

import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, 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 import (
						    LiltForQuestionAnswering,
						    LiltForSequenceClassification,
						    LiltForTokenClassification,
						    LiltModel,
						)
						from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST




class      UpperCamelCase :

				def __init__(			self :      int							,    snake_case__ :      List[Any]							,    snake_case__ :      str=1_3							,    snake_case__ :      Optional[Any]=7							,    snake_case__ :      str=True							,    snake_case__ :      Optional[int]=True							,    snake_case__ :      Tuple=True							,    snake_case__ :      Tuple=True							,    snake_case__ :      Tuple=9_9							,    snake_case__ :      List[str]=2_4							,    snake_case__ :      Union[str, Any]=2							,    snake_case__ :      Union[str, Any]=6							,    snake_case__ :      List[str]=3_7							,    snake_case__ :      List[str]="gelu"							,    snake_case__ :      List[str]=0.1							,    snake_case__ :      str=0.1							,    snake_case__ :      Any=5_1_2							,    snake_case__ :      Tuple=1_6							,    snake_case__ :      Any=2							,    snake_case__ :      List[Any]=0.02							,    snake_case__ :      Dict=3							,    snake_case__ :      Dict=None							,    snake_case__ :      Optional[int]=1_0_0_0							,    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			parent
					SCREAMING_SNAKE_CASE								=			batch_size
					SCREAMING_SNAKE_CASE								=			seq_length
					SCREAMING_SNAKE_CASE								=			is_training
					SCREAMING_SNAKE_CASE								=			use_input_mask
					SCREAMING_SNAKE_CASE								=			use_token_type_ids
					SCREAMING_SNAKE_CASE								=			use_labels
					SCREAMING_SNAKE_CASE								=			vocab_size
					SCREAMING_SNAKE_CASE								=			hidden_size
					SCREAMING_SNAKE_CASE								=			num_hidden_layers
					SCREAMING_SNAKE_CASE								=			num_attention_heads
					SCREAMING_SNAKE_CASE								=			intermediate_size
					SCREAMING_SNAKE_CASE								=			hidden_act
					SCREAMING_SNAKE_CASE								=			hidden_dropout_prob
					SCREAMING_SNAKE_CASE								=			attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE								=			max_position_embeddings
					SCREAMING_SNAKE_CASE								=			type_vocab_size
					SCREAMING_SNAKE_CASE								=			type_sequence_label_size
					SCREAMING_SNAKE_CASE								=			initializer_range
					SCREAMING_SNAKE_CASE								=			num_labels
					SCREAMING_SNAKE_CASE								=			scope
					SCREAMING_SNAKE_CASE								=			range_bbox

				def UpperCamelCase			(			self :      Tuple    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

					SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length, 4]							,    self.range_bbox    )
					# 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]:
								SCREAMING_SNAKE_CASE								=			bbox[i, j, 3]
								SCREAMING_SNAKE_CASE								=			bbox[i, j, 1]
								SCREAMING_SNAKE_CASE								=			t
							if bbox[i, j, 2] < bbox[i, j, 0]:
								SCREAMING_SNAKE_CASE								=			bbox[i, j, 2]
								SCREAMING_SNAKE_CASE								=			bbox[i, j, 0]
								SCREAMING_SNAKE_CASE								=			t

					SCREAMING_SNAKE_CASE								=			None
					if self.use_input_mask:
						SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    vocab_size=2    )

					SCREAMING_SNAKE_CASE								=			None
					if self.use_token_type_ids:
						SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.type_vocab_size    )

					SCREAMING_SNAKE_CASE								=			None
					SCREAMING_SNAKE_CASE								=			None
					if self.use_labels:
						SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size]							,    self.type_sequence_label_size    )
						SCREAMING_SNAKE_CASE								=			ids_tensor([self.batch_size, self.seq_length]							,    self.num_labels    )

					SCREAMING_SNAKE_CASE								=			self.get_config()

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

				def UpperCamelCase			(			self :      Tuple    ):

					"""simple docstring"""






					return LiltConfig(
					    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							,    )

				def UpperCamelCase			(			self :      int							,    snake_case__ :      Tuple							,    snake_case__ :      Optional[int]							,    snake_case__ :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      Dict							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]							,    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			LiltModel(config=snake_case__    )
					model.to(snake_case__    )
					model.eval()
					SCREAMING_SNAKE_CASE								=			model(snake_case__							,    bbox=snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__    )
					SCREAMING_SNAKE_CASE								=			model(snake_case__							,    bbox=snake_case__							,    token_type_ids=snake_case__    )
					SCREAMING_SNAKE_CASE								=			model(snake_case__							,    bbox=snake_case__    )

