


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
_A			=						logging.get_logger(__name__)

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Tuple      ,					SCREAMING_SNAKE_CASE__      : Union[str, Any]      ,					SCREAMING_SNAKE_CASE__      : Any    ):
     __UpperCamelCase   							=os.path.abspath(__A    )
     logger.info(F'Converting TensorFlow checkpoint from {tf_path}'    )
     # Load weights from TF model
     __UpperCamelCase   							=tf.train.list_variables(__A    )
     __UpperCamelCase   							=[]
     __UpperCamelCase   							=[]
     __UpperCamelCase   							=[]
     for full_name, shape in init_vars:
          # logger.info(f"Loading TF weight {name} with shape {shape}")
          __UpperCamelCase   							=full_name.split('/'    )
          if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
               logger.info(F'Skipping non-model layer {full_name}'    )
               continue
          if "optimizer" in full_name:
               logger.info(F'Skipping optimization layer {full_name}'    )
               continue
          if name[0] == "model":
               # ignore initial 'model'
               __UpperCamelCase   							=name[1:]
          # figure out how many levels deep the name is
          __UpperCamelCase   							=0
          for _name in name:
               if _name.startswith('layer_with_weights'    ):
                    depth += 1
               else:
                    break
          layer_depth.append(__A    )
          # read data
          __UpperCamelCase   							=tf.train.load_variable(__A      ,					__A    )
          names.append('/'.join(__A    )    )
          arrays.append(__A    )
     logger.info(F'Read a total of {len(__A    ):,} layers'    )

     # Sanity check
     if len(set(__A    )    ) != 1:
          raise ValueError(F'Found layer names with different depths (layer depth {list(set(__A    )    )})'    )
     __UpperCamelCase   							=list(set(__A    )    )[0]
     if layer_depth != 1:
          raise ValueError(
              'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
              ' heads.'    )

