



'''simple docstring'''



import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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


if is_torch_available():
						import torch

						from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
						from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def 					UpperCAmelCase   (						A  :				Dict					,		A  :				Union[str, Any]					,		A  :				Tuple					,		A  :				Optional[int]=None					,		A  :				List[str]=None					,		A  :				int=None					,		A  :				Dict=None					,		A  :				List[Any]=None					,		):
							if attention_mask is None:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			input_ids.ne(config.pad_token_id				)
							if decoder_attention_mask is None:
														SCREAMING_SNAKE_CASE					:      List[str]     					=			decoder_input_ids.ne(config.pad_token_id				)
							if head_mask is None:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.ones(config.encoder_layers					,		config.encoder_attention_heads					,		device=A				)
							if decoder_head_mask is None:
														SCREAMING_SNAKE_CASE					:      Tuple     					=			torch.ones(config.decoder_layers					,		config.decoder_attention_heads					,		device=A				)
							if cross_attn_head_mask is None:
														SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.ones(config.decoder_layers					,		config.decoder_attention_heads					,		device=A				)
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}
class 	lowerCamelCase_							:







						def __init__(  self   :      Dict				,    lowerCAmelCase__   :      Any				,    lowerCAmelCase__   :      Tuple=13				,    lowerCAmelCase__   :      List[Any]=7				,    lowerCAmelCase__   :      Optional[int]=True				,    lowerCAmelCase__   :      Tuple=False				,    lowerCAmelCase__   :      List[Any]=99				,    lowerCAmelCase__   :      List[Any]=16				,    lowerCAmelCase__   :      Optional[int]=2				,    lowerCAmelCase__   :      Optional[Any]=4				,    lowerCAmelCase__   :      str=4				,    lowerCAmelCase__   :      List[Any]="relu"				,    lowerCAmelCase__   :      Dict=0.1				,    lowerCAmelCase__   :      Tuple=0.1				,    lowerCAmelCase__   :      List[Any]=0.0				,    lowerCAmelCase__   :      str=0.0				,    lowerCAmelCase__   :      List[Any]=20				,    lowerCAmelCase__   :      Any=2				,    lowerCAmelCase__   :      Union[str, Any]=1				,    lowerCAmelCase__   :      Union[str, Any]=0				,    ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			parent
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			batch_size
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			seq_length
													SCREAMING_SNAKE_CASE					:      Tuple     					=			is_training
													SCREAMING_SNAKE_CASE					:      Any     					=			use_labels
													SCREAMING_SNAKE_CASE					:      List[str]     					=			vocab_size
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			hidden_size
													SCREAMING_SNAKE_CASE					:      List[str]     					=			num_hidden_layers
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			num_attention_heads
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			intermediate_size
													SCREAMING_SNAKE_CASE					:      Tuple     					=			hidden_act
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			hidden_dropout_prob
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE					:      List[str]     					=			encoder_layerdrop
													SCREAMING_SNAKE_CASE					:      Dict     					=			decoder_layerdrop
													SCREAMING_SNAKE_CASE					:      List[str]     					=			max_position_embeddings
													SCREAMING_SNAKE_CASE					:      List[str]     					=			eos_token_id
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			pad_token_id
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			bos_token_id







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)
													SCREAMING_SNAKE_CASE					:      str     					=			self.eos_token_id  # Eos Token
													SCREAMING_SNAKE_CASE					:      Any     					=			ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size				)

													# we need to clamp the input ids here to avoid having pad token in between
													# this is because for M2M100 the position_ids are prepared such that
													# all pad tokens have pos id = 2 and rest are between 2..seq_length
													# and the seq_length here is seq_length - num_pad_tokens
													# but when using past, there is no way of knowing if the past input ids had
													# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
													# position_ids being off by num_pad_tokens in past input
													SCREAMING_SNAKE_CASE					:      int     					=			input_ids.clamp(self.pad_token_id + 1				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			decoder_input_ids.clamp(self.pad_token_id + 1				)

													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			self.get_config()
													SCREAMING_SNAKE_CASE					:      Dict     					=			prepare_mam_aaa_inputs_dict(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				)
													return config, inputs_dict







						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													return MaMaaaConfig(
													    vocab_size=self.vocab_size				,    d_model=self.hidden_size				,    encoder_layers=self.num_hidden_layers				,    decoder_layers=self.num_hidden_layers				,    encoder_attention_heads=self.num_attention_heads				,    decoder_attention_heads=self.num_attention_heads				,    encoder_ffn_dim=self.intermediate_size				,    decoder_ffn_dim=self.intermediate_size				,    dropout=self.hidden_dropout_prob				,    attention_dropout=self.attention_probs_dropout_prob				,    encoder_layerdrop=self.encoder_layerdrop				,    decoder_layerdrop=self.decoder_layerdrop				,    max_position_embeddings=self.max_position_embeddings				,    eos_token_id=self.eos_token_id				,    bos_token_id=self.bos_token_id				,    pad_token_id=self.pad_token_id				,    )







