


from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers.models.layoutlm.modeling_tf_layoutlm import (
						    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLayoutLMForMaskedLM,
						    TFLayoutLMForQuestionAnswering,
						    TFLayoutLMForSequenceClassification,
						    TFLayoutLMForTokenClassification,
						    TFLayoutLMModel,
						)







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase=1_3   ,						__lowercase=7   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=True   ,						__lowercase=9_9   ,						__lowercase=3_2   ,						__lowercase=2   ,						__lowercase=4   ,						__lowercase=3_7   ,						__lowercase="gelu"   ,						__lowercase=0.1   ,						__lowercase=0.1   ,						__lowercase=5_1_2   ,						__lowercase=1_6   ,						__lowercase=2   ,						__lowercase=0.02   ,						__lowercase=3   ,						__lowercase=4   ,						__lowercase=None   ,						__lowercase=1_0_0_0   ,						)					->    str:
										lowerCAmelCase_ :      Dict    				=				parent
										lowerCAmelCase_ :      Union[str, Any]    				=				batch_size
										lowerCAmelCase_ :      int    				=				seq_length
										lowerCAmelCase_ :      Dict    				=				is_training
										lowerCAmelCase_ :      List[str]    				=				use_input_mask
										lowerCAmelCase_ :      Union[str, Any]    				=				use_token_type_ids
										lowerCAmelCase_ :      List[Any]    				=				use_labels
										lowerCAmelCase_ :      Dict    				=				vocab_size
										lowerCAmelCase_ :      Optional[Any]    				=				hidden_size
										lowerCAmelCase_ :      str    				=				num_hidden_layers
										lowerCAmelCase_ :      Dict    				=				num_attention_heads
										lowerCAmelCase_ :      str    				=				intermediate_size
										lowerCAmelCase_ :      Union[str, Any]    				=				hidden_act
										lowerCAmelCase_ :      str    				=				hidden_dropout_prob
										lowerCAmelCase_ :      List[Any]    				=				attention_probs_dropout_prob
										lowerCAmelCase_ :      Any    				=				max_position_embeddings
										lowerCAmelCase_ :      List[str]    				=				type_vocab_size
										lowerCAmelCase_ :      List[str]    				=				type_sequence_label_size
										lowerCAmelCase_ :      int    				=				initializer_range
										lowerCAmelCase_ :      Dict    				=				num_labels
										lowerCAmelCase_ :      str    				=				num_choices
										lowerCAmelCase_ :      int    				=				scope
										lowerCAmelCase_ :      int    				=				range_bbox




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Any    				=				ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size							)

										# convert bbox to numpy since TF does not support item assignment
										lowerCAmelCase_ :      Tuple    				=				ids_tensor([self.batch_size, self.seq_length, 4]   ,						self.range_bbox							).numpy()
										# Ensure that bbox is legal
										for i in range(bbox.shape[0]							):
														for j in range(bbox.shape[1]							):
																		if bbox[i, j, 3] < bbox[i, j, 1]:
																						lowerCAmelCase_ :      Optional[int]    				=				bbox[i, j, 3]
																						lowerCAmelCase_ :      str    				=				bbox[i, j, 1]
																						lowerCAmelCase_ :      Any    				=				t
																		if bbox[i, j, 2] < bbox[i, j, 0]:
																						lowerCAmelCase_ :      List[Any]    				=				bbox[i, j, 2]
																						lowerCAmelCase_ :      Any    				=				bbox[i, j, 0]
																						lowerCAmelCase_ :      Dict    				=				t
										lowerCAmelCase_ :      Any    				=				tf.convert_to_tensor(__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				None
										if self.use_input_mask:
														lowerCAmelCase_ :      str    				=				random_attention_mask([self.batch_size, self.seq_length]							)

										lowerCAmelCase_ :      Any    				=				None
										if self.use_token_type_ids:
														lowerCAmelCase_ :      Optional[int]    				=				ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size							)

