



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()
__snake_case       :	int            =logging.get_logger(__name__)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Union[str, Any]):
				lowerCAmelCase__  :       Optional[Any]				=  os.path.abspath(lowerCamelCase_)
				logger.info(f"""Converting TensorFlow checkpoint from {tf_path}""")
				# Load weights from TF model
				lowerCAmelCase__  :       Dict				=  tf.train.list_variables(lowerCamelCase_)
				lowerCAmelCase__  :       List[str]				=  []
				lowerCAmelCase__  :       Union[str, Any]				=  []
				lowerCAmelCase__  :       Optional[Any]				=  []
				for full_name, shape in init_vars:
								# logger.info(f"Loading TF weight {name} with shape {shape}")
								lowerCAmelCase__  :       Optional[Any]				=  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'
												lowerCAmelCase__  :       str				=  name[1:]
								# figure out how many levels deep the name is
								lowerCAmelCase__  :       Dict				=  0
								for _name in name:
												if _name.startswith('''layer_with_weights'''):
																depth += 1
												else:
																break
								layer_depth.append(lowerCamelCase_)
								# read data
								lowerCAmelCase__  :       Any				=  tf.train.load_variable(lowerCamelCase_ ,lowerCamelCase_)
								names.append('''/'''.join(lowerCamelCase_))
								arrays.append(lowerCamelCase_)
				logger.info(f"""Read a total of {len(lowerCamelCase_):,} layers""")

