





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


A__    :       Dict              =logging.get_logger(__name__)




class 			UpperCAmelCase					(      snake_case_       ):
      _lowercase:      Optional[Any]											=       ['''pixel_values''']




      def __init__(      self      :			Optional[Any]      ,      __snake_case      :			bool = True      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			PILImageResampling = PIL.Image.BICUBIC      ,      __snake_case      :			bool = True      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			Union[int, float] = 1 / 2_55      ,      __snake_case      :			bool = True      ,      __snake_case      :			bool = True      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      **__snake_case      :			List[str]      ,      )    ->				None:
            super().__init__(**__snake_case  )
            _lowerCAmelCase												=							size if size is not None else {"""height""": 2_56, """width""": 2_56}
            _lowerCAmelCase												=							get_size_dict(__snake_case  )
            _lowerCAmelCase												=							crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      param_name="""crop_size"""  )

            _lowerCAmelCase												=							do_resize
            _lowerCAmelCase												=							size
            _lowerCAmelCase												=							resample
            _lowerCAmelCase												=							do_center_crop
            _lowerCAmelCase												=							crop_size
            _lowerCAmelCase												=							do_rescale
            _lowerCAmelCase												=							rescale_factor
            _lowerCAmelCase												=							do_normalize
            _lowerCAmelCase												=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            _lowerCAmelCase												=							image_std if image_std is not None else IMAGENET_STANDARD_STD




      def     lowercase__   (      self      :			int      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Dict[str, int]      ,      __snake_case      :			PILImageResampling = PIL.Image.BICUBIC      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			Optional[int]      ,      )    ->				np.ndarray:
            _lowerCAmelCase												=							get_size_dict(__snake_case  )
            if "height" not in size or "width" not in size:
                  raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"  )
            return resize(
                __snake_case      ,      size=(size["""height"""], size["""width"""])      ,      resample=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			str      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Dict[str, int]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			List[Any]      ,      )    ->				np.ndarray:
            _lowerCAmelCase												=							get_size_dict(__snake_case  )
            if "height" not in size or "width" not in size:
                  raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"  )
            return center_crop(__snake_case      ,      size=(size["""height"""], size["""width"""])      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			List[Any]      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Union[int, float]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			List[Any]      ,      )    ->				Optional[int]:
            return rescale(__snake_case      ,      scale=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			int      ,      __snake_case      :			np.ndarray      ,      __snake_case      :			Union[float, List[float]]      ,      __snake_case      :			Union[float, List[float]]      ,      __snake_case      :			Optional[Union[str, ChannelDimension]] = None      ,      **__snake_case      :			Optional[int]      ,      )    ->				np.ndarray:
            return normalize(__snake_case      ,      mean=__snake_case      ,      std=__snake_case      ,      data_format=__snake_case      ,      **__snake_case  )




      def     lowercase__   (      self      :			Dict      ,      __snake_case      :			ImageInput      ,      __snake_case      :			bool = None      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			Tuple=None      ,      __snake_case      :			bool = None      ,      __snake_case      :			Dict[str, int] = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			float = None      ,      __snake_case      :			bool = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			Optional[Union[float, List[float]]] = None      ,      __snake_case      :			Optional[Union[str, TensorType]] = None      ,      __snake_case      :			ChannelDimension = ChannelDimension.FIRST      ,      **__snake_case      :			List[str]      ,      )    ->				PIL.Image.Image:
            _lowerCAmelCase												=							do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase												=							resample if resample is not None else self.resample
            _lowerCAmelCase												=							do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase												=							do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase												=							rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase												=							do_normalize if do_normalize is not None else self.do_normalize
            _lowerCAmelCase												=							image_mean if image_mean is not None else self.image_mean
            _lowerCAmelCase												=							image_std if image_std is not None else self.image_std

            _lowerCAmelCase												=							size if size is not None else self.size
            _lowerCAmelCase												=							get_size_dict(__snake_case  )
            _lowerCAmelCase												=							crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase												=							get_size_dict(__snake_case      ,      param_name="""crop_size"""  )