					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 UpperCamelCase			(			self :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      Tuple							,    snake_case__ :      Union[str, Any]							,    snake_case__ :      Dict							,    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.num_labels
					SCREAMING_SNAKE_CASE								=			LiltForTokenClassification(config=snake_case__    )
					model.to(snake_case__    )
					model.eval()
					SCREAMING_SNAKE_CASE								=			model(
					    snake_case__							,    bbox=snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    labels=snake_case__    )
					self.parent.assertEqual(result.logits.shape							,    (self.batch_size, self.seq_length, self.num_labels)    )

				def UpperCamelCase			(			self :      Dict							,    snake_case__ :      str							,    snake_case__ :      List[Any]							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      Dict							,    snake_case__ :      Tuple							,    snake_case__ :      Union[str, Any]							,    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			LiltForQuestionAnswering(config=snake_case__    )
					model.to(snake_case__    )
					model.eval()
					SCREAMING_SNAKE_CASE								=			model(
					    snake_case__							,    bbox=snake_case__							,    attention_mask=snake_case__							,    token_type_ids=snake_case__							,    start_positions=snake_case__							,    end_positions=snake_case__							,    )
					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 UpperCamelCase			(			self :      Optional[int]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.prepare_config_and_inputs()
					(
					    (
					    SCREAMING_SNAKE_CASE	
					)						,	(
					    SCREAMING_SNAKE_CASE	
					)						,	(
					    SCREAMING_SNAKE_CASE	
					)						,	(
					    SCREAMING_SNAKE_CASE	
					)						,	(
					    SCREAMING_SNAKE_CASE	
					)						,	(
					    SCREAMING_SNAKE_CASE	
					)						,	(
					    SCREAMING_SNAKE_CASE	
					)						,		
					)								=			config_and_inputs
					SCREAMING_SNAKE_CASE								=			{
					    'input_ids': input_ids,
					    'bbox': bbox,
					    'token_type_ids': token_type_ids,
					    'attention_mask': input_mask,
					}
					return config, inputs_dict




@require_torch
class      UpperCamelCase (     SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			SCREAMING_SNAKE_CASE	,			unittest.TestCase   ):
				__UpperCamelCase         =(
				    (
				        LiltModel,
				        LiltForSequenceClassification,
				        LiltForTokenClassification,
				        LiltForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				__UpperCamelCase         =(
				    {
				        "feature-extraction": LiltModel,
				        "question-answering": LiltForQuestionAnswering,
				        "text-classification": LiltForSequenceClassification,
				        "token-classification": LiltForTokenClassification,
				        "zero-shot": LiltForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				__UpperCamelCase         =False
				__UpperCamelCase         =False

				def UpperCamelCase			(			self :      int							,    snake_case__ :      int							,    snake_case__ :      Optional[int]							,    snake_case__ :      Optional[Any]							,    snake_case__ :      Any							,    snake_case__ :      Union[str, Any]    ):

					"""simple docstring"""






					return True

				def UpperCamelCase			(			self :      Tuple    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			LiltModelTester(self    )
					SCREAMING_SNAKE_CASE								=			ConfigTester(self							,    config_class=snake_case__							,    hidden_size=3_7    )

				def UpperCamelCase			(			self :      str    ):

					"""simple docstring"""






					self.config_tester.run_common_tests()

				def UpperCamelCase			(			self :      Any    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*snake_case__    )

				def UpperCamelCase			(			self :      Union[str, Any]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
					for type in ["absolute", "relative_key", "relative_key_query"]:
						SCREAMING_SNAKE_CASE								=			type
						self.model_tester.create_and_check_model(*snake_case__    )

				def UpperCamelCase			(			self :      Optional[int]    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_token_classification(*snake_case__    )

				def UpperCamelCase			(			self :      int    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_question_answering(*snake_case__    )




				@slow
				def UpperCamelCase			(			self :      List[Any]    ):

					"""simple docstring"""






					for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						SCREAMING_SNAKE_CASE								=			LiltModel.from_pretrained(snake_case__    )
						self.assertIsNotNone(snake_case__    )




@require_torch
@slow
class      UpperCamelCase (     unittest.TestCase   ):

				def UpperCamelCase			(			self :      Any    ):

					"""simple docstring"""






					SCREAMING_SNAKE_CASE								=			LiltModel.from_pretrained('SCUT-DLVCLab/lilt-roberta-en-base'    ).to(snake_case__    )

					SCREAMING_SNAKE_CASE								=			torch.tensor([[1, 2]]							,    device=snake_case__    )
					SCREAMING_SNAKE_CASE								=			torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]							,    device=snake_case__    )

					# forward pass
					with torch.no_grad():
						SCREAMING_SNAKE_CASE								=			model(input_ids=snake_case__							,    bbox=snake_case__    )

					SCREAMING_SNAKE_CASE								=			torch.Size([1, 2, 7_6_8]    )
					SCREAMING_SNAKE_CASE								=			torch.tensor(
					    [[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]]							,    device=snake_case__							,    )

					self.assertTrue(outputs.last_hidden_state.shape							,    snake_case__    )
					self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]							,    snake_case__							,    atol=1E-3    )    )




# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    "pipelines_utils",
    "0.22.0",
    "Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.",
    standard_warn=False,
    stacklevel=3,
)

