






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()
__lowerCamelCase      :    Any   		=   logging.get_logger("""transformers.models.speecht5""")

__lowerCamelCase      :    List[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""",
}
__lowerCamelCase      :    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""",
}
__lowerCamelCase      :    List[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""",
}
__lowerCamelCase      :    Any   		=   {
    """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""",
}
__lowerCamelCase      :    str   		=   {
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
__lowerCamelCase      :    str   		=   {
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
__lowerCamelCase      :    Optional[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""",
}
__lowerCamelCase      :    List[str]   		=   {
    """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""",
}
__lowerCamelCase      :    Optional[int]   		=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
__lowerCamelCase      :    List[str]   		=   {
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase      :    Tuple   		=   {
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
__lowerCamelCase      :    Dict   		=   []
__lowerCamelCase      :    Dict   		=   [
    """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""",
]
__lowerCamelCase      :    Any   		=   IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
__lowerCamelCase      :    Dict   		=   IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
__lowerCamelCase      :    Union[str, Any]   		=   IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Optional[Any]  ,						snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]  ,						snake_case_      :							Any  ,						snake_case_      :							List[Any]     ):
	for attribute in key.split("."     ):
		snake_case__				:   Union[str, Any]     =						getattr(snake_case_  ,						snake_case_     )

	if weight_type is not None:
		snake_case__				:   Union[str, Any]     =						getattr(snake_case_  ,						snake_case_     ).shape
	else:
		snake_case__				:   Tuple     =						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":
		snake_case__				:   Dict     =						value
	elif weight_type == "weight_g":
		snake_case__				:   List[Any]     =						value
	elif weight_type == "weight_v":
		snake_case__				:   int     =						value
	elif weight_type == "bias":
		snake_case__				:   List[Any]     =						value
	elif weight_type == "running_mean":
		snake_case__				:   Optional[Any]     =						value
	elif weight_type == "running_var":
		snake_case__				:   Dict     =						value
	elif weight_type == "num_batches_tracked":
		snake_case__				:   Optional[int]     =						value
	else:
		snake_case__				:   Tuple     =						value

	logger.info(F'''{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}.'''     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							Tuple  ,						snake_case_      :							Union[str, Any]     ):
	for key in ignore_keys:
		if key.endswith(".*"     ):
			if name.startswith(key[:-1]     ):
				return True
		elif ".*." in key:
			snake_case__,		snake_case__				:   Optional[Any]     =						key.split(".*."     )
			if prefix in name and suffix in name:
				return True
		elif key in name:
			return True
	return False







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							Dict  ,						snake_case_      :							Dict     ):
	snake_case__				:   Optional[Any]     =						[]

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

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

		snake_case__				:   Any     =						False
		if "conv_layers" in name:
			load_conv_layer(
			    snake_case_  ,						snake_case_  ,						snake_case_  ,						snake_case_  ,						hf_model.config.feat_extract_norm == "group"  ,						)
			snake_case__				:   Dict     =						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:
					snake_case__,		snake_case__				:   Tuple     =						key.split(".*."     )
					if prefix in name and suffix in name:
						snake_case__				:   Union[str, Any]     =						suffix

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

	logger.warning(F'''Unused weights: {unused_weights}'''     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							str  ,						snake_case_      :							Tuple  ,						snake_case_      :							Optional[Any]  ,						snake_case_      :							int     ):
	snake_case__				:   Optional[int]     =						full_name.split("conv_layers."     )[-1]
	snake_case__				:   Optional[Any]     =						name.split("."     )
	snake_case__				:   Optional[Any]     =						int(items[0]     )
	snake_case__				:   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.'''     )
			snake_case__				:   Any     =						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.'''     )
			snake_case__				:   Union[str, 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.'''     )
			snake_case__				:   str     =						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.'''     )
			snake_case__				:   Dict     =						value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''     )
	else:
		unused_weights.append(snake_case_     )







