

"""simple docstring"""





import unittest

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

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


if is_torch_available():
   import torch

   from transformers import (
       MPNetForMaskedLM,
       MPNetForMultipleChoice,
       MPNetForQuestionAnswering,
       MPNetForSequenceClassification,
       MPNetForTokenClassification,
       MPNetModel,
   )




class 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			List[Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[Any]=1_3,					UpperCAmelCase__							:			Union[str, Any]=7,					UpperCAmelCase__							:			Tuple=True,					UpperCAmelCase__							:			Optional[Any]=True,					UpperCAmelCase__							:			Optional[int]=False,					UpperCAmelCase__							:			Union[str, Any]=True,					UpperCAmelCase__							:			Tuple=9_9,					UpperCAmelCase__							:			Optional[Any]=6_4,					UpperCAmelCase__							:			str=5,					UpperCAmelCase__							:			Optional[Any]=4,					UpperCAmelCase__							:			List[str]=6_4,					UpperCAmelCase__							:			Any="gelu",					UpperCAmelCase__							:			int=0.1,					UpperCAmelCase__							:			Tuple=0.1,					UpperCAmelCase__							:			Optional[Any]=5_1_2,					UpperCAmelCase__							:			Union[str, Any]=1_6,					UpperCAmelCase__							:			List[Any]=2,					UpperCAmelCase__							:			Tuple=0.02,					UpperCAmelCase__							:			str=3,					UpperCAmelCase__							:			List[str]=4,					UpperCAmelCase__							:			Dict=None,					):
    __lowercase								=						parent
    __lowercase								=						batch_size
    __lowercase								=						seq_length
    __lowercase								=						is_training
    __lowercase								=						use_input_mask
    __lowercase								=						use_token_type_ids
    __lowercase								=						use_labels
    __lowercase								=						vocab_size
    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						max_position_embeddings
    __lowercase								=						type_vocab_size
    __lowercase								=						type_sequence_label_size
    __lowercase								=						initializer_range
    __lowercase								=						num_labels
    __lowercase								=						num_choices
    __lowercase								=						scope


 def 					_lowercase (			self							:			List[str]	):
    return MPNetConfig.from_pretrained("microsoft/mpnet-base"	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						ids_tensor([self.batch_size, self.seq_length],					self.vocab_size	)

    __lowercase								=						None
    if self.use_input_mask:
       __lowercase								=						random_attention_mask([self.batch_size, self.seq_length]	)

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

    __lowercase								=						self.get_config()
    return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels


 def 					_lowercase (			self							:			Dict	):
    return MPNetConfig(
        vocab_size=self.vocab_size,					hidden_size=self.hidden_size,					num_hidden_layers=self.num_hidden_layers,					num_attention_heads=self.num_attention_heads,					intermediate_size=self.intermediate_size,					hidden_act=self.hidden_act,					hidden_dropout_prob=self.hidden_dropout_prob,					attention_probs_dropout_prob=self.attention_probs_dropout_prob,					max_position_embeddings=self.max_position_embeddings,					initializer_range=self.initializer_range,					)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			Optional[Any]	):
    __lowercase								=						MPNetModel(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)	)
    self.parent.assertEqual(result.pooler_output.shape,					(self.batch_size, self.hidden_size)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			str	):
    __lowercase								=						MPNetForQuestionAnswering(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					start_positions=UpperCAmelCase__,					end_positions=UpperCAmelCase__,					)
    self.parent.assertEqual(result.start_logits.shape,					(self.batch_size, self.seq_length)	)
    self.parent.assertEqual(result.end_logits.shape,					(self.batch_size, self.seq_length)	)


 def 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.num_labels
    __lowercase								=						MPNetForSequenceClassification(UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)	)


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str]	):
    __lowercase								=						self.num_choices
    __lowercase								=						MPNetForMultipleChoice(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						input_ids.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
    __lowercase								=						input_mask.unsqueeze(1	).expand(-1,					self.num_choices,					-1	).contiguous()
    __lowercase								=						model(
        UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__,					)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_choices)	)


 def 					_lowercase (			self							:			Optional[int],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Optional[Any],					UpperCAmelCase__							:			List[Any]	):
    __lowercase								=						self.num_labels
    __lowercase								=						MPNetForTokenClassification(config=UpperCAmelCase__	)
    model.to(UpperCAmelCase__	)
    model.eval()
    __lowercase								=						model(UpperCAmelCase__,					attention_mask=UpperCAmelCase__,					labels=UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.num_labels)	)



 def 					_lowercase (			self							:			int	):
    __lowercase								=						self.prepare_config_and_inputs()
    ((__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase)		,(__lowercase))								=						config_and_inputs
    __lowercase								=						{"input_ids": input_ids, "attention_mask": input_mask}
    return config, inputs_dict







