


import inspect
import unittest

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

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


if is_torch_available():
	import torch
	from torch import nn

	from transformers import YolosForObjectDetection, YolosModel
	from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor



class 				A       :
					'''simple docstring'''






					def __init__(self       :		str ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Union[str, Any]=13 ,   _UpperCAmelCase       :		Dict=[30, 30] ,   _UpperCAmelCase       :		Optional[int]=2 ,   _UpperCAmelCase       :		Optional[Any]=3 ,   _UpperCAmelCase       :		Union[str, Any]=True ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		Optional[int]=32 ,   _UpperCAmelCase       :		List[Any]=5 ,   _UpperCAmelCase       :		int=4 ,   _UpperCAmelCase       :		Optional[Any]=37 ,   _UpperCAmelCase       :		Union[str, Any]="gelu" ,   _UpperCAmelCase       :		Tuple=0.1 ,   _UpperCAmelCase       :		int=0.1 ,   _UpperCAmelCase       :		List[str]=10 ,   _UpperCAmelCase       :		List[str]=0.02 ,   _UpperCAmelCase       :		str=3 ,   _UpperCAmelCase       :		int=None ,   _UpperCAmelCase       :		List[Any]=8 ,   _UpperCAmelCase       :		Dict=10 ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						image_size
							lowercase__	     =						patch_size
							lowercase__	     =						num_channels
							lowercase__	     =						is_training
							lowercase__	     =						use_labels
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_act
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						type_sequence_label_size
							lowercase__	     =						initializer_range
							lowercase__	     =						num_labels
							lowercase__	     =						scope
							lowercase__	     =						n_targets
							lowercase__	     =						num_detection_tokens
							# we set the expected sequence length (which is used in several tests)
							# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
							lowercase__	     =						(image_size[1] // patch_size) * (image_size[0] // patch_size)
							lowercase__	     =						num_patches + 1 + self.num_detection_tokens

					def     lowerCamelCase__	(self       :		Optional[int]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)

							lowercase__	     =						None
							if self.use_labels:
									# labels is a list of Dict (each Dict being the labels for a given example in the batch)
									lowercase__	     =						[]
									for i in range(self.batch_size						):
											lowercase__	     =						{}
											lowercase__	     =						torch.randint(
											    high=self.num_labels ,   size=(self.n_targets,) ,   device=_UpperCAmelCase						)
											lowercase__	     =						torch.rand(self.n_targets ,   4 ,   device=_UpperCAmelCase						)
											labels.append(_UpperCAmelCase						)

							lowercase__	     =						self.get_config()

							return config, pixel_values, labels

					def     lowerCamelCase__	(self       :		Tuple						)		->  List[Any]:




							"""simple docstring"""


							return YolosConfig(
							    image_size=self.image_size ,   patch_size=self.patch_size ,   num_channels=self.num_channels ,   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 ,   is_decoder=_UpperCAmelCase ,   initializer_range=self.initializer_range ,   num_detection_tokens=self.num_detection_tokens ,   num_labels=self.num_labels ,   )

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						YolosModel(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(
							    result.last_hidden_state.shape ,   (self.batch_size, self.expected_seq_len, self.hidden_size)						)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		int						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						YolosForObjectDetection(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(pixel_values=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase						)

							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_detection_tokens, self.num_labels + 1)						)
							self.parent.assertEqual(result.pred_boxes.shape ,   (self.batch_size, self.num_detection_tokens, 4)						)

							lowercase__	     =						model(pixel_values=_UpperCAmelCase ,   labels=_UpperCAmelCase						)

							self.parent.assertEqual(result.loss.shape ,   ()						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_detection_tokens, self.num_labels + 1)						)
							self.parent.assertEqual(result.pred_boxes.shape ,   (self.batch_size, self.num_detection_tokens, 4)						)


					def     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							lowercase__     ,      lowercase__     ,      lowercase__	     =						config_and_inputs
							lowercase__	     =						{"""pixel_values""": pixel_values}
							return config, inputs_dict






@require_torch
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
					A__     =					(
					    {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
					)

					A__     =					False
					A__     =					False
					A__     =					False
					A__     =					False

					def     lowerCamelCase__	(self       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		int=False						)		->  str:




