



'''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 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  def get_masked_lm_array(_A    ):
    a						: Optional[Any]					       =					f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    a						: List[str]					       =					tf.train.load_variable(UpperCAmelCase__			,      UpperCAmelCase__    )

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

    return torch.from_numpy(UpperCAmelCase__    )

  def get_encoder_array(_A    ):
    a						: Dict					       =					f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    a						: Tuple					       =					tf.train.load_variable(UpperCAmelCase__			,      UpperCAmelCase__    )

    if "kernel" in name:
      a						: Optional[int]					       =					array.transpose()

    return torch.from_numpy(UpperCAmelCase__    )

  def get_encoder_layer_array(_A			,      _A    ):
    a						: Optional[Any]					       =					f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    a						: Dict					       =					tf.train.load_variable(UpperCAmelCase__			,      UpperCAmelCase__    )

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

    return torch.from_numpy(UpperCAmelCase__    )

  def get_encoder_attention_layer_array(_A			,      _A			,      _A    ):
    a						: str					       =					f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
    a						: Tuple					       =					tf.train.load_variable(UpperCAmelCase__			,      UpperCAmelCase__    )
    a						: Optional[Any]					       =					array.reshape(UpperCAmelCase__    )

    if "kernel" in name:
      a						: Optional[int]					       =					array.transpose()

    return torch.from_numpy(UpperCAmelCase__    )

  print(f"""Loading model based on config from {config_path}..."""    )
  a						: Optional[Any]					       =					BertConfig.from_json_file(UpperCAmelCase__    )
  a						: List[Any]					       =					BertForMaskedLM(UpperCAmelCase__    )

  # 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						: Union[str, Any]					       =					get_encoder_attention_layer_array(
        UpperCAmelCase__			,      '_query_dense/kernel'			,      self_attn.query.weight.data.shape    )
    a						: str					       =					get_encoder_attention_layer_array(
        UpperCAmelCase__			,      '_query_dense/bias'			,      self_attn.query.bias.data.shape    )
    a						: List[Any]					       =					get_encoder_attention_layer_array(
        UpperCAmelCase__			,      '_key_dense/kernel'			,      self_attn.key.weight.data.shape    )
    a						: str					       =					get_encoder_attention_layer_array(
        UpperCAmelCase__			,      '_key_dense/bias'			,      self_attn.key.bias.data.shape    )
    a						: Tuple					       =					get_encoder_attention_layer_array(
        UpperCAmelCase__			,      '_value_dense/kernel'			,      self_attn.value.weight.data.shape    )
    a						: str					       =					get_encoder_attention_layer_array(
        UpperCAmelCase__			,      '_value_dense/bias'			,      self_attn.value.bias.data.shape    )

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

    a						: List[Any]					       =					get_encoder_attention_layer_array(
        UpperCAmelCase__			,      '_output_dense/kernel'			,      self_output.dense.weight.data.shape    )
    a						: Union[str, Any]					       =					get_encoder_attention_layer_array(
        UpperCAmelCase__			,      '_output_dense/bias'			,      self_output.dense.bias.data.shape    )

    a						: int					       =					get_encoder_layer_array(UpperCAmelCase__			,      '_attention_layer_norm/gamma'    )
    a						: Optional[Any]					       =					get_encoder_layer_array(UpperCAmelCase__			,      '_attention_layer_norm/beta'    )

    # Intermediate
    a						: BertIntermediate					       =					layer.intermediate

    a						: List[Any]					       =					get_encoder_layer_array(UpperCAmelCase__			,      '_intermediate_dense/kernel'    )
    a						: Any					       =					get_encoder_layer_array(UpperCAmelCase__			,      '_intermediate_dense/bias'    )

    # Output
    a						: BertOutput					       =					layer.output

    a						: Optional[int]					       =					get_encoder_layer_array(UpperCAmelCase__			,      '_output_dense/kernel'    )
    a						: Optional[Any]					       =					get_encoder_layer_array(UpperCAmelCase__			,      '_output_dense/bias'    )

    a						: Dict					       =					get_encoder_layer_array(UpperCAmelCase__			,      '_output_layer_norm/gamma'    )
    a						: Any					       =					get_encoder_layer_array(UpperCAmelCase__			,      '_output_layer_norm/beta'    )

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

  # LM Head
  a						: Optional[int]					       =					model.cls.predictions.transform

  a						: str					       =					get_masked_lm_array('dense/kernel'    )
  a						: List[Any]					       =					get_masked_lm_array('dense/bias'    )

  a						: int					       =					get_masked_lm_array('layer_norm/gamma'    )
  a						: Optional[Any]					       =					get_masked_lm_array('layer_norm/beta'    )

  a						: int					       =					get_masked_lm_array('embedding_table'    )

  # Pooling
  a						: int					       =					BertPooler(config=UpperCAmelCase__    )
  a						: BertPooler					       =					get_encoder_array('_pooler_layer/kernel'    )
  a						: BertPooler					       =					get_encoder_array('_pooler_layer/bias'    )