@require_torch
class 					_lowerCAmelCase  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Union[str, Any]   		=	(
     (
         MPNetForMaskedLM,
         MPNetForMultipleChoice,
         MPNetForQuestionAnswering,
         MPNetForSequenceClassification,
         MPNetForTokenClassification,
         MPNetModel,
     )
     if is_torch_available()
     else ()
 )
 __UpperCAmelCase	:					Optional[int]   		=	(
     {
         "feature-extraction": MPNetModel,
         "fill-mask": MPNetForMaskedLM,
         "question-answering": MPNetForQuestionAnswering,
         "text-classification": MPNetForSequenceClassification,
         "token-classification": MPNetForTokenClassification,
         "zero-shot": MPNetForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __UpperCAmelCase	:					Any   		=	False
 __UpperCAmelCase	:					List[Any]   		=	True


 def 					_lowercase (			self							:			int	):
    __lowercase								=						MPNetModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					hidden_size=3_7	)


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


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_sequence_classification(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_multiple_choice(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Union[str, Any]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_token_classification(*UpperCAmelCase__	)



 def 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_mpnet_for_question_answering(*UpperCAmelCase__	)







@require_torch
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @slow
 def 					_lowercase (			self							:			Dict	):
    __lowercase								=						MPNetModel.from_pretrained("microsoft/mpnet-base"	)
    __lowercase								=						torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]	)
    __lowercase								=						model(UpperCAmelCase__	)[0]
    __lowercase								=						torch.Size((1, 1_1, 7_6_8)	)
    self.assertEqual(output.shape,					UpperCAmelCase__	)
    __lowercase								=						torch.tensor(
        [[[-0.0_550, 0.1_943, -0.0_740], [-0.0_562, 0.2_211, -0.0_579], [-0.0_437, 0.3_337, -0.0_641]]]	)
    # compare the actual values for a slice.
    self.assertTrue(torch.allclose(output[:, :3, :3],					UpperCAmelCase__,					atol=1E-4	)	)






'''simple docstring'''







import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowercase :  List[Any]    							=       logging.get_logger(__name__)

_lowercase :  List[Any]    							=       "▁"

_lowercase :  Tuple    							=       {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowercase :  List[str]    							=       {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

_lowercase :  List[str]    							=       {
    "facebook/m2m100_418M": 1_0_2_4,
}

# fmt: off
_lowercase :  Tuple    							=       {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     VOCAB_FILES_NAMES
					lowerCAmelCase_        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_        =     PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_        =     ['''input_ids''', '''attention_mask''']

					lowerCAmelCase_        =     []
					lowerCAmelCase_        =     []






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE="<s>"				,							__SCREAMING_SNAKE_CASE="</s>"				,							__SCREAMING_SNAKE_CASE="</s>"				,							__SCREAMING_SNAKE_CASE="<pad>"				,							__SCREAMING_SNAKE_CASE="<unk>"				,							__SCREAMING_SNAKE_CASE="m2m100"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE=8				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       {} if sp_model_kwargs is None else sp_model_kwargs

											lowercase_	:     List[Any]													=       language_codes
											lowercase_	:     Optional[int]													=       FAIRSEQ_LANGUAGE_CODES[language_codes]
											lowercase_	:     List[Any]													=       {lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

											lowercase_	:     Union[str, Any]													=       kwargs.get('''additional_special_tokens'''				,							[]		)
											kwargs["additional_special_tokens"] += [
											    self.get_lang_token(__SCREAMING_SNAKE_CASE		)
											    for lang_code in fairseq_language_code
											    if self.get_lang_token(__SCREAMING_SNAKE_CASE		) not in kwargs["additional_special_tokens"]
											]

											super().__init__(
											    src_lang=__SCREAMING_SNAKE_CASE				,							tgt_lang=__SCREAMING_SNAKE_CASE				,							bos_token=__SCREAMING_SNAKE_CASE				,							eos_token=__SCREAMING_SNAKE_CASE				,							sep_token=__SCREAMING_SNAKE_CASE				,							unk_token=__SCREAMING_SNAKE_CASE				,							pad_token=__SCREAMING_SNAKE_CASE				,							language_codes=__SCREAMING_SNAKE_CASE				,							sp_model_kwargs=self.sp_model_kwargs				,							num_madeup_words=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     int													=       vocab_file
											lowercase_	:     Any													=       load_json(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       {v: k for k, v in self.encoder.items()}
											lowercase_	:     Optional[int]													=       spm_file
											lowercase_	:     Any													=       load_spm(__SCREAMING_SNAKE_CASE				,							self.sp_model_kwargs		)

											lowercase_	:     List[Any]													=       len(self.encoder		)

											lowercase_	:     Dict													=       {
											    self.get_lang_token(__SCREAMING_SNAKE_CASE		): self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE		)
											}
											lowercase_	:     Optional[int]													=       {lang_code: self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE		)}
											lowercase_	:     Union[str, Any]													=       {v: k for k, v in self.lang_token_to_id.items()}

											lowercase_	:     Tuple													=       src_lang if src_lang is not None else '''en'''
											lowercase_	:     Optional[int]													=       tgt_lang
											lowercase_	:     Any													=       self.get_lang_id(self._src_lang		)
											self.set_src_lang_special_tokens(self._src_lang		)

											lowercase_	:     Dict													=       num_madeup_words






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return len(self.encoder		) + len(self.lang_token_to_id		)






