



import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_image_processing import CustomImageProcessor  # noqa E402


lowercase_	       =			get_tests_dir('''fixtures''')






class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    Any)->							Union[str, Any]:
						__lowerCAmelCase	   =mock.Mock()
						__lowerCAmelCase	   =5_00
						__lowerCAmelCase	   ={}
						__lowerCAmelCase	   =HTTPError
						__lowerCAmelCase	   ={}

						# Download this model to make sure it's in the cache.
						__lowerCAmelCase	   =ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""")
						# Under the mock environment we get a 500 error when trying to reach the model.
						with mock.patch("""requests.Session.request"""				,	return_value=__A) as mock_head:
											__lowerCAmelCase	   =ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""")
											# This check we did call the fake head request
											mock_head.assert_called()



	def 		UpperCamelCase							(						self							:    Dict)->							List[str]:
						__lowerCAmelCase	   =ViTImageProcessor.from_pretrained(
						    """https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json""")



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



						with self.assertRaises(__A):
											# config is in subfolder, the following should not work without specifying the subfolder
											__lowerCAmelCase	   =AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants""")

						__lowerCAmelCase	   =AutoImageProcessor.from_pretrained(
						    """hf-internal-testing/stable-diffusion-all-variants"""				,	subfolder="""feature_extractor""")

						self.assertIsNotNone(__A)






@is_staging_test
class 						__a (     unittest.TestCase				):



	@classmethod
	def 		UpperCamelCase							(						cls							:    int)->							str:
						__lowerCAmelCase	   =TOKEN
						HfFolder.save_token(__A)



	@classmethod
	def 		UpperCamelCase							(						cls							:    Optional[int])->							Optional[Any]:



						try:
											delete_repo(token=cls._token				,	repo_id="""test-image-processor""")
						except HTTPError:
											pass

						try:
											delete_repo(token=cls._token				,	repo_id="""valid_org/test-image-processor-org""")
						except HTTPError:
											pass

						try:
											delete_repo(token=cls._token				,	repo_id="""test-dynamic-image-processor""")
						except HTTPError:
											pass



	def 		UpperCamelCase							(						self							:    Tuple)->							List[str]:
						__lowerCAmelCase	   =ViTImageProcessor.from_pretrained(__A)
						image_processor.push_to_hub("""test-image-processor"""				,	use_auth_token=self._token)

						__lowerCAmelCase	   =ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor""")
						for k, v in image_processor.__dict__.items():
											self.assertEqual(__A				,	getattr(__A				,	__A))

						# Reset repo
						delete_repo(token=self._token				,	repo_id="""test-image-processor""")

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(
											    __A				,	repo_id="""test-image-processor"""				,	push_to_hub=__A				,	use_auth_token=self._token)

						__lowerCAmelCase	   =ViTImageProcessor.from_pretrained(F"""{USER}/test-image-processor""")
						for k, v in image_processor.__dict__.items():
											self.assertEqual(__A				,	getattr(__A				,	__A))



	def 		UpperCamelCase							(						self							:    int)->							Optional[int]:
						__lowerCAmelCase	   =ViTImageProcessor.from_pretrained(__A)
						image_processor.push_to_hub("""valid_org/test-image-processor"""				,	use_auth_token=self._token)

						__lowerCAmelCase	   =ViTImageProcessor.from_pretrained("""valid_org/test-image-processor""")
						for k, v in image_processor.__dict__.items():
											self.assertEqual(__A				,	getattr(__A				,	__A))

						# Reset repo
						delete_repo(token=self._token				,	repo_id="""valid_org/test-image-processor""")

						# Push to hub via save_pretrained
						with tempfile.TemporaryDirectory() as tmp_dir:
											image_processor.save_pretrained(
											    __A				,	repo_id="""valid_org/test-image-processor-org"""				,	push_to_hub=__A				,	use_auth_token=self._token)

						__lowerCAmelCase	   =ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org""")
						for k, v in image_processor.__dict__.items():
											self.assertEqual(__A				,	getattr(__A				,	__A))



	def 		UpperCamelCase							(						self							:    Optional[Any])->							int:
						CustomImageProcessor.register_for_auto_class()
						__lowerCAmelCase	   =CustomImageProcessor.from_pretrained(__A)

						image_processor.push_to_hub("""test-dynamic-image-processor"""				,	use_auth_token=self._token)

