

"""simple docstring"""





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     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_					):
	def get_masked_lm_array(A_					):
		lowerCAmelCase__      :		Optional[Any]    							=      f'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
		lowerCAmelCase__      :		Optional[int]    							=      tf.train.load_variable(A_		,	A_					)

		if "kernel" in name:
			lowerCAmelCase__      :		Tuple    							=      array.transpose()

		return torch.from_numpy(A_					)

	def get_encoder_array(A_					):
		lowerCAmelCase__      :		List[Any]    							=      f'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
		lowerCAmelCase__      :		str    							=      tf.train.load_variable(A_		,	A_					)

		if "kernel" in name:
			lowerCAmelCase__      :		Dict    							=      array.transpose()

		return torch.from_numpy(A_					)

	def get_encoder_layer_array(A_		,	A_					):
		lowerCAmelCase__      :		List[Any]    							=      f'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
		lowerCAmelCase__      :		Union[str, Any]    							=      tf.train.load_variable(A_		,	A_					)

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

		return torch.from_numpy(A_					)

	def get_encoder_attention_layer_array(A_		,	A_		,	A_					):
		lowerCAmelCase__      :		List[Any]    							=      f'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
		lowerCAmelCase__      :		Any    							=      tf.train.load_variable(A_		,	A_					)
		lowerCAmelCase__      :		Dict    							=      array.reshape(A_					)

		if "kernel" in name:
			lowerCAmelCase__      :		List[str]    							=      array.transpose()

		return torch.from_numpy(A_					)

	print(f'Loading model based on config from {config_path}...'					)
	lowerCAmelCase__      :		Optional[Any]    							=      BertConfig.from_json_file(A_					)
	lowerCAmelCase__      :		Dict    							=      BertForMaskedLM(A_					)

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

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

		lowerCAmelCase__      :		Union[str, Any]    							=      get_encoder_attention_layer_array(
		    A_		,	'''_query_dense/kernel'''		,	self_attn.query.weight.data.shape					)
		lowerCAmelCase__      :		Optional[Any]    							=      get_encoder_attention_layer_array(
		    A_		,	'''_query_dense/bias'''		,	self_attn.query.bias.data.shape					)
		lowerCAmelCase__      :		Optional[int]    							=      get_encoder_attention_layer_array(
		    A_		,	'''_key_dense/kernel'''		,	self_attn.key.weight.data.shape					)
		lowerCAmelCase__      :		Union[str, Any]    							=      get_encoder_attention_layer_array(
		    A_		,	'''_key_dense/bias'''		,	self_attn.key.bias.data.shape					)
		lowerCAmelCase__      :		List[Any]    							=      get_encoder_attention_layer_array(
		    A_		,	'''_value_dense/kernel'''		,	self_attn.value.weight.data.shape					)
		lowerCAmelCase__      :		List[str]    							=      get_encoder_attention_layer_array(
		    A_		,	'''_value_dense/bias'''		,	self_attn.value.bias.data.shape					)

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

		lowerCAmelCase__      :		Union[str, Any]    							=      get_encoder_attention_layer_array(
		    A_		,	'''_output_dense/kernel'''		,	self_output.dense.weight.data.shape					)
		lowerCAmelCase__      :		Optional[int]    							=      get_encoder_attention_layer_array(
		    A_		,	'''_output_dense/bias'''		,	self_output.dense.bias.data.shape					)

		lowerCAmelCase__      :		List[Any]    							=      get_encoder_layer_array(A_		,	'''_attention_layer_norm/gamma'''					)
		lowerCAmelCase__      :		Optional[int]    							=      get_encoder_layer_array(A_		,	'''_attention_layer_norm/beta'''					)

		# Intermediate
		lowerCAmelCase__      :		BertIntermediate    							=      layer.intermediate

		lowerCAmelCase__      :		Union[str, Any]    							=      get_encoder_layer_array(A_		,	'''_intermediate_dense/kernel'''					)
		lowerCAmelCase__      :		Any    							=      get_encoder_layer_array(A_		,	'''_intermediate_dense/bias'''					)

		# Output
		lowerCAmelCase__      :		BertOutput    							=      layer.output

		lowerCAmelCase__      :		str    							=      get_encoder_layer_array(A_		,	'''_output_dense/kernel'''					)
		lowerCAmelCase__      :		int    							=      get_encoder_layer_array(A_		,	'''_output_dense/bias'''					)

		lowerCAmelCase__      :		Optional[int]    							=      get_encoder_layer_array(A_		,	'''_output_layer_norm/gamma'''					)
		lowerCAmelCase__      :		Dict    							=      get_encoder_layer_array(A_		,	'''_output_layer_norm/beta'''					)

	# Embeddings
	lowerCAmelCase__      :		Union[str, Any]    							=      get_encoder_array('''_position_embedding_layer/embeddings'''					)
	lowerCAmelCase__      :		Dict    							=      get_encoder_array('''_type_embedding_layer/embeddings'''					)
	lowerCAmelCase__      :		Union[str, Any]    							=      get_encoder_array('''_embedding_norm_layer/gamma'''					)
	lowerCAmelCase__      :		Dict    							=      get_encoder_array('''_embedding_norm_layer/beta'''					)