     # convert layers
     logger.info('Converting weights...'    )
     for full_name, array in zip(__A      ,					__A    ):
          __UpperCamelCase   							=full_name.split('/'    )
          __UpperCamelCase   							=model
          __UpperCamelCase   							=[]
          for i, m_name in enumerate(__A    ):
               if m_name == ".ATTRIBUTES":
                    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                    break
               if m_name.startswith('layer_with_weights'    ):
                    __UpperCamelCase   							=int(m_name.split('-'    )[-1]    )
                    if layer_num <= 2:
                         # embedding layers
                         # layer_num 0: word_embeddings
                         # layer_num 1: position_embeddings
                         # layer_num 2: token_type_embeddings
                         continue
                    elif layer_num == 3:
                         # embedding LayerNorm
                         trace.extend(['embeddings', 'LayerNorm']    )
                         __UpperCamelCase   							=getattr(__A      ,					'embeddings'    )
                         __UpperCamelCase   							=getattr(__A      ,					'LayerNorm'    )
                    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                         # encoder layers
                         trace.extend(['encoder', 'layer', str(layer_num - 4    )]    )
                         __UpperCamelCase   							=getattr(__A      ,					'encoder'    )
                         __UpperCamelCase   							=getattr(__A      ,					'layer'    )
                         __UpperCamelCase   							=pointer[layer_num - 4]
                    elif layer_num == config.num_hidden_layers + 4:
                         # pooler layer
                         trace.extend(['pooler', 'dense']    )
                         __UpperCamelCase   							=getattr(__A      ,					'pooler'    )
                         __UpperCamelCase   							=getattr(__A      ,					'dense'    )
               elif m_name == "embeddings":
                    trace.append('embeddings'    )
                    __UpperCamelCase   							=getattr(__A      ,					'embeddings'    )
                    if layer_num == 0:
                         trace.append('word_embeddings'    )
                         __UpperCamelCase   							=getattr(__A      ,					'word_embeddings'    )
                    elif layer_num == 1:
                         trace.append('position_embeddings'    )
                         __UpperCamelCase   							=getattr(__A      ,					'position_embeddings'    )
                    elif layer_num == 2:
                         trace.append('token_type_embeddings'    )
                         __UpperCamelCase   							=getattr(__A      ,					'token_type_embeddings'    )
                    else:
                         raise ValueError(F'Unknown embedding layer with name {full_name}'    )
                    trace.append('weight'    )
                    __UpperCamelCase   							=getattr(__A      ,					'weight'    )
               elif m_name == "_attention_layer":
                    # self-attention layer
                    trace.extend(['attention', 'self']    )
                    __UpperCamelCase   							=getattr(__A      ,					'attention'    )
                    __UpperCamelCase   							=getattr(__A      ,					'self'    )
               elif m_name == "_attention_layer_norm":
                    # output attention norm
                    trace.extend(['attention', 'output', 'LayerNorm']    )
                    __UpperCamelCase   							=getattr(__A      ,					'attention'    )
                    __UpperCamelCase   							=getattr(__A      ,					'output'    )
                    __UpperCamelCase   							=getattr(__A      ,					'LayerNorm'    )
               elif m_name == "_attention_output_dense":
                    # output attention dense
                    trace.extend(['attention', 'output', 'dense']    )
                    __UpperCamelCase   							=getattr(__A      ,					'attention'    )
                    __UpperCamelCase   							=getattr(__A      ,					'output'    )
                    __UpperCamelCase   							=getattr(__A      ,					'dense'    )
               elif m_name == "_output_dense":
                    # output dense
                    trace.extend(['output', 'dense']    )
                    __UpperCamelCase   							=getattr(__A      ,					'output'    )
                    __UpperCamelCase   							=getattr(__A      ,					'dense'    )
               elif m_name == "_output_layer_norm":
                    # output dense
                    trace.extend(['output', 'LayerNorm']    )
                    __UpperCamelCase   							=getattr(__A      ,					'output'    )
                    __UpperCamelCase   							=getattr(__A      ,					'LayerNorm'    )
               elif m_name == "_key_dense":
                    # attention key
                    trace.append('key'    )
                    __UpperCamelCase   							=getattr(__A      ,					'key'    )
               elif m_name == "_query_dense":
                    # attention query
                    trace.append('query'    )
                    __UpperCamelCase   							=getattr(__A      ,					'query'    )
               elif m_name == "_value_dense":
                    # attention value
                    trace.append('value'    )
                    __UpperCamelCase   							=getattr(__A      ,					'value'    )
               elif m_name == "_intermediate_dense":
                    # attention intermediate dense
                    trace.extend(['intermediate', 'dense']    )
                    __UpperCamelCase   							=getattr(__A      ,					'intermediate'    )
                    __UpperCamelCase   							=getattr(__A      ,					'dense'    )
               elif m_name == "_output_layer_norm":
                    # output layer norm
                    trace.append('output'    )
                    __UpperCamelCase   							=getattr(__A      ,					'output'    )
               # weights & biases
               elif m_name in ["bias", "beta"]:
                    trace.append('bias'    )
                    __UpperCamelCase   							=getattr(__A      ,					'bias'    )
               elif m_name in ["kernel", "gamma"]:
                    trace.append('weight'    )
                    __UpperCamelCase   							=getattr(__A      ,					'weight'    )
               else:
                    logger.warning(F'Ignored {m_name}'    )
        # for certain layers reshape is necessary
          __UpperCamelCase   							='.'.join(__A    )
          if re.match(r'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'      ,					__A    ) or re.match(
              r'(\S+)\.attention\.output\.dense\.weight'      ,					__A    ):
               __UpperCamelCase   							=array.reshape(pointer.data.shape    )
          if "kernel" in full_name:
               __UpperCamelCase   							=array.transpose()
          if pointer.shape == array.shape:
               __UpperCamelCase   							=torch.from_numpy(__A    )
          else:
               raise ValueError(
                   F'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                   F' {array.shape}'    )
          logger.info(F'Successfully set variable {full_name} to PyTorch layer {trace}'    )
     return model

def 						_UpperCAmelCase    (      SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : Optional[Any]      ,					SCREAMING_SNAKE_CASE__      : int    ):
     logger.info(F'Loading model based on config from {config_path}...'    )
     __UpperCamelCase   							=BertConfig.from_json_file(__A    )
     __UpperCamelCase   							=BertModel(__A    )

     # Load weights from checkpoint
     logger.info(F'Loading weights from checkpoint {tf_checkpoint_path}...'    )
     load_tfa_weights_in_bert(__A      ,					__A      ,					__A    )