  # Export final model
  model.save_pretrained(UpperCAmelCase__    )

  # Integration test - should load without any errors ;)
  a						: Dict					       =					BertForMaskedLM.from_pretrained(UpperCAmelCase__    )
  print(new_model.eval()    )

  print('Model conversion was done sucessfully!'    )


if __name__ == "__main__":
     lowerCAmelCase: Optional[int]							=						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.',
     )
     lowerCAmelCase: Tuple							=						parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




'''simple docstring'''






import argparse
import os
import re

import packaging.version


lowerCAmelCase: List[str]							=						'examples/'
lowerCAmelCase: List[Any]							=						{
    'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(r'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(r'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), r'\1version="VERSION",'),
    'doc': (re.compile(r'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
lowerCAmelCase: str							=						{
    'init': 'src/transformers/__init__.py',
    'setup': 'setup.py',
}
lowerCAmelCase: str							=						'README.md'
def 				lowerCamelCase__	(						_A			,      _A			,      _A    ):
  with open(_A			,      'r'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    a						: Tuple					       =					f.read()
  a       ,     a						: Tuple					       =					REPLACE_PATTERNS[pattern]
  a						: Dict					       =					replace.replace('VERSION'			,      _A    )
  a						: Dict					       =					re_pattern.sub(_A			,      _A    )
  with open(_A			,      'w'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    f.write(_A    )
def 				lowerCamelCase__	(						_A    ):
  for folder, directories, fnames in os.walk(_A    ):
    # Removing some of the folders with non-actively maintained examples from the walk
    if "research_projects" in directories:
      directories.remove('research_projects'    )
    if "legacy" in directories:
      directories.remove('legacy'    )
    for fname in fnames:
      if fname.endswith('.py'    ):
        update_version_in_file(os.path.join(_A			,      _A    )			,      _A			,      pattern='examples'    )
def 				lowerCamelCase__	(						_A			,      _A=False    ):
  for pattern, fname in REPLACE_FILES.items():
    update_version_in_file(_A			,      _A			,      _A    )
  if not patch:
    update_version_in_examples(_A    )
def 				lowerCamelCase__	(						):
  a						: Tuple					       =					'🤗 Transformers currently provides the following architectures'
  a						: Any					       =					'1. Want to contribute a new model?'
  with open(_A			,      'r'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    a						: Tuple					       =					f.readlines()

  # Find the start of the list.
  a						: Optional[int]					       =					0
  while not lines[start_index].startswith(_start_prompt    ):
    start_index += 1
  start_index += 1

  a						: Optional[int]					       =					start_index
  # Update the lines in the model list.
  while not lines[index].startswith(_end_prompt    ):
    if lines[index].startswith('1.'    ):
      a						: List[Any]					       =					lines[index].replace(
          'https://huggingface.co/docs/transformers/main/model_doc'			,      'https://huggingface.co/docs/transformers/model_doc'			,      )
    index += 1

  with open(_A			,      'w'			,      encoding='utf-8'			,      newline='\n'    ) as f:
    f.writelines(_A    )
def 				lowerCamelCase__	(						):
  with open(REPLACE_FILES['init']			,      'r'    ) as f:
    a						: Union[str, Any]					       =					f.read()
  a						: Tuple					       =					REPLACE_PATTERNS['init'][0].search(_A    ).groups()[0]
  return packaging.version.parse(_A    )
def 				lowerCamelCase__	(						_A=False    ):
  a						: int					       =					get_version()
  if patch and default_version.is_devrelease:
    raise ValueError('Can\'t create a patch version from the dev branch, checkout a released version!'    )
  if default_version.is_devrelease:
    a						: Any					       =					default_version.base_version
  elif patch:
    a						: Dict					       =					f"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
  else:
    a						: Union[str, Any]					       =					f"""{default_version.major}.{default_version.minor + 1}.0"""

  # Now let's ask nicely if that's the right one.
  a						: List[Any]					       =					input(f"""Which version are you releasing? [{default_version}]"""    )
  if len(_A    ) == 0:
    a						: Union[str, Any]					       =					default_version

  print(f"""Updating version to {version}."""    )
  global_version_update(_A			,      patch=_A    )
  if not patch:
    print('Cleaning main README, don\'t forget to run `make fix-copies`.'    )
    clean_main_ref_in_model_list()
def 				lowerCamelCase__	(						):
  a						: int					       =					get_version()
  a						: Any					       =					f"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
  a						: int					       =					current_version.base_version

  # Check with the user we got that right.
  a						: Tuple					       =					input(f"""Which version are we developing now? [{dev_version}]"""    )
  if len(_A    ) == 0:
    a						: Optional[int]					       =					dev_version

  print(f"""Updating version to {version}."""    )
  global_version_update(_A    )
  print('Cleaning main README, don\'t forget to run `make fix-copies`.'    )
  clean_main_ref_in_model_list()


if __name__ == "__main__":
     lowerCAmelCase: Tuple							=						argparse.ArgumentParser()
     parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
     parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
     lowerCAmelCase: Optional[Any]							=						parser.parse_args()
     if not args.post_release:
          pre_release_work(patch=args.patch)
     elif args.patch:
          print('Nothing to do after a patch :-)')
     else:
          post_release_work()