


"""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       A_   (   _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str,     _lowerCAmelCase     :					str       ):






     """simple docstring"""

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

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

          return torch.from_numpy(_lowerCAmelCase       )

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

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

          return torch.from_numpy(_lowerCAmelCase       )

     def get_encoder_layer_array(_lowerCAmelCase     :					int,     _lowerCAmelCase     :					str       ):
          _a    	=				f'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
          _a    	=				tf.train.load_variable(_lowerCAmelCase,     _lowerCAmelCase       )

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

          return torch.from_numpy(_lowerCAmelCase       )

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

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

          return torch.from_numpy(_lowerCAmelCase       )

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

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

          # Self-attention
          _a    	=				layer.attention.self

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

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

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

          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_attention_layer_norm/gamma'''       )
          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_attention_layer_norm/beta'''       )

          # Intermediate
          _a    	=				layer.intermediate

          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_intermediate_dense/kernel'''       )
          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_intermediate_dense/bias'''       )

          # Output
          _a    	=				layer.output

          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_output_dense/kernel'''       )
          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_output_dense/bias'''       )

          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_output_layer_norm/gamma'''       )
          _a    	=				get_encoder_layer_array(_lowerCAmelCase,     '''_output_layer_norm/beta'''       )

     # Embeddings
     _a    	=				get_encoder_array('''_position_embedding_layer/embeddings'''       )
     _a    	=				get_encoder_array('''_type_embedding_layer/embeddings'''       )
     _a    	=				get_encoder_array('''_embedding_norm_layer/gamma'''       )
     _a    	=				get_encoder_array('''_embedding_norm_layer/beta'''       )

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

     _a    	=				get_masked_lm_array('''dense/kernel'''       )
     _a    	=				get_masked_lm_array('''dense/bias'''       )

     _a    	=				get_masked_lm_array('''layer_norm/gamma'''       )
     _a    	=				get_masked_lm_array('''layer_norm/beta'''       )

     _a    	=				get_masked_lm_array('''embedding_table'''       )

     # Pooling
     _a    	=				BertPooler(config=_lowerCAmelCase       )
     _a    	=				get_encoder_array('''_pooler_layer/kernel'''       )
     _a    	=				get_encoder_array('''_pooler_layer/bias'''       )

     # Export final model
     model.save_pretrained(_lowerCAmelCase       )

     # Integration test - should load without any errors ;)
     _a    	=				BertForMaskedLM.from_pretrained(_lowerCAmelCase       )
     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"""






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,
    get_resize_output_image_size,
    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


__snake_case		=       logging.get_logger(__name__)


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Any    =		['pixel_values']


     def __init__(      self    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = PILImageResampling.BILINEAR    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = 1 / 255    ,				__UpperCAmelCase = True    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          super().__init__(**__UpperCAmelCase							)
          _a    	=				size if size is not None else {'''shortest_edge''': 384}
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)

          _a    	=				do_resize
          _a    	=				size
          # Default value set here for backwards compatibility where the value in config is None
          _a    	=				crop_pct if crop_pct is not None else 224 / 256
          _a    	=				resample
          _a    	=				do_rescale
          _a    	=				rescale_factor
          _a    	=				do_normalize
          _a    	=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _a    	=				image_std if image_std is not None else IMAGENET_STANDARD_STD


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = PILImageResampling.BICUBIC    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
          if "shortest_edge" not in size:
               raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'							)
          _a    	=				size['''shortest_edge''']

          if shortest_edge < 384:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               _a    	=				int(shortest_edge / crop_pct							)
               _a    	=				get_resize_output_image_size(__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)
               _a    	=				resize(image=__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=__UpperCAmelCase    ,				size=(shortest_edge, shortest_edge)    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)
          else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   __UpperCAmelCase    ,				size=(shortest_edge, shortest_edge)    ,				resample=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							Optional[int]:
          return rescale(__UpperCAmelCase    ,				scale=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							np.ndarray:
          return normalize(__UpperCAmelCase    ,				mean=__UpperCAmelCase    ,				std=__UpperCAmelCase    ,				data_format=__UpperCAmelCase    ,				**__UpperCAmelCase							)





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = ChannelDimension.FIRST    ,				**__UpperCAmelCase    ,				)       ->							PIL.Image.Image:
          _a    	=				do_resize if do_resize is not None else self.do_resize
          _a    	=				crop_pct if crop_pct is not None else self.crop_pct
          _a    	=				resample if resample is not None else self.resample
          _a    	=				do_rescale if do_rescale is not None else self.do_rescale
          _a    	=				rescale_factor if rescale_factor is not None else self.rescale_factor
          _a    	=				do_normalize if do_normalize is not None else self.do_normalize
          _a    	=				image_mean if image_mean is not None else self.image_mean
          _a    	=				image_std if image_std is not None else self.image_std

          _a    	=				size if size is not None else self.size
          _a    	=				get_size_dict(__UpperCAmelCase    ,				default_to_square=__UpperCAmelCase							)

          _a    	=				make_list_of_images(__UpperCAmelCase							)

          if not valid_images(__UpperCAmelCase							):
               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_resize and size["shortest_edge"] < 384 and crop_pct is None:
               raise ValueError('''crop_pct must be specified if size < 384.'''							)

          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.
          _a    	=				[to_numpy_array(__UpperCAmelCase							) for image in images]

          if do_resize:
               _a    	=				[self.resize(image=__UpperCAmelCase    ,				size=__UpperCAmelCase    ,				crop_pct=__UpperCAmelCase    ,				resample=__UpperCAmelCase							) for image in images]

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

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

          _a    	=				[to_channel_dimension_format(__UpperCAmelCase    ,				__UpperCAmelCase							) for image in images]

          _a    	=				{'''pixel_values''': images}
          return BatchFeature(data=__UpperCAmelCase    ,				tensor_type=__UpperCAmelCase							)