



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_	       =			get_tests_dir('''fixtures/test_sentencepiece.model''')






@require_sentencepiece
@require_tokenizers
class 						__a (     lowercase__	,    unittest.TestCase				):
	SCREAMING_SNAKE_CASE       =       XGLMTokenizer
	SCREAMING_SNAKE_CASE       =       XGLMTokenizerFast
	SCREAMING_SNAKE_CASE       =       True
	SCREAMING_SNAKE_CASE       =       True



	def 		UpperCamelCase							(						self							:    List[Any])->							str:
						super().setUp()

						# We have a SentencePiece fixture for testing
						__lowerCAmelCase	   =XGLMTokenizer(__lowercase				,	keep_accents=__lowercase)
						tokenizer.save_pretrained(self.tmpdirname)



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							int:
						__lowerCAmelCase	   ="""<pad>"""
						__lowerCAmelCase	   =1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase)				,	__lowercase)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase)				,	__lowercase)



	def 		UpperCamelCase							(						self							:    Tuple)->							Tuple:
						__lowerCAmelCase	   =list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]				,	"""<s>""")
						self.assertEqual(vocab_keys[1]				,	"""<pad>""")
						self.assertEqual(len(__lowercase)				,	10_08)



	def 		UpperCamelCase							(						self							:    Tuple)->							Optional[Any]:
						self.assertEqual(self.get_tokenizer().vocab_size				,	10_08)



	def 		UpperCamelCase							(						self							:    List[str])->							Dict:
						__lowerCAmelCase	   =XGLMTokenizer(__lowercase				,	keep_accents=__lowercase)

						__lowerCAmelCase	   =tokenizer.tokenize("""This is a test""")
						self.assertListEqual(__lowercase				,	["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__lowercase)				,	[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				,	)

						__lowerCAmelCase	   =tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
						self.assertListEqual(
						    __lowercase				,	[
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """9""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """é""",
						        """.""",
						    ]				,	)
						__lowerCAmelCase	   =tokenizer.convert_tokens_to_ids(__lowercase)
						self.assertListEqual(
						    __lowercase				,	[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						    ]				,	)

						__lowerCAmelCase	   =tokenizer.convert_ids_to_tokens(__lowercase)
						self.assertListEqual(
						    __lowercase				,	[
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """<unk>""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """<unk>""",
						        """.""",
						    ]				,	)



	@cached_property
	def 		UpperCamelCase							(						self							:    List[str])->							Any:
						return XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")



	def 		UpperCamelCase							(						self							:    int)->							Any:
						with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(__lowercase				,	f.name)
											__lowerCAmelCase	   =XGLMTokenizer(f.name				,	keep_accents=__lowercase)
											__lowerCAmelCase	   =pickle.dumps(__lowercase)
						pickle.loads(__lowercase)



	def 		UpperCamelCase							(						self							:    Dict)->							int:
						if not self.test_rust_tokenizer:
											return

						__lowerCAmelCase	   =self.get_tokenizer()
						__lowerCAmelCase	   =self.get_rust_tokenizer()

						__lowerCAmelCase	   ="""I was born in 92000, and this is falsé."""

						__lowerCAmelCase	   =tokenizer.tokenize(__lowercase)
						__lowerCAmelCase	   =rust_tokenizer.tokenize(__lowercase)
						self.assertListEqual(__lowercase				,	__lowercase)

						__lowerCAmelCase	   =tokenizer.encode(__lowercase				,	add_special_tokens=__lowercase)
						__lowerCAmelCase	   =rust_tokenizer.encode(__lowercase				,	add_special_tokens=__lowercase)
						self.assertListEqual(__lowercase				,	__lowercase)

						__lowerCAmelCase	   =self.get_rust_tokenizer()
						__lowerCAmelCase	   =tokenizer.encode(__lowercase)
						__lowerCAmelCase	   =rust_tokenizer.encode(__lowercase)
						self.assertListEqual(__lowercase				,	__lowercase)



	@slow
	def 		UpperCamelCase							(						self							:    List[Any])->							Tuple:
						__lowerCAmelCase	   ="""Hello World!"""
						__lowerCAmelCase	   =[2, 3_12_27, 44_47, 35]

						self.assertListEqual(__lowercase				,	self.big_tokenizer.encode(__lowercase))



	@slow
	def 		UpperCamelCase							(						self							:    Union[str, Any])->							List[Any]:
						__lowerCAmelCase	   =(
						    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
						    """ add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"""
						)
						# fmt: off
						__lowerCAmelCase	   =[2, 10_18, 67, 11, 19_88, 26_17, 56_31, 2_78, 11, 34_07, 48, 7_16_30, 2_80_85, 4, 32_34, 1_57, 13, 6, 5, 6, 4, 35_26, 7_68, 15, 6_59, 57, 2_98, 39_83, 8_64, 1_29, 21, 6, 5, 1_36_75, 3_77, 6_52, 75_80, 1_03_41, 1_55, 28_17, 4_22, 16_66, 7, 16_74, 53, 1_13, 20_22_77, 1_78_92, 33, 60, 87, 4, 32_34, 1_57, 61, 26_67, 5_23_76, 19, 88, 23, 7_35]
						# fmt: on

						self.assertListEqual(__lowercase				,	self.big_tokenizer.encode(__lowercase))



	@slow
	def 		UpperCamelCase							(						self							:    Tuple)->							List[Any]:
						# fmt: off
						__lowerCAmelCase	   ={
						    """input_ids""": [[2, 10_88_25, 11_63, 15, 8_80_10, 4_73, 1_58_98, 1_57, 1_36_72, 18_57, 3_12, 8, 23_80_21, 11_63, 53, 1_36_72, 18_57, 3_12, 8, 5_32_83, 18_23_96, 8, 1_85_66, 16, 3_67_33, 41_01, 8, 2_30, 24_40_17, 12_25_53, 7, 15, 13_25_97, 4, 2_93, 1_25_11, 76_10, 4, 34_14, 13_25_97, 9, 4, 3_23_61, 3_62, 4, 7_34, 2_85_12, 3_25_69, 18, 4, 3_23_61, 2_60_96, 1_49_82, 73, 1_87_15, 2_14_33, 23_52_61, 15, 4_92, 1_24_27, 16, 53, 1_87_15, 2_14_33, 6_54_54, 15, 2_36_59, 5_63, 16, 2_78, 5_97, 28_43, 5_95, 79_31, 18_23_96, 6_41_86, 22, 8_86, 5_95, 13_29_81, 53, 2_55_40, 34_49, 4_39_82, 3_99_01, 59_51, 8_78, 3_30, 4, 2_76_94, 8_02_69, 3_12, 53, 65_17, 1_17_80, 6_11, 2_04_08, 5], [2, 6, 13_25_97, 67, 4_28_97, 33, 5_92, 8, 16_37_29, 2_55_40, 3_61, 13_69_97, 10_95_14, 17_32_30, 7, 5_01, 60, 10_29_13, 1_96, 56_31, 2_35, 6_32_43, 4_73, 6, 23_17_57, 74, 52_77, 79_05, 53, 30_95, 3_73_17, 22, 4_54, 18_38_74, 5], [2, 2_68, 3_12_98, 4_65_30, 6, 13_29_35, 4_38_31, 7, 5_97, 32, 24, 36_88, 98_65, 5]],
						    """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
						}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__lowercase				,	model_name="""facebook/xglm-564M"""				,	padding=__lowercase				,	)









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_)




