




"""simple docstring"""







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       SCREAMING_SNAKE_CASE__					(					__lowerCamelCase					):


						def __init__(							self				:			Optional[Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[int]=13						,						lowerCAmelCase				:			str=7						,						lowerCAmelCase				:			str=True						,						lowerCAmelCase				:			Optional[Any]=True						,						lowerCAmelCase				:			List[str]=True						,						lowerCAmelCase				:			Optional[int]=True						,						lowerCAmelCase				:			Dict=True						,						lowerCAmelCase				:			int=False						,						lowerCAmelCase				:			Union[str, Any]=False						,						lowerCAmelCase				:			str=False						,						lowerCAmelCase				:			Any=2						,						lowerCAmelCase				:			List[Any]=99						,						lowerCAmelCase				:			Dict=0						,						lowerCAmelCase				:			str=32						,						lowerCAmelCase				:			Optional[Any]=5						,						lowerCAmelCase				:			Union[str, Any]=4						,						lowerCAmelCase				:			Any=0.1						,						lowerCAmelCase				:			Optional[Any]=0.1						,						lowerCAmelCase				:			int=512						,						lowerCAmelCase				:			Union[str, Any]=12						,						lowerCAmelCase				:			int=2						,						lowerCAmelCase				:			str=0.02						,						lowerCAmelCase				:			Tuple=3						,						lowerCAmelCase				:			Any=4						,						lowerCAmelCase				:			List[str]="last"						,						lowerCAmelCase				:			Optional[Any]=None						,						lowerCAmelCase				:			List[Any]=None						,						):
										lowerCAmelCase  =				parent
										lowerCAmelCase  =				batch_size
										lowerCAmelCase  =				seq_length
										lowerCAmelCase  =				is_training
										lowerCAmelCase  =				use_input_lengths
										lowerCAmelCase  =				use_token_type_ids
										lowerCAmelCase  =				use_labels
										lowerCAmelCase  =				gelu_activation
										lowerCAmelCase  =				sinusoidal_embeddings
										lowerCAmelCase  =				causal
										lowerCAmelCase  =				asm
										lowerCAmelCase  =				n_langs
										lowerCAmelCase  =				vocab_size
										lowerCAmelCase  =				n_special
										lowerCAmelCase  =				hidden_size
										lowerCAmelCase  =				num_hidden_layers
										lowerCAmelCase  =				num_attention_heads
										lowerCAmelCase  =				hidden_dropout_prob
										lowerCAmelCase  =				attention_probs_dropout_prob
										lowerCAmelCase  =				max_position_embeddings
										lowerCAmelCase  =				type_vocab_size
										lowerCAmelCase  =				type_sequence_label_size
										lowerCAmelCase  =				initializer_range
										lowerCAmelCase  =				num_labels
										lowerCAmelCase  =				num_choices
										lowerCAmelCase  =				summary_type
										lowerCAmelCase  =				use_proj
										lowerCAmelCase  =				scope


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				ids_tensor([self.batch_size, self.seq_length]						,						self.vocab_size					)
										lowerCAmelCase  =				random_attention_mask([self.batch_size, self.seq_length]					)

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

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

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

										lowerCAmelCase  =				self.get_config()

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


						def   __lowercase (							self				:			Optional[int]					):
										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   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			List[Any]						,						):
										lowerCAmelCase  =				FlaubertModel(config=a_					)
										model.to(a_					)
										model.eval()
										lowerCAmelCase  =				model(a_						,						lengths=a_						,						langs=a_					)
										lowerCAmelCase  =				model(a_						,						langs=a_					)
										lowerCAmelCase  =				model(a_					)
										self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)					)


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Union[str, Any]						,						):
										lowerCAmelCase  =				FlaubertWithLMHeadModel(a_					)
										model.to(a_					)
										model.eval()

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


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			str						,						):
										lowerCAmelCase  =				FlaubertForQuestionAnsweringSimple(a_					)
										model.to(a_					)
										model.eval()

										lowerCAmelCase  =				model(a_					)

										lowerCAmelCase  =				model(a_						,						start_positions=a_						,						end_positions=a_					)
										self.parent.assertEqual(result.start_logits.shape						,						(self.batch_size, self.seq_length)					)
										self.parent.assertEqual(result.end_logits.shape						,						(self.batch_size, self.seq_length)					)


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Dict						,						):
										lowerCAmelCase  =				FlaubertForQuestionAnswering(a_					)
										model.to(a_					)
										model.eval()

										lowerCAmelCase  =				model(a_					)

										lowerCAmelCase  =				model(
										    a_						,						start_positions=a_						,						end_positions=a_						,						cls_index=a_						,						is_impossible=a_						,						p_mask=a_						,						)

										lowerCAmelCase  =				model(
										    a_						,						start_positions=a_						,						end_positions=a_						,						cls_index=a_						,						is_impossible=a_						,						)