	# LM Head
	lowerCAmelCase__      :		Union[str, Any]    							=      model.cls.predictions.transform

	lowerCAmelCase__      :		str    							=      get_masked_lm_array('''dense/kernel'''					)
	lowerCAmelCase__      :		Dict    							=      get_masked_lm_array('''dense/bias'''					)

	lowerCAmelCase__      :		Optional[Any]    							=      get_masked_lm_array('''layer_norm/gamma'''					)
	lowerCAmelCase__      :		Dict    							=      get_masked_lm_array('''layer_norm/beta'''					)

	lowerCAmelCase__      :		str    							=      get_masked_lm_array('''embedding_table'''					)

	# Pooling
	lowerCAmelCase__      :		List[str]    							=      BertPooler(config=A_					)
	lowerCAmelCase__      :		BertPooler    							=      get_encoder_array('''_pooler_layer/kernel'''					)
	lowerCAmelCase__      :		BertPooler    							=      get_encoder_array('''_pooler_layer/bias'''					)

	# Export final model
	model.save_pretrained(A_					)

	# Integration test - should load without any errors ;)
	lowerCAmelCase__      :		Optional[Any]    							=      BertForMaskedLM.from_pretrained(A_					)
	print(new_model.eval()					)

	print('''Model conversion was done sucessfully!'''					)


if __name__ == "__main__":
							__UpperCamelCase    : Any				 =						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.''',
							)
							__UpperCamelCase    : int				 =						parser.parse_args()
							convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer


__UpperCamelCase    : List[Any]				 =						logging.get_logger(__name__)

__UpperCamelCase    : Dict				 =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : List[Any]				 =						{
    '''vocab_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
        ),
        '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
        '''distilbert-base-uncased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
        '''distilbert-base-cased-distilled-squad''': (
            '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-german-cased''': (
            '''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
        ),
        '''distilbert-base-multilingual-cased''': (
            '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCamelCase    : Union[str, Any]				 =						{
    '''distilbert-base-uncased''': 5_1_2,
    '''distilbert-base-uncased-distilled-squad''': 5_1_2,
    '''distilbert-base-cased''': 5_1_2,
    '''distilbert-base-cased-distilled-squad''': 5_1_2,
    '''distilbert-base-german-cased''': 5_1_2,
    '''distilbert-base-multilingual-cased''': 5_1_2,
}


__UpperCamelCase    : int				 =						{
    '''distilbert-base-uncased''': {'''do_lower_case''': True},
    '''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
    '''distilbert-base-cased''': {'''do_lower_case''': False},
    '''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
    '''distilbert-base-german-cased''': {'''do_lower_case''': False},
    '''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			PRETRAINED_INIT_CONFIGURATION
					lowercase__												=			["input_ids", "attention_mask"]
					lowercase__												=			DistilBertTokenizer







					def __init__(			self     :							int  ,lowercase_     :							List[str]=None  ,lowercase_     :							int=None  ,lowercase_     :							str=True  ,lowercase_     :							Union[str, Any]="[UNK]"  ,lowercase_     :							List[str]="[SEP]"  ,lowercase_     :							List[str]="[PAD]"  ,lowercase_     :							List[str]="[CLS]"  ,lowercase_     :							List[str]="[MASK]"  ,lowercase_     :							Tuple=True  ,lowercase_     :							Tuple=None  ,**lowercase_     :							str  ,):
						super().__init__(
						    lowercase_  ,tokenizer_file=lowercase_  ,do_lower_case=lowercase_  ,unk_token=lowercase_  ,sep_token=lowercase_  ,pad_token=lowercase_  ,cls_token=lowercase_  ,mask_token=lowercase_  ,tokenize_chinese_chars=lowercase_  ,strip_accents=lowercase_  ,**lowercase_  ,)

						lowerCAmelCase__      :		Tuple    							=      json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
						if (
						    normalizer_state.get('''lowercase'''  ,lowercase_						) != do_lower_case
						    or normalizer_state.get('''strip_accents'''  ,lowercase_						) != strip_accents
						    or normalizer_state.get('''handle_chinese_chars'''  ,lowercase_						) != tokenize_chinese_chars
						):
							lowerCAmelCase__      :		Union[str, Any]    							=      getattr(lowercase_  ,normalizer_state.pop('''type'''						)						)
							lowerCAmelCase__      :		Optional[int]    							=      do_lower_case
							lowerCAmelCase__      :		List[str]    							=      strip_accents
							lowerCAmelCase__      :		int    							=      tokenize_chinese_chars
							lowerCAmelCase__      :		Dict    							=      normalizer_class(**lowercase_						)

						lowerCAmelCase__      :		Tuple    							=      do_lower_case







					def 			__lowerCAmelCase					(			self     :							str  ,lowercase_     :							Tuple  ,lowercase_     :							Optional[Any]=None						):
						lowerCAmelCase__      :		Any    							=      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output







					def 			__lowerCAmelCase					(			self     :							int  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		Dict    							=      [self.sep_token_id]
						lowerCAmelCase__      :		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						) * [0] + len(token_ids_a + sep						) * [1]







					def 			__lowerCAmelCase					(			self     :							Any  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						lowerCAmelCase__      :		Any    							=      self._tokenizer.model.save(lowercase_  ,name=lowercase_						)
						return tuple(lowercase_						)