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							int  ,						snake_case_      :							Tuple  ,						snake_case_      :							Tuple  ,						snake_case_      :							int=None  ,						snake_case_      :							str=None  ,						snake_case_      :							int=None  ,						):
	if config_path is not None:
		snake_case__				:   List[Any]     =						SpeechTaConfig.from_pretrained(snake_case_     )
	else:
		snake_case__				:   List[Any]     =						SpeechTaConfig()

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

	if vocab_path:
		snake_case__				:   Any     =						SpeechTaTokenizer(snake_case_  ,						model_max_length=config.max_text_positions     )

		# Mask token behaves like a normal word, i.e. include the space before it
		snake_case__				:   List[str]     =						AddedToken("<mask>"  ,						lstrip=snake_case_  ,						rstrip=snake_case_     )
		snake_case__				:   List[Any]     =						mask_token
		tokenizer.add_special_tokens({"mask_token": mask_token}     )
		tokenizer.add_tokens(["<ctc_blank>"]     )

	snake_case__				:   Any     =						SpeechTaFeatureExtractor()
	snake_case__				:   Union[str, Any]     =						SpeechTaProcessor(tokenizer=snake_case_  ,						feature_extractor=snake_case_     )
	processor.save_pretrained(snake_case_     )

	snake_case__				:   str     =						torch.load(snake_case_     )
	recursively_load_weights(fairseq_checkpoint["model"]  ,						snake_case_  ,						snake_case_     )

	model.save_pretrained(snake_case_     )

	if repo_id:
		print("Pushing to the hub..."     )
		processor.push_to_hub(snake_case_     )
		model.push_to_hub(snake_case_     )


if __name__ == "__main__":
	__lowerCamelCase      :    Tuple   		=   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."""
	)

	__lowerCamelCase      :    int   		=   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,
	)











import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__lowerCamelCase      :    List[str]   		=   logging.get_logger(__name__)


__lowerCamelCase      :    Optional[Any]   		=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
__lowerCamelCase      :    Tuple   		=   {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase      :    Dict   		=   {
    """allenai/led-base-16384""": 1_6384,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def   SCREAMING_SNAKE_CASE       (				):
	snake_case__				:   Optional[int]     =						(
	    list(range(ord("!"     )  ,						ord("~"     ) + 1     )     ) + list(range(ord("¡"     )  ,						ord("¬"     ) + 1     )     ) + list(range(ord("®"     )  ,						ord("ÿ"     ) + 1     )     )
	)
	snake_case__				:   Optional[int]     =						bs[:]
	snake_case__				:   Any     =						0
	for b in range(2**8     ):
		if b not in bs:
			bs.append(snake_case_     )
			cs.append(2**8 + n     )
			n += 1
	snake_case__				:   Dict     =						[chr(snake_case_     ) for n in cs]
	return dict(zip(snake_case_  ,						snake_case_     )     )







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):
	snake_case__				:   Dict     =						set()
	snake_case__				:   Tuple     =						word[0]
	for char in word[1:]:
		pairs.add((prev_char, char)     )
		snake_case__				:   List[Any]     =						char
	return pairs





class 							SCREAMING_SNAKE_CASE__		(       UpperCamelCase_		):





					"""simple docstring"""






					a_			=	VOCAB_FILES_NAMES
					a_			=	PRETRAINED_VOCAB_FILES_MAP
					a_			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_			=	["input_ids", "attention_mask"]



					def __init__(      self  :						List[str]							,	__A  :						Any							,	__A  :						List[str]							,	__A  :						Optional[Any]="replace"							,	__A  :						Optional[int]="<s>"							,	__A  :						Union[str, Any]="</s>"							,	__A  :						Tuple="</s>"							,	__A  :						List[Any]="<s>"							,	__A  :						Dict="<unk>"							,	__A  :						Any="<pad>"							,	__A  :						Optional[int]="<mask>"							,	__A  :						List[str]=False							,	**__A  :						Union[str, Any]							,	):
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else bos_token
						snake_case__				:   List[str]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else eos_token
						snake_case__				:   Any     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else sep_token
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else cls_token
						snake_case__				:   Tuple     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else unk_token
						snake_case__				:   List[Any]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else pad_token

