






import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
		import numpy as np
		import torch

		from . import (
		    AlbertForPreTraining,
		    BartForConditionalGeneration,
		    BertForPreTraining,
		    BertForQuestionAnswering,
		    BertForSequenceClassification,
		    CamembertForMaskedLM,
		    CTRLLMHeadModel,
		    DistilBertForMaskedLM,
		    DistilBertForQuestionAnswering,
		    DPRContextEncoder,
		    DPRQuestionEncoder,
		    DPRReader,
		    ElectraForPreTraining,
		    FlaubertWithLMHeadModel,
		    GPTaLMHeadModel,
		    LayoutLMForMaskedLM,
		    LxmertForPreTraining,
		    LxmertVisualFeatureEncoder,
		    OpenAIGPTLMHeadModel,
		    RobertaForMaskedLM,
		    RobertaForSequenceClassification,
		    TaForConditionalGeneration,
		    TransfoXLLMHeadModel,
		    XLMRobertaForMaskedLM,
		    XLMWithLMHeadModel,
		    XLNetLMHeadModel,
		)


logging.set_verbosity_info()

_a      	=  {
    '''bart''': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''bert''': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-uncased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-large-cased-whole-word-masking-finetuned-squad''': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''bert-base-cased-finetuned-mrpc''': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''dpr''': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''gpt2''': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlnet''': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm''': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''xlm-roberta''': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''transfo-xl''': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''openai-gpt''': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''roberta''': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''layoutlm''': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    '''roberta-large-mnli''': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''camembert''': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''flaubert''': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert''': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''distilbert-base-distilled-squad''': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert''': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''lxmert-visual-feature-encoder''': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''ctrl''': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''albert''': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''t5''': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''electra''': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    '''wav2vec2''': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=False ,		__lowerCAmelCase=True	)->  Optional[Any]:





						"""simple docstring"""



						if model_type not in MODEL_CLASSES:
												raise ValueError(F"""Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()	)}."""	)

						_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			MODEL_CLASSES[model_type]

						# Initialise TF model
						if config_file in aws_config_map:
												_UpperCAmelCase									=			cached_file(__lowerCAmelCase ,		__lowerCAmelCase ,		force_download=not use_cached_models	)
						_UpperCAmelCase									=			config_class.from_json_file(__lowerCAmelCase	)
						_UpperCAmelCase									=			True
						_UpperCAmelCase									=			True
						print(F"""Building TensorFlow model from configuration: {config}"""	)
						_UpperCAmelCase									=			model_class(__lowerCAmelCase	)

						# Load weights from tf checkpoint
						if pytorch_checkpoint_path in aws_config_map.keys():
												_UpperCAmelCase									=			cached_file(
												    __lowerCAmelCase ,		__lowerCAmelCase ,		force_download=not use_cached_models	)
						# Load PyTorch checkpoint in tf2 model:
						_UpperCAmelCase									=			load_pytorch_checkpoint_in_tfa_model(__lowerCAmelCase ,		__lowerCAmelCase	)

						if compare_with_pt_model:
												_UpperCAmelCase									=			tf_model(tf_model.dummy_inputs ,		training=__lowerCAmelCase	)  # build the network

												_UpperCAmelCase									=			torch.load(__lowerCAmelCase ,		map_location='cpu'	)
												_UpperCAmelCase									=			pt_model_class.from_pretrained(
												    pretrained_model_name_or_path=__lowerCAmelCase ,		config=__lowerCAmelCase ,		state_dict=__lowerCAmelCase	)

												with torch.no_grad():
																		_UpperCAmelCase									=			pt_model(**pt_model.dummy_inputs	)

												_UpperCAmelCase									=			pto[0].numpy()
												_UpperCAmelCase									=			tfo[0].numpy()
												_UpperCAmelCase									=			np.amax(np.abs(np_pt - np_tf	)	)
												print(F"""Max absolute difference between models outputs {diff}"""	)
												assert diff <= 2E-2, F"""Error, model absolute difference is >2e-2: {diff}"""

						# Save pytorch-model
						print(F"""Save TensorFlow model to {tf_dump_path}"""	)
						tf_model.save_weights(__lowerCAmelCase ,		save_format='h5'	)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None ,		__lowerCAmelCase=False ,		__lowerCAmelCase=False ,		__lowerCAmelCase=False ,		__lowerCAmelCase=False ,		)->  Tuple:





						"""simple docstring"""



						if args_model_type is None:
												_UpperCAmelCase									=			list(MODEL_CLASSES.keys()	)
						else:
												_UpperCAmelCase									=			[args_model_type]

						for j, model_type in enumerate(__lowerCAmelCase ,		start=1	):
												print('=' * 100	)
												print(F""" Converting model type {j}/{len(__lowerCAmelCase	)}: {model_type}"""	)
												print('=' * 100	)
												if model_type not in MODEL_CLASSES:
																		raise ValueError(F"""Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()	)}."""	)