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self._src_lang






					@src_lang.setter
					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     str													=       new_src_lang
											self.set_src_lang_special_tokens(self._src_lang		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.sp_model.encode(__SCREAMING_SNAKE_CASE				,							out_type=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if token in self.lang_token_to_id:
																	return self.lang_token_to_id[token]
											return self.encoder.get(__SCREAMING_SNAKE_CASE				,							self.encoder[self.unk_token]		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if index in self.id_to_lang_token:
																	return self.id_to_lang_token[index]
											return self.decoder.get(__SCREAMING_SNAKE_CASE				,							self.unk_token		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       []
											lowercase_	:     List[str]													=       ''''''
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE		) + token
																							lowercase_	:     Optional[Any]													=       []
																	else:
																							current_sub_tokens.append(__SCREAMING_SNAKE_CASE		)
											out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE		)
											return out_string.strip()






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=__SCREAMING_SNAKE_CASE				,							token_ids_a=__SCREAMING_SNAKE_CASE				,							already_has_special_tokens=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       [1] * len(self.prefix_tokens		)
											lowercase_	:     Any													=       [1] * len(self.suffix_tokens		)
											if token_ids_a is None:
																	return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE		)) + suffix_ones
											return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE		)) + ([0] * len(__SCREAMING_SNAKE_CASE		)) + suffix_ones






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if token_ids_a is None:
																	return self.prefix_tokens + token_ids_a + self.suffix_tokens
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab






					def __getstate__(   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       self.__dict__.copy()
											lowercase_	:     List[Any]													=       None
											return state






					def __setstate__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       d

											# for backward compatibility
											if not hasattr(self				,							'''sp_model_kwargs'''		):
																	lowercase_	:     List[Any]													=       {}

											lowercase_	:     Union[str, Any]													=       load_spm(self.spm_file				,							self.sp_model_kwargs		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       Path(__SCREAMING_SNAKE_CASE		)
											if not save_dir.is_dir():
																	raise OSError(F'''{save_directory} should be a directory'''		)
											lowercase_	:     Dict													=       save_dir / (
											    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
											)
											lowercase_	:     Dict													=       save_dir / (
											    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
											)

											save_json(self.encoder				,							__SCREAMING_SNAKE_CASE		)

											if os.path.abspath(self.spm_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		) and os.path.isfile(self.spm_file		):
																	copyfile(self.spm_file				,							__SCREAMING_SNAKE_CASE		)
											elif not os.path.isfile(self.spm_file		):
																	with open(__SCREAMING_SNAKE_CASE				,							'''wb'''		) as fi:
																							lowercase_	:     int													=       self.sp_model.serialized_model_proto()
																							fi.write(__SCREAMING_SNAKE_CASE		)

											return (str(__SCREAMING_SNAKE_CASE		), str(__SCREAMING_SNAKE_CASE		))






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = "en"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "ro"				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       src_lang
											lowercase_	:     List[str]													=       tgt_lang
											self.set_src_lang_special_tokens(self.src_lang		)
											return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if src_lang is None or tgt_lang is None:
																	raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''		)
											lowercase_	:     Tuple													=       src_lang
											lowercase_	:     Any													=       self(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       self.get_lang_id(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       tgt_lang_id
											return inputs






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.set_src_lang_special_tokens(self.src_lang		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.set_tgt_lang_special_tokens(self.tgt_lang		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       self.get_lang_token(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       self.lang_token_to_id[lang_token]
											lowercase_	:     Optional[Any]													=       [self.cur_lang_id]
											lowercase_	:     Union[str, Any]													=       [self.eos_token_id]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       self.get_lang_token(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       self.lang_token_to_id[lang_token]
											lowercase_	:     str													=       [self.cur_lang_id]
											lowercase_	:     List[str]													=       [self.eos_token_id]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.lang_code_to_token[lang]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       self.get_lang_token(__SCREAMING_SNAKE_CASE		)
											return self.lang_token_to_id[lang_token]







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Dict[str, Any]							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       sentencepiece.SentencePieceProcessor(**__SCREAMING_SNAKE_CASE							)
						spm.Load(str(__SCREAMING_SNAKE_CASE							)							)
						return spm
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						with open(__SCREAMING_SNAKE_CASE      ,      '''r'''							) as f:
												return json.load(__SCREAMING_SNAKE_CASE							)




def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						with open(__SCREAMING_SNAKE_CASE      ,      '''w'''							) as f:
												json.dump(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      indent=2							)