            _lowerCAmelCase												=							make_list_of_images(__snake_case  )

            if not valid_images(__snake_case  ):
                  raise ValueError(
                      """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                      """torch.Tensor, tf.Tensor or jax.ndarray."""  )

            if do_resize and size is None or resample is None:
                  raise ValueError("""Size and resample must be specified if do_resize is True."""  )

            if do_center_crop and crop_size is None:
                  raise ValueError("""Crop size must be specified if do_center_crop is True."""  )

            if do_rescale and rescale_factor is None:
                  raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

            if do_normalize and (image_mean is None or image_std is None):
                  raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

            # All transformations expect numpy arrays.
            _lowerCAmelCase												=							[to_numpy_array(__snake_case  ) for image in images]

            if do_resize:
                  _lowerCAmelCase												=							[self.resize(image=__snake_case      ,      size=__snake_case      ,      resample=__snake_case  ) for image in images]

            if do_center_crop:
                  _lowerCAmelCase												=							[self.center_crop(image=__snake_case      ,      size=__snake_case  ) for image in images]

            if do_rescale:
                  _lowerCAmelCase												=							[self.rescale(image=__snake_case      ,      scale=__snake_case  ) for image in images]

            if do_normalize:
                  _lowerCAmelCase												=							[self.normalize(image=__snake_case      ,      mean=__snake_case      ,      std=__snake_case  ) for image in images]

            _lowerCAmelCase												=							[to_channel_dimension_format(__snake_case      ,      __snake_case  ) for image in images]

            _lowerCAmelCase												=							{"""pixel_values""": images}
            return BatchFeature(data=__snake_case      ,      tensor_type=__snake_case  )













'''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__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      def get_masked_lm_array(lowerCAmelCase  ):
            _lowerCAmelCase												=							f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

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

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_array(lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

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

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_layer_array(lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )

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

            return torch.from_numpy(lowerCAmelCase  )

      def get_encoder_attention_layer_array(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
            _lowerCAmelCase												=							tf.train.load_variable(lowerCAmelCase							,     lowerCAmelCase  )
            _lowerCAmelCase												=							array.reshape(lowerCAmelCase  )

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

            return torch.from_numpy(lowerCAmelCase  )

      print(f"Loading model based on config from {config_path}..."  )
      _lowerCAmelCase												=							BertConfig.from_json_file(lowerCAmelCase  )
      _lowerCAmelCase												=							BertForMaskedLM(lowerCAmelCase  )

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

            # Self-attention
            _lowerCAmelCase												=							layer.attention.self

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/kernel"""							,     self_attn.query.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/bias"""							,     self_attn.query.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/kernel"""							,     self_attn.key.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/bias"""							,     self_attn.key.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/kernel"""							,     self_attn.value.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/bias"""							,     self_attn.value.bias.data.shape  )

            # Self-attention Output
            _lowerCAmelCase												=							layer.attention.output

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/kernel"""							,     self_output.dense.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/bias"""							,     self_output.dense.bias.data.shape  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/beta"""  )

            # Intermediate
            _lowerCAmelCase												=							layer.intermediate

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/bias"""  )

            # Output
            _lowerCAmelCase												=							layer.output

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/bias"""  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/beta"""  )

      # Embeddings
      _lowerCAmelCase												=							get_encoder_array("""_position_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_type_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/gamma"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/beta"""  )

      # LM Head
      _lowerCAmelCase												=							model.cls.predictions.transform

      _lowerCAmelCase												=							get_masked_lm_array("""dense/kernel"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""dense/bias"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/gamma"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/beta"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""embedding_table"""  )

      # Pooling
      _lowerCAmelCase												=							BertPooler(config=lowerCAmelCase  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/kernel"""  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/bias"""  )

      # Export final model
      model.save_pretrained(lowerCAmelCase  )

      # Integration test - should load without any errors ;)
      _lowerCAmelCase												=							BertForMaskedLM.from_pretrained(lowerCAmelCase  )
      print(new_model.eval()  )

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


if __name__ == "__main__":
      A__    :       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.''',
      )
      A__    :       Dict              =parser.parse_args()
      convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






