


"""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     __lowerCAmelCase		(		lowercase    :    str		,				lowercase    :    str		,				lowercase    :    str					)      ->		Union[str, Any]:


    """simple docstring"""





    def get_masked_lm_array(lowercase    :    str					):
        snake_case       : List[str]    =       F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
        snake_case       : List[Any]    =       tf.train.load_variable(lowercase		,				lowercase					)

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

        return torch.from_numpy(lowercase					)

    def get_encoder_array(lowercase    :    str					):
        snake_case       : int    =       F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
        snake_case       : Dict    =       tf.train.load_variable(lowercase		,				lowercase					)

        if "kernel" in name:
            snake_case       : List[Any]    =       array.transpose()

        return torch.from_numpy(lowercase					)

    def get_encoder_layer_array(lowercase    :    int		,				lowercase    :    str					):
        snake_case       : List[Any]    =       F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
        snake_case       : Optional[int]    =       tf.train.load_variable(lowercase		,				lowercase					)

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

        return torch.from_numpy(lowercase					)

    def get_encoder_attention_layer_array(lowercase    :    int		,				lowercase    :    str		,				lowercase    :    Optional[Any]					):
        snake_case       : int    =       F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
        snake_case       : Optional[Any]    =       tf.train.load_variable(lowercase		,				lowercase					)
        snake_case       : Optional[int]    =       array.reshape(lowercase					)

        if "kernel" in name:
            snake_case       : int    =       array.transpose()

        return torch.from_numpy(lowercase					)

    print(F'Loading model based on config from {config_path}...'					)
    snake_case       : Optional[int]    =       BertConfig.from_json_file(lowercase					)
    snake_case       : Union[str, Any]    =       BertForMaskedLM(lowercase					)

    # 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       : List[Any]    =       get_encoder_attention_layer_array(
            lowercase		,				"_query_dense/kernel"		,				self_attn.query.weight.data.shape					)
        snake_case       : List[str]    =       get_encoder_attention_layer_array(
            lowercase		,				"_query_dense/bias"		,				self_attn.query.bias.data.shape					)
        snake_case       : List[Any]    =       get_encoder_attention_layer_array(
            lowercase		,				"_key_dense/kernel"		,				self_attn.key.weight.data.shape					)
        snake_case       : Any    =       get_encoder_attention_layer_array(
            lowercase		,				"_key_dense/bias"		,				self_attn.key.bias.data.shape					)
        snake_case       : Union[str, Any]    =       get_encoder_attention_layer_array(
            lowercase		,				"_value_dense/kernel"		,				self_attn.value.weight.data.shape					)
        snake_case       : Dict    =       get_encoder_attention_layer_array(
            lowercase		,				"_value_dense/bias"		,				self_attn.value.bias.data.shape					)

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

        snake_case       : Optional[Any]    =       get_encoder_attention_layer_array(
            lowercase		,				"_output_dense/kernel"		,				self_output.dense.weight.data.shape					)
        snake_case       : Optional[Any]    =       get_encoder_attention_layer_array(
            lowercase		,				"_output_dense/bias"		,				self_output.dense.bias.data.shape					)

        snake_case       : List[Any]    =       get_encoder_layer_array(lowercase		,				"_attention_layer_norm/gamma"					)
        snake_case       : Optional[Any]    =       get_encoder_layer_array(lowercase		,				"_attention_layer_norm/beta"					)

        # Intermediate
        snake_case       : BertIntermediate    =       layer.intermediate

        snake_case       : Union[str, Any]    =       get_encoder_layer_array(lowercase		,				"_intermediate_dense/kernel"					)
        snake_case       : List[str]    =       get_encoder_layer_array(lowercase		,				"_intermediate_dense/bias"					)

        # Output
        snake_case       : BertOutput    =       layer.output

        snake_case       : Any    =       get_encoder_layer_array(lowercase		,				"_output_dense/kernel"					)
        snake_case       : Dict    =       get_encoder_layer_array(lowercase		,				"_output_dense/bias"					)

        snake_case       : Optional[Any]    =       get_encoder_layer_array(lowercase		,				"_output_layer_norm/gamma"					)
        snake_case       : int    =       get_encoder_layer_array(lowercase		,				"_output_layer_norm/beta"					)

    # Embeddings
    snake_case       : List[Any]    =       get_encoder_array("_position_embedding_layer/embeddings"					)
    snake_case       : int    =       get_encoder_array("_type_embedding_layer/embeddings"					)
    snake_case       : str    =       get_encoder_array("_embedding_norm_layer/gamma"					)
    snake_case       : Optional[Any]    =       get_encoder_array("_embedding_norm_layer/beta"					)

    # LM Head
    snake_case       : int    =       model.cls.predictions.transform

    snake_case       : int    =       get_masked_lm_array("dense/kernel"					)
    snake_case       : Tuple    =       get_masked_lm_array("dense/bias"					)

    snake_case       : str    =       get_masked_lm_array("layer_norm/gamma"					)
    snake_case       : List[Any]    =       get_masked_lm_array("layer_norm/beta"					)

    snake_case       : Optional[int]    =       get_masked_lm_array("embedding_table"					)

    # Pooling
    snake_case       : Any    =       BertPooler(config=lowercase					)
    snake_case       : BertPooler    =       get_encoder_array("_pooler_layer/kernel"					)
    snake_case       : BertPooler    =       get_encoder_array("_pooler_layer/bias"					)

    # Export final model
    model.save_pretrained(lowercase					)

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

    print("Model conversion was done sucessfully!"					)


if __name__ == "__main__":
  __snake_case       =       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.""",
  )
  __snake_case       =       parser.parse_args()
  convert_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_segformer import SegformerImageProcessor


__snake_case       =       logging.get_logger(__name__)







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      *UpperCamelCase__		,      **UpperCamelCase__		)      ->						None:

       '''simple docstring'''




       warnings.warn(
           "The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
           " Please use SegformerImageProcessor instead."		,      UpperCamelCase__		,      )
       super().__init__(*UpperCamelCase__		,      **UpperCamelCase__		)