										lowerCAmelCase_ :      str    				=				None
										lowerCAmelCase_ :      Dict    				=				None
										lowerCAmelCase_ :      Union[str, Any]    				=				None
										if self.use_labels:
														lowerCAmelCase_ :      Union[str, Any]    				=				ids_tensor([self.batch_size]   ,						self.type_sequence_label_size							)
														lowerCAmelCase_ :      Optional[Any]    				=				ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels							)
														lowerCAmelCase_ :      Tuple    				=				ids_tensor([self.batch_size]   ,						self.num_choices							)

										lowerCAmelCase_ :      Optional[int]    				=				LayoutLMConfig(
										    vocab_size=self.vocab_size   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						type_vocab_size=self.type_vocab_size   ,						initializer_range=self.initializer_range   ,						)

										return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Optional[int]:
										lowerCAmelCase_ :      str    				=				TFLayoutLMModel(config=__lowercase							)

										lowerCAmelCase_ :      str    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)
										lowerCAmelCase_ :      List[Any]    				=				model(__lowercase   ,						__lowercase   ,						token_type_ids=__lowercase							)
										lowerCAmelCase_ :      Any    				=				model(__lowercase   ,						__lowercase							)
										self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)							)
										self.parent.assertEqual(result.pooler_output.shape   ,						(self.batch_size, self.hidden_size)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      Dict    				=				TFLayoutLMForMaskedLM(config=__lowercase							)

										lowerCAmelCase_ :      int    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase   ,						labels=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    List[Any]:
										lowerCAmelCase_ :      int    				=				self.num_labels
										lowerCAmelCase_ :      Dict    				=				TFLayoutLMForSequenceClassification(config=__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    List[Any]:
										lowerCAmelCase_ :      List[str]    				=				self.num_labels
										lowerCAmelCase_ :      Union[str, Any]    				=				TFLayoutLMForTokenClassification(config=__lowercase							)

										lowerCAmelCase_ :      int    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase   ,						labels=__lowercase							)
										self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels)							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    str:
										lowerCAmelCase_ :      Tuple    				=				TFLayoutLMForQuestionAnswering(config=__lowercase							)

										lowerCAmelCase_ :      Dict    				=				model(__lowercase   ,						__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)
										self.parent.assertEqual(result.start_logits.shape   ,						(self.batch_size, self.seq_length)							)
										self.parent.assertEqual(result.end_logits.shape   ,						(self.batch_size, self.seq_length)							)




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      List[Any]    				=				self.prepare_config_and_inputs()
										(
										    lowerCAmelCase_  
										) :      Any    				=				config_and_inputs
										lowerCAmelCase_ :      List[Any]    				=				{
										    '''input_ids''': input_ids,
										    '''bbox''': bbox,
										    '''token_type_ids''': token_type_ids,
										    '''attention_mask''': input_mask,
										}
										return config, inputs_dict







@require_tf
class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  Dict					=							(
						    (
						        TFLayoutLMModel,
						        TFLayoutLMForMaskedLM,
						        TFLayoutLMForTokenClassification,
						        TFLayoutLMForSequenceClassification,
						        TFLayoutLMForQuestionAnswering,
						    )
						    if is_tf_available()
						    else ()
						)
						SCREAMING_SNAKE_CASE__      :  List[str]					=							(
						    {
						        """feature-extraction""": TFLayoutLMModel,
						        """fill-mask""": TFLayoutLMForMaskedLM,
						        """text-classification""": TFLayoutLMForSequenceClassification,
						        """token-classification""": TFLayoutLMForTokenClassification,
						        """zero-shot""": TFLayoutLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						SCREAMING_SNAKE_CASE__      :  Tuple					=							False
						SCREAMING_SNAKE_CASE__      :  Any					=							True
						SCREAMING_SNAKE_CASE__      :  Union[str, Any]					=							10




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_ :      Union[str, Any]    				=				TFLayoutLMModelTester(self							)
										lowerCAmelCase_ :      str    				=				ConfigTester(self   ,						config_class=__lowercase   ,						hidden_size=3_7							)




						def 						lowercase_ (				self							)					->    int:
										self.config_tester.run_common_tests()




						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      Optional[Any]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowercase							)




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_ :      int    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*__lowercase							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      List[str]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*__lowercase							)




