

import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM

@require_torch
class 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=99      ,      lowercase=13      ,      lowercase=16      ,      lowercase=7      ,      lowercase=True      ,      lowercase=True      ,      lowercase=True      ,      lowercase=False      ,      lowercase=True      ,      lowercase=2      ,      lowercase=32      ,      lowercase=4      ,      lowercase=4      ,      lowercase=30      ,      lowercase=0      ,      lowercase=1      ,      lowercase=2      ,      lowercase=None      ,      )  ->						Any:
							'''simple docstring'''






							A__        =			parent
							A__        =			batch_size
							A__        =			decoder_seq_length
							# For common tests
							A__        =			self.decoder_seq_length
							A__        =			is_training
							A__        =			use_attention_mask
							A__        =			use_labels

							A__        =			vocab_size
							A__        =			d_model
							A__        =			d_model
							A__        =			decoder_layers
							A__        =			decoder_layers
							A__        =			decoder_ffn_dim
							A__        =			decoder_attention_heads
							A__        =			decoder_attention_heads
							A__        =			eos_token_id
							A__        =			bos_token_id
							A__        =			pad_token_id
							A__        =			decoder_start_token_id
							A__        =			use_cache
							A__        =			max_position_embeddings

							A__        =			None
							A__        =			decoder_seq_length
							A__        =			2
							A__        =			1


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			ids_tensor([self.batch_size, self.decoder_seq_length]      ,      self.vocab_size       )

							A__        =			None
							if self.use_attention_mask:
											A__        =			ids_tensor([self.batch_size, self.decoder_seq_length]      ,      vocab_size=2       )

							A__        =			None
							if self.use_labels:
											A__        =			ids_tensor([self.batch_size, self.decoder_seq_length]      ,      self.vocab_size       )

							A__        =			TrOCRConfig(
							    vocab_size=self.vocab_size      ,      d_model=self.d_model      ,      decoder_layers=self.decoder_layers      ,      decoder_ffn_dim=self.decoder_ffn_dim      ,      decoder_attention_heads=self.decoder_attention_heads      ,      eos_token_id=self.eos_token_id      ,      bos_token_id=self.bos_token_id      ,      use_cache=self.use_cache      ,      pad_token_id=self.pad_token_id      ,      decoder_start_token_id=self.decoder_start_token_id      ,      max_position_embeddings=self.max_position_embeddings      ,      )

							return (config, input_ids, attention_mask, lm_labels)


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      )  ->						List[str]:
							'''simple docstring'''






							A__        =			True
							A__        =			TrOCRDecoder(config=lowercase       ).to(lowercase       ).eval()
							A__        =			input_ids[:2]

							input_ids[input_ids == 0] += 1
							# first forward pass
							A__        =			model(lowercase      ,      use_cache=lowercase       )
							A__        =			model(lowercase       )
							A__        =			model(lowercase      ,      use_cache=lowercase       )

							self.parent.assertTrue(len(lowercase       ) == len(lowercase       )       )
							self.parent.assertTrue(len(lowercase       ) == len(lowercase       ) + 1       )

							A__        =			outputs["past_key_values"]

							# create hypothetical next token and extent to next_input_ids
							A__        =			ids_tensor((2, 1)      ,      config.vocab_size - 1       ) + 1

							# append to next input_ids and
							A__        =			torch.cat([input_ids, next_tokens]      ,      dim=-1       )

							A__        =			model(lowercase       )["last_hidden_state"]
							A__        =			model(lowercase      ,      past_key_values=lowercase       )["last_hidden_state"]

							# select random slice
							A__        =			ids_tensor((1,)      ,      output_from_past.shape[-1]       ).item()
							A__        =			output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
							A__        =			output_from_past[:, 0, random_slice_idx].detach()

							# test that outputs are equal for slice
							assert torch.allclose(lowercase      ,      lowercase      ,      atol=1e-3       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			self.prepare_config_and_inputs()
							A__    ,				A__    ,				A__    ,				A__        =			config_and_inputs

