"""simple docstring"""
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()
SCREAMING_SNAKE_CASE__   :      Optional[int]							=logging.get_logger(__name__)



def 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->List[Any]:
    _lowerCamelCase	: Dict       =       os.path.abspath(_A )
    logger.info(F'''Converting TensorFlow checkpoint from {tf_path}''' )
    # Load weights from TF model
    _lowerCamelCase	: Union[str, Any]       =       tf.train.list_variables(_A )
    _lowerCamelCase	: int       =       []
    _lowerCamelCase	: str       =       []
    _lowerCamelCase	: List[Any]       =       []
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        _lowerCamelCase	: List[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'
            _lowerCamelCase	: Tuple       =       name[1:]
        # figure out how many levels deep the name is
        _lowerCamelCase	: List[Any]       =       0
        for _name in name:
            if _name.startswith('''layer_with_weights''' ):
                depth += 1
            else:
                break
        layer_depth.append(_A )
        # read data
        _lowerCamelCase	: Dict       =       tf.train.load_variable(_A						,	_A )
        names.append('''/'''.join(_A ) )
        arrays.append(_A )
    logger.info(F'''Read a total of {len(_A ):,} layers''' )

    # Sanity check
    if len(set(_A ) ) != 1:
        raise ValueError(F'''Found layer names with different depths (layer depth {list(set(_A ) )})''' )
    _lowerCamelCase	: Union[str, Any]       =       list(set(_A ) )[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(_A						,	_A ):
        _lowerCamelCase	: Tuple       =       full_name.split('''/''' )
        _lowerCamelCase	: Tuple       =       model
        _lowerCamelCase	: str       =       []
        for i, m_name in enumerate(_A ):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith('''layer_with_weights''' ):
                _lowerCamelCase	: Any       =       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'''] )
                    _lowerCamelCase	: List[Any]       =       getattr(_A						,	'''embeddings''' )
                    _lowerCamelCase	: Union[str, Any]       =       getattr(_A						,	'''LayerNorm''' )
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(['''encoder''', '''layer''', str(layer_num - 4 )] )
                    _lowerCamelCase	: List[Any]       =       getattr(_A						,	'''encoder''' )
                    _lowerCamelCase	: int       =       getattr(_A						,	'''layer''' )
                    _lowerCamelCase	: Union[str, Any]       =       pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(['''pooler''', '''dense'''] )
                    _lowerCamelCase	: Optional[int]       =       getattr(_A						,	'''pooler''' )
                    _lowerCamelCase	: Union[str, Any]       =       getattr(_A						,	'''dense''' )
            elif m_name == "embeddings":
                trace.append('''embeddings''' )
                _lowerCamelCase	: str       =       getattr(_A						,	'''embeddings''' )
                if layer_num == 0:
                    trace.append('''word_embeddings''' )
                    _lowerCamelCase	: List[str]       =       getattr(_A						,	'''word_embeddings''' )
                elif layer_num == 1:
                    trace.append('''position_embeddings''' )
                    _lowerCamelCase	: int       =       getattr(_A						,	'''position_embeddings''' )
                elif layer_num == 2:
                    trace.append('''token_type_embeddings''' )
                    _lowerCamelCase	: List[Any]       =       getattr(_A						,	'''token_type_embeddings''' )
                else:
                    raise ValueError(F'''Unknown embedding layer with name {full_name}''' )
                trace.append('''weight''' )
                _lowerCamelCase	: Optional[int]       =       getattr(_A						,	'''weight''' )
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(['''attention''', '''self'''] )
                _lowerCamelCase	: Optional[int]       =       getattr(_A						,	'''attention''' )
                _lowerCamelCase	: Any       =       getattr(_A						,	'''self''' )
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(['''attention''', '''output''', '''LayerNorm'''] )
                _lowerCamelCase	: List[str]       =       getattr(_A						,	'''attention''' )
                _lowerCamelCase	: Optional[int]       =       getattr(_A						,	'''output''' )
                _lowerCamelCase	: Dict       =       getattr(_A						,	'''LayerNorm''' )
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(['''attention''', '''output''', '''dense'''] )
                _lowerCamelCase	: Optional[int]       =       getattr(_A						,	'''attention''' )
                _lowerCamelCase	: List[Any]       =       getattr(_A						,	'''output''' )
                _lowerCamelCase	: int       =       getattr(_A						,	'''dense''' )
            elif m_name == "_output_dense":
                # output dense
                trace.extend(['''output''', '''dense'''] )
                _lowerCamelCase	: Optional[Any]       =       getattr(_A						,	'''output''' )
                _lowerCamelCase	: List[str]       =       getattr(_A						,	'''dense''' )
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(['''output''', '''LayerNorm'''] )
                _lowerCamelCase	: Optional[int]       =       getattr(_A						,	'''output''' )
                _lowerCamelCase	: Optional[Any]       =       getattr(_A						,	'''LayerNorm''' )
            elif m_name == "_key_dense":
                # attention key
                trace.append('''key''' )
                _lowerCamelCase	: int       =       getattr(_A						,	'''key''' )
            elif m_name == "_query_dense":
                # attention query
                trace.append('''query''' )
                _lowerCamelCase	: Union[str, Any]       =       getattr(_A						,	'''query''' )
            elif m_name == "_value_dense":
                # attention value
                trace.append('''value''' )
                _lowerCamelCase	: Optional[int]       =       getattr(_A						,	'''value''' )
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(['''intermediate''', '''dense'''] )
                _lowerCamelCase	: int       =       getattr(_A						,	'''intermediate''' )
                _lowerCamelCase	: List[str]       =       getattr(_A						,	'''dense''' )
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append('''output''' )
                _lowerCamelCase	: Tuple       =       getattr(_A						,	'''output''' )
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append('''bias''' )
                _lowerCamelCase	: Any       =       getattr(_A						,	'''bias''' )
            elif m_name in ["kernel", "gamma"]:
                trace.append('''weight''' )
                _lowerCamelCase	: str       =       getattr(_A						,	'''weight''' )
            else:
                logger.warning(F'''Ignored {m_name}''' )
        # for certain layers reshape is necessary
        _lowerCamelCase	: List[Any]       =       '.'.join(_A )
        if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''						,	_A ) or re.match(
            R'''(\S+)\.attention\.output\.dense\.weight'''						,	_A ):
            _lowerCamelCase	: List[str]       =       array.reshape(pointer.data.shape )
        if "kernel" in full_name:
            _lowerCamelCase	: int       =       array.transpose()
        if pointer.shape == array.shape:
            _lowerCamelCase	: List[str]       =       torch.from_numpy(_A )
        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 					UpperCamelCase					(       SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_ )      ->Optional[Any]:
    logger.info(F'''Loading model based on config from {config_path}...''' )
    _lowerCamelCase	: int       =       BertConfig.from_json_file(_A )
    _lowerCamelCase	: List[str]       =       BertModel(_A )

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE__   :      Dict							=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).',
  )
  SCREAMING_SNAKE_CASE__   :      Union[str, Any]							=parser.parse_args()
  convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase     :  Any											=    {
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """CanineForMultipleChoice""",
           """CanineForQuestionAnswering""",
           """CanineForSequenceClassification""",
           """CanineForTokenClassification""",
           """CanineLayer""",
           """CanineModel""",
           """CaninePreTrainedModel""",
           """load_tf_weights_in_canine""",
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCAmelCase     :  Optional[Any]											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

