



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:      int		 =		logging.get_logger(__name__)
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Optional[Any]       =  os.path.abspath(lowerCamelCase_       )
  logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""       )
  # Load weights from TF model
  a		:      Dict       =  tf.train.list_variables(lowerCamelCase_       )
  a		:      List[str]       =  []
  a		:      Union[str, Any]       =  []
  a		:      Optional[Any]       =  []
  for full_name, shape in init_vars:
    # logger.info(f"Loading TF weight {name} with shape {shape}")
    a		:      Optional[Any]       =  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'
      a		:      str       =  name[1:]
    # figure out how many levels deep the name is
    a		:      Dict       =  0
    for _name in name:
      if _name.startswith('layer_with_weights'       ):
        depth += 1
      else:
        break
    layer_depth.append(lowerCamelCase_       )
    # read data
    a		:      Any       =  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_       )       )})"""       )
  a		:      List[str]       =  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_       ):
    a		:      int       =  full_name.split('/'       )
    a		:      Tuple       =  model
    a		:      int       =  []
    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'       ):
        a		:      Optional[int]       =  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']       )
          a		:      str       =  getattr(lowerCamelCase_				,    'embeddings'       )
          a		:      Optional[int]       =  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       )]       )
          a		:      List[str]       =  getattr(lowerCamelCase_				,    'encoder'       )
          a		:      Any       =  getattr(lowerCamelCase_				,    'layer'       )
          a		:      List[Any]       =  pointer[layer_num - 4]
        elif layer_num == config.num_hidden_layers + 4:
          # pooler layer
          trace.extend(['pooler', 'dense']       )
          a		:      str       =  getattr(lowerCamelCase_				,    'pooler'       )
          a		:      Optional[Any]       =  getattr(lowerCamelCase_				,    'dense'       )
      elif m_name == "embeddings":
        trace.append('embeddings'       )
        a		:      Any       =  getattr(lowerCamelCase_				,    'embeddings'       )
        if layer_num == 0:
          trace.append('word_embeddings'       )
          a		:      List[str]       =  getattr(lowerCamelCase_				,    'word_embeddings'       )
        elif layer_num == 1:
          trace.append('position_embeddings'       )
          a		:      Dict       =  getattr(lowerCamelCase_				,    'position_embeddings'       )
        elif layer_num == 2:
          trace.append('token_type_embeddings'       )
          a		:      List[Any]       =  getattr(lowerCamelCase_				,    'token_type_embeddings'       )
        else:
          raise ValueError(f"""Unknown embedding layer with name {full_name}"""       )
        trace.append('weight'       )
        a		:      List[Any]       =  getattr(lowerCamelCase_				,    'weight'       )
      elif m_name == "_attention_layer":
        # self-attention layer
        trace.extend(['attention', 'self']       )
        a		:      Optional[Any]       =  getattr(lowerCamelCase_				,    'attention'       )
        a		:      str       =  getattr(lowerCamelCase_				,    'self'       )
      elif m_name == "_attention_layer_norm":
        # output attention norm
        trace.extend(['attention', 'output', 'LayerNorm']       )
        a		:      Dict       =  getattr(lowerCamelCase_				,    'attention'       )
        a		:      Dict       =  getattr(lowerCamelCase_				,    'output'       )
        a		:      Any       =  getattr(lowerCamelCase_				,    'LayerNorm'       )
      elif m_name == "_attention_output_dense":
        # output attention dense
        trace.extend(['attention', 'output', 'dense']       )
        a		:      int       =  getattr(lowerCamelCase_				,    'attention'       )
        a		:      str       =  getattr(lowerCamelCase_				,    'output'       )
        a		:      str       =  getattr(lowerCamelCase_				,    'dense'       )
      elif m_name == "_output_dense":
        # output dense
        trace.extend(['output', 'dense']       )
        a		:      List[Any]       =  getattr(lowerCamelCase_				,    'output'       )
        a		:      Dict       =  getattr(lowerCamelCase_				,    'dense'       )
      elif m_name == "_output_layer_norm":
        # output dense
        trace.extend(['output', 'LayerNorm']       )
        a		:      Optional[int]       =  getattr(lowerCamelCase_				,    'output'       )
        a		:      Dict       =  getattr(lowerCamelCase_				,    'LayerNorm'       )
      elif m_name == "_key_dense":
        # attention key
        trace.append('key'       )
        a		:      List[str]       =  getattr(lowerCamelCase_				,    'key'       )
      elif m_name == "_query_dense":
        # attention query
        trace.append('query'       )
        a		:      List[str]       =  getattr(lowerCamelCase_				,    'query'       )
      elif m_name == "_value_dense":
        # attention value
        trace.append('value'       )
        a		:      int       =  getattr(lowerCamelCase_				,    'value'       )
      elif m_name == "_intermediate_dense":
        # attention intermediate dense
        trace.extend(['intermediate', 'dense']       )
        a		:      str       =  getattr(lowerCamelCase_				,    'intermediate'       )
        a		:      List[str]       =  getattr(lowerCamelCase_				,    'dense'       )
      elif m_name == "_output_layer_norm":
        # output layer norm
        trace.append('output'       )
        a		:      int       =  getattr(lowerCamelCase_				,    'output'       )
      # weights & biases
      elif m_name in ["bias", "beta"]:
        trace.append('bias'       )
        a		:      List[Any]       =  getattr(lowerCamelCase_				,    'bias'       )
      elif m_name in ["kernel", "gamma"]:
        trace.append('weight'       )
        a		:      List[str]       =  getattr(lowerCamelCase_				,    'weight'       )
      else:
        logger.warning(f"""Ignored {m_name}"""       )
        # for certain layers reshape is necessary
    a		:      str       =  '''.'''.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_       ):
      a		:      Tuple       =  array.reshape(pointer.data.shape       )
    if "kernel" in full_name:
      a		:      Tuple       =  array.transpose()
    if pointer.shape == array.shape:
      a		:      Union[str, Any]       =  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    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  logger.info(f"""Loading model based on config from {config_path}..."""       )
  a		:      Tuple       =  BertConfig.from_json_file(lowerCamelCase_       )
  a		:      Union[str, Any]       =  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:      int		 =		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:      Tuple		 =		parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase:      Optional[Any]		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    *__snake_case				:						List[str]		,    **__snake_case				:						Union[str, Any]			):
      warnings.warn(
          'The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
          ' Please use ChineseCLIPImageProcessor instead.'		,    __snake_case		,    )
      super().__init__(*__snake_case		,    **__snake_case			)