



'''simple docstring'''



import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase		       =				logging.get_logger(__name__)


lowerCamelCase		       =				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCamelCase		       =				{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

lowerCamelCase		       =				{"facebook/blenderbot_small-90M": 512}



def      _A				(      _lowerCAmelCase    ):



							"""simple docstring"""



							__lowercase						=set()
							__lowercase						=word[0]
							for char in word[1:]:
														pairs.add((prev_char, char)    )
														__lowercase						=char

							__lowercase						=set(_lowerCAmelCase    )
							return pairs





class _UpperCamelCase						(   _SCREAMING_SNAKE_CASE						):





				'''simple docstring'''





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

				def __init__(							self     :     Union[str, Any]     ,				_lowerCAmelCase     :     Dict     ,				_lowerCAmelCase     :     int     ,				_lowerCAmelCase     :     int="__start__"     ,				_lowerCAmelCase     :     Union[str, Any]="__end__"     ,				_lowerCAmelCase     :     Any="__unk__"     ,				_lowerCAmelCase     :     Any="__null__"     ,				**_lowerCAmelCase     :     str     ,				):







											'''simple docstring'''

											super().__init__(unk_token=A_     ,				bos_token=A_     ,				eos_token=A_     ,				pad_token=A_     ,				**A_)

											with open(A_     ,				encoding='utf-8') as vocab_handle:
																		__lowercase						=json.load(A_)
											__lowercase						={v: k for k, v in self.encoder.items()}
											with open(A_     ,				encoding='utf-8') as merges_handle:
																		__lowercase						=merges_handle.read().split('\n')[1:-1]
											__lowercase						=[tuple(merge.split()) for merge in merges]
											__lowercase						=dict(zip(A_     ,				range(len(A_))))
											__lowercase						={}

				@property
				def  __lowerCamelCase   (							self     :     int):







											'''simple docstring'''

											return len(self.encoder)

				def  __lowerCamelCase   (							self     :     Optional[Any]):







											'''simple docstring'''

											return dict(self.encoder     ,				**self.added_tokens_encoder)

				def  __lowerCamelCase   (							self     :     Dict     ,				_lowerCAmelCase     :     Union[str, Any]):







											'''simple docstring'''

											if token in self.cache:
																		return self.cache[token]
											__lowercase						=re.sub('([.,!?()])'     ,				R' \1'     ,				A_)
											__lowercase						=re.sub('(\')'     ,				R' \1 '     ,				A_)
											__lowercase						=re.sub(R'\s{2,}'     ,				' '     ,				A_)
											if "\n" in token:
																		__lowercase						=token.replace('\n'     ,				' __newln__')

											__lowercase						=token.split(' ')
											__lowercase						=[]
											for token in tokens:
																		if not len(A_):
																									continue

																		__lowercase						=token.lower()
																		__lowercase						=tuple(A_)
																		__lowercase						=tuple(list(word[:-1]) + [word[-1] + '</w>'])
																		__lowercase						=get_pairs(A_)

																		if not pairs:
																									words.append(A_)
																									continue

																		while True:
																									__lowercase						=min(A_     ,				key=lambda _lowerCAmelCase: self.bpe_ranks.get(A_     ,				float('inf')))
																									if bigram not in self.bpe_ranks:
																																break
																									__lowercase		, __lowercase						=bigram
																									__lowercase						=[]
																									__lowercase						=0

																									while i < len(A_):
																																try:
																																							__lowercase						=word.index(A_     ,				A_)
																																							new_word.extend(word[i:j])
																																							__lowercase						=j
																																except ValueError:
																																							new_word.extend(word[i:])
																																							break

																																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
																									__lowercase						=tuple(A_)
																									__lowercase						=new_word
																									if len(A_) == 1:
																																break
																									else:
																																__lowercase						=get_pairs(A_)
																		__lowercase						='@@ '.join(A_)
																		__lowercase						=word[:-4]

																		__lowercase						=word
																		words.append(A_)
											return " ".join(A_)

				def  __lowerCamelCase   (							self     :     str     ,				_lowerCAmelCase     :     Optional[Any]):







											'''simple docstring'''

											__lowercase						=[]

											__lowercase						=re.findall(R'\S+\n?'     ,				A_)

											for token in words:
																		split_tokens.extend(list(self.bpe(A_).split(' ')))
											return split_tokens

				def  __lowerCamelCase   (							self     :     List[str]     ,				_lowerCAmelCase     :     List[Any]):







											'''simple docstring'''

											__lowercase						=token.lower()
											return self.encoder.get(A_     ,				self.encoder.get(self.unk_token))

				def  __lowerCamelCase   (							self     :     Optional[Any]     ,				_lowerCAmelCase     :     Optional[int]):