				# Sanity check
				if len(set(lowerCamelCase_)) != 1:
								raise ValueError(f"""Found layer names with different depths (layer depth {list(set(lowerCamelCase_))})""")
				lowerCAmelCase__  :       List[str]				=  list(set(lowerCamelCase_))[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(lowerCamelCase_ ,lowerCamelCase_):
								lowerCAmelCase__  :       int				=  full_name.split('''/''')
								lowerCAmelCase__  :       Tuple				=  model
								lowerCAmelCase__  :       int				=  []
								for i, m_name in enumerate(lowerCamelCase_):
												if m_name == ".ATTRIBUTES":
																# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																break
												if m_name.startswith('''layer_with_weights'''):
																lowerCAmelCase__  :       Optional[int]				=  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'''])
																				lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''embeddings''')
																				lowerCAmelCase__  :       Optional[int]				=  getattr(lowerCamelCase_ ,'''LayerNorm''')
																elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																				# encoder layers
																				trace.extend(['''encoder''', '''layer''', str(layer_num - 4)])
																				lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''encoder''')
																				lowerCAmelCase__  :       Any				=  getattr(lowerCamelCase_ ,'''layer''')
																				lowerCAmelCase__  :       List[Any]				=  pointer[layer_num - 4]
																elif layer_num == config.num_hidden_layers + 4:
																				# pooler layer
																				trace.extend(['''pooler''', '''dense'''])
																				lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''pooler''')
																				lowerCAmelCase__  :       Optional[Any]				=  getattr(lowerCamelCase_ ,'''dense''')
												elif m_name == "embeddings":
																trace.append('''embeddings''')
																lowerCAmelCase__  :       Any				=  getattr(lowerCamelCase_ ,'''embeddings''')
																if layer_num == 0:
																				trace.append('''word_embeddings''')
																				lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''word_embeddings''')
																elif layer_num == 1:
																				trace.append('''position_embeddings''')
																				lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''position_embeddings''')
																elif layer_num == 2:
																				trace.append('''token_type_embeddings''')
																				lowerCAmelCase__  :       List[Any]				=  getattr(lowerCamelCase_ ,'''token_type_embeddings''')
																else:
																				raise ValueError(f"""Unknown embedding layer with name {full_name}""")
																trace.append('''weight''')
																lowerCAmelCase__  :       List[Any]				=  getattr(lowerCamelCase_ ,'''weight''')
												elif m_name == "_attention_layer":
																# self-attention layer
																trace.extend(['''attention''', '''self'''])
																lowerCAmelCase__  :       Optional[Any]				=  getattr(lowerCamelCase_ ,'''attention''')
																lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''self''')
												elif m_name == "_attention_layer_norm":
																# output attention norm
																trace.extend(['''attention''', '''output''', '''LayerNorm'''])
																lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''attention''')
																lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''output''')
																lowerCAmelCase__  :       Any				=  getattr(lowerCamelCase_ ,'''LayerNorm''')
												elif m_name == "_attention_output_dense":
																# output attention dense
																trace.extend(['''attention''', '''output''', '''dense'''])
																lowerCAmelCase__  :       int				=  getattr(lowerCamelCase_ ,'''attention''')
																lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''output''')
																lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''dense''')
												elif m_name == "_output_dense":
																# output dense
																trace.extend(['''output''', '''dense'''])
																lowerCAmelCase__  :       List[Any]				=  getattr(lowerCamelCase_ ,'''output''')
																lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''dense''')
												elif m_name == "_output_layer_norm":
																# output dense
																trace.extend(['''output''', '''LayerNorm'''])
																lowerCAmelCase__  :       Optional[int]				=  getattr(lowerCamelCase_ ,'''output''')
																lowerCAmelCase__  :       Dict				=  getattr(lowerCamelCase_ ,'''LayerNorm''')
												elif m_name == "_key_dense":
																# attention key
																trace.append('''key''')
																lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''key''')
												elif m_name == "_query_dense":
																# attention query
																trace.append('''query''')
																lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''query''')
												elif m_name == "_value_dense":
																# attention value
																trace.append('''value''')
																lowerCAmelCase__  :       int				=  getattr(lowerCamelCase_ ,'''value''')
												elif m_name == "_intermediate_dense":
																# attention intermediate dense
																trace.extend(['''intermediate''', '''dense'''])
																lowerCAmelCase__  :       str				=  getattr(lowerCamelCase_ ,'''intermediate''')
																lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''dense''')
												elif m_name == "_output_layer_norm":
																# output layer norm
																trace.append('''output''')
																lowerCAmelCase__  :       int				=  getattr(lowerCamelCase_ ,'''output''')
												# weights & biases
												elif m_name in ["bias", "beta"]:
																trace.append('''bias''')
																lowerCAmelCase__  :       List[Any]				=  getattr(lowerCamelCase_ ,'''bias''')
												elif m_name in ["kernel", "gamma"]:
																trace.append('''weight''')
																lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,'''weight''')
												else:
																logger.warning(f"""Ignored {m_name}""")
        # for certain layers reshape is necessary
								lowerCAmelCase__  :       str				=  '''.'''.join(lowerCamelCase_)
								if re.match(r'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)''' ,lowerCamelCase_) or re.match(
								    r'''(\S+)\.attention\.output\.dense\.weight''' ,lowerCamelCase_):
												lowerCAmelCase__  :       Tuple				=  array.reshape(pointer.data.shape)
								if "kernel" in full_name:
												lowerCAmelCase__  :       Tuple				=  array.transpose()
								if pointer.shape == array.shape:
												lowerCAmelCase__  :       Union[str, Any]				=  torch.from_numpy(lowerCamelCase_)
								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 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	str ,lowerCamelCase_	:	List[str]):
				logger.info(f"""Loading model based on config from {config_path}...""")
				lowerCAmelCase__  :       Tuple				=  BertConfig.from_json_file(lowerCamelCase_)
				lowerCAmelCase__  :       Union[str, Any]				=  BertModel(lowerCamelCase_)

				# Load weights from checkpoint
				logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}...""")
				load_tfa_weights_in_bert(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)

				# Save pytorch-model
				logger.info(f"""Saving PyTorch model to {pytorch_dump_path}...""")
				torch.save(model.state_dict() ,lowerCamelCase_)


if __name__ == "__main__":
							__snake_case       :	int            =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).',
							)
							__snake_case       :	Tuple            =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