												_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			MODEL_CLASSES[model_type]

												if model_shortcut_names_or_path is None:
																		_UpperCAmelCase									=			list(aws_model_maps.keys()	)
												if config_shortcut_names_or_path is None:
																		_UpperCAmelCase									=			model_shortcut_names_or_path

												for i, (model_shortcut_name, config_shortcut_name) in enumerate(
												    zip(__lowerCAmelCase ,		__lowerCAmelCase	) ,		start=1	):
																		print('-' * 100	)
																		if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																								if not only_convert_finetuned_models:
																														print(F"""    Skipping finetuned checkpoint {model_shortcut_name}"""	)
																														continue
																								_UpperCAmelCase									=			model_shortcut_name
																		elif only_convert_finetuned_models:
																								print(F"""    Skipping not finetuned checkpoint {model_shortcut_name}"""	)
																								continue
																		print(
																		    F"""    Converting checkpoint {i}/{len(__lowerCAmelCase	)}: {model_shortcut_name} - model_type {model_type}"""	)
																		print('-' * 100	)

																		if config_shortcut_name in aws_config_map:
																								_UpperCAmelCase									=			cached_file(__lowerCAmelCase ,		__lowerCAmelCase ,		force_download=not use_cached_models	)
																		else:
																								_UpperCAmelCase									=			config_shortcut_name

																		if model_shortcut_name in aws_model_maps:
																								_UpperCAmelCase									=			cached_file(__lowerCAmelCase ,		__lowerCAmelCase ,		force_download=not use_cached_models	)
																		else:
																								_UpperCAmelCase									=			model_shortcut_name

																		if os.path.isfile(__lowerCAmelCase	):
																								_UpperCAmelCase									=			'converted_model'

																		convert_pt_checkpoint_to_tf(
																		    model_type=__lowerCAmelCase ,		pytorch_checkpoint_path=__lowerCAmelCase ,		config_file=__lowerCAmelCase ,		tf_dump_path=os.path.join(__lowerCAmelCase ,		model_shortcut_name + '-tf_model.h5'	) ,		compare_with_pt_model=__lowerCAmelCase ,		)
																		if remove_cached_files:
																								os.remove(__lowerCAmelCase	)
																								os.remove(__lowerCAmelCase	)


if __name__ == "__main__":
		_a      	=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_dump_path''', default=None, type=str, required=True, help='''Path to the output Tensorflow dump file.'''
		)
		parser.add_argument(
		    '''--model_type''',
		    default=None,
		    type=str,
		    help=(
		        F'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
		        '''convert all the models from AWS.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_checkpoint_path''',
		    default=None,
		    type=str,
		    help=(
		        '''Path to the PyTorch checkpoint path or shortcut name to download from AWS. '''
		        '''If not given, will download and convert all the checkpoints from AWS.'''
		    ),
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    help=(
		        '''The config json file corresponding to the pre-trained model. \n'''
		        '''This specifies the model architecture. If not given and '''
		        '''--pytorch_checkpoint_path is not given or is a shortcut name '''
		        '''use the configuration associated to the shortcut name on the AWS'''
		    ),
		)
		parser.add_argument(
		    '''--compare_with_pt_model''', action='''store_true''', help='''Compare Tensorflow and PyTorch model predictions.'''
		)
		parser.add_argument(
		    '''--use_cached_models''',
		    action='''store_true''',
		    help='''Use cached models if possible instead of updating to latest checkpoint versions.''',
		)
		parser.add_argument(
		    '''--remove_cached_files''',
		    action='''store_true''',
		    help='''Remove pytorch models after conversion (save memory when converting in batches).''',
		)
		parser.add_argument('''--only_convert_finetuned_models''', action='''store_true''', help='''Only convert finetuned models.''')
		_a      	=  parser.parse_args()

		# if args.pytorch_checkpoint_path is not None:
		#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
		#                                 args.pytorch_checkpoint_path,
		#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
		#                                 args.tf_dump_path,
		#                                 compare_with_pt_model=args.compare_with_pt_model,
		#                                 use_cached_models=args.use_cached_models)
		# else:
		convert_all_pt_checkpoints_to_tf(
		    args.model_type.lower() if args.model_type is not None else None,
		    args.tf_dump_path,
		    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
		    if args.pytorch_checkpoint_path is not None
		    else None,
		    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
		    compare_with_pt_model=args.compare_with_pt_model,
		    use_cached_models=args.use_cached_models,
		    remove_cached_files=args.remove_cached_files,
		    only_convert_finetuned_models=args.only_convert_finetuned_models,
		)


'''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							)