							"""simple docstring"""


							lowercase__	     =						super()._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase ,   return_labels=_UpperCAmelCase						)

							if return_labels:
									if model_class.__name__ == "YolosForObjectDetection":
											lowercase__	     =						[]
											for i in range(self.model_tester.batch_size						):
													lowercase__	     =						{}
													lowercase__	     =						torch.ones(
													    size=(self.model_tester.n_targets,) ,   device=_UpperCAmelCase ,   dtype=torch.long						)
													lowercase__	     =						torch.ones(
													    self.model_tester.n_targets ,   4 ,   device=_UpperCAmelCase ,   dtype=torch.float						)
													labels.append(_UpperCAmelCase						)
											lowercase__	     =						labels

							return inputs_dict

					def     lowerCamelCase__	(self       :		Dict						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						YolosModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   has_text_modality=_UpperCAmelCase ,   hidden_size=37						)

					def     lowerCamelCase__	(self       :		Dict						)		->  str:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  List[str]:




							"""simple docstring"""


							pass

					def     lowerCamelCase__	(self       :		Tuple						)		->  str:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__	     =						model_class(_UpperCAmelCase						)
									self.assertIsInstance(model.get_input_embeddings() ,   (nn.Module)						)
									lowercase__	     =						model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(_UpperCAmelCase ,   nn.Linear						)						)

					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()

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

									lowercase__	     =						["""pixel_values"""]
									self.assertListEqual(arg_names[:1] ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  int:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Dict						)		->  List[str]:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__	     =						True

							# in YOLOS, the seq_len is different
							lowercase__	     =						self.model_tester.expected_seq_len
							for model_class in self.all_model_classes:
									lowercase__	     =						True
									lowercase__	     =						False
									lowercase__	     =						True
									lowercase__	     =						model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									with torch.no_grad():
											lowercase__	     =						model(**self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)						)
									lowercase__	     =						outputs.attentions
									self.assertEqual(len(_UpperCAmelCase						) ,   self.model_tester.num_hidden_layers						)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									lowercase__	     =						True
									lowercase__	     =						model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									with torch.no_grad():
											lowercase__	     =						model(**self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)						)
									lowercase__	     =						outputs.attentions
									self.assertEqual(len(_UpperCAmelCase						) ,   self.model_tester.num_hidden_layers						)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]						) ,   [self.model_tester.num_attention_heads, seq_len, seq_len] ,   )
									lowercase__	     =						len(_UpperCAmelCase						)

									# Check attention is always last and order is fine
									lowercase__	     =						True
									lowercase__	     =						True
									lowercase__	     =						model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()
									with torch.no_grad():
											lowercase__	     =						model(**self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)						)

									lowercase__	     =						1
									self.assertEqual(out_len + added_hidden_states ,   len(_UpperCAmelCase						)						)

									lowercase__	     =						outputs.attentions

									self.assertEqual(len(_UpperCAmelCase						) ,   self.model_tester.num_hidden_layers						)
									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]						) ,   [self.model_tester.num_attention_heads, seq_len, seq_len] ,   )

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Tuple:




							"""simple docstring"""


							def check_hidden_states_output(_UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Any						):
									lowercase__	     =						model_class(_UpperCAmelCase						)
									model.to(_UpperCAmelCase						)
									model.eval()

									with torch.no_grad():
											lowercase__	     =						model(**self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)						)

									lowercase__	     =						outputs.hidden_states

									lowercase__	     =						getattr(
									    self.model_tester ,   """expected_num_hidden_layers""" ,   self.model_tester.num_hidden_layers + 1						)
									self.assertEqual(len(_UpperCAmelCase						) ,   _UpperCAmelCase						)

									# YOLOS has a different seq_length
									lowercase__	     =						self.model_tester.expected_seq_len

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]						) ,   [seq_length, self.model_tester.hidden_size] ,   )

							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									lowercase__	     =						True
									check_hidden_states_output(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									lowercase__	     =						True

									check_hidden_states_output(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Any						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase						)


					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Dict:




							"""simple docstring"""


							for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__	     =						YolosModel.from_pretrained(_UpperCAmelCase						)
									self.assertIsNotNone(_UpperCAmelCase						)






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




		lowercase__	     =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
		return image







@require_torch
@require_vision
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@cached_property
					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""						) if is_vision_available() else None


					@slow
					def     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""						).to(_UpperCAmelCase						)

							lowercase__	     =						self.default_image_processor
							lowercase__	     =						prepare_img()
							lowercase__	     =						image_processor(images=_UpperCAmelCase ,   return_tensors="""pt"""						).to(_UpperCAmelCase						)