						# This has added the proper auto_map field to the config
						self.assertDictEqual(
						    image_processor.auto_map				,	{"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""}				,	)

						__lowerCAmelCase	   =AutoImageProcessor.from_pretrained(
						    F"""{USER}/test-dynamic-image-processor"""				,	trust_remote_code=__A)
						# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
						self.assertEqual(new_image_processor.__class__.__name__				,	"""CustomImageProcessor""")









import unittest

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

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


if is_torch_available():
				import torch

				from transformers import (
				    XLMForMultipleChoice,
				    XLMForQuestionAnswering,
				    XLMForQuestionAnsweringSimple,
				    XLMForSequenceClassification,
				    XLMForTokenClassification,
				    XLMModel,
				    XLMWithLMHeadModel,
				)
				from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST






class 						__a :



	def __init__(						self							:    Optional[int]				,	snake_case_							:    int				,	snake_case_							:    Optional[Any]=13				,	snake_case_							:    List[str]=7				,	snake_case_							:    Tuple=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    str=True				,	snake_case_							:    Optional[Any]=True				,	snake_case_							:    List[Any]=False				,	snake_case_							:    str=False				,	snake_case_							:    List[str]=False				,	snake_case_							:    List[Any]=2				,	snake_case_							:    List[Any]=99				,	snake_case_							:    Tuple=0				,	snake_case_							:    Optional[int]=32				,	snake_case_							:    Union[str, Any]=5				,	snake_case_							:    Any=4				,	snake_case_							:    str=0.1				,	snake_case_							:    List[Any]=0.1				,	snake_case_							:    Optional[Any]=5_12				,	snake_case_							:    str=2				,	snake_case_							:    List[str]=0.0_2				,	snake_case_							:    Optional[int]=2				,	snake_case_							:    Union[str, Any]=4				,	snake_case_							:    str="last"				,	snake_case_							:    Union[str, Any]=True				,	snake_case_							:    int=None				,	snake_case_							:    Any=0				,	)->							Tuple:
						__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_sequence_label_size
						__lowerCAmelCase	   =initializer_range
						__lowerCAmelCase	   =num_labels
						__lowerCAmelCase	   =num_choices
						__lowerCAmelCase	   =summary_type
						__lowerCAmelCase	   =use_proj
						__lowerCAmelCase	   =scope
						__lowerCAmelCase	   =bos_token_id



	def 		UpperCamelCase							(						self							:    Tuple)->							int:
						__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 		UpperCamelCase							(						self							:    Tuple)->							Union[str, Any]:
						return XLMConfig(
						    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				,	num_labels=self.num_labels				,	bos_token_id=self.bos_token_id				,	)



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    List[str]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Any				,	snake_case_							:    Optional[int]				,	snake_case_							:    List[Any]				,	)->							Tuple:
						__lowerCAmelCase	   =XLMModel(config=snake_case_)
						model.to(snake_case_)
						model.eval()
						__lowerCAmelCase	   =model(snake_case_				,	lengths=snake_case_				,	langs=snake_case_)
						__lowerCAmelCase	   =model(snake_case_				,	langs=snake_case_)
						__lowerCAmelCase	   =model(snake_case_)
						self.parent.assertEqual(result.last_hidden_state.shape				,	(self.batch_size, self.seq_length, self.hidden_size))



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    str				,	snake_case_							:    List[Any]				,	snake_case_							:    str				,	snake_case_							:    Dict				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Dict				,	snake_case_							:    Optional[int]				,	snake_case_							:    int				,	snake_case_							:    int				,	)->							Dict:
						__lowerCAmelCase	   =XLMWithLMHeadModel(snake_case_)
						model.to(snake_case_)
						model.eval()

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



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    Any				,	snake_case_							:    str				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Dict				,	snake_case_							:    int				,	snake_case_							:    List[Any]				,	snake_case_							:    Tuple				,	)->							Dict:
						__lowerCAmelCase	   =XLMForQuestionAnsweringSimple(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)

						__lowerCAmelCase	   =model(snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_)
						__lowerCAmelCase	   =outputs
						self.parent.assertEqual(result.start_logits.shape				,	(self.batch_size, self.seq_length))
						self.parent.assertEqual(result.end_logits.shape				,	(self.batch_size, self.seq_length))



	def 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    int				,	snake_case_							:    int				,	snake_case_							:    str				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Tuple				,	)->							Optional[int]:
						__lowerCAmelCase	   =XLMForQuestionAnswering(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)

