
'''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


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__	=							{
    "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"
        ),
    },
}

lowerCamelCase__	=							{
    "distilbert-base-uncased": 512,
    "distilbert-base-uncased-distilled-squad": 512,
    "distilbert-base-cased": 512,
    "distilbert-base-cased-distilled-squad": 512,
    "distilbert-base-german-cased": 512,
    "distilbert-base-multilingual-cased": 512,
}


lowerCamelCase__	=							{
    "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        lowerCAmelCase__      (   __lowerCAmelCase	):
						UpperCamelCase_							: int	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: str	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: Tuple	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: Union[str, Any]	=		PRETRAINED_INIT_CONFIGURATION
						UpperCamelCase_							: Any	=		["input_ids", "attention_mask"]
						UpperCamelCase_							: str	=		DistilBertTokenizer

						def __init__(			self       ,							a=None       ,							a=None       ,							a=True       ,							a="[UNK]"       ,							a="[SEP]"       ,							a="[PAD]"       ,							a="[CLS]"       ,							a="[MASK]"       ,							a=True       ,							a=None       ,							**a       ,							)					->      str:


								'''simple docstring'''

								super().__init__(
								    _UpperCamelCase       ,							tokenizer_file=_UpperCamelCase       ,							do_lower_case=_UpperCamelCase       ,							unk_token=_UpperCamelCase       ,							sep_token=_UpperCamelCase       ,							pad_token=_UpperCamelCase       ,							cls_token=_UpperCamelCase       ,							mask_token=_UpperCamelCase       ,							tokenize_chinese_chars=_UpperCamelCase       ,							strip_accents=_UpperCamelCase       ,							**_UpperCamelCase       ,							)

								_UpperCamelCase						    =	json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
								if (
								    normalizer_state.get("""lowercase"""       ,							_UpperCamelCase				) != do_lower_case
								    or normalizer_state.get("""strip_accents"""       ,							_UpperCamelCase				) != strip_accents
								    or normalizer_state.get("""handle_chinese_chars"""       ,							_UpperCamelCase				) != tokenize_chinese_chars
								):
										_UpperCamelCase						    =	getattr(_UpperCamelCase       ,							normalizer_state.pop("""type"""				)				)
										_UpperCamelCase						    =	do_lower_case
										_UpperCamelCase						    =	strip_accents
										_UpperCamelCase						    =	tokenize_chinese_chars
										_UpperCamelCase						    =	normalizer_class(**_UpperCamelCase				)

								_UpperCamelCase						    =	do_lower_case

						def  A_				(			self       ,							a       ,							a=None				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	[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  A_				(			self       ,							a       ,							a = None				)					->      List[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.sep_token_id]
								_UpperCamelCase						    =	[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  A_				(			self       ,							a       ,							a = None				)					->      Tuple[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._tokenizer.model.save(_UpperCamelCase       ,							name=_UpperCamelCase				)
								return tuple(_UpperCamelCase				)





import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	os.path.abspath(lowerCAmelCase )
		logger.info(F'Converting TensorFlow checkpoint from {tf_path}' )
		# Load weights from TF model
		_UpperCamelCase						    =	tf.train.list_variables(lowerCAmelCase )
		_UpperCamelCase						    =	[]
		_UpperCamelCase						    =	[]
		_UpperCamelCase						    =	[]
		for full_name, shape in init_vars:
				# logger.info(f"Loading TF weight {name} with shape {shape}")
				_UpperCamelCase						    =	full_name.split("""/""" )
				if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
						logger.info(F'Skipping non-model layer {full_name}' )
						continue
				if "optimizer" in full_name:
						logger.info(F'Skipping optimization layer {full_name}' )
						continue
				if name[0] == "model":
						# ignore initial 'model'
						_UpperCamelCase						    =	name[1:]
				# figure out how many levels deep the name is
				_UpperCamelCase						    =	0
				for _name in name:
						if _name.startswith("""layer_with_weights""" ):
								depth += 1
						else:
								break
				layer_depth.append(lowerCAmelCase )
				# read data
				_UpperCamelCase						    =	tf.train.load_variable(lowerCAmelCase						,  lowerCAmelCase )
				names.append("""/""".join(lowerCAmelCase ) )
				arrays.append(lowerCAmelCase )
		logger.info(F'Read a total of {len(lowerCAmelCase ):,} layers' )

		# Sanity check
		if len(set(lowerCAmelCase ) ) != 1:
				raise ValueError(F'Found layer names with different depths (layer depth {list(set(lowerCAmelCase ) )})' )
		_UpperCamelCase						    =	list(set(lowerCAmelCase ) )[0]
		if layer_depth != 1:
				raise ValueError(
				    """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
				    """ heads.""" )