										(lowerCAmelCase)  =				result_with_labels.to_tuple()

										lowerCAmelCase  =				model(a_						,						start_positions=a_						,						end_positions=a_					)

										(lowerCAmelCase)  =				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   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			Optional[int]						,						lowerCAmelCase				:			List[Any]						,						):
										lowerCAmelCase  =				FlaubertForSequenceClassification(a_					)
										model.to(a_					)
										model.eval()

										lowerCAmelCase  =				model(a_					)
										lowerCAmelCase  =				model(a_						,						labels=a_					)

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


						def   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Optional[Any]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Any						,						):
										lowerCAmelCase  =				self.num_labels
										lowerCAmelCase  =				FlaubertForTokenClassification(a_					)
										model.to(a_					)
										model.eval()

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


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			Dict						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			Union[str, Any]						,						lowerCAmelCase				:			int						,						):
										lowerCAmelCase  =				self.num_choices
										lowerCAmelCase  =				FlaubertForMultipleChoice(config=a_					)
										model.to(a_					)
										model.eval()
										lowerCAmelCase  =				input_ids.unsqueeze(1					).expand(-1						,						self.num_choices						,						-1					).contiguous()
										lowerCAmelCase  =				token_type_ids.unsqueeze(1					).expand(-1						,						self.num_choices						,						-1					).contiguous()
										lowerCAmelCase  =				input_mask.unsqueeze(1					).expand(-1						,						self.num_choices						,						-1					).contiguous()
										lowerCAmelCase  =				model(
										    a_						,						attention_mask=a_						,						token_type_ids=a_						,						labels=a_						,						)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_choices)					)




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






@require_torch
class       SCREAMING_SNAKE_CASE__					(					__lowerCamelCase						,				__lowerCamelCase						,				unittest.TestCase					):
						_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   __lowercase (							self				:			Dict						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			int					):
										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   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple=False					):
										lowerCAmelCase  =				super()._prepare_for_class(a_						,						a_						,						return_labels=a_					)

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

										return inputs_dict


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				FlaubertModelTester(self					)
										lowerCAmelCase  =				ConfigTester(self						,						config_class=a_						,						emb_dim=37					)


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


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_flaubert_model(*a_					)


						def   __lowercase (							self				:			str					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_flaubert_lm_head(*a_					)


						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_flaubert_simple_qa(*a_					)


						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_flaubert_qa(*a_					)


						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_flaubert_sequence_classif(*a_					)


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_flaubert_token_classif(*a_					)


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_flaubert_multiple_choice(*a_					)


						@slow
						def   __lowercase (							self				:			List[str]					):
										for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase  =				FlaubertModel.from_pretrained(a_					)
														self.assertIsNotNone(a_					)




						@slow
						@require_torch_gpu
						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				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

														lowerCAmelCase  =				True
														lowerCAmelCase  =				model_class(config=a_					)

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

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






@require_torch
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@slow
						def   __lowercase (							self				:			Dict					):
										lowerCAmelCase  =				FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased"""					)
										lowerCAmelCase  =				torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]					)
										with torch.no_grad():
														lowerCAmelCase  =				model(a_					)[0]
										lowerCAmelCase  =				torch.Size((1, 11, 768)					)
										self.assertEqual(output.shape						,						a_					)
										lowerCAmelCase  =				torch.tensor(
										    [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]]					)

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









"""simple docstring"""







import inspect
import unittest

from transformers import ViTMSNConfig
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, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			List[Any]						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Optional[int]=13						,						lowerCAmelCase				:			List[str]=30						,						lowerCAmelCase				:			Dict=2						,						lowerCAmelCase				:			Any=3						,						lowerCAmelCase				:			str=True						,						lowerCAmelCase				:			List[Any]=True						,						lowerCAmelCase				:			Dict=32						,						lowerCAmelCase				:			Dict=5						,						lowerCAmelCase				:			str=4						,						lowerCAmelCase				:			List[Any]=37						,						lowerCAmelCase				:			List[Any]="gelu"						,						lowerCAmelCase				:			List[Any]=0.1						,						lowerCAmelCase				:			List[Any]=0.1						,						lowerCAmelCase				:			Optional[int]=10						,						lowerCAmelCase				:			List[str]=0.02						,						lowerCAmelCase				:			List[str]=None						,						):
										lowerCAmelCase  =				parent
										lowerCAmelCase  =				batch_size
										lowerCAmelCase  =				image_size
										lowerCAmelCase  =				patch_size
										lowerCAmelCase  =				num_channels
										lowerCAmelCase  =				is_training
										lowerCAmelCase  =				use_labels
										lowerCAmelCase  =				hidden_size
										lowerCAmelCase  =				num_hidden_layers
										lowerCAmelCase  =				num_attention_heads
										lowerCAmelCase  =				intermediate_size
										lowerCAmelCase  =				hidden_act
										lowerCAmelCase  =				hidden_dropout_prob
										lowerCAmelCase  =				attention_probs_dropout_prob
										lowerCAmelCase  =				type_sequence_label_size
										lowerCAmelCase  =				initializer_range
										lowerCAmelCase  =				scope

										# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										lowerCAmelCase  =				(image_size // patch_size) ** 2
										lowerCAmelCase  =				num_patches + 1


						def   __lowercase (							self				:			List[str]					):
										lowerCAmelCase  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										lowerCAmelCase  =				None
										if self.use_labels:
														lowerCAmelCase  =				ids_tensor([self.batch_size]						,						self.type_sequence_label_size					)

										lowerCAmelCase  =				self.get_config()

										return config, pixel_values, labels


						def   __lowercase (							self				:			Tuple					):
										return ViTMSNConfig(
										    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						,						initializer_range=self.initializer_range						,						)


						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Tuple					):
										lowerCAmelCase  =				ViTMSNModel(config=lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.last_hidden_state.shape						,						(self.batch_size, self.seq_length, self.hidden_size)					)


						def   __lowercase (							self				:			List[str]						,						lowerCAmelCase				:			int						,						lowerCAmelCase				:			str						,						lowerCAmelCase				:			int					):
										lowerCAmelCase  =				self.type_sequence_label_size
										lowerCAmelCase  =				ViTMSNForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()
										lowerCAmelCase  =				model(lowerCAmelCase						,						labels=lowerCAmelCase					)
										print("""Pixel and labels shape: {pixel_values.shape}, {labels.shape}"""					)
										print("""Labels: {labels}"""					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										lowerCAmelCase  =				1
										lowerCAmelCase  =				ViTMSNForImageClassification(lowerCAmelCase					)
										model.to(lowerCAmelCase					)
										model.eval()

										lowerCAmelCase  =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										lowerCAmelCase  =				model(lowerCAmelCase					)
										self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.type_sequence_label_size)					)




						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				self.prepare_config_and_inputs()
										lowerCAmelCase ,						lowerCAmelCase ,						lowerCAmelCase  =				config_and_inputs
										lowerCAmelCase  =				{"""pixel_values""": pixel_values}
										return config, inputs_dict






@require_torch
class       SCREAMING_SNAKE_CASE__					(					_a						,				_a						,				unittest.TestCase					):
						_a    						=						(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						_a    						=						(
						    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_a    						=						False
						_a    						=						False
						_a    						=						False
						_a    						=						False


						def   __lowercase (							self				:			Tuple					):
										lowerCAmelCase  =				ViTMSNModelTester(self					)
										lowerCAmelCase  =				ConfigTester(self						,						config_class=lowerCAmelCase						,						has_text_modality=lowerCAmelCase						,						hidden_size=37					)


						def   __lowercase (							self				:			Dict					):
										self.config_tester.run_common_tests()


						@unittest.skip(reason="""ViTMSN does not use inputs_embeds"""					)
						def   __lowercase (							self				:			Optional[Any]					):
										pass


						def   __lowercase (							self				:			int					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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


						def   __lowercase (							self				:			Optional[int]					):
										lowerCAmelCase ,						lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs_for_common()

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

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


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase					)


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase					)




						@slow
						def   __lowercase (							self				:			Optional[Any]					):
										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase  =				ViTMSNModel.from_pretrained(lowerCAmelCase					)
														self.assertIsNotNone(lowerCAmelCase					)





def 							lowercase					()     ->  Dict:


				'''simple docstring'''






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






@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						@cached_property
						def   __lowercase (							self				:			Dict					):
										return ViTImageProcessor.from_pretrained("""facebook/vit-msn-small"""					) if is_vision_available() else None




						@slow
						def   __lowercase (							self				:			Tuple					):
										torch.manual_seed(2					)
										lowerCAmelCase  =				ViTMSNForImageClassification.from_pretrained("""facebook/vit-msn-small"""					).to(lowerCAmelCase					)

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

										# forward pass
										with torch.no_grad():
														lowerCAmelCase  =				model(**lowerCAmelCase					)

										# verify the logits
										lowerCAmelCase  =				torch.Size((1, 1000)					)
										self.assertEqual(outputs.logits.shape						,						lowerCAmelCase					)

										lowerCAmelCase  =				torch.tensor([-0.0803, -0.4454, -0.2375]					).to(lowerCAmelCase					)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]						,						lowerCAmelCase						,						atol=1e-4					)					)





