"""simple docstring"""






import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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 (
						    LayoutLMForMaskedLM,
						    LayoutLMForQuestionAnswering,
						    LayoutLMForSequenceClassification,
						    LayoutLMForTokenClassification,
						    LayoutLMModel,
						)
class     snake_case	:




					"""simple docstring"""

					def __init__(    self       :     Union[str, Any]   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[str]=13   ,lowerCamelCase__       :     int=7   ,lowerCamelCase__       :     List[str]=True   ,lowerCamelCase__       :     Optional[Any]=True   ,lowerCamelCase__       :     Tuple=True   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     int=99   ,lowerCamelCase__       :     List[str]=32   ,lowerCamelCase__       :     Optional[int]=5   ,lowerCamelCase__       :     List[Any]=4   ,lowerCamelCase__       :     int=37   ,lowerCamelCase__       :     Union[str, Any]="gelu"   ,lowerCamelCase__       :     Dict=0.1   ,lowerCamelCase__       :     List[Any]=0.1   ,lowerCamelCase__       :     Dict=512   ,lowerCamelCase__       :     List[Any]=16   ,lowerCamelCase__       :     List[Any]=2   ,lowerCamelCase__       :     Any=0.0_2   ,lowerCamelCase__       :     Optional[int]=3   ,lowerCamelCase__       :     Dict=4   ,lowerCamelCase__       :     Optional[Any]=None   ,lowerCamelCase__       :     List[Any]=1_000   ,):
									UpperCAmelCase__					=							parent
									UpperCAmelCase__					=							batch_size
									UpperCAmelCase__					=							seq_length
									UpperCAmelCase__					=							is_training
									UpperCAmelCase__					=							use_input_mask
									UpperCAmelCase__					=							use_token_type_ids
									UpperCAmelCase__					=							use_labels
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							hidden_size
									UpperCAmelCase__					=							num_hidden_layers
									UpperCAmelCase__					=							num_attention_heads
									UpperCAmelCase__					=							intermediate_size
									UpperCAmelCase__					=							hidden_act
									UpperCAmelCase__					=							hidden_dropout_prob
									UpperCAmelCase__					=							attention_probs_dropout_prob
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							type_vocab_size
									UpperCAmelCase__					=							type_sequence_label_size
									UpperCAmelCase__					=							initializer_range
									UpperCAmelCase__					=							num_labels
									UpperCAmelCase__					=							num_choices
									UpperCAmelCase__					=							scope
									UpperCAmelCase__					=							range_bbox







					def 		__lowerCAmelCase						(    self       :     int      ):
									UpperCAmelCase__					=							ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size      )

									UpperCAmelCase__					=							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]:
																					UpperCAmelCase__					=							bbox[i, j, 3]
																					UpperCAmelCase__					=							bbox[i, j, 1]
																					UpperCAmelCase__					=							t
																	if bbox[i, j, 2] < bbox[i, j, 0]:
																					UpperCAmelCase__					=							bbox[i, j, 2]
																					UpperCAmelCase__					=							bbox[i, j, 0]
																					UpperCAmelCase__					=							t

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

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

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

									UpperCAmelCase__					=							self.get_config()

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







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									return 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   ,)







					def 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Optional[int]   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Union[str, Any]      ):
									UpperCAmelCase__					=							LayoutLMModel(config=UpperCamelCase__      )
									model.to(UpperCamelCase__      )
									model.eval()
									UpperCAmelCase__					=							model(UpperCamelCase__   ,UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__      )
									UpperCAmelCase__					=							model(UpperCamelCase__   ,UpperCamelCase__   ,token_type_ids=UpperCamelCase__      )
									UpperCAmelCase__					=							model(UpperCamelCase__   ,UpperCamelCase__      )
									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 		__lowerCAmelCase						(    self       :     List[Any]   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     List[str]   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     int   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Dict      ):
									UpperCAmelCase__					=							LayoutLMForMaskedLM(config=UpperCamelCase__      )
									model.to(UpperCamelCase__      )
									model.eval()
									UpperCAmelCase__					=							model(UpperCamelCase__   ,UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__   ,labels=UpperCamelCase__      )
									self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.vocab_size)      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]   ,lowerCamelCase__       :     Optional[int]   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     Optional[int]   ,lowerCamelCase__       :     Optional[int]   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     Optional[int]   ,lowerCamelCase__       :     List[Any]      ):
									UpperCAmelCase__					=							self.num_labels
									UpperCAmelCase__					=							LayoutLMForSequenceClassification(UpperCamelCase__      )
									model.to(UpperCamelCase__      )
									model.eval()
									UpperCAmelCase__					=							model(
									    UpperCamelCase__   ,UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__   ,labels=UpperCamelCase__      )
									self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)      )