		# convert layers
		logger.info("""Converting weights...""" )
		for full_name, array in zip(lowerCAmelCase						,  lowerCAmelCase ):
				_UpperCamelCase						    =	full_name.split("""/""" )
				_UpperCamelCase						    =	model
				_UpperCamelCase						    =	[]
				for i, m_name in enumerate(lowerCAmelCase ):
						if m_name == ".ATTRIBUTES":
								# variable names end with .ATTRIBUTES/VARIABLE_VALUE
								break
						if m_name.startswith("""layer_with_weights""" ):
								_UpperCamelCase						    =	int(m_name.split("""-""" )[-1] )
								if layer_num <= 2:
										# embedding layers
										# layer_num 0: word_embeddings
										# layer_num 1: position_embeddings
										# layer_num 2: token_type_embeddings
										continue
								elif layer_num == 3:
										# embedding LayerNorm
										trace.extend(["""embeddings""", """LayerNorm"""] )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """LayerNorm""" )
								elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
										# encoder layers
										trace.extend(["""encoder""", """layer""", str(layer_num - 4 )] )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """encoder""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """layer""" )
										_UpperCamelCase						    =	pointer[layer_num - 4]
								elif layer_num == config.num_hidden_layers + 4:
										# pooler layer
										trace.extend(["""pooler""", """dense"""] )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """pooler""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "embeddings":
								trace.append("""embeddings""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """embeddings""" )
								if layer_num == 0:
										trace.append("""word_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """word_embeddings""" )
								elif layer_num == 1:
										trace.append("""position_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """position_embeddings""" )
								elif layer_num == 2:
										trace.append("""token_type_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """token_type_embeddings""" )
								else:
										raise ValueError(F'Unknown embedding layer with name {full_name}' )
								trace.append("""weight""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """weight""" )
						elif m_name == "_attention_layer":
								# self-attention layer
								trace.extend(["""attention""", """self"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """self""" )
						elif m_name == "_attention_layer_norm":
								# output attention norm
								trace.extend(["""attention""", """output""", """LayerNorm"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """LayerNorm""" )
						elif m_name == "_attention_output_dense":
								# output attention dense
								trace.extend(["""attention""", """output""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_dense":
								# output dense
								trace.extend(["""output""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_layer_norm":
								# output dense
								trace.extend(["""output""", """LayerNorm"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """LayerNorm""" )
						elif m_name == "_key_dense":
								# attention key
								trace.append("""key""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """key""" )
						elif m_name == "_query_dense":
								# attention query
								trace.append("""query""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """query""" )
						elif m_name == "_value_dense":
								# attention value
								trace.append("""value""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """value""" )
						elif m_name == "_intermediate_dense":
								# attention intermediate dense
								trace.extend(["""intermediate""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """intermediate""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_layer_norm":
								# output layer norm
								trace.append("""output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
						# weights & biases
						elif m_name in ["bias", "beta"]:
								trace.append("""bias""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """bias""" )
						elif m_name in ["kernel", "gamma"]:
								trace.append("""weight""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """weight""" )
						else:
								logger.warning(F'Ignored {m_name}' )
        # for certain layers reshape is necessary
				_UpperCamelCase						    =	""".""".join(lowerCAmelCase )
				if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""						,  lowerCAmelCase ) or re.match(
				    R"""(\S+)\.attention\.output\.dense\.weight"""						,  lowerCAmelCase ):
						_UpperCamelCase						    =	array.reshape(pointer.data.shape )
				if "kernel" in full_name:
						_UpperCamelCase						    =	array.transpose()
				if pointer.shape == array.shape:
						_UpperCamelCase						    =	torch.from_numpy(lowerCAmelCase )
				else:
						raise ValueError(
						    F'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
						    F' {array.shape}' )
				logger.info(F'Successfully set variable {full_name} to PyTorch layer {trace}' )
		return model


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		logger.info(F'Loading model based on config from {config_path}...' )
		_UpperCamelCase						    =	BertConfig.from_json_file(lowerCAmelCase )
		_UpperCamelCase						    =	BertModel(lowerCAmelCase )

		# Load weights from checkpoint
		logger.info(F'Loading weights from checkpoint {tf_checkpoint_path}...' )
		load_tfa_weights_in_bert(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )

		# Save pytorch-model
		logger.info(F'Saving PyTorch model to {pytorch_dump_path}...' )
		torch.save(model.state_dict()						,  lowerCAmelCase )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		parser.add_argument(
		    "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow 2.x 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 (must include filename).",
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



