


'''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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
   def get_masked_lm_array(UpperCamelCase__					):
      UpperCAmelCase__  :					List[Any]        =     f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
      UpperCAmelCase__  :					Union[str, Any]        =     tf.train.load_variable(UpperCamelCase__ ,      UpperCamelCase__					)

      if "kernel" in name:
         UpperCAmelCase__  :					List[str]        =     array.transpose()

      return torch.from_numpy(UpperCamelCase__					)

   def get_encoder_array(UpperCamelCase__					):
      UpperCAmelCase__  :					Union[str, Any]        =     f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
      UpperCAmelCase__  :					str        =     tf.train.load_variable(UpperCamelCase__ ,      UpperCamelCase__					)

      if "kernel" in name:
         UpperCAmelCase__  :					Any        =     array.transpose()

      return torch.from_numpy(UpperCamelCase__					)

   def get_encoder_layer_array(UpperCamelCase__ ,      UpperCamelCase__					):
      UpperCAmelCase__  :					Union[str, Any]        =     f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
      UpperCAmelCase__  :					Optional[int]        =     tf.train.load_variable(UpperCamelCase__ ,      UpperCamelCase__					)

      if "kernel" in name:
         UpperCAmelCase__  :					Union[str, Any]        =     array.transpose()

      return torch.from_numpy(UpperCamelCase__					)

   def get_encoder_attention_layer_array(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
      UpperCAmelCase__  :					Optional[int]        =     f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
      UpperCAmelCase__  :					List[str]        =     tf.train.load_variable(UpperCamelCase__ ,      UpperCamelCase__					)
      UpperCAmelCase__  :					Optional[int]        =     array.reshape(UpperCamelCase__					)

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

      return torch.from_numpy(UpperCamelCase__					)

   print(f'''Loading model based on config from {config_path}...'''					)
   UpperCAmelCase__  :					List[str]        =     BertConfig.from_json_file(UpperCamelCase__					)
   UpperCAmelCase__  :					Optional[int]        =     BertForMaskedLM(UpperCamelCase__					)

   # Layers
   for layer_index in range(0 ,      config.num_hidden_layers					):
      UpperCAmelCase__  :					BertLayer        =     model.bert.encoder.layer[layer_index]

      # Self-attention
      UpperCAmelCase__  :					BertSelfAttention        =     layer.attention.self

      UpperCAmelCase__  :					List[Any]        =     get_encoder_attention_layer_array(
          UpperCamelCase__ ,      """_query_dense/kernel""" ,      self_attn.query.weight.data.shape					)
      UpperCAmelCase__  :					str        =     get_encoder_attention_layer_array(
          UpperCamelCase__ ,      """_query_dense/bias""" ,      self_attn.query.bias.data.shape					)
      UpperCAmelCase__  :					Optional[int]        =     get_encoder_attention_layer_array(
          UpperCamelCase__ ,      """_key_dense/kernel""" ,      self_attn.key.weight.data.shape					)
      UpperCAmelCase__  :					Dict        =     get_encoder_attention_layer_array(
          UpperCamelCase__ ,      """_key_dense/bias""" ,      self_attn.key.bias.data.shape					)
      UpperCAmelCase__  :					Optional[Any]        =     get_encoder_attention_layer_array(
          UpperCamelCase__ ,      """_value_dense/kernel""" ,      self_attn.value.weight.data.shape					)
      UpperCAmelCase__  :					Optional[int]        =     get_encoder_attention_layer_array(
          UpperCamelCase__ ,      """_value_dense/bias""" ,      self_attn.value.bias.data.shape					)

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

      UpperCAmelCase__  :					Optional[int]        =     get_encoder_attention_layer_array(
          UpperCamelCase__ ,      """_output_dense/kernel""" ,      self_output.dense.weight.data.shape					)
      UpperCAmelCase__  :					Dict        =     get_encoder_attention_layer_array(
          UpperCamelCase__ ,      """_output_dense/bias""" ,      self_output.dense.bias.data.shape					)

      UpperCAmelCase__  :					List[str]        =     get_encoder_layer_array(UpperCamelCase__ ,      """_attention_layer_norm/gamma"""					)
      UpperCAmelCase__  :					int        =     get_encoder_layer_array(UpperCamelCase__ ,      """_attention_layer_norm/beta"""					)

      # Intermediate
      UpperCAmelCase__  :					BertIntermediate        =     layer.intermediate

      UpperCAmelCase__  :					Tuple        =     get_encoder_layer_array(UpperCamelCase__ ,      """_intermediate_dense/kernel"""					)
      UpperCAmelCase__  :					Dict        =     get_encoder_layer_array(UpperCamelCase__ ,      """_intermediate_dense/bias"""					)

      # Output
      UpperCAmelCase__  :					BertOutput        =     layer.output

      UpperCAmelCase__  :					List[Any]        =     get_encoder_layer_array(UpperCamelCase__ ,      """_output_dense/kernel"""					)
      UpperCAmelCase__  :					Optional[Any]        =     get_encoder_layer_array(UpperCamelCase__ ,      """_output_dense/bias"""					)