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.prepare_config_and_inputs()
													return config, inputs_dict







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      Union[str, Any]				,    lowerCAmelCase__   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			MaMaaaModel(config=lowerCAmelCase__				).get_decoder().to(lowerCAmelCase__				).eval()
													SCREAMING_SNAKE_CASE					:      str     					=			inputs_dict['''input_ids''']
													SCREAMING_SNAKE_CASE					:      Tuple     					=			inputs_dict['''attention_mask''']
													SCREAMING_SNAKE_CASE					:      int     					=			inputs_dict['''head_mask''']

													# first forward pass
													SCREAMING_SNAKE_CASE					:      Any     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    head_mask=lowerCAmelCase__				,    use_cache=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Tuple     					=			outputs.to_tuple()

													# create hypothetical multiple next token and extent to next_input_ids
													SCREAMING_SNAKE_CASE					:      str     					=			ids_tensor((self.batch_size, 3)				,    config.vocab_size				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			ids_tensor((self.batch_size, 3)				,    2				)

													# append to next input_ids and
													SCREAMING_SNAKE_CASE					:      str     					=			torch.cat([input_ids, next_tokens]				,    dim=-1				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			torch.cat([attention_mask, next_attn_mask]				,    dim=-1				)

													SCREAMING_SNAKE_CASE					:      Dict     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)['''last_hidden_state''']
													SCREAMING_SNAKE_CASE					:      str     					=			model(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				,    past_key_values=lowerCAmelCase__				)[
													    '''last_hidden_state'''
													]

													# select random slice
													SCREAMING_SNAKE_CASE					:      Any     					=			ids_tensor((1,)				,    output_from_past.shape[-1]				).item()
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			output_from_no_past[:, -3:, random_slice_idx].detach()
													SCREAMING_SNAKE_CASE					:      str     					=			output_from_past[:, :, random_slice_idx].detach()

													self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]				)

													# test that outputs are equal for slice
													self.parent.assertTrue(torch.allclose(lowerCAmelCase__				,    lowerCAmelCase__				,    atol=1e-2				)				)







						def 					__lowercase    (  self   :      List[str]				,    lowerCAmelCase__   :      Tuple				,    lowerCAmelCase__   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			MaMaaaModel(config=lowerCAmelCase__				).to(lowerCAmelCase__				).eval()
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			model(**lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			outputs.encoder_last_hidden_state
													SCREAMING_SNAKE_CASE					:      int     					=			outputs.last_hidden_state

													with tempfile.TemporaryDirectory() as tmpdirname:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model.get_encoder()
																				encoder.save_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			MaMaaaEncoder.from_pretrained(lowerCAmelCase__				).to(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			encoder(inputs_dict['''input_ids''']				,    attention_mask=inputs_dict['''attention_mask''']				)[
													    0
													]

													self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model.get_decoder()
																				decoder.save_pretrained(lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      int     					=			MaMaaaDecoder.from_pretrained(lowerCAmelCase__				).to(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			decoder(
													    input_ids=inputs_dict['''decoder_input_ids''']				,    attention_mask=inputs_dict['''decoder_attention_mask''']				,    encoder_hidden_states=lowerCAmelCase__				,    encoder_attention_mask=inputs_dict['''attention_mask''']				,    )[0]

													self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3				)


@require_torch
class 	lowerCamelCase_							(					snake_case_    ,				snake_case_    ,				snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Union[str, Any]	     =				(
						    (
						        MaMaaaModel,
						        MaMaaaForConditionalGeneration,
						    )
						    if is_torch_available()
						    else ()
						)
						_lowerCAmelCase     :   List[Any]	     =				(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
						_lowerCAmelCase     :   str	     =				(
						    {
						        'conversational': MaMaaaForConditionalGeneration,
						        'feature-extraction': MaMaaaModel,
						        'summarization': MaMaaaForConditionalGeneration,
						        'text2text-generation': MaMaaaForConditionalGeneration,
						        'translation': MaMaaaForConditionalGeneration,
						    }
						    if is_torch_available()
						    else {}
						)
						_lowerCAmelCase     :   Optional[int]	     =				True
						_lowerCAmelCase     :   Any	     =				True
						_lowerCAmelCase     :   Tuple	     =				False
						_lowerCAmelCase     :   str	     =				False







						def 					__lowercase    (  self   :      Union[str, Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      List[Any]				,    lowerCAmelCase__   :      Dict				,    lowerCAmelCase__   :      str				,    lowerCAmelCase__   :      Dict				):

