





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       A__							(   SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)       ->   int:
  def get_masked_lm_array(SCREAMING_SNAKE_CASE__):
    __snake_case:						Optional[int]			  =						F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    __snake_case:						Dict			  =						tf.train.load_variable(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

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

    return torch.from_numpy(SCREAMING_SNAKE_CASE__)

  def get_encoder_array(SCREAMING_SNAKE_CASE__):
    __snake_case:						List[str]			  =						F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    __snake_case:						List[str]			  =						tf.train.load_variable(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

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

    return torch.from_numpy(SCREAMING_SNAKE_CASE__)

  def get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    __snake_case:						List[str]			  =						F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    __snake_case:						Tuple			  =						tf.train.load_variable(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)

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

    return torch.from_numpy(SCREAMING_SNAKE_CASE__)

  def get_encoder_attention_layer_array(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__):
    __snake_case:						str			  =						F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
    __snake_case:						Any			  =						tf.train.load_variable(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
    __snake_case:						str			  =						array.reshape(SCREAMING_SNAKE_CASE__)

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

    return torch.from_numpy(SCREAMING_SNAKE_CASE__)

  print(F'''Loading model based on config from {config_path}...''')
  __snake_case:						List[str]			  =						BertConfig.from_json_file(SCREAMING_SNAKE_CASE__)
  __snake_case:						Union[str, Any]			  =						BertForMaskedLM(SCREAMING_SNAKE_CASE__)

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

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

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

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

    __snake_case:						str			  =						get_encoder_attention_layer_array(
        SCREAMING_SNAKE_CASE__ , """_output_dense/kernel""" , self_output.dense.weight.data.shape)
    __snake_case:						List[Any]			  =						get_encoder_attention_layer_array(
        SCREAMING_SNAKE_CASE__ , """_output_dense/bias""" , self_output.dense.bias.data.shape)

    __snake_case:						Any			  =						get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , """_attention_layer_norm/gamma""")
    __snake_case:						str			  =						get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , """_attention_layer_norm/beta""")

    # Intermediate
    __snake_case:						BertIntermediate			  =						layer.intermediate

    __snake_case:						Optional[Any]			  =						get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , """_intermediate_dense/kernel""")
    __snake_case:						List[Any]			  =						get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , """_intermediate_dense/bias""")

    # Output
    __snake_case:						BertOutput			  =						layer.output

    __snake_case:						Optional[Any]			  =						get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , """_output_dense/kernel""")
    __snake_case:						Optional[int]			  =						get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , """_output_dense/bias""")

    __snake_case:						Any			  =						get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , """_output_layer_norm/gamma""")
    __snake_case:						str			  =						get_encoder_layer_array(SCREAMING_SNAKE_CASE__ , """_output_layer_norm/beta""")

  # Embeddings
  __snake_case:						Dict			  =						get_encoder_array("""_position_embedding_layer/embeddings""")
  __snake_case:						Dict			  =						get_encoder_array("""_type_embedding_layer/embeddings""")
  __snake_case:						Dict			  =						get_encoder_array("""_embedding_norm_layer/gamma""")
  __snake_case:						Optional[Any]			  =						get_encoder_array("""_embedding_norm_layer/beta""")

  # LM Head
  __snake_case:						Optional[Any]			  =						model.cls.predictions.transform

  __snake_case:						Optional[Any]			  =						get_masked_lm_array("""dense/kernel""")
  __snake_case:						Optional[int]			  =						get_masked_lm_array("""dense/bias""")

  __snake_case:						str			  =						get_masked_lm_array("""layer_norm/gamma""")
  __snake_case:						str			  =						get_masked_lm_array("""layer_norm/beta""")

  __snake_case:						List[str]			  =						get_masked_lm_array("""embedding_table""")

  # Pooling
  __snake_case:						int			  =						BertPooler(config=SCREAMING_SNAKE_CASE__)
  __snake_case:						BertPooler			  =						get_encoder_array("""_pooler_layer/kernel""")
  __snake_case:						BertPooler			  =						get_encoder_array("""_pooler_layer/bias""")

  # Export final model
  model.save_pretrained(SCREAMING_SNAKE_CASE__)

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

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


if __name__ == "__main__":
     __UpperCAmelCase	:       str    	=							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.",
     )
     __UpperCAmelCase	:       Tuple    	=							parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)













from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import (
          ImageTextPipelineOutput,
          UniDiffuserPipeline,
      )
else:
      from .modeling_text_decoder import UniDiffuserTextDecoder
      from .modeling_uvit import UniDiffuserModel, UTransformeraDModel
      from .pipeline_unidiffuser import ImageTextPipelineOutput, UniDiffuserPipeline