      UpperCAmelCase__  :					int        =     get_encoder_layer_array(UpperCamelCase__ ,      """_output_layer_norm/gamma"""					)
      UpperCAmelCase__  :					List[str]        =     get_encoder_layer_array(UpperCamelCase__ ,      """_output_layer_norm/beta"""					)

   # Embeddings
   UpperCAmelCase__  :					Dict        =     get_encoder_array("""_position_embedding_layer/embeddings"""					)
   UpperCAmelCase__  :					Dict        =     get_encoder_array("""_type_embedding_layer/embeddings"""					)
   UpperCAmelCase__  :					Union[str, Any]        =     get_encoder_array("""_embedding_norm_layer/gamma"""					)
   UpperCAmelCase__  :					Optional[int]        =     get_encoder_array("""_embedding_norm_layer/beta"""					)

   # LM Head
   UpperCAmelCase__  :					List[str]        =     model.cls.predictions.transform

   UpperCAmelCase__  :					Dict        =     get_masked_lm_array("""dense/kernel"""					)
   UpperCAmelCase__  :					Tuple        =     get_masked_lm_array("""dense/bias"""					)

   UpperCAmelCase__  :					str        =     get_masked_lm_array("""layer_norm/gamma"""					)
   UpperCAmelCase__  :					Dict        =     get_masked_lm_array("""layer_norm/beta"""					)

   UpperCAmelCase__  :					Optional[Any]        =     get_masked_lm_array("""embedding_table"""					)

   # Pooling
   UpperCAmelCase__  :					str        =     BertPooler(config=UpperCamelCase__					)
   UpperCAmelCase__  :					BertPooler        =     get_encoder_array("""_pooler_layer/kernel"""					)
   UpperCAmelCase__  :					BertPooler        =     get_encoder_array("""_pooler_layer/bias"""					)

   # Export final model
   model.save_pretrained(UpperCamelCase__					)

   # Integration test - should load without any errors ;)
   UpperCAmelCase__  :					Optional[int]        =     BertForMaskedLM.from_pretrained(UpperCamelCase__					)
   print(new_model.eval()					)

   print("""Model conversion was done sucessfully!"""					)


if __name__ == "__main__":
  __A	=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.',
  )
  __A	=parser.parse_args()
  convert_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_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A	={
    'configuration_bert': ['BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BertConfig', 'BertOnnxConfig'],
    'tokenization_bert': ['BasicTokenizer', 'BertTokenizer', 'WordpieceTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['BertTokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BertForMaskedLM',
		    'BertForMultipleChoice',
		    'BertForNextSentencePrediction',
		    'BertForPreTraining',
		    'BertForQuestionAnswering',
		    'BertForSequenceClassification',
		    'BertForTokenClassification',
		    'BertLayer',
		    'BertLMHeadModel',
		    'BertModel',
		    'BertPreTrainedModel',
		    'load_tf_weights_in_bert',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBertEmbeddings',
		    'TFBertForMaskedLM',
		    'TFBertForMultipleChoice',
		    'TFBertForNextSentencePrediction',
		    'TFBertForPreTraining',
		    'TFBertForQuestionAnswering',
		    'TFBertForSequenceClassification',
		    'TFBertForTokenClassification',
		    'TFBertLMHeadModel',
		    'TFBertMainLayer',
		    'TFBertModel',
		    'TFBertPreTrainedModel',
		]
try:
		if not is_tensorflow_text_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['TFBertTokenizer']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'FlaxBertForCausalLM',
		    'FlaxBertForMaskedLM',
		    'FlaxBertForMultipleChoice',
		    'FlaxBertForNextSentencePrediction',
		    'FlaxBertForPreTraining',
		    'FlaxBertForQuestionAnswering',
		    'FlaxBertForSequenceClassification',
		    'FlaxBertForTokenClassification',
		    'FlaxBertModel',
		    'FlaxBertPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
		from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_fast import BertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bert import (
				    BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BertForMaskedLM,
				    BertForMultipleChoice,
				    BertForNextSentencePrediction,
				    BertForPreTraining,
				    BertForQuestionAnswering,
				    BertForSequenceClassification,
				    BertForTokenClassification,
				    BertLayer,
				    BertLMHeadModel,
				    BertModel,
				    BertPreTrainedModel,
				    load_tf_weights_in_bert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_bert import (
				    TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBertEmbeddings,
				    TFBertForMaskedLM,
				    TFBertForMultipleChoice,
				    TFBertForNextSentencePrediction,
				    TFBertForPreTraining,
				    TFBertForQuestionAnswering,
				    TFBertForSequenceClassification,
				    TFBertForTokenClassification,
				    TFBertLMHeadModel,
				    TFBertMainLayer,
				    TFBertModel,
				    TFBertPreTrainedModel,
				)

		try:
				if not is_tensorflow_text_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bert_tf import TFBertTokenizer

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_bert import (
				    FlaxBertForCausalLM,
				    FlaxBertForMaskedLM,
				    FlaxBertForMultipleChoice,
				    FlaxBertForNextSentencePrediction,
				    FlaxBertForPreTraining,
				    FlaxBertForQuestionAnswering,
				    FlaxBertForSequenceClassification,
				    FlaxBertForTokenClassification,
				    FlaxBertModel,
				    FlaxBertPreTrainedModel,
				)

else:
		import sys

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