													"""simple docstring"""
													if pipeline_test_casse_name == "TranslationPipelineTests":
																				# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
																				# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
																				return True

													return False







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Dict     					=			MaMaaaModelTester(self				)
													SCREAMING_SNAKE_CASE					:      int     					=			ConfigTester(self				,    config_class=lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													self.config_tester.run_common_tests()







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			self.model_tester.prepare_config_and_inputs()
													for model_class in self.all_model_classes:
																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model_class(lowerCAmelCase__				)

																				with tempfile.TemporaryDirectory() as tmpdirname:
																											model.save_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      str     					=			model_class.from_pretrained(lowerCAmelCase__				,    output_loading_info=lowerCAmelCase__				)
																				self.assertEqual(info['''missing_keys''']				,    []				)







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Union[str, Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			self.model_tester.prepare_config_and_inputs_for_common()
													self.model_tester.check_encoder_decoder_model_standalone(*lowerCAmelCase__				)







						def 					__lowercase    (  self   :      str				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
																				SCREAMING_SNAKE_CASE					:      Tuple     					=			model_class(lowerCAmelCase__				)
																				model.to(lowerCAmelCase__				)
																				model.eval()

																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			copy.deepcopy(self._prepare_for_class(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																				if not self.is_encoder_decoder:
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			inputs['''input_ids''']
																											del inputs["input_ids"]
																				else:
																											SCREAMING_SNAKE_CASE					:      str     					=			inputs['''input_ids''']
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			inputs.get('''decoder_input_ids'''				,    lowerCAmelCase__				)
																											del inputs["input_ids"]
																											inputs.pop('''decoder_input_ids'''				,    lowerCAmelCase__				)

																				SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model.get_input_embeddings()
																				if not self.is_encoder_decoder:
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			wte(lowerCAmelCase__				)
																				else:
																											SCREAMING_SNAKE_CASE					:      int     					=			wte(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			wte(lowerCAmelCase__				)

																				with torch.no_grad():
																											model(**lowerCAmelCase__				)[0]







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			self.model_tester.prepare_config_and_inputs()
													SCREAMING_SNAKE_CASE					:      Any     					=			input_dict['''input_ids''']
													SCREAMING_SNAKE_CASE					:      List[str]     					=			input_ids.ne(1				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      str     					=			MaMaaaForConditionalGeneration(lowerCAmelCase__				).eval().to(lowerCAmelCase__				)
													if torch_device == "cuda":
																				model.half()
													model.generate(lowerCAmelCase__				,    attention_mask=lowerCAmelCase__				)
													model.generate(num_beams=4				,    do_sample=lowerCAmelCase__				,    early_stopping=lowerCAmelCase__				,    num_return_sequences=3				)


def 					UpperCAmelCase   (						A  :				Optional[Any]				):
							return torch.tensor(A					,		dtype=torch.long					,		device=A				)


lowerCAmelCase_     :       Union[str, Any]								=						1E-4






@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 	lowerCamelCase_							(					unittest.TestCase			):







						@cached_property
						def 					__lowercase    (  self   :      Any				):

													"""simple docstring"""
													return MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''				)







						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			MaMaaaModel.from_pretrained('''facebook/m2m100_418M'''				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Any     					=			_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]				)
													SCREAMING_SNAKE_CASE					:      int     					=			_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]				)
													SCREAMING_SNAKE_CASE					:      int     					=			prepare_mam_aaa_inputs_dict(model.config				,    lowerCAmelCase__				,    lowerCAmelCase__				)
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE					:      List[Any]     					=			model(**lowerCAmelCase__				)[0]
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			torch.Size((1, 11, 10_24)				)
													self.assertEqual(output.shape				,    lowerCAmelCase__				)
													# change to expected output here
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			torch.tensor(
													    [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]				,    device=lowerCAmelCase__				)
													self.assertTrue(torch.allclose(output[:, :3, :3]				,    lowerCAmelCase__				,    atol=lowerCAmelCase__				)				)







						def 					__lowercase    (  self   :      Optional[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Any     					=			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''				).to(lowerCAmelCase__				)