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      Tuple    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*__lowercase							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      Optional[Any]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*__lowercase							)




						@slow
						def 						lowercase_ (				self							)					->    Dict:
										for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_ :      str    				=				TFLayoutLMModel.from_pretrained(__lowercase							)
														self.assertIsNotNone(__lowercase							)




						@unittest.skip('''Onnx compliancy broke with TF 2.10'''							)
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										pass






def 			lowerCAmelCase (		)-> List[Any]:
				# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
				# fmt: off
				lowerCAmelCase_ :      Union[str, Any]    				=				tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]] )  # noqa: E231
				lowerCAmelCase_ :      Dict    				=				tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] )  # noqa: E231
				lowerCAmelCase_ :      Union[str, Any]    				=				tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]] )  # noqa: E231
				lowerCAmelCase_ :      List[Any]    				=				tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] )  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				lowerCAmelCase_ :      List[str]    				=				tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]] )  # noqa: E231
				# fmt: on

				return input_ids, attention_mask, bbox, token_type_ids, labels







@require_tf
class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						@slow
						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_ :      int    				=				TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''							)

										lowerCAmelCase_ :      Dict    				=				prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCAmelCase_ :      str    				=				model(input_ids=__lowercase   ,						bbox=__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)

										# test the sequence output on [0, :3, :3]
										lowerCAmelCase_ :      List[str]    				=				tf.convert_to_tensor(
										    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]   ,						)

										self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]   ,						__lowercase   ,						atol=1e-3							)							)

										# test the pooled output on [1, :3]
										lowerCAmelCase_ :      Any    				=				tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]							)

										self.assertTrue(np.allclose(outputs.pooler_output[1, :3]   ,						__lowercase   ,						atol=1e-3							)							)




						@slow
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										# initialize model with randomly initialized sequence classification head
										lowerCAmelCase_ :      str    				=				TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''   ,						num_labels=2							)

										lowerCAmelCase_ :      str    				=				prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCAmelCase_ :      Optional[int]    				=				model(
										    input_ids=__lowercase   ,						bbox=__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase   ,						labels=tf.convert_to_tensor([1, 1]							)   ,						)

										# test whether we get a loss as a scalar
										lowerCAmelCase_ :      List[str]    				=				outputs.loss
										lowerCAmelCase_ :      Optional[int]    				=				(2,)
										self.assertEqual(loss.shape   ,						__lowercase							)

										# test the shape of the logits
										lowerCAmelCase_ :      List[str]    				=				outputs.logits
										lowerCAmelCase_ :      Union[str, Any]    				=				(2, 2)
										self.assertEqual(logits.shape   ,						__lowercase							)




						@slow
						def 						lowercase_ (				self							)					->    List[Any]:
										# initialize model with randomly initialized token classification head
										lowerCAmelCase_ :      int    				=				TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''   ,						num_labels=1_3							)

										lowerCAmelCase_ :      Optional[Any]    				=				prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCAmelCase_ :      Optional[Any]    				=				model(
										    input_ids=__lowercase   ,						bbox=__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase   ,						labels=__lowercase							)

										# test the shape of the logits
										lowerCAmelCase_ :      Tuple    				=				outputs.logits
										lowerCAmelCase_ :      List[Any]    				=				tf.convert_to_tensor((2, 2_5, 1_3)							)
										self.assertEqual(logits.shape   ,						__lowercase							)




						@slow
						def 						lowercase_ (				self							)					->    Tuple:
										# initialize model with randomly initialized token classification head
										lowerCAmelCase_ :      Dict    				=				TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''							)

										lowerCAmelCase_ :      List[str]    				=				prepare_layoutlm_batch_inputs()

										# forward pass
										lowerCAmelCase_ :      Any    				=				model(input_ids=__lowercase   ,						bbox=__lowercase   ,						attention_mask=__lowercase   ,						token_type_ids=__lowercase							)