						__lowerCAmelCase	   =model(
						    snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_				,	cls_index=snake_case_				,	is_impossible=snake_case_				,	p_mask=snake_case_				,	)

						__lowerCAmelCase	   =model(
						    snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_				,	cls_index=snake_case_				,	is_impossible=snake_case_				,	)

						((__lowerCAmelCase)   ,	)	   =result_with_labels.to_tuple()

						__lowerCAmelCase	   =model(snake_case_				,	start_positions=snake_case_				,	end_positions=snake_case_)

						((__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 		UpperCamelCase							(						self							:    Tuple				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    str				,	snake_case_							:    int				,	snake_case_							:    Optional[int]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    Optional[Any]				,	)->							int:
						__lowerCAmelCase	   =XLMForSequenceClassification(snake_case_)
						model.to(snake_case_)
						model.eval()

						__lowerCAmelCase	   =model(snake_case_)
						__lowerCAmelCase	   =model(snake_case_				,	labels=snake_case_)
						self.parent.assertEqual(result.loss.shape				,	())
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.type_sequence_label_size))



	def 		UpperCamelCase							(						self							:    Optional[Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    int				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    str				,	snake_case_							:    List[str]				,	snake_case_							:    Union[str, Any]				,	)->							Optional[Any]:
						__lowerCAmelCase	   =self.num_labels
						__lowerCAmelCase	   =XLMForTokenClassification(snake_case_)
						model.to(snake_case_)
						model.eval()

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



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Tuple				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[int]				,	snake_case_							:    Optional[Any]				,	snake_case_							:    Union[str, Any]				,	snake_case_							:    Optional[int]				,	snake_case_							:    Tuple				,	snake_case_							:    List[str]				,	snake_case_							:    Tuple				,	)->							List[Any]:
						__lowerCAmelCase	   =self.num_choices
						__lowerCAmelCase	   =XLMForMultipleChoice(config=snake_case_)
						model.to(snake_case_)
						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(
						    snake_case_				,	attention_mask=snake_case_				,	token_type_ids=snake_case_				,	labels=snake_case_				,	)
						self.parent.assertEqual(result.logits.shape				,	(self.batch_size, self.num_choices))



	def 		UpperCamelCase							(						self							:    Optional[Any])->							List[Any]:
						__lowerCAmelCase	   =self.prepare_config_and_inputs()
						(
						    (
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	(
						    __lowerCAmelCase
						)   ,	
						)	   =config_and_inputs
						__lowerCAmelCase	   ={"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
						return config, inputs_dict






@require_torch
class 						__a (     SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       (
	    (
	        XLMModel,
	        XLMWithLMHeadModel,
	        XLMForQuestionAnswering,
	        XLMForSequenceClassification,
	        XLMForQuestionAnsweringSimple,
	        XLMForTokenClassification,
	        XLMForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE       =       (
	    (XLMWithLMHeadModel,) if is_torch_available() else ()
	)  # TODO (PVP): Check other models whether language generation is also applicable
	SCREAMING_SNAKE_CASE       =       (
	    {
	        "feature-extraction": XLMModel,
	        "fill-mask": XLMWithLMHeadModel,
	        "question-answering": XLMForQuestionAnsweringSimple,
	        "text-classification": XLMForSequenceClassification,
	        "text-generation": XLMWithLMHeadModel,
	        "token-classification": XLMForTokenClassification,
	        "zero-shot": XLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)



	def 		UpperCamelCase							(						self							:    List[str]				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    str				,	snake_case_							:    int				,	snake_case_							:    str)->							Tuple:
						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 		UpperCamelCase							(						self							:    int				,	snake_case_							:    Tuple				,	snake_case_							:    Any				,	snake_case_							:    str=False)->							str:
						__lowerCAmelCase	   =super()._prepare_for_class(snake_case_				,	snake_case_				,	return_labels=snake_case_)