											'''simple docstring'''

											return self.decoder.get(A_     ,				self.unk_token)

				def  __lowerCamelCase   (							self     :     Optional[Any]     ,				_lowerCAmelCase     :     Union[str, Any]):







											'''simple docstring'''

											__lowercase						=' '.join(A_).replace('@@ '     ,				'').strip()
											return out_string

				def  __lowerCamelCase   (							self     :     Union[str, Any]     ,				_lowerCAmelCase     :     Dict     ,				_lowerCAmelCase     :     int = None):







											'''simple docstring'''

											if not os.path.isdir(A_):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
																		return
											__lowercase						=os.path.join(
											    A_     ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
											__lowercase						=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')

											__lowercase						=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 _lowerCAmelCase: 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!')
																																__lowercase						=token_index
																									writer.write(' '.join(A_) + '\n')
																									index += 1

											return vocab_file, merge_file





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def       A       (					lowercase )   ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase		=     [
		    'encoder.version',
		    'decoder.version',
		    'model.encoder.version',
		    'model.decoder.version',
		    'decoder.output_projection.weight',
		    '_float_tensor',
		    'encoder.embed_positions._float_tensor',
		    'decoder.embed_positions._float_tensor',
		]
		for k in ignore_keys:
				state_dict.pop(lowercase    ,       lowercase )


def       A       (					lowercase )   ->  List[Any]:
		'''simple docstring'''

		UpperCamelCase		=     list(s_dict.keys() )
		for key in keys:
				if "transformer_layers" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )
				elif "subsample" in key:
						UpperCamelCase		=     s_dict.pop(lowercase )


def       A       (					lowercase )   ->  Tuple:
		'''simple docstring'''

		UpperCamelCase	,  UpperCamelCase		=     emb.weight.shape
		UpperCamelCase		=     nn.Linear(lowercase    ,       lowercase    ,       bias=lowercase )
		UpperCamelCase		=     emb.weight.data
		return lin_layer


def       A       (					lowercase    ,       lowercase )   ->  Dict:
		'''simple docstring'''

		UpperCamelCase		=     torch.load(lowercase    ,       map_location='cpu' )
		UpperCamelCase		=     mam_aaa['args']
		UpperCamelCase		=     mam_aaa['model']
		UpperCamelCase		=     state_dict['decoder.output_projection.weight']

		remove_ignore_keys_(lowercase )
		rename_keys(lowercase )

		UpperCamelCase		=     state_dict['decoder.embed_tokens.weight'].shape[0]

		UpperCamelCase		=     args.share_decoder_input_output_embed

		UpperCamelCase		=     [int(lowercase ) for i in args.conv_kernel_sizes.split(',' )]
		UpperCamelCase		=     SpeechaTextConfig(
		    vocab_size=lowercase    ,       max_source_positions=args.max_source_positions    ,       max_target_positions=args.max_target_positions    ,       encoder_layers=args.encoder_layers    ,       decoder_layers=args.decoder_layers    ,       encoder_attention_heads=args.encoder_attention_heads    ,       decoder_attention_heads=args.decoder_attention_heads    ,       encoder_ffn_dim=args.encoder_ffn_embed_dim    ,       decoder_ffn_dim=args.decoder_ffn_embed_dim    ,       d_model=args.encoder_embed_dim    ,       dropout=args.dropout    ,       attention_dropout=args.attention_dropout    ,       activation_dropout=args.activation_dropout    ,       activation_function='relu'    ,       num_conv_layers=len(lowercase )    ,       conv_channels=args.conv_channels    ,       conv_kernel_sizes=lowercase    ,       input_feat_per_channel=args.input_feat_per_channel    ,       input_channels=args.input_channels    ,       tie_word_embeddings=lowercase    ,       num_beams=5    ,       max_length=200    ,       use_cache=lowercase    ,       decoder_start_token_id=2    ,       early_stopping=lowercase    ,       )

		UpperCamelCase		=     SpeechaTextForConditionalGeneration(lowercase )
		UpperCamelCase	,  UpperCamelCase		=     model.model.load_state_dict(lowercase    ,       strict=lowercase )
		if len(lowercase ) > 0 and not set(lowercase ) <= {
		    "encoder.embed_positions.weights",
		    "decoder.embed_positions.weights",
		}:
				raise ValueError(
				    'Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'
				    f''' but all the following weights are missing {missing}''' )

		if tie_embeds:
				UpperCamelCase		=     make_linear_from_emb(model.model.decoder.embed_tokens )
		else:
				UpperCamelCase		=     lm_head_weights

		model.save_pretrained(lowercase )


if __name__ == "__main__":
					_UpperCAmelCase					: Optional[Any]      =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					_UpperCAmelCase					: Dict      =       parser.parse_args()
					convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)