							# forward pass
							with torch.no_grad():
									lowercase__	     =						model(inputs.pixel_values						)

							# verify outputs
							lowercase__	     =						torch.Size((1, 100, 92)						)
							self.assertEqual(outputs.logits.shape ,   _UpperCAmelCase						)

							lowercase__	     =						torch.tensor(
							    [[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] ,   device=_UpperCAmelCase ,   )
							lowercase__	     =						torch.tensor(
							    [[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] ,   device=_UpperCAmelCase						)
							self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,   _UpperCAmelCase ,   atol=1E-4						)						)
							self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] ,   _UpperCAmelCase ,   atol=1E-4						)						)

							# verify postprocessing
							lowercase__	     =						image_processor.post_process_object_detection(
							    _UpperCAmelCase ,   threshold=0.3 ,   target_sizes=[image.size[::-1]]						)[0]
							lowercase__	     =						torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861]						).to(_UpperCAmelCase						)
							lowercase__	     =						[75, 75, 17, 63, 17]
							lowercase__	     =						torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495]						).to(_UpperCAmelCase						)

							self.assertEqual(len(results["""scores"""]						) ,   5						)
							self.assertTrue(torch.allclose(results["""scores"""] ,   _UpperCAmelCase ,   atol=1E-4						)						)
							self.assertSequenceEqual(results["""labels"""].tolist() ,   _UpperCAmelCase						)
							self.assertTrue(torch.allclose(results["""boxes"""][0, :] ,   _UpperCAmelCase						)						)





import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    FlaubertForMultipleChoice,
	    FlaubertForQuestionAnswering,
	    FlaubertForQuestionAnsweringSimple,
	    FlaubertForSequenceClassification,
	    FlaubertForTokenClassification,
	    FlaubertModel,
	    FlaubertWithLMHeadModel,
	)
	from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					def __init__(self       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Any=13 ,   _UpperCAmelCase       :		str=7 ,   _UpperCAmelCase       :		str=True ,   _UpperCAmelCase       :		List[Any]=True ,   _UpperCAmelCase       :		Optional[int]=True ,   _UpperCAmelCase       :		List[Any]=True ,   _UpperCAmelCase       :		Tuple=True ,   _UpperCAmelCase       :		Optional[int]=False ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]=False ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		Optional[int]=99 ,   _UpperCAmelCase       :		Any=0 ,   _UpperCAmelCase       :		Dict=32 ,   _UpperCAmelCase       :		Tuple=5 ,   _UpperCAmelCase       :		Union[str, Any]=4 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[Any]=0.1 ,   _UpperCAmelCase       :		List[str]=512 ,   _UpperCAmelCase       :		Tuple=12 ,   _UpperCAmelCase       :		int=2 ,   _UpperCAmelCase       :		Tuple=0.02 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Dict="last" ,   _UpperCAmelCase       :		Any=None ,   _UpperCAmelCase       :		int=None ,   )		->  int:




							"""simple docstring"""


							lowercase__	     =						parent
							lowercase__	     =						batch_size
							lowercase__	     =						seq_length
							lowercase__	     =						is_training
							lowercase__	     =						use_input_lengths
							lowercase__	     =						use_token_type_ids
							lowercase__	     =						use_labels
							lowercase__	     =						gelu_activation
							lowercase__	     =						sinusoidal_embeddings
							lowercase__	     =						causal
							lowercase__	     =						asm
							lowercase__	     =						n_langs
							lowercase__	     =						vocab_size
							lowercase__	     =						n_special
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						type_sequence_label_size
							lowercase__	     =						initializer_range
							lowercase__	     =						num_labels
							lowercase__	     =						num_choices
							lowercase__	     =						summary_type
							lowercase__	     =						use_proj
							lowercase__	     =						scope

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[str]:




							"""simple docstring"""


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

							lowercase__	     =						None
							if self.use_input_lengths:
									lowercase__	     =						(
									    ids_tensor([self.batch_size] ,   vocab_size=2						) + self.seq_length - 2
									)  # small variation of seq_length

							lowercase__	     =						None
							if self.use_token_type_ids:
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.n_langs						)

							lowercase__	     =						None
							lowercase__	     =						None
							lowercase__	     =						None
							if self.use_labels:
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.type_sequence_label_size						)
									lowercase__	     =						ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels						)
									lowercase__	     =						ids_tensor([self.batch_size] ,   2						).float()
									lowercase__	     =						ids_tensor([self.batch_size] ,   self.num_choices						)

							lowercase__	     =						self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_lengths,
							    sequence_labels,
							    token_labels,
							    is_impossible_labels,
							    choice_labels,
							    input_mask,
							)

					def     lowerCamelCase__	(self       :		int						)		->  Dict:




							"""simple docstring"""


							return FlaubertConfig(
							    vocab_size=self.vocab_size ,   n_special=self.n_special ,   emb_dim=self.hidden_size ,   n_layers=self.num_hidden_layers ,   n_heads=self.num_attention_heads ,   dropout=self.hidden_dropout_prob ,   attention_dropout=self.attention_probs_dropout_prob ,   gelu_activation=self.gelu_activation ,   sinusoidal_embeddings=self.sinusoidal_embeddings ,   asm=self.asm ,   causal=self.causal ,   n_langs=self.n_langs ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   summary_type=self.summary_type ,   use_proj=self.use_proj ,   )

					def     lowerCamelCase__	(self       :		Tuple ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Tuple ,   )		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertModel(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						model(_UpperCAmelCase ,   lengths=_UpperCAmelCase ,   langs=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase ,   langs=_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size)						)

					def     lowerCamelCase__	(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		List[str] ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						FlaubertWithLMHeadModel(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   token_type_ids=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.loss.shape ,   ()						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		Union[str, Any] ,   )		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForQuestionAnsweringSimple(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(_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       :		str ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Optional[int] ,   )		->  List[Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForQuestionAnswering(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)

							lowercase__	     =						model(
							    _UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase ,   cls_index=_UpperCAmelCase ,   is_impossible=_UpperCAmelCase ,   p_mask=_UpperCAmelCase ,   )

							lowercase__	     =						model(
							    _UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase ,   cls_index=_UpperCAmelCase ,   is_impossible=_UpperCAmelCase ,   )

							((lowercase__)     ,      )	     =						result_with_labels.to_tuple()

							lowercase__	     =						model(_UpperCAmelCase ,   start_positions=_UpperCAmelCase ,   end_positions=_UpperCAmelCase						)

							((lowercase__)     ,      )	     =						result_with_labels.to_tuple()

							self.parent.assertEqual(result_with_labels.loss.shape ,   ()						)
							self.parent.assertEqual(result.start_top_log_probs.shape ,   (self.batch_size, model.config.start_n_top)						)
							self.parent.assertEqual(result.start_top_index.shape ,   (self.batch_size, model.config.start_n_top)						)
							self.parent.assertEqual(
							    result.end_top_log_probs.shape ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
							self.parent.assertEqual(
							    result.end_top_index.shape ,   (self.batch_size, model.config.start_n_top * model.config.end_n_top)						)
							self.parent.assertEqual(result.cls_logits.shape ,   (self.batch_size,)						)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Any ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		List[str] ,   )		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						FlaubertForSequenceClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase						)
							lowercase__	     =						model(_UpperCAmelCase ,   labels=_UpperCAmelCase						)

							self.parent.assertEqual(result.loss.shape ,   ()						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.type_sequence_label_size)						)

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		Union[str, Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		str ,   _UpperCAmelCase       :		Dict ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Optional[Any] ,   )		->  str:




							"""simple docstring"""


							lowercase__	     =						self.num_labels
							lowercase__	     =						FlaubertForTokenClassification(_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()

							lowercase__	     =						model(_UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   labels=_UpperCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.num_labels)						)

					def     lowerCamelCase__	(self       :		List[str] ,   _UpperCAmelCase       :		Optional[Any] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Optional[int] ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   )		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						self.num_choices
							lowercase__	     =						FlaubertForMultipleChoice(config=_UpperCAmelCase						)
							model.to(_UpperCAmelCase						)
							model.eval()
							lowercase__	     =						input_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						token_type_ids.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						input_mask.unsqueeze(1						).expand(-1 ,   self.num_choices ,   -1						).contiguous()
							lowercase__	     =						model(
							    _UpperCAmelCase ,   attention_mask=_UpperCAmelCase ,   token_type_ids=_UpperCAmelCase ,   labels=_UpperCAmelCase ,   )
							self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_choices)						)


					def     lowerCamelCase__	(self       :		Dict						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.prepare_config_and_inputs()
							(
							    (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      (
							    lowercase__
							)     ,      
							)	     =						config_and_inputs
							lowercase__	     =						{
							    """input_ids""": input_ids,
							    """token_type_ids""": token_type_ids,
							    """lengths""": input_lengths,
							    """attention_mask""": input_mask,
							}
							return config, inputs_dict