							A__        =			{"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict

@require_torch
class 					a__						(					snake_case							,	snake_case							,	snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
			__lowerCamelCase        =       (TrOCRForCausalLM,) if is_torch_available() else ()
			__lowerCamelCase        =       {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
			__lowerCamelCase        =       True
			__lowerCamelCase        =       False


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			TrOCRStandaloneDecoderModelTester(self      ,      is_training=lowercase       )
							A__        =			ConfigTester(self      ,      config_class=lowercase       )


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past(*lowercase       )


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							return


			@unittest.skip("The model doesn't support left padding"       )  # and it's not used enough to be worth fixing :)
			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							pass




import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DetaImageProcessor

class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=7      ,      lowercase=3      ,      lowercase=30      ,      lowercase=400      ,      lowercase=True      ,      lowercase=None      ,      lowercase=True      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=[0.5, 0.5, 0.5]      ,      lowercase=True      ,      lowercase=1 / 255      ,      lowercase=True      ,      )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
							A__        =			parent
							A__        =			batch_size
							A__        =			num_channels
							A__        =			min_resolution
							A__        =			max_resolution
							A__        =			do_resize
							A__        =			size
							A__        =			do_normalize
							A__        =			image_mean
							A__        =			image_std
							A__        =			do_rescale
							A__        =			rescale_factor
							A__        =			do_pad


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_rescale": self.do_rescale,
							    "rescale_factor": self.rescale_factor,
							    "do_pad": self.do_pad,
							}


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=False       )  ->						int:
							'''simple docstring'''






							if not batched:
											A__        =			image_inputs[0]
											if isinstance(lowercase      ,      Image.Image       ):
															A__    ,				A__        =			image.size
											else:
															A__    ,				A__        =			image.shape[1], image.shape[2]
											if w < h:
															A__        =			int(self.size["shortest_edge"] * h / w       )
															A__        =			self.size["shortest_edge"]
											elif w > h:
															A__        =			self.size["shortest_edge"]
															A__        =			int(self.size["shortest_edge"] * w / h       )
											else:
															A__        =			self.size["shortest_edge"]
															A__        =			self.size["shortest_edge"]

							else:
											A__        =			[]
											for image in image_inputs:
															A__    ,				A__        =			self.get_expected_values([image]       )
															expected_values.append((expected_height, expected_width)       )
											A__        =			max(lowercase      ,      key=lambda lowercase       : item[0]       )[0]
											A__        =			max(lowercase      ,      key=lambda lowercase       : item[1]       )[1]

							return expected_height, expected_width

@require_torch
@require_vision
class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       DetaImageProcessor if is_vision_available() else None


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			DetaImageProcessingTester(self       )


			@property
			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							return self.image_processor_tester.prepare_image_processor_dict()


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							self.assertTrue(hasattr(lowercase      ,      "image_mean"       )       )
							self.assertTrue(hasattr(lowercase      ,      "image_std"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_normalize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_resize"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_rescale"       )       )
							self.assertTrue(hasattr(lowercase      ,      "do_pad"       )       )
							self.assertTrue(hasattr(lowercase      ,      "size"       )       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			self.image_processing_class.from_dict(self.image_processor_dict       )
							self.assertEqual(image_processor.size      ,      {"shortest_edge": 18, "longest_edge": 1333}       )
							self.assertEqual(image_processor.do_pad      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PIL images
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      Image.Image       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase       )

							self.assertEqual(
							    encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )

							# Test batched
							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase      ,      batched=lowercase       )

							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,      )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random numpy tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      numpify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      np.ndarray       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase       )

							self.assertEqual(
							    encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase      ,      batched=lowercase       )

							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,      )


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			self.image_processing_class(**self.image_processor_dict       )
							# create random PyTorch tensors
							A__        =			prepare_image_inputs(self.image_processor_tester      ,      equal_resolution=lowercase      ,      torchify=lowercase       )
							for image in image_inputs:
											self.assertIsInstance(lowercase      ,      torch.Tensor       )