										# test the shape of the logits
										lowerCAmelCase_ :      Optional[int]    				=				tf.convert_to_tensor((2, 2_5)							)
										self.assertEqual(outputs.start_logits.shape   ,						__lowercase							)
										self.assertEqual(outputs.end_logits.shape   ,						__lowercase							)



import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel

						if is_vision_available():
												from transformers import MaskFormerImageProcessor

if is_vision_available():
						from PIL import Image







class 				snake_case__:







						'''simple docstring'''




						def __init__(				self   ,						__lowercase   ,						__lowercase=2   ,						__lowercase=True   ,						__lowercase=False   ,						__lowercase=1_0   ,						__lowercase=3   ,						__lowercase=3_2 * 4   ,						__lowercase=3_2 * 6   ,						__lowercase=4   ,						__lowercase=3_2   ,						)					->    Union[str, Any]:
										lowerCAmelCase_ :      str    				=				parent
										lowerCAmelCase_ :      Optional[Any]    				=				batch_size
										lowerCAmelCase_ :      List[Any]    				=				is_training
										lowerCAmelCase_ :      Optional[Any]    				=				use_auxiliary_loss
										lowerCAmelCase_ :      List[Any]    				=				num_queries
										lowerCAmelCase_ :      str    				=				num_channels
										lowerCAmelCase_ :      Dict    				=				min_size
										lowerCAmelCase_ :      List[str]    				=				max_size
										lowerCAmelCase_ :      Any    				=				num_labels
										lowerCAmelCase_ :      str    				=				mask_feature_size




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      Optional[Any]    				=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]							).to(
										    __lowercase							)

										lowerCAmelCase_ :      Optional[Any]    				=				torch.ones([self.batch_size, self.min_size, self.max_size]   ,						device=__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				(
										    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]   ,						device=__lowercase							) > 0.5
										).float()
										lowerCAmelCase_ :      List[str]    				=				(torch.rand((self.batch_size, self.num_labels)   ,						device=__lowercase							) > 0.5).long()

										lowerCAmelCase_ :      Dict    				=				self.get_config()
										return config, pixel_values, pixel_mask, mask_labels, class_labels




						def 						lowercase_ (				self							)					->    List[str]:
										return MaskFormerConfig.from_backbone_and_decoder_configs(
										    backbone_config=SwinConfig(
										        depths=[1, 1, 1, 1]   ,						)   ,						decoder_config=DetrConfig(
										        decoder_ffn_dim=1_2_8   ,						num_queries=self.num_queries   ,						decoder_attention_heads=2   ,						d_model=self.mask_feature_size   ,						)   ,						mask_feature_size=self.mask_feature_size   ,						fpn_feature_size=self.mask_feature_size   ,						num_channels=self.num_channels   ,						num_labels=self.num_labels   ,						)




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      int    				=				self.prepare_config_and_inputs()
										lowerCAmelCase_ :      Union[str, Any]    				=				{'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
										return config, inputs_dict




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase							)					->    Any:
										lowerCAmelCase_ :      Optional[int]    				=				output.encoder_hidden_states
										lowerCAmelCase_ :      List[Any]    				=				output.pixel_decoder_hidden_states
										lowerCAmelCase_ :      Optional[Any]    				=				output.transformer_decoder_hidden_states

										self.parent.assertTrue(len(__lowercase							)   ,						len(config.backbone_config.depths							)							)
										self.parent.assertTrue(len(__lowercase							)   ,						len(config.backbone_config.depths							)							)
										self.parent.assertTrue(len(__lowercase							)   ,						config.decoder_config.decoder_layers							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase=False							)					->    int:
										with torch.no_grad():
														lowerCAmelCase_ :      List[Any]    				=				MaskFormerModel(config=__lowercase							)
														model.to(__lowercase							)
														model.eval()

														lowerCAmelCase_ :      Optional[Any]    				=				model(pixel_values=__lowercase   ,						pixel_mask=__lowercase							)
														lowerCAmelCase_ :      Optional[int]    				=				model(__lowercase   ,						output_hidden_states=__lowercase							)
										# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
										# encoder and pixel decoder
										self.parent.assertEqual(
										    output.transformer_decoder_last_hidden_state.shape   ,						(self.batch_size, self.num_queries, self.mask_feature_size)   ,						)
										# let's ensure the other two hidden state exists
										self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None							)
										self.parent.assertTrue(output.encoder_last_hidden_state is not None							)

										if output_hidden_states:
														self.check_output_hidden_state(__lowercase   ,						__lowercase							)