													# change to intended input
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			_long_tensor([[12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38, 2]]				)
													SCREAMING_SNAKE_CASE					:      Dict     					=			_long_tensor([[2, 12_80_28, 98, 12, 3_05_27, 27_32, 1_59, 77_55, 6_19_04, 3_91_44, 38]]				)
													SCREAMING_SNAKE_CASE					:      str     					=			prepare_mam_aaa_inputs_dict(model.config				,    lowerCAmelCase__				,    lowerCAmelCase__				)
													with torch.no_grad():
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			model(**lowerCAmelCase__				)[0]
													SCREAMING_SNAKE_CASE					:      str     					=			torch.Size((1, 11, model.config.vocab_size)				)
													self.assertEqual(output.shape				,    lowerCAmelCase__				)
													# change to expected output here
													SCREAMING_SNAKE_CASE					:      List[str]     					=			torch.tensor(
													    [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]				,    device=lowerCAmelCase__				)
													self.assertTrue(torch.allclose(output[:, :3, :3]				,    lowerCAmelCase__				,    atol=lowerCAmelCase__				)				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			MaMaaaForConditionalGeneration.from_pretrained('''facebook/m2m100_418M'''				).to(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			MaMaaaTokenizer.from_pretrained('''facebook/m2m100_418M'''				,    src_lang='''fr'''				,    tgt_lang='''en'''				)

													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[
													    '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
													    '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
													    '''Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'''
													    ''' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'''
													    ''' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.''',
													]

													# The below article tests that we don't add any hypotheses outside of the top n_beams
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tokenizer(lowerCAmelCase__				,    padding=lowerCAmelCase__				,    return_tensors='''pt'''				)

													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			model.generate(
													    input_ids=dct['''input_ids'''].to(lowerCAmelCase__				)				,    attention_mask=dct['''attention_mask'''].to(lowerCAmelCase__				)				,    num_beams=5				,    forced_bos_token_id=tokenizer.get_lang_id('''en'''				)				,    )

													SCREAMING_SNAKE_CASE					:      Tuple     					=			[
													    '''The NSA case highlights the total absence of intelligence debate''',
													    '''I think there are two levels of response from the French government.''',
													    '''When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'''
													    ''' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'''
													    ''' communications in France.''',
													]

													SCREAMING_SNAKE_CASE					:      Tuple     					=			tokenizer.batch_decode(
													    hypotheses_batch.tolist()				,    clean_up_tokenization_spaces=lowerCAmelCase__				,    skip_special_tokens=lowerCAmelCase__				)
													assert generated == expected_en









'''simple docstring'''



import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
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


lowerCAmelCase_     :       int								=						get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase_							(					snake_case_    ,				unittest.TestCase			):
						_lowerCAmelCase     :   Optional[int]	     =				XLMRobertaTokenizer
						_lowerCAmelCase     :   Optional[Any]	     =				XLMRobertaTokenizerFast
						_lowerCAmelCase     :   List[str]	     =				True
						_lowerCAmelCase     :   Union[str, Any]	     =				True







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													super().setUp()

													# We have a SentencePiece fixture for testing
													SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)
													tokenizer.save_pretrained(self.tmpdirname				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''<pad>'''
													SCREAMING_SNAKE_CASE					:      Tuple     					=			1

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







						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			list(self.get_tokenizer().get_vocab().keys()				)

													self.assertEqual(vocab_keys[0]				,    '''<s>'''				)
													self.assertEqual(vocab_keys[1]				,    '''<pad>'''				)
													self.assertEqual(vocab_keys[-1]				,    '''<mask>'''				)
													self.assertEqual(len(lowerCAmelCase__				)				,    10_02				)







						def 					__lowercase    (  self   :      List[Any]				):

													"""simple docstring"""
													self.assertEqual(self.get_tokenizer().vocab_size				,    10_02				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			XLMRobertaTokenizer(lowerCAmelCase__				,    keep_accents=lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      List[str]     					=			tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCAmelCase__				,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

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

													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        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''',
													        '''.''',
													    ]				,    )
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.convert_tokens_to_ids(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        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]
													        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
													    ]				,    )

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
													self.assertListEqual(
													    lowerCAmelCase__				,    [
													        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>''',
													        '''.''',
													    ]				,    )







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													if not self.test_slow_tokenizer:
																				# as we don't have a slow version, we can't compare the outputs between slow and fast versions
																				return

													SCREAMING_SNAKE_CASE					:      Tuple     					=			(self.rust_tokenizer_class, '''hf-internal-testing/tiny-xlm-roberta''', {})
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																											SCREAMING_SNAKE_CASE					:      int     					=			self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			self.tokenizer_class.from_pretrained(lowerCAmelCase__				,    **lowerCAmelCase__				)