							# Test not batched input
							A__        =			image_processing(image_inputs[0]      ,      return_tensors="pt"       ).pixel_values

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase       )

							self.assertEqual(
							    encoded_images.shape      ,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,      )

							# Test batched
							A__        =			image_processing(lowercase      ,      return_tensors="pt"       ).pixel_values

							A__    ,				A__        =			self.image_processor_tester.get_expected_values(lowercase      ,      batched=lowercase       )

							self.assertEqual(
							    encoded_images.shape      ,      (
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,      )


			@slow
			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"      ,      "r"       ) as f:
											A__        =			json.loads(f.read()       )

							A__        =			{"image_id": 39769, "annotations": target}

							# encode them
							A__        =			DetaImageProcessor()
							A__        =			image_processing(images=lowercase      ,      annotations=lowercase      ,      return_tensors="pt"       )

							# verify pixel values
							A__        =			torch.Size([1, 3, 800, 1066]       )
							self.assertEqual(encoding["pixel_values"].shape      ,      lowercase       )

							A__        =			torch.tensor([0.2796, 0.3138, 0.3481]       )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,      lowercase      ,      atol=1e-4       )       )

							# verify area
							A__        =			torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,      lowercase       )       )
							# verify boxes
							A__        =			torch.Size([6, 4]       )
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      lowercase       )
							A__        =			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      lowercase      ,      atol=1e-3       )       )
							# verify image_id
							A__        =			torch.tensor([39769]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      lowercase       )       )
							# verify is_crowd
							A__        =			torch.tensor([0, 0, 0, 0, 0, 0]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      lowercase       )       )
							# verify class_labels
							A__        =			torch.tensor([75, 75, 63, 65, 17, 17]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      lowercase       )       )
							# verify orig_size
							A__        =			torch.tensor([480, 640]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      lowercase       )       )
							# verify size
							A__        =			torch.tensor([800, 1066]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      lowercase       )       )


			@slow
			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
							with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"      ,      "r"       ) as f:
											A__        =			json.loads(f.read()       )

							A__        =			{"file_name": "000000039769.png", "image_id": 39769, "segments_info": target}

							A__        =			pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"       )

							# encode them
							A__        =			DetaImageProcessor(format="coco_panoptic"       )
							A__        =			image_processing(images=lowercase      ,      annotations=lowercase      ,      masks_path=lowercase      ,      return_tensors="pt"       )

							# verify pixel values
							A__        =			torch.Size([1, 3, 800, 1066]       )
							self.assertEqual(encoding["pixel_values"].shape      ,      lowercase       )

							A__        =			torch.tensor([0.2796, 0.3138, 0.3481]       )
							self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]      ,      lowercase      ,      atol=1e-4       )       )

							# verify area
							A__        =			torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["area"]      ,      lowercase       )       )
							# verify boxes
							A__        =			torch.Size([6, 4]       )
							self.assertEqual(encoding["labels"][0]["boxes"].shape      ,      lowercase       )
							A__        =			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]      ,      lowercase      ,      atol=1e-3       )       )
							# verify image_id
							A__        =			torch.tensor([39769]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]      ,      lowercase       )       )
							# verify is_crowd
							A__        =			torch.tensor([0, 0, 0, 0, 0, 0]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]      ,      lowercase       )       )
							# verify class_labels
							A__        =			torch.tensor([17, 17, 63, 75, 75, 93]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]      ,      lowercase       )       )
							# verify masks
							A__        =			822873
							self.assertEqual(encoding["labels"][0]["masks"].sum().item()      ,      lowercase       )
							# verify orig_size
							A__        =			torch.tensor([480, 640]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]      ,      lowercase       )       )
							# verify size
							A__        =			torch.tensor([800, 1066]       )
							self.assertTrue(torch.allclose(encoding["labels"][0]["size"]      ,      lowercase       )       )