					def 		__lowerCAmelCase						(    self       :     Dict   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     Tuple   ,lowerCamelCase__       :     Dict   ,lowerCamelCase__       :     Optional[Any]      ):
									UpperCAmelCase__					=							self.num_labels
									UpperCAmelCase__					=							LayoutLMForTokenClassification(config=UpperCamelCase__      )
									model.to(UpperCamelCase__      )
									model.eval()
									UpperCAmelCase__					=							model(UpperCamelCase__   ,UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__   ,labels=UpperCamelCase__      )
									self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.seq_length, self.num_labels)      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Any   ,lowerCamelCase__       :     List[Any]   ,lowerCamelCase__       :     Tuple   ,lowerCamelCase__       :     Tuple   ,lowerCamelCase__       :     Optional[Any]   ,lowerCamelCase__       :     str   ,lowerCamelCase__       :     Any      ):
									UpperCAmelCase__					=							LayoutLMForQuestionAnswering(config=UpperCamelCase__      )
									model.to(UpperCamelCase__      )
									model.eval()
									UpperCAmelCase__					=							model(
									    UpperCamelCase__   ,bbox=UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__   ,start_positions=UpperCamelCase__   ,end_positions=UpperCamelCase__   ,)
									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 		__lowerCAmelCase						(    self       :     Optional[Any]      ):
									UpperCAmelCase__					=							self.prepare_config_and_inputs()
									(
									    UpperCAmelCase__				
									)					=							config_and_inputs
									UpperCAmelCase__					=							{
									    "input_ids": input_ids,
									    "bbox": bbox,
									    "token_type_ids": token_type_ids,
									    "attention_mask": input_mask,
									}
									return config, inputs_dict






@require_torch
class     snake_case	(			lowerCAmelCase__				,   lowerCAmelCase__				,   unittest.TestCase    ):




					"""simple docstring"""

					snake_case__       =			(
					    (
					        LayoutLMModel,
					        LayoutLMForMaskedLM,
					        LayoutLMForSequenceClassification,
					        LayoutLMForTokenClassification,
					        LayoutLMForQuestionAnswering,
					    )
					    if is_torch_available()
					    else None
					)
					snake_case__       =			(
					    {
					        """document-question-answering""": LayoutLMForQuestionAnswering,
					        """feature-extraction""": LayoutLMModel,
					        """fill-mask""": LayoutLMForMaskedLM,
					        """text-classification""": LayoutLMForSequenceClassification,
					        """token-classification""": LayoutLMForTokenClassification,
					        """zero-shot""": LayoutLMForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)
					snake_case__       =			True







					def 		__lowerCAmelCase						(    self       :     Dict      ):
									UpperCAmelCase__					=							LayoutLMModelTester(self      )
									UpperCAmelCase__					=							ConfigTester(self   ,config_class=UpperCamelCase__   ,hidden_size=37      )







					def 		__lowerCAmelCase						(    self       :     str      ):
									self.config_tester.run_common_tests()







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     Optional[int]      ):
									UpperCAmelCase__					=							self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
													UpperCAmelCase__					=							type
													self.model_tester.create_and_check_model(*UpperCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     int      ):
									UpperCAmelCase__					=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     str      ):
									UpperCAmelCase__					=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*UpperCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*UpperCamelCase__      )







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






def   a_						( ):
				# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
				# fmt: off
				UpperCAmelCase__					=							torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]] ,      device=_SCREAMING_SNAKE_CASE	)  # noqa: E231
				UpperCAmelCase__					=							torch.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],] ,      device=_SCREAMING_SNAKE_CASE	)  # noqa: E231
				UpperCAmelCase__					=							torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]] ,      device=_SCREAMING_SNAKE_CASE	)  # noqa: E231
				UpperCAmelCase__					=							torch.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]] ,      device=_SCREAMING_SNAKE_CASE	)  # noqa: E231
				# these are sequence labels (i.e. at the token level)
				UpperCAmelCase__					=							torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]] ,      device=_SCREAMING_SNAKE_CASE	)  # noqa: E231
				# fmt: on

				return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class     snake_case	(			unittest.TestCase    ):