						if return_labels:
											if model_class.__name__ == "XLMForQuestionAnswering":
																__lowerCAmelCase	   =torch.zeros(
																    self.model_tester.batch_size				,	dtype=torch.long				,	device=snake_case_)
																__lowerCAmelCase	   =torch.zeros(
																    self.model_tester.batch_size				,	dtype=torch.long				,	device=snake_case_)

						return inputs_dict



	def 		UpperCamelCase							(						self							:    int)->							List[Any]:
						__lowerCAmelCase	   =XLMModelTester(self)
						__lowerCAmelCase	   =ConfigTester(self				,	config_class=snake_case_				,	emb_dim=37)



	def 		UpperCamelCase							(						self							:    List[str])->							Any:
						self.config_tester.run_common_tests()



	def 		UpperCamelCase							(						self							:    Optional[int])->							Union[str, Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*snake_case_)



	def 		UpperCamelCase							(						self							:    Any)->							int:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*snake_case_)



	def 		UpperCamelCase							(						self							:    Dict)->							str:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_simple_qa(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any])->							Union[str, Any]:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[str])->							Dict:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*snake_case_)



	def 		UpperCamelCase							(						self							:    List[Any])->							int:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_token_classif(*snake_case_)



	def 		UpperCamelCase							(						self							:    str)->							str:
						__lowerCAmelCase	   =self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any]				,	snake_case_							:    Tuple				,	snake_case_							:    Optional[Any]				,	snake_case_							:    List[Any]				,	snake_case_							:    Any				,	snake_case_							:    int				,	snake_case_							:    Optional[int]=False				,	snake_case_							:    List[Any]=1)->							Optional[Any]:
						self.assertIsInstance(snake_case_				,	snake_case_)
						self.assertListEqual(
						    [isinstance(snake_case_				,	snake_case_) for iter_attentions in attentions]				,	[True] * len(snake_case_))
						self.assertEqual(len(snake_case_)				,	(max_length - min_length) * num_beam_groups)

						for idx, iter_attentions in enumerate(snake_case_):
											# adds PAD dummy token
											__lowerCAmelCase	   =min_length + idx + 1
											__lowerCAmelCase	   =min_length + idx + 1

											__lowerCAmelCase	   =(
											    batch_size * num_beam_groups,
											    config.num_attention_heads,
											    tgt_len,
											    src_len,
											)
											# check attn size
											self.assertListEqual(
											    [layer_attention.shape for layer_attention in iter_attentions]				,	[expected_shape] * len(snake_case_))



	def 		UpperCamelCase							(						self							:    List[Any]				,	snake_case_							:    Any				,	snake_case_							:    str				,	snake_case_							:    Any				,	snake_case_							:    List[Any]				,	snake_case_							:    Dict				,	snake_case_							:    List[str]=False				,	snake_case_							:    Any=1)->							Any:
						self.assertIsInstance(snake_case_				,	snake_case_)
						self.assertListEqual(
						    [isinstance(snake_case_				,	snake_case_) for iter_hidden_states in hidden_states]				,	[True] * len(snake_case_)				,	)
						self.assertEqual(len(snake_case_)				,	(max_length - min_length) * num_beam_groups)

						for idx, iter_hidden_states in enumerate(snake_case_):
											# adds PAD dummy token
											__lowerCAmelCase	   =min_length + idx + 1
											__lowerCAmelCase	   =(batch_size * num_beam_groups, seq_len, config.hidden_size)
											# check hidden size
											self.assertListEqual(
											    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]				,	[expected_shape] * len(snake_case_)				,	)
						pass



	@slow
	def 		UpperCamelCase							(						self							:    Any)->							List[str]:
						for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCAmelCase	   =XLMModel.from_pretrained(snake_case_)
											self.assertIsNotNone(snake_case_)






@require_torch
class 						__a (     unittest.TestCase				):



	@slow
	def 		UpperCamelCase							(						self							:    int)->							List[str]:
						__lowerCAmelCase	   =XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048""")
						model.to(snake_case_)
						__lowerCAmelCase	   =torch.tensor([[14, 4_47]]				,	dtype=torch.long				,	device=snake_case_)  # the president
						__lowerCAmelCase	   =[
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						]  # the president the president the president the president the president the president the president the president the president the president
						# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
						__lowerCAmelCase	   =model.generate(snake_case_				,	do_sample=snake_case_)
						self.assertListEqual(output_ids[0].cpu().numpy().tolist()				,	snake_case_)