__snake_case       :	str            =logging.get_logger(__name__)


if is_vision_available():
							import PIL



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=["""pixel_values"""]



		def __init__(self	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = PILImageResampling.BICUBIC	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = True	,__lowerCamelCase = 1 / 2_55	,__lowerCamelCase = True	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = True	,**__lowerCamelCase	,)  ->    None:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  size if size is not None else {'''shortest_edge''': 2_24}
						lowerCAmelCase__  :       Union[str, Any]				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
						lowerCAmelCase__  :       Optional[int]				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase	,param_name='''crop_size'''				)

						lowerCAmelCase__  :       Optional[int]				=  do_resize
						lowerCAmelCase__  :       Any				=  size
						lowerCAmelCase__  :       int				=  resample
						lowerCAmelCase__  :       Dict				=  do_center_crop
						lowerCAmelCase__  :       str				=  crop_size
						lowerCAmelCase__  :       Dict				=  do_rescale
						lowerCAmelCase__  :       Optional[Any]				=  rescale_factor
						lowerCAmelCase__  :       Dict				=  do_normalize
						lowerCAmelCase__  :       Tuple				=  image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						lowerCAmelCase__  :       Union[str, Any]				=  image_std if image_std is not None else OPENAI_CLIP_STD
						lowerCAmelCase__  :       int				=  do_convert_rgb



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = PILImageResampling.BICUBIC	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  get_size_dict(__lowerCamelCase	,default_to_square=__lowerCamelCase				)
						if "shortest_edge" not in size:
										raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""				)
						lowerCAmelCase__  :       Optional[int]				=  get_resize_output_image_size(__lowerCamelCase	,size=size['''shortest_edge''']	,default_to_square=__lowerCamelCase				)
						return resize(__lowerCamelCase	,size=__lowerCamelCase	,resample=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  get_size_dict(__lowerCamelCase				)
						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(__lowerCamelCase	,size=(size['''height'''], size['''width'''])	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    int:

						"""simple docstring"""






						return rescale(__lowerCamelCase	,scale=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    np.ndarray:

						"""simple docstring"""






						return normalize(__lowerCamelCase	,mean=__lowerCamelCase	,std=__lowerCamelCase	,data_format=__lowerCamelCase	,**__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = ChannelDimension.FIRST	,**__lowerCamelCase	,)  ->    PIL.Image.Image:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__  :       Tuple				=  size if size is not None else self.size
						lowerCAmelCase__  :       Any				=  get_size_dict(__lowerCamelCase	,param_name='''size'''	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Tuple				=  resample if resample is not None else self.resample
						lowerCAmelCase__  :       Dict				=  do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__  :       List[Any]				=  crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__  :       str				=  get_size_dict(__lowerCamelCase	,param_name='''crop_size'''	,default_to_square=__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__  :       int				=  rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__  :       List[Any]				=  do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__  :       Dict				=  image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__  :       int				=  image_std if image_std is not None else self.image_std
						lowerCAmelCase__  :       Dict				=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						lowerCAmelCase__  :       Optional[int]				=  make_list_of_images(__lowerCamelCase				)

						if not valid_images(__lowerCamelCase				):
										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:
										lowerCAmelCase__  :       Union[str, Any]				=  [convert_to_rgb(__lowerCamelCase				) for image in images]

						# All transformations expect numpy arrays.
						lowerCAmelCase__  :       Any				=  [to_numpy_array(__lowerCamelCase				) for image in images]

						if do_resize:
										lowerCAmelCase__  :       str				=  [self.resize(image=__lowerCamelCase	,size=__lowerCamelCase	,resample=__lowerCamelCase				) for image in images]

						if do_center_crop:
										lowerCAmelCase__  :       Dict				=  [self.center_crop(image=__lowerCamelCase	,size=__lowerCamelCase				) for image in images]

						if do_rescale:
										lowerCAmelCase__  :       Union[str, Any]				=  [self.rescale(image=__lowerCamelCase	,scale=__lowerCamelCase				) for image in images]

						if do_normalize:
										lowerCAmelCase__  :       Optional[Any]				=  [self.normalize(image=__lowerCamelCase	,mean=__lowerCamelCase	,std=__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       List[Any]				=  [to_channel_dimension_format(__lowerCamelCase	,__lowerCamelCase				) for image in images]

						lowerCAmelCase__  :       Optional[int]				=  {'''pixel_values''': images}
						return BatchFeature(data=__lowerCamelCase	,tensor_type=__lowerCamelCase				)