					"""simple docstring"""

					@slow
					def 		__lowerCAmelCase						(    self       :     str      ):
									UpperCAmelCase__					=							LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'      ).to(UpperCamelCase__      )

									UpperCAmelCase__					=							prepare_layoutlm_batch_inputs()

									# forward pass
									UpperCAmelCase__					=							model(input_ids=UpperCamelCase__   ,bbox=UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__      )

									# test the sequence output on [0, :3, :3]
									UpperCAmelCase__					=							torch.tensor(
									    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]   ,device=UpperCamelCase__   ,)

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

									# test the pooled output on [1, :3]
									UpperCAmelCase__					=							torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]   ,device=UpperCamelCase__      )

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







					@slow
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									# initialize model with randomly initialized sequence classification head
									UpperCAmelCase__					=							LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'   ,num_labels=2      ).to(
									    UpperCamelCase__      )

									UpperCAmelCase__					=							prepare_layoutlm_batch_inputs()

									# forward pass
									UpperCAmelCase__					=							model(
									    input_ids=UpperCamelCase__   ,bbox=UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__   ,labels=torch.tensor([1, 1]   ,device=UpperCamelCase__      )   ,)

									# test whether we get a loss as a scalar
									UpperCAmelCase__					=							outputs.loss
									UpperCAmelCase__					=							torch.Size([]      )
									self.assertEqual(loss.shape   ,UpperCamelCase__      )

									# test the shape of the logits
									UpperCAmelCase__					=							outputs.logits
									UpperCAmelCase__					=							torch.Size((2, 2)      )
									self.assertEqual(logits.shape   ,UpperCamelCase__      )







					@slow
					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									# initialize model with randomly initialized token classification head
									UpperCAmelCase__					=							LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'   ,num_labels=13      ).to(
									    UpperCamelCase__      )

									UpperCAmelCase__					=							prepare_layoutlm_batch_inputs()

									# forward pass
									UpperCAmelCase__					=							model(
									    input_ids=UpperCamelCase__   ,bbox=UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__   ,labels=UpperCamelCase__      )

									# test the loss calculation to be around 2.65
									# expected_loss = torch.tensor(2.65, device=torch_device)

									# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
									# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

									# test the shape of the logits
									UpperCAmelCase__					=							outputs.logits
									UpperCAmelCase__					=							torch.Size((2, 25, 13)      )
									self.assertEqual(logits.shape   ,UpperCamelCase__      )







					@slow
					def 		__lowerCAmelCase						(    self       :     List[str]      ):
									# initialize model with randomly initialized token classification head
									UpperCAmelCase__					=							LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'      ).to(UpperCamelCase__      )

									UpperCAmelCase__					=							prepare_layoutlm_batch_inputs()

									# forward pass
									UpperCAmelCase__					=							model(input_ids=UpperCamelCase__   ,bbox=UpperCamelCase__   ,attention_mask=UpperCamelCase__   ,token_type_ids=UpperCamelCase__      )

									# test the shape of the logits
									UpperCAmelCase__					=							torch.Size((2, 25)      )
									self.assertEqual(outputs.start_logits.shape   ,UpperCamelCase__      )
									self.assertEqual(outputs.end_logits.shape   ,UpperCamelCase__      )




"""simple docstring"""






import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


lowerCAmelCase__  :  List[Any]											=     '\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class     snake_case	(			unittest.TestCase				,   __UpperCAmelCase    ):




					"""simple docstring"""

					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							load_tool('summarization'      )
									self.tool.setup()
									UpperCAmelCase__					=							load_tool('summarization'   ,remote=lowerCamelCase__      )







					def 		__lowerCAmelCase						(    self       :     Union[str, Any]      ):
									UpperCAmelCase__					=							self.tool(lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.remote_tool(lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     List[Any]      ):
									UpperCAmelCase__					=							self.tool(text=lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)







					def 		__lowerCAmelCase						(    self       :     Tuple      ):
									UpperCAmelCase__					=							self.remote_tool(text=lowerCamelCase__      )
									self.assertEqual(
									    lowerCamelCase__   ,'Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'   ,)