						# Mask token behave like a normal word, i.e. include the space before it
						snake_case__				:   List[str]     =						AddedToken(__A							,	lstrip=__A							,	rstrip=__A ) if isinstance(__A							,	__A ) else mask_token

						super().__init__(
						    errors=__A							,	bos_token=__A							,	eos_token=__A							,	unk_token=__A							,	sep_token=__A							,	cls_token=__A							,	pad_token=__A							,	mask_token=__A							,	add_prefix_space=__A							,	**__A							,	)

						with open(__A							,	encoding="utf-8" ) as vocab_handle:
							snake_case__				:   Any     =						json.load(__A )
						snake_case__				:   Optional[Any]     =						{v: k for k, v in self.encoder.items()}
						snake_case__				:   Union[str, Any]     =						errors  # how to handle errors in decoding
						snake_case__				:   Any     =						bytes_to_unicode()
						snake_case__				:   Optional[Any]     =						{v: k for k, v in self.byte_encoder.items()}
						with open(__A							,	encoding="utf-8" ) as merges_handle:
							snake_case__				:   str     =						merges_handle.read().split("\n" )[1:-1]
						snake_case__				:   int     =						[tuple(merge.split() ) for merge in bpe_merges]
						snake_case__				:   str     =						dict(zip(__A							,	range(len(__A ) ) ) )
						snake_case__				:   Optional[int]     =						{}
						snake_case__				:   Any     =						add_prefix_space

						# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
						snake_case__				:   Union[str, Any]     =						re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )



					@property
					# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
					def  _lowercase  (      self  :						List[Any] ):
						return len(self.encoder )



					def  _lowercase  (      self  :						Any ):
						return dict(self.encoder							,	**self.added_tokens_encoder )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Optional[int] ):
						if token in self.cache:
							return self.cache[token]
						snake_case__				:   Union[str, Any]     =						tuple(__A )
						snake_case__				:   List[Any]     =						get_pairs(__A )

						if not pairs:
							return token

						while True:
							snake_case__				:   Tuple     =						min(__A							,	key=lambda __A : self.bpe_ranks.get(__A							,	float("inf" ) ) )
							if bigram not in self.bpe_ranks:
								break
							snake_case__,		snake_case__				:   Dict     =						bigram
							snake_case__				:   str     =						[]
							snake_case__				:   Union[str, Any]     =						0
							while i < len(__A ):
								try:
									snake_case__				:   Dict     =						word.index(__A							,	__A )
								except ValueError:
									new_word.extend(word[i:] )
									break
								else:
									new_word.extend(word[i:j] )
									snake_case__				:   str     =						j

								if word[i] == first and i < len(__A ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
								else:
									new_word.append(word[i] )
									i += 1
							snake_case__				:   str     =						tuple(__A )
							snake_case__				:   int     =						new_word
							if len(__A ) == 1:
								break
							else:
								snake_case__				:   List[str]     =						get_pairs(__A )
						snake_case__				:   List[Any]     =						" ".join(__A )
						snake_case__				:   Optional[int]     =						word
						return word



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						Optional[Any] ):
						snake_case__				:   List[str]     =						[]
						for token in re.findall(self.pat							,	__A ):
							snake_case__				:   Dict     =						"".join(
							    self.byte_encoder[b] for b in token.encode("utf-8" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
							bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A ).split(" " ) )
						return bpe_tokens



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[int] ):
						return self.encoder.get(__A							,	self.encoder.get(self.unk_token ) )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						Optional[Any] ):
						return self.decoder.get(__A )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Dict ):
						snake_case__				:   Optional[Any]     =						"".join(__A )
						snake_case__				:   int     =						bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,	errors=self.errors )
						return text



