
'''simple docstring'''







import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
_lowercase :  Union[str, Any]    							=       logging.get_logger("transformers.models.speecht5")

_lowercase :  Optional[Any]    							=       {
    "speech_encoder_prenet.layer_norm": "speecht5.encoder.prenet.feature_projection.layer_norm",
    "speech_encoder_prenet.post_extract_proj": "speecht5.encoder.prenet.feature_projection.projection",
    "speech_encoder_prenet.pos_conv.0": "speecht5.encoder.prenet.pos_conv_embed.conv",
    "speech_encoder_prenet.mask_emb": "speecht5.encoder.prenet.masked_spec_embed",
}
_lowercase :  List[str]    							=       {
    "text_encoder_prenet.encoder_prenet.0": "speecht5.encoder.prenet.embed_tokens",
    "text_encoder_prenet.encoder_prenet.1.alpha": "speecht5.encoder.prenet.encode_positions.alpha",
}
_lowercase :  Any    							=       {
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0": "speecht5.decoder.prenet.layers.0",
    "speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0": "speecht5.decoder.prenet.layers.1",
    "speech_decoder_prenet.decoder_prenet.0.1": "speecht5.decoder.prenet.final_layer",
    "speech_decoder_prenet.decoder_prenet.1.alpha": "speecht5.decoder.prenet.encode_positions.alpha",
    "speech_decoder_prenet.spkembs_layer.0": "speecht5.decoder.prenet.speaker_embeds_layer",
}
_lowercase :  Tuple    							=       {
    "speech_decoder_postnet.feat_out": "speech_decoder_postnet.feat_out",
    "speech_decoder_postnet.prob_out": "speech_decoder_postnet.prob_out",
    "speech_decoder_postnet.postnet.postnet.0.0": "speech_decoder_postnet.layers.0.conv",
    "speech_decoder_postnet.postnet.postnet.0.1": "speech_decoder_postnet.layers.0.batch_norm",
    "speech_decoder_postnet.postnet.postnet.1.0": "speech_decoder_postnet.layers.1.conv",
    "speech_decoder_postnet.postnet.postnet.1.1": "speech_decoder_postnet.layers.1.batch_norm",
    "speech_decoder_postnet.postnet.postnet.2.0": "speech_decoder_postnet.layers.2.conv",
    "speech_decoder_postnet.postnet.postnet.2.1": "speech_decoder_postnet.layers.2.batch_norm",
    "speech_decoder_postnet.postnet.postnet.3.0": "speech_decoder_postnet.layers.3.conv",
    "speech_decoder_postnet.postnet.postnet.3.1": "speech_decoder_postnet.layers.3.batch_norm",
    "speech_decoder_postnet.postnet.postnet.4.0": "speech_decoder_postnet.layers.4.conv",
    "speech_decoder_postnet.postnet.postnet.4.1": "speech_decoder_postnet.layers.4.batch_norm",
}
_lowercase :  Any    							=       {
    "text_decoder_prenet.embed_tokens": "speecht5.decoder.prenet.embed_tokens",
}
_lowercase :  int    							=       {
    "text_decoder_postnet.output_projection": "text_decoder_postnet.lm_head",
}
_lowercase :  int    							=       {
    "encoder.layers.*.self_attn.k_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj",
    "encoder.layers.*.self_attn.v_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj",
    "encoder.layers.*.self_attn.q_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj",
    "encoder.layers.*.self_attn.out_proj": "speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj",
    "encoder.layers.*.self_attn_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.layer_norm",
    "encoder.layers.*.fc1": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense",
    "encoder.layers.*.fc2": "speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense",
    "encoder.layers.*.final_layer_norm": "speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "speecht5.encoder.wrapped_encoder.layer_norm",
    "encoder.pos_emb.pe_k": "speecht5.encoder.wrapped_encoder.embed_positions.pe_k",
}
_lowercase :  Any    							=       {
    "decoder.layers.*.self_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj",
    "decoder.layers.*.self_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj",
    "decoder.layers.*.self_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj",
    "decoder.layers.*.self_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj",
    "decoder.layers.*.self_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm",
    "decoder.layers.*.encoder_attn.k_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj",
    "decoder.layers.*.encoder_attn.v_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj",
    "decoder.layers.*.encoder_attn.q_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj",
    "decoder.layers.*.encoder_attn.out_proj": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj",
    "decoder.layers.*.encoder_attn_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm",
    "decoder.layers.*.fc1": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense",
    "decoder.layers.*.fc2": "speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense",
    "decoder.layers.*.final_layer_norm": "speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm",
}
_lowercase :  Tuple    							=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
_lowercase :  Optional[Any]    							=       {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowercase :  Optional[Any]    							=       {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
_lowercase :  List[str]    							=       []
_lowercase :  Tuple    							=       [
    "encoder.version",
    "encoder.layers.*.norm_k.weight",
    "encoder.layers.*.norm_k.bias",
    "decoder.version",
    "decoder.layers.*.norm_k.weight",
    "decoder.layers.*.norm_k.bias",
    "decoder.pos_emb.pe_k",
    "speech_encoder_prenet.embed_positions._float_tensor",
    "text_decoder_prenet.embed_positions._float_tensor",
]
_lowercase :  Optional[int]    							=       IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "speech_decoder_prenet.*",
    "speech_decoder_postnet.*",
]
_lowercase :  int    							=       IGNORE_KEYS + [
    "encoder.proj",
    "speech_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
_lowercase :  Tuple    							=       IGNORE_KEYS + [
    "encoder.proj",
    "text_encoder_prenet.*",
    "text_decoder_prenet.*",
    "text_decoder_postnet.*",
]
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: int							):