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase   ,						__lowercase							)					->    Any:
										lowerCAmelCase_ :      Any    				=				MaskFormerForInstanceSegmentation(config=__lowercase							)
										model.to(__lowercase							)
										model.eval()

										def comm_check_on_output(__lowercase							):
														# let's still check that all the required stuff is there
														self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None							)
														self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None							)
														self.parent.assertTrue(result.encoder_last_hidden_state is not None							)
														# okay, now we need to check the logits shape
														# due to the encoder compression, masks have a //4 spatial size
														self.parent.assertEqual(
														    result.masks_queries_logits.shape   ,						(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)   ,						)
														# + 1 for null class
														self.parent.assertEqual(
														    result.class_queries_logits.shape   ,						(self.batch_size, self.num_queries, self.num_labels + 1)							)

										with torch.no_grad():
														lowerCAmelCase_ :      int    				=				model(pixel_values=__lowercase   ,						pixel_mask=__lowercase							)
														lowerCAmelCase_ :      Any    				=				model(__lowercase							)

														comm_check_on_output(__lowercase							)

														lowerCAmelCase_ :      List[Any]    				=				model(
														    pixel_values=__lowercase   ,						pixel_mask=__lowercase   ,						mask_labels=__lowercase   ,						class_labels=__lowercase							)

										comm_check_on_output(__lowercase							)

										self.parent.assertTrue(result.loss is not None							)
										self.parent.assertEqual(result.loss.shape   ,						torch.Size([1]							)							)







@require_torch
class 				snake_case__(							UpperCAmelCase__,						UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[str]					=							(MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
						SCREAMING_SNAKE_CASE__      :  Tuple					=							(
						    {"""feature-extraction""": MaskFormerModel, """image-segmentation""": MaskFormerForInstanceSegmentation}
						    if is_torch_available()
						    else {}
						)

						SCREAMING_SNAKE_CASE__      :  Tuple					=							False
						SCREAMING_SNAKE_CASE__      :  Dict					=							False
						SCREAMING_SNAKE_CASE__      :  Tuple					=							False
						SCREAMING_SNAKE_CASE__      :  List[str]					=							False




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      Any    				=				MaskFormerModelTester(self							)
										lowerCAmelCase_ :      str    				=				ConfigTester(self   ,						config_class=__lowercase   ,						has_text_modality=__lowercase							)




						def 						lowercase_ (				self							)					->    Any:
										self.config_tester.run_common_tests()




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_				,  lowerCAmelCase_ :      Any    				=				self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskformer_model(__lowercase   ,						**__lowercase   ,						output_hidden_states=__lowercase							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      Optional[Any]    				=				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*__lowercase							)




						@unittest.skip(reason='''MaskFormer does not use inputs_embeds'''							)
						def 						lowercase_ (				self							)					->    str:
										pass




						@unittest.skip(reason='''MaskFormer does not have a get_input_embeddings method'''							)
						def 						lowercase_ (				self							)					->    Optional[Any]:
										pass




						@unittest.skip(reason='''MaskFormer is not a generative model'''							)
						def 						lowercase_ (				self							)					->    Optional[Any]:
										pass




						@unittest.skip(reason='''MaskFormer does not use token embeddings'''							)
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										pass




						@require_torch_multi_gpu
						@unittest.skip(
						    reason='''MaskFormer has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''							)
						def 						lowercase_ (				self							)					->    Optional[Any]:
										pass




						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''							)
						def 						lowercase_ (				self							)					->    Dict:
										pass




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_				,  lowerCAmelCase_ :      str    				=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase_ :      Tuple    				=				model_class(__lowercase							)
														lowerCAmelCase_ :      Dict    				=				inspect.signature(model.forward							)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														lowerCAmelCase_ :      str    				=				[*signature.parameters.keys()]

														lowerCAmelCase_ :      Tuple    				=				['''pixel_values''']
														self.assertListEqual(arg_names[:1]   ,						__lowercase							)