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						str							,	__A  :						Optional[str] = None ):
						if not os.path.isdir(__A ):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
							return
						snake_case__				:   List[Any]     =						os.path.join(
						    __A							,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
						snake_case__				:   str     =						os.path.join(
						    __A							,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

						with open(__A							,	"w"							,	encoding="utf-8" ) as f:
							f.write(json.dumps(self.encoder							,	indent=2							,	sort_keys=__A							,	ensure_ascii=__A ) + "\n" )

						snake_case__				:   str     =						0
						with open(__A							,	"w"							,	encoding="utf-8" ) as writer:
							writer.write("#version: 0.2\n" )
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,	key=lambda __A : kv[1] ):
								if index != token_index:
									logger.warning(
									    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    " Please check that the tokenizer is not corrupted!" )
									snake_case__				:   int     =						token_index
								writer.write(" ".join(__A ) + "\n" )
								index += 1

						return vocab_file, merge_file



					def  _lowercase  (      self  :						int							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						snake_case__				:   Tuple     =						[self.cls_token_id]
						snake_case__				:   List[Any]     =						[self.sep_token_id]
						return cls + token_ids_a + sep + sep + token_ids_a + sep



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None							,	__A  :						bool = False ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=__A							,	token_ids_a=__A							,	already_has_special_tokens=__A )

						if token_ids_a is None:
							return [1] + ([0] * len(__A )) + [1]
						return [1] + ([0] * len(__A )) + [1, 1] + ([0] * len(__A )) + [1]



					def  _lowercase  (      self  :						List[Any]							,	__A  :						List[int]							,	__A  :						Optional[List[int]] = None ):
						snake_case__				:   Any     =						[self.sep_token_id]
						snake_case__				:   List[Any]     =						[self.cls_token_id]

						if token_ids_a is None:
							return len(cls + token_ids_a + sep ) * [0]
						return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						int							,	__A  :						int=False							,	**__A  :						Dict ):
						snake_case__				:   Optional[int]     =						kwargs.pop("add_prefix_space"							,	self.add_prefix_space )
						if (is_split_into_words or add_prefix_space) and (len(__A ) > 0 and not text[0].isspace()):
							snake_case__				:   Optional[int]     =						" " + text
						return (text, kwargs)






					def  _lowercase  (      self  :						Any							,	__A  :						Union[Dict[str, EncodedInput], BatchEncoding]							,	__A  :						Optional[int] = None							,	__A  :						PaddingStrategy = PaddingStrategy.DO_NOT_PAD							,	__A  :						Optional[int] = None							,	__A  :						Optional[bool] = None							,	):
						snake_case__				:   Optional[Any]     =						super()._pad(
						    encoded_inputs=__A							,	max_length=__A							,	padding_strategy=__A							,	pad_to_multiple_of=__A							,	return_attention_mask=__A							,	)

						# Load from model defaults
						if return_attention_mask is None:
							snake_case__				:   Union[str, Any]     =						"attention_mask" in self.model_input_names

						if return_attention_mask and "global_attention_mask" in encoded_inputs:
							snake_case__				:   Union[str, Any]     =						encoded_inputs[self.model_input_names[0]]
							# `global_attention_mask` need to have the same length as other (sequential) inputs.
							snake_case__				:   Tuple     =						len(encoded_inputs["global_attention_mask"] ) != len(__A )

							if needs_to_be_padded:
								snake_case__				:   int     =						len(__A ) - len(encoded_inputs["global_attention_mask"] )

								if self.padding_side == "right":
									# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
									snake_case__				:   int     =						(
									    encoded_inputs["global_attention_mask"] + [-1] * difference
									)
								elif self.padding_side == "left":
									snake_case__				:   Tuple     =						[-1] * difference + encoded_inputs[
									    "global_attention_mask"
									]
								else:
									raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

						return encoded_inputs