						"""simple docstring"""





						for attribute in key.split('''.'''							):
												lowercase_	:     Tuple													=       getattr(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						if weight_type is not None:
												lowercase_	:     Optional[Any]													=       getattr(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							).shape
						else:
												lowercase_	:     Dict													=       hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
												    F''' {value.shape} for {full_name}'''							)

						if weight_type == "weight":
												lowercase_	:     List[Any]													=       value
						elif weight_type == "weight_g":
												lowercase_	:     Optional[int]													=       value
						elif weight_type == "weight_v":
												lowercase_	:     Optional[int]													=       value
						elif weight_type == "bias":
												lowercase_	:     Dict													=       value
						elif weight_type == "running_mean":
												lowercase_	:     Any													=       value
						elif weight_type == "running_var":
												lowercase_	:     Optional[int]													=       value
						elif weight_type == "num_batches_tracked":
												lowercase_	:     Any													=       value
						else:
												lowercase_	:     Optional[Any]													=       value

						logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						for key in ignore_keys:
												if key.endswith('''.*'''							):
																		if name.startswith(key[:-1]							):
																								return True
												elif ".*." in key:
																		lowercase_						, lowercase_	:     Optional[int]													=       key.split('''.*.'''							)
																		if prefix in name and suffix in name:
																								return True
												elif key in name:
																		return True
						return False
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						lowercase_	:     Union[str, Any]													=       []

						if task == "s2t":
												lowercase_	:     str													=       hf_model.speechta.encoder.prenet.feature_encoder
												lowercase_	:     List[Any]													=       MAPPING_S2T
												lowercase_	:     Optional[int]													=       IGNORE_KEYS_S2T
						elif task == "t2s":
												lowercase_	:     str													=       None
												lowercase_	:     Optional[int]													=       MAPPING_T2S
												lowercase_	:     str													=       IGNORE_KEYS_T2S
						elif task == "s2s":
												lowercase_	:     str													=       hf_model.speechta.encoder.prenet.feature_encoder
												lowercase_	:     int													=       MAPPING_S2S
												lowercase_	:     Optional[Any]													=       IGNORE_KEYS_S2S
						else:
												raise ValueError(F'''Unsupported task: {task}'''							)

						for name, value in fairseq_dict.items():
												if should_ignore(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																		logger.info(F'''{name} was ignored'''							)
																		continue

												lowercase_	:     Optional[Any]													=       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      hf_model.config.feat_extract_norm == '''group'''      ,      )
																		lowercase_	:     Tuple													=       True
												else:
																		for key, mapped_key in MAPPING.items():
																								# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

																								if "*" in key:
																														lowercase_						, lowercase_	:     Any													=       key.split('''.*.'''							)
																														if prefix in name and suffix in name:
																																				lowercase_	:     List[Any]													=       suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
																								if key in name:
																														lowercase_	:     Any													=       True
																														if "*" in mapped_key:
																																				lowercase_	:     Optional[int]													=       name.split(__SCREAMING_SNAKE_CASE							)[0].split('''.'''							)[-2]
																																				lowercase_	:     Any													=       mapped_key.replace('''*'''      ,      __SCREAMING_SNAKE_CASE							)
																														if "weight_g" in name:
																																				lowercase_	:     int													=       '''weight_g'''
																														elif "weight_v" in name:
																																				lowercase_	:     Optional[int]													=       '''weight_v'''
																														elif "bias" in name:
																																				lowercase_	:     Tuple													=       '''bias'''
																														elif "weight" in name:
																																				lowercase_	:     Union[str, Any]													=       '''weight'''
																														elif "running_mean" in name:
																																				lowercase_	:     str													=       '''running_mean'''
																														elif "running_var" in name:
																																				lowercase_	:     Optional[Any]													=       '''running_var'''
																														elif "num_batches_tracked" in name:
																																				lowercase_	:     Tuple													=       '''num_batches_tracked'''
																														else:
																																				lowercase_	:     int													=       None
																														set_recursively(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)
																								continue
												if not is_used:
																		unused_weights.append(__SCREAMING_SNAKE_CASE							)

						logger.warning(F'''Unused weights: {unused_weights}'''							)
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Optional[Any]      ,      __SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: int      ,      __SCREAMING_SNAKE_CASE					: Dict							):