						@slow
						def 						lowercase_ (				self							)					->    Optional[int]:
										for model_name in ["facebook/maskformer-swin-small-coco"]:
														lowerCAmelCase_ :      str    				=				MaskFormerModel.from_pretrained(__lowercase							)
														self.assertIsNotNone(__lowercase							)




						def 						lowercase_ (				self							)					->    List[Any]:
										lowerCAmelCase_ :      Tuple    				=				(self.model_tester.min_size,) * 2
										lowerCAmelCase_ :      List[Any]    				=				{
										    '''pixel_values''': torch.randn((2, 3, *size)   ,						device=__lowercase							),
										    '''mask_labels''': torch.randn((2, 1_0, *size)   ,						device=__lowercase							),
										    '''class_labels''': torch.zeros(2   ,						1_0   ,						device=__lowercase							).long(),
										}

										lowerCAmelCase_ :      Tuple    				=				MaskFormerForInstanceSegmentation(MaskFormerConfig()							).to(__lowercase							)
										lowerCAmelCase_ :      Dict    				=				model(**__lowercase							)
										self.assertTrue(outputs.loss is not None							)




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_				,  lowerCAmelCase_ :      List[Any]    				=				self.model_tester.prepare_config_and_inputs_for_common()
										self.model_tester.create_and_check_maskformer_model(__lowercase   ,						**__lowercase   ,						output_hidden_states=__lowercase							)




						def 						lowercase_ (				self							)					->    int:
										lowerCAmelCase_				,  lowerCAmelCase_ :      Tuple    				=				self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase_ :      List[str]    				=				model_class(__lowercase							).to(__lowercase							)
														lowerCAmelCase_ :      int    				=				model(**__lowercase   ,						output_attentions=__lowercase							)
														self.assertTrue(outputs.attentions is not None							)




						def 						lowercase_ (				self							)					->    List[str]:
										if not self.model_tester.is_training:
														return
										# only MaskFormerForInstanceSegmentation has the loss
										lowerCAmelCase_ :      int    				=				self.all_model_classes[1]
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      str    				=				self.model_tester.prepare_config_and_inputs()

										lowerCAmelCase_ :      Optional[Any]    				=				model_class(__lowercase							)
										model.to(__lowercase							)
										model.train()

										lowerCAmelCase_ :      Optional[Any]    				=				model(__lowercase   ,						mask_labels=__lowercase   ,						class_labels=__lowercase							).loss
										loss.backward()




						def 						lowercase_ (				self							)					->    Optional[int]:
										# only MaskFormerForInstanceSegmentation has the loss
										lowerCAmelCase_ :      Any    				=				self.all_model_classes[1]
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      List[str]    				=				self.model_tester.prepare_config_and_inputs()
										lowerCAmelCase_ :      Tuple    				=				True
										lowerCAmelCase_ :      Tuple    				=				True

										lowerCAmelCase_ :      Any    				=				model_class(__lowercase							)
										model.to(__lowercase							)
										model.train()

										lowerCAmelCase_ :      Any    				=				model(__lowercase   ,						mask_labels=__lowercase   ,						class_labels=__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				outputs.encoder_hidden_states[0]
										encoder_hidden_states.retain_grad()

										lowerCAmelCase_ :      str    				=				outputs.pixel_decoder_hidden_states[0]
										pixel_decoder_hidden_states.retain_grad()
										# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
										lowerCAmelCase_ :      str    				=				outputs.transformer_decoder_hidden_states[0]
										transformer_decoder_hidden_states.retain_grad()

										lowerCAmelCase_ :      Union[str, Any]    				=				outputs.attentions[0]
										attentions.retain_grad()

										outputs.loss.backward(retain_graph=__lowercase							)

										self.assertIsNotNone(encoder_hidden_states.grad							)
										self.assertIsNotNone(pixel_decoder_hidden_states.grad							)
										self.assertIsNotNone(transformer_decoder_hidden_states.grad							)
										self.assertIsNotNone(attentions.grad							)