@require_torch
class 				A       (			UpperCAmelCase__					,    UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					(
					    (
					        FlaubertModel,
					        FlaubertWithLMHeadModel,
					        FlaubertForQuestionAnswering,
					        FlaubertForQuestionAnsweringSimple,
					        FlaubertForSequenceClassification,
					        FlaubertForTokenClassification,
					        FlaubertForMultipleChoice,
					    )
					    if is_torch_available()
					    else ()
					)
					A__     =					(
					    {
					        '''feature-extraction''': FlaubertModel,
					        '''fill-mask''': FlaubertWithLMHeadModel,
					        '''question-answering''': FlaubertForQuestionAnsweringSimple,
					        '''text-classification''': FlaubertForSequenceClassification,
					        '''token-classification''': FlaubertForTokenClassification,
					        '''zero-shot''': FlaubertForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)

					def     lowerCamelCase__	(self       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		Tuple ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[Any] ,   _UpperCAmelCase       :		int						)		->  str:




							"""simple docstring"""


							if (
							    pipeline_test_casse_name == "QAPipelineTests"
							    and tokenizer_name is not None
							    and not tokenizer_name.endswith("""Fast"""						)
							):
									# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
									# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
									# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
									return True

							return False

					def     lowerCamelCase__	(self       :		Any ,   _UpperCAmelCase       :		int ,   _UpperCAmelCase       :		List[str] ,   _UpperCAmelCase       :		Union[str, Any]=False						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						super()._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase ,   return_labels=_UpperCAmelCase						)

							if return_labels:
									if model_class.__name__ == "FlaubertForQuestionAnswering":
											lowercase__	     =						torch.zeros(
											    self.model_tester.batch_size ,   dtype=torch.long ,   device=_UpperCAmelCase						)
											lowercase__	     =						torch.zeros(
											    self.model_tester.batch_size ,   dtype=torch.long ,   device=_UpperCAmelCase						)

							return inputs_dict

					def     lowerCamelCase__	(self       :		str						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						FlaubertModelTester(self						)
							lowercase__	     =						ConfigTester(self ,   config_class=_UpperCAmelCase ,   emb_dim=37						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Tuple:




							"""simple docstring"""


							self.config_tester.run_common_tests()

					def     lowerCamelCase__	(self       :		List[Any]						)		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_model(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[Any]						)		->  str:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_lm_head(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Tuple:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_simple_qa(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_qa(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		List[str]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_sequence_classif(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Union[str, Any]						)		->  Union[str, Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_token_classif(*_UpperCAmelCase						)

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							lowercase__	     =						self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_multiple_choice(*_UpperCAmelCase						)

					@slow
					def     lowerCamelCase__	(self       :		int						)		->  List[Any]:




							"""simple docstring"""


							for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase__	     =						FlaubertModel.from_pretrained(_UpperCAmelCase						)
									self.assertIsNotNone(_UpperCAmelCase						)


					@slow
					@require_torch_gpu
					def     lowerCamelCase__	(self       :		Dict						)		->  int:




							"""simple docstring"""


							lowercase__     ,      lowercase__	     =						self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
									# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == FlaubertForMultipleChoice:
											return

									lowercase__	     =						True
									lowercase__	     =						model_class(config=_UpperCAmelCase						)

									lowercase__	     =						self._prepare_for_class(_UpperCAmelCase ,   _UpperCAmelCase						)
									lowercase__	     =						torch.jit.trace(
									    _UpperCAmelCase ,   (inputs_dict["""input_ids"""].to("""cpu"""						), inputs_dict["""attention_mask"""].to("""cpu"""						))						)

									with tempfile.TemporaryDirectory() as tmp:
											torch.jit.save(_UpperCAmelCase ,   os.path.join(_UpperCAmelCase ,   """traced_model.pt"""						)						)
											lowercase__	     =						torch.jit.load(os.path.join(_UpperCAmelCase ,   """traced_model.pt"""						) ,   map_location=_UpperCAmelCase						)
											loaded(inputs_dict["""input_ids"""].to(_UpperCAmelCase						) ,   inputs_dict["""attention_mask"""].to(_UpperCAmelCase						)						)






@require_torch
class 				A       (			unittest.TestCase				):
					'''simple docstring'''






					@slow
					def     lowerCamelCase__	(self       :		Tuple						)		->  Dict:




							"""simple docstring"""


							lowercase__	     =						FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""						)
							lowercase__	     =						torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
							with torch.no_grad():
									lowercase__	     =						model(_UpperCAmelCase						)[0]
							lowercase__	     =						torch.Size((1, 11, 768)						)
							self.assertEqual(output.shape ,   _UpperCAmelCase						)
							lowercase__	     =						torch.tensor(
							    [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]]						)

							self.assertTrue(torch.allclose(output[:, :3, :3] ,   _UpperCAmelCase ,   atol=1E-4						)						)