						"""simple docstring"""





						lowercase_	:     List[str]													=       full_name.split('''conv_layers.'''							)[-1]
						lowercase_	:     List[Any]													=       name.split('''.'''							)
						lowercase_	:     Union[str, Any]													=       int(items[0]							)
						lowercase_	:     Optional[int]													=       int(items[1]							)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    F'''{full_name} has size {value.shape}, but'''
																								    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
																		lowercase_	:     str													=       value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    F'''{full_name} has size {value.shape}, but'''
																								    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
																		lowercase_	:     Any													=       value
																		logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
						elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    F'''{full_name} has size {value.shape}, but'''
																								    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''							)
																		lowercase_	:     Optional[int]													=       value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    F'''{full_name} has size {value.shape}, but'''
																								    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''							)
																		lowercase_	:     Any													=       value
																		logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
						else:
												unused_weights.append(__SCREAMING_SNAKE_CASE							)
@torch.no_grad()
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Optional[int]      ,      __SCREAMING_SNAKE_CASE					: List[str]=None      ,      __SCREAMING_SNAKE_CASE					: Optional[int]=None      ,      __SCREAMING_SNAKE_CASE					: Tuple=None      ,      ):



						"""simple docstring"""





						if config_path is not None:
												lowercase_	:     str													=       SpeechTaConfig.from_pretrained(__SCREAMING_SNAKE_CASE							)
						else:
												lowercase_	:     Optional[Any]													=       SpeechTaConfig()

						if task == "s2t":
												lowercase_	:     str													=       config.max_text_positions
												lowercase_	:     int													=       SpeechTaForSpeechToText(__SCREAMING_SNAKE_CASE							)
						elif task == "t2s":
												lowercase_	:     Union[str, Any]													=       1876
												lowercase_	:     str													=       600
												lowercase_	:     Union[str, Any]													=       config.max_speech_positions
												lowercase_	:     List[Any]													=       SpeechTaForTextToSpeech(__SCREAMING_SNAKE_CASE							)
						elif task == "s2s":
												lowercase_	:     Any													=       1876
												lowercase_	:     Tuple													=       config.max_speech_positions
												lowercase_	:     int													=       SpeechTaForSpeechToSpeech(__SCREAMING_SNAKE_CASE							)
						else:
												raise ValueError(F'''Unknown task name: {task}'''							)

						if vocab_path:
												lowercase_	:     List[str]													=       SpeechTaTokenizer(__SCREAMING_SNAKE_CASE      ,      model_max_length=config.max_text_positions							)

												# Mask token behaves like a normal word, i.e. include the space before it
												lowercase_	:     Optional[Any]													=       AddedToken('''<mask>'''      ,      lstrip=__SCREAMING_SNAKE_CASE      ,      rstrip=__SCREAMING_SNAKE_CASE							)
												lowercase_	:     Tuple													=       mask_token
												tokenizer.add_special_tokens({'''mask_token''': mask_token}							)
												tokenizer.add_tokens(['''<ctc_blank>''']							)

						lowercase_	:     Optional[int]													=       SpeechTaFeatureExtractor()
						lowercase_	:     Optional[Any]													=       SpeechTaProcessor(tokenizer=__SCREAMING_SNAKE_CASE      ,      feature_extractor=__SCREAMING_SNAKE_CASE							)
						processor.save_pretrained(__SCREAMING_SNAKE_CASE							)

						lowercase_	:     str													=       torch.load(__SCREAMING_SNAKE_CASE							)
						recursively_load_weights(fairseq_checkpoint['''model''']      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)

						model.save_pretrained(__SCREAMING_SNAKE_CASE							)

						if repo_id:
												print('''Pushing to the hub...'''							)
												processor.push_to_hub(__SCREAMING_SNAKE_CASE							)
												model.push_to_hub(__SCREAMING_SNAKE_CASE							)


if __name__ == "__main__":
							_lowercase :  List[str]    							=       argparse.ArgumentParser()
							parser.add_argument(
							    "--task",
							    default="s2t",
							    type=str,
							    help="Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.",
							)
							parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--vocab_path", default=None, type=str, help="Path to SentencePiece model")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							parser.add_argument(
							    "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
							)
							parser.add_argument(
							    "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
							)

							_lowercase :  Union[str, Any]    							=       parser.parse_args()
							convert_speechta_checkpoint(
							    args.task,
							    args.checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.config_path,
							    args.vocab_path,
							    args.push_to_hub,
							)





'''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							)