     # Save pytorch-model
     logger.info(F'Saving PyTorch model to {pytorch_dump_path}...'    )
     torch.save(model.state_dict()      ,					__A    )


if __name__ == "__main__":
   _A			=						argparse.ArgumentParser()
   parser.add_argument(
       '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow 2.x 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 (must include filename).',
   )
   _A			=						parser.parse_args()
   convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_A			=						logging.get_logger(__name__)


if is_vision_available():
   import PIL






class    UpperCAmelCase__	( A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]              =  ["pixel_values"]






    def __init__(      self ,       A_ = True ,       A_ = None ,       A_ = PILImageResampling.BICUBIC ,       A_ = True ,       A_ = None ,       A_ = True ,       A_ = 1 / 255 ,       A_ = True ,       A_ = None ,       A_ = None ,       A_ = True ,       **A_ ,       )  ->	None:
         super().__init__(**A_   )
         __UpperCamelCase   							=size if size is not None else {'shortest_edge': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         __UpperCamelCase   							=crop_size if crop_size is not None else {'height': 224, 'width': 224}
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_ ,       param_name='crop_size'   )

         __UpperCamelCase   							=do_resize
         __UpperCamelCase   							=size
         __UpperCamelCase   							=resample
         __UpperCamelCase   							=do_center_crop
         __UpperCamelCase   							=crop_size
         __UpperCamelCase   							=do_rescale
         __UpperCamelCase   							=rescale_factor
         __UpperCamelCase   							=do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         __UpperCamelCase   							=image_std if image_std is not None else OPENAI_CLIP_STD
         __UpperCamelCase   							=do_convert_rgb






    def _a (      self ,       A_ ,       A_ ,       A_ = PILImageResampling.BICUBIC ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_ ,       default_to_square=A_   )
         if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'   )
         __UpperCamelCase   							=get_resize_output_image_size(A_ ,       size=size['shortest_edge'] ,       default_to_square=A_   )
         return resize(A_ ,       size=A_ ,       resample=A_ ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	np.ndarray:
         __UpperCamelCase   							=get_size_dict(A_   )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'   )
         return center_crop(A_ ,       size=(size['height'], size['width']) ,       data_format=A_ ,       **A_   )






    def _a (      self ,       A_ ,       A_ ,       A_ = None ,       **A_ ,       )  ->	Union[str, Any]:
         return rescale(A_ ,       scale=A_ ,       data_format=A_ ,       **A_   )






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






    def _a (      self ,       A_ ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = None ,       A_ = ChannelDimension.FIRST ,       **A_ ,       )  ->	PIL.Image.Image:
         __UpperCamelCase   							=do_resize if do_resize is not None else self.do_resize
         __UpperCamelCase   							=size if size is not None else self.size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='size' ,       default_to_square=A_   )
         __UpperCamelCase   							=resample if resample is not None else self.resample
         __UpperCamelCase   							=do_center_crop if do_center_crop is not None else self.do_center_crop
         __UpperCamelCase   							=crop_size if crop_size is not None else self.crop_size
         __UpperCamelCase   							=get_size_dict(A_ ,       param_name='crop_size' ,       default_to_square=A_   )
         __UpperCamelCase   							=do_rescale if do_rescale is not None else self.do_rescale
         __UpperCamelCase   							=rescale_factor if rescale_factor is not None else self.rescale_factor
         __UpperCamelCase   							=do_normalize if do_normalize is not None else self.do_normalize
         __UpperCamelCase   							=image_mean if image_mean is not None else self.image_mean
         __UpperCamelCase   							=image_std if image_std is not None else self.image_std
         __UpperCamelCase   							=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         __UpperCamelCase   							=make_list_of_images(A_   )

         if not valid_images(A_   ):
              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:
              raise ValueError('Size 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.'   )

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
              __UpperCamelCase   							=[convert_to_rgb(A_   ) for image in images]

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

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

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

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

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

         __UpperCamelCase   							=[to_channel_dimension_format(A_ ,       A_   ) for image in images]

         __UpperCamelCase   							={'pixel_values': images}
         return BatchFeature(data=A_ ,       tensor_type=A_   )





