


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()
UpperCAmelCase_      : Union[str, Any]  					=       logging.get_logger(__name__)







def        SCREAMING_SNAKE_CASE_						(			__A :			Any  ,    __A :			int  ,    __A :			Optional[int]				)	->		Tuple:

       """simple docstring"""

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

       """simple docstring"""

       logger.info(F"""Loading model based on config from {config_path}..."""				)
       a_					:		str							=  BertConfig.from_json_file(__A				)
       a_					:		Optional[Any]							=  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__":
   UpperCAmelCase_      : Any  					=       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).',
   )
   UpperCAmelCase_      : Tuple  					=       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


__lowercase          =  {
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowercase          =  [
						    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''NezhaForNextSentencePrediction''',
						    '''NezhaForMaskedLM''',
						    '''NezhaForPreTraining''',
						    '''NezhaForMultipleChoice''',
						    '''NezhaForQuestionAnswering''',
						    '''NezhaForSequenceClassification''',
						    '''NezhaForTokenClassification''',
						    '''NezhaModel''',
						    '''NezhaPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nezha import (
												    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NezhaForMaskedLM,
												    NezhaForMultipleChoice,
												    NezhaForNextSentencePrediction,
												    NezhaForPreTraining,
												    NezhaForQuestionAnswering,
												    NezhaForSequenceClassification,
												    NezhaForTokenClassification,
												    NezhaModel,
												    NezhaPreTrainedModel,
												)


else:
						import sys

						__lowercase          =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