_UpperCAmelCase			:  Dict     =1E-4



def 			lowerCAmelCase (		)-> Any:
				lowerCAmelCase_ :      Optional[int]    				=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
				return image







@require_vision
@slow
class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						@cached_property
						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return (
										    MaskFormerImageProcessor.from_pretrained('''facebook/maskformer-swin-small-coco'''							)
										    if is_vision_available()
										    else None
										)




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Optional[Any]    				=				MaskFormerModel.from_pretrained('''facebook/maskformer-swin-small-coco'''							).to(__lowercase							)
										lowerCAmelCase_ :      Dict    				=				self.default_image_processor
										lowerCAmelCase_ :      int    				=				prepare_img()
										lowerCAmelCase_ :      Any    				=				image_processor(__lowercase   ,						return_tensors='''pt'''							).to(__lowercase							)
										lowerCAmelCase_ :      Any    				=				inputs['''pixel_values'''].shape
										# check size is divisible by 32
										self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0							)
										# check size
										self.assertEqual(__lowercase   ,						(1, 3, 8_0_0, 1_0_8_8)							)

										with torch.no_grad():
														lowerCAmelCase_ :      List[str]    				=				model(**__lowercase							)

										lowerCAmelCase_ :      Union[str, Any]    				=				torch.tensor(
										    [[-0.04_82, 0.92_28, 0.49_51], [-0.25_47, 0.80_17, 0.85_27], [-0.00_69, 0.33_85, -0.00_89]]							).to(__lowercase							)
										self.assertTrue(
										    torch.allclose(
										        outputs.encoder_last_hidden_state[0, 0, :3, :3]   ,						__lowercase   ,						atol=__lowercase							)							)

										lowerCAmelCase_ :      List[Any]    				=				torch.tensor(
										    [[-0.84_22, -0.84_34, -0.97_18], [-1.01_44, -0.55_65, -0.41_95], [-1.00_38, -0.44_84, -0.19_61]]							).to(__lowercase							)
										self.assertTrue(
										    torch.allclose(
										        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]   ,						__lowercase   ,						atol=__lowercase							)							)

										lowerCAmelCase_ :      int    				=				torch.tensor(
										    [[0.28_52, -0.01_59, 0.97_35], [0.62_54, 0.18_58, 0.85_29], [-0.06_80, -0.41_16, 1.84_13]]							).to(__lowercase							)
										self.assertTrue(
										    torch.allclose(
										        outputs.transformer_decoder_last_hidden_state[0, :3, :3]   ,						__lowercase   ,						atol=__lowercase							)							)




						def 						lowercase_ (				self							)					->    Dict:
										lowerCAmelCase_ :      Optional[Any]    				=				(
										    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''							)
										    .to(__lowercase							)
										    .eval()
										)
										lowerCAmelCase_ :      Tuple    				=				self.default_image_processor
										lowerCAmelCase_ :      Optional[Any]    				=				prepare_img()
										lowerCAmelCase_ :      int    				=				image_processor(__lowercase   ,						return_tensors='''pt'''							).to(__lowercase							)
										lowerCAmelCase_ :      Tuple    				=				inputs['''pixel_values'''].shape
										# check size is divisible by 32
										self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0							)
										# check size
										self.assertEqual(__lowercase   ,						(1, 3, 8_0_0, 1_0_8_8)							)

