



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

import regex as re

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


snake_case    :						str  =logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
snake_case    :						Optional[Any]  ={
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

snake_case    :						Any  ={
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}
@lru_cache()
def 	snake_case__       (					)		->		Dict:







						"""simple docstring"""

						A__	:							Union[str, Any]						    = (
						    list(range(ord("!"       )							,     ord("~"       ) + 1       )       ) + list(range(ord("¡"       )							,     ord("¬"       ) + 1       )       ) + list(range(ord("®"       )							,     ord("ÿ"       ) + 1       )       )
						)
						A__	:							List[Any]						    = bs[:]
						A__	:							Union[str, Any]						    = 0
						for b in range(2**8       ):
												if b not in bs:
																		bs.append(SCREAMING_SNAKE_CASE_       )
																		cs.append(2**8 + n       )
																		n += 1
						A__	:							Union[str, Any]						    = [chr(SCREAMING_SNAKE_CASE_       ) for n in cs]
						return dict(zip(SCREAMING_SNAKE_CASE_							,     SCREAMING_SNAKE_CASE_       )       )
def 	snake_case__       (					__lowercase       )		->		List[str]:







						"""simple docstring"""

						A__	:							str						    = set()
						A__	:							List[str]						    = word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)       )
												A__	:							Tuple						    = char
						return pairs




class 							lowerCAmelCase__  (	lowercase__						):
					__A     :    Union[str, Any]           =						VOCAB_FILES_NAMES
					__A     :    List[Any]           =						PRETRAINED_VOCAB_FILES_MAP
					__A     :    str           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A     :    List[str]           =						["""input_ids""", """attention_mask"""]







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

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

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

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

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







					@property
					def  _lowercase			(       self      :     List[str]):
											return len(self.encoder)







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







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     Dict):
											if token in self.cache:
																	return self.cache[token]
											A__	:							int						    = tuple(__lowercase)
											A__	:							str						    = get_pairs(__lowercase)

											if not pairs:
																	return token

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

																							if word[i] == first and i < len(__lowercase) - 1 and word[i + 1] == second:
																													new_word.append(first + second)
																													i += 2
																							else:
																													new_word.append(word[i])
																													i += 1
																	A__	:							List[Any]						    = tuple(__lowercase)
																	A__	:							Any						    = new_word
																	if len(__lowercase) == 1:
																							break
																	else:
																							A__	:							Tuple						    = get_pairs(__lowercase)
											A__	:							str						    = " ".join(__lowercase)
											A__	:							Union[str, Any]						    = word
											return word







					def  _lowercase			(       self      :     Tuple			,		_A      :     Dict):
											A__	:							Union[str, Any]						    = []
											for token in re.findall(self.pat			,		__lowercase):
																	A__	:							int						    = "".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(__lowercase).split(" "))
											return bpe_tokens







					def  _lowercase			(       self      :     Union[str, Any]			,		_A      :     Union[str, Any]):
											return self.encoder.get(__lowercase			,		self.encoder.get(self.unk_token))







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







					def  _lowercase			(       self      :     List[str]			,		_A      :     Union[str, Any]):
											A__	:							List[Any]						    = "".join(__lowercase)
											A__	:							Tuple						    = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"			,		errors=self.errors)
											return text







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

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

											A__	:							List[str]						    = 0
											with open(__lowercase			,		"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!")
																													A__	:							Union[str, Any]						    = token_index
																							writer.write(" ".join(__lowercase) + "\n")
																							index += 1

											return vocab_file, merge_file







					def  _lowercase			(       self      :     Optional[Any]			,		_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]
											A__	:							Tuple						    = [self.cls_token_id]
											A__	:							Tuple						    = [self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep







					def  _lowercase			(       self      :     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=__lowercase			,		token_ids_a=__lowercase			,		already_has_special_tokens=__lowercase)

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







					def  _lowercase			(       self      :     str			,		_A      :     List[int]			,		_A      :     Optional[List[int]] = None):
											A__	:							Tuple						    = [self.sep_token_id]
											A__	:							Dict						    = [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      :     Any			,		_A      :     Dict=False			,		**_A      :     Union[str, Any]):
											A__	:							Any						    = kwargs.pop("add_prefix_space"			,		self.add_prefix_space)
											if (is_split_into_words or add_prefix_space) and (len(__lowercase) > 0 and not text[0].isspace()):
																	A__	:							Optional[int]						    = " " + text
											return (text, kwargs)




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							str						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_attention_layer_array(__lowercase							,     __lowercase							,     __lowercase       ):
												A__	:							List[str]						    = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							List[Any]						    = tf.train.load_variable(__lowercase							,     __lowercase       )
												A__	:							int						    = array.reshape(__lowercase       )

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# Layers
						for layer_index in range(0							,     config.num_hidden_layers       ):
												A__	:							BertLayer						    = model.bert.encoder.layer[layer_index]

												# Self-attention
												A__	:							BertSelfAttention						    = layer.attention.self

												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

												# Self-attention Output
												A__	:							BertSelfOutput						    = layer.attention.output

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/beta"       )

						# Embeddings
						A__	:							str						    = get_encoder_array("_position_embedding_layer/embeddings"       )
						A__	:							List[str]						    = get_encoder_array("_type_embedding_layer/embeddings"       )
						A__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)