																											SCREAMING_SNAKE_CASE					:      int     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it save with the same files + the tokenizer.json file for the fast one
																											self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)
																											SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      int     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)
																																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=True
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it save with the same files
																											self.assertSequenceEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      str     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																											shutil.rmtree(lowerCAmelCase__				)

																											# Save tokenizer rust, legacy_format=False
																											SCREAMING_SNAKE_CASE					:      List[Any]     					=			tempfile.mkdtemp()

																											SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer_r.save_pretrained(lowerCAmelCase__				,    legacy_format=lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Optional[int]     					=			tokenizer_p.save_pretrained(lowerCAmelCase__				)

																											# Checks it saved the tokenizer.json file
																											self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)

																											# Checks everything loads correctly in the same way
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_r.from_pretrained(lowerCAmelCase__				)
																											SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer_p.from_pretrained(lowerCAmelCase__				)

																											# Check special tokens are set accordingly on Rust and Python
																											for key in tokenizer_pp.special_tokens_map:
																																		self.assertTrue(hasattr(lowerCAmelCase__				,    lowerCAmelCase__				)				)

																											shutil.rmtree(lowerCAmelCase__				)







						@cached_property
						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''				)







						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													with tempfile.NamedTemporaryFile() as f:
																				shutil.copyfile(lowerCAmelCase__				,    f.name				)
																				SCREAMING_SNAKE_CASE					:      str     					=			XLMRobertaTokenizer(f.name				,    keep_accents=lowerCAmelCase__				)
																				SCREAMING_SNAKE_CASE					:      str     					=			pickle.dumps(lowerCAmelCase__				)
													pickle.loads(lowerCAmelCase__				)







						def 					__lowercase    (  self   :      Dict				):

													"""simple docstring"""
													if not self.test_rust_tokenizer:
																				return

													SCREAMING_SNAKE_CASE					:      int     					=			self.get_tokenizer()
													SCREAMING_SNAKE_CASE					:      int     					=			self.get_rust_tokenizer()

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			'''I was born in 92000, and this is falsé.'''

													SCREAMING_SNAKE_CASE					:      int     					=			tokenizer.tokenize(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			rust_tokenizer.tokenize(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      Any     					=			tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				,    add_special_tokens=lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      str     					=			self.get_rust_tokenizer()
													SCREAMING_SNAKE_CASE					:      Dict     					=			tokenizer.encode(lowerCAmelCase__				)
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			rust_tokenizer.encode(lowerCAmelCase__				)
													self.assertListEqual(lowerCAmelCase__				,    lowerCAmelCase__				)







						@slow
						def 					__lowercase    (  self   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			'''Hello World!'''
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			[0, 3_53_78, 66_61, 38, 2]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

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







						@slow
						def 					__lowercase    (  self   :      Optional[int]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      str     					=			(
													    '''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'''
													)
													SCREAMING_SNAKE_CASE					:      Any     					=			[
													    0,
													    32_93,
													    83,
													    10,
													    45_52,
													    49_89,
													    79_86,
													    6_78,
													    10,
													    59_15,
													    1_11,
													    17_94_59,
													    12_48_50,
													    4,
													    60_44,
													    2_37,
													    12,
													    6,
													    5,
													    6,
													    4,
													    67_80,
													    7_05,
													    15,
													    13_88,
													    44,
													    3_78,
													    1_01_14,
													    7_11,
													    1_52,
													    20,
													    6,
													    5,
													    2_23_76,
													    6_42,
													    12_21,
													    1_51_90,
													    3_41_53,
													    4_50,
													    56_08,
													    9_59,
													    11_19,
													    5_77_02,
													    1_36,
													    1_86,
													    47,
													    10_98,
													    2_93_67,
													    47,
													    # 4426, # What fairseq tokenizes from "<unk>": "_<"
													    # 3678, # What fairseq tokenizes from "<unk>": "unk"
													    # 2740, # What fairseq tokenizes from "<unk>": ">"
													    3,  # What we tokenize from "<unk>": "<unk>"
													    6,  # Residue from the tokenization: an extra sentencepiece underline
													    4,
													    60_44,
													    2_37,
													    62_84,
													    5_09_01,
													    5_28,
													    31,
													    90,
													    34,
													    9_27,
													    2,
													]
													# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
													# xlmr.eval()
													# xlmr.encode(symbols)

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







						@slow
						def 					__lowercase    (  self   :      List[str]				):

													"""simple docstring"""
													# fmt: off
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			{'''input_ids''': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
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													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCAmelCase__				,    model_name='''xlm-roberta-base'''				,    revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''				,    )