										with torch.no_grad():
														lowerCAmelCase_ :      Dict    				=				model(**__lowercase							)
										# masks_queries_logits
										lowerCAmelCase_ :      Optional[int]    				=				outputs.masks_queries_logits
										self.assertEqual(
										    masks_queries_logits.shape   ,						(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   ,						)
										lowerCAmelCase_ :      Tuple    				=				[
										    [-1.3_73_71_24, -1.7_72_49_37, -1.9_36_42_33],
										    [-1.5_97_72_81, -1.9_86_79_39, -2.1_52_36_95],
										    [-1.5_79_53_98, -1.9_26_98_32, -2.09_39_42],
										]
										lowerCAmelCase_ :      int    				=				torch.tensor(__lowercase							).to(__lowercase							)
										self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,						__lowercase   ,						atol=__lowercase							)							)
										# class_queries_logits
										lowerCAmelCase_ :      List[Any]    				=				outputs.class_queries_logits
										self.assertEqual(
										    class_queries_logits.shape   ,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
										lowerCAmelCase_ :      Dict    				=				torch.tensor(
										    [
										        [1.6_512e00, -5.2_572e00, -3.3_519e00],
										        [3.6_169e-02, -5.9_025e00, -2.9_313e00],
										        [1.0_766e-04, -7.7_630e00, -5.1_263e00],
										    ]							).to(__lowercase							)
										self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,						__lowercase   ,						atol=__lowercase							)							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      str    				=				(
										    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-resnet101-coco-stuff'''							)
										    .to(__lowercase							)
										    .eval()
										)
										lowerCAmelCase_ :      int    				=				self.default_image_processor
										lowerCAmelCase_ :      Optional[Any]    				=				prepare_img()
										lowerCAmelCase_ :      Dict    				=				image_processor(__lowercase   ,						return_tensors='''pt'''							).to(__lowercase							)
										lowerCAmelCase_ :      Optional[Any]    				=				inputs['''pixel_values'''].shape
										# check size is divisible by 32
										self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0							)
										# check size
										self.assertEqual(__lowercase   ,						(1, 3, 8_0_0, 1_0_8_8)							)

										with torch.no_grad():
														lowerCAmelCase_ :      str    				=				model(**__lowercase							)
										# masks_queries_logits
										lowerCAmelCase_ :      List[str]    				=				outputs.masks_queries_logits
										self.assertEqual(
										    masks_queries_logits.shape   ,						(1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)   ,						)
										lowerCAmelCase_ :      Any    				=				[[-0.90_46, -2.63_66, -4.60_62], [-3.41_79, -5.78_90, -8.80_57], [-4.91_79, -7.65_60, -10.77_11]]
										lowerCAmelCase_ :      str    				=				torch.tensor(__lowercase							).to(__lowercase							)
										self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]   ,						__lowercase   ,						atol=__lowercase							)							)
										# class_queries_logits
										lowerCAmelCase_ :      Optional[int]    				=				outputs.class_queries_logits
										self.assertEqual(
										    class_queries_logits.shape   ,						(1, model.config.decoder_config.num_queries, model.config.num_labels + 1)							)
										lowerCAmelCase_ :      int    				=				torch.tensor(
										    [[4.71_88, -3.25_85, -2.88_57], [6.68_71, -2.91_81, -1.24_87], [7.24_49, -2.27_64, -2.18_74]]							).to(__lowercase							)
										self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]   ,						__lowercase   ,						atol=__lowercase							)							)




						def 						lowercase_ (				self							)					->    Optional[Any]:
										lowerCAmelCase_ :      Dict    				=				(
										    MaskFormerForInstanceSegmentation.from_pretrained('''facebook/maskformer-swin-small-coco'''							)
										    .to(__lowercase							)
										    .eval()
										)
										lowerCAmelCase_ :      str    				=				self.default_image_processor

										lowerCAmelCase_ :      Union[str, Any]    				=				image_processor(
										    [np.zeros((3, 8_0_0, 1_3_3_3)							), np.zeros((3, 8_0_0, 1_3_3_3)							)]   ,						segmentation_maps=[np.zeros((3_8_4, 3_8_4)							).astype(np.floataa							), np.zeros((3_8_4, 3_8_4)							).astype(np.floataa							)]   ,						return_tensors='''pt'''   ,						)

										lowerCAmelCase_ :      Optional[Any]    				=				inputs['''pixel_values'''].to(__lowercase							)
										lowerCAmelCase_ :      int    				=				[el.to(__lowercase							) for el in inputs['''mask_labels''']]
										lowerCAmelCase_ :      Optional[Any]    				=				[el.to(__lowercase							) for el in inputs['''class_labels''']]

										with torch.no_grad():
														lowerCAmelCase_ :      str    				=				model(**__lowercase							)

										self.assertTrue(outputs.loss is not None							)