
'''simple docstring'''





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class        lowerCAmelCase__      (   lowercase__	):
						UpperCamelCase_							: str	=		["""image_processor""", """tokenizer"""]
						UpperCamelCase_							: Dict	=		"""CLIPImageProcessor"""
						UpperCamelCase_							: int	=		("""CLIPTokenizer""", """CLIPTokenizerFast""")

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""       ,							__lowercase       ,							)
										_UpperCamelCase						    =	kwargs.pop("""feature_extractor"""				)

								_UpperCamelCase						    =	image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""				)
								if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""				)

								super().__init__(__lowercase       ,							__lowercase				)

						def __call__(			self       ,							a=None       ,							a=None       ,							a=None       ,							**a				)					->      Any:


								'''simple docstring'''

								if text is None and images is None:
										raise ValueError("""You have to specify either text or images. Both cannot be none."""				)

								if text is not None:
										_UpperCamelCase						    =	self.tokenizer(__lowercase       ,							return_tensors=__lowercase       ,							**__lowercase				)

								if images is not None:
										_UpperCamelCase						    =	self.image_processor(__lowercase       ,							return_tensors=__lowercase       ,							**__lowercase				)

								if text is not None and images is not None:
										_UpperCamelCase						    =	image_features.pixel_values
										return encoding
								elif text is not None:
										return encoding
								else:
										return BatchEncoding(data=dict(**__lowercase				)       ,							tensor_type=__lowercase				)

						def  A_				(			self       ,							*a       ,							**a				)					->      Dict:


								'''simple docstring'''

								return self.tokenizer.batch_decode(*__lowercase       ,							**__lowercase				)

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

								return self.tokenizer.decode(*__lowercase       ,							**__lowercase				)

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer.model_input_names
								_UpperCamelCase						    =	self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

						@property
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							__lowercase       ,							)
								return self.image_processor_class







						@property
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,							__lowercase       ,							)
								return self.image_processor





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()
lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	os.path.abspath(lowerCAmelCase )
		logger.info(F'Converting TensorFlow checkpoint from {tf_path}' )
		# Load weights from TF model
		_UpperCamelCase						    =	tf.train.list_variables(lowerCAmelCase )
		_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(lowerCAmelCase )
				# read data
				_UpperCamelCase						    =	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 ) )})' )
		_UpperCamelCase						    =	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 ):
				_UpperCamelCase						    =	full_name.split("""/""" )
				_UpperCamelCase						    =	model
				_UpperCamelCase						    =	[]
				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""" ):
								_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(lowerCAmelCase						,  """embeddings""" )
										_UpperCamelCase						    =	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 )] )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """encoder""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """layer""" )
										_UpperCamelCase						    =	pointer[layer_num - 4]
								elif layer_num == config.num_hidden_layers + 4:
										# pooler layer
										trace.extend(["""pooler""", """dense"""] )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """pooler""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "embeddings":
								trace.append("""embeddings""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """embeddings""" )
								if layer_num == 0:
										trace.append("""word_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """word_embeddings""" )
								elif layer_num == 1:
										trace.append("""position_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """position_embeddings""" )
								elif layer_num == 2:
										trace.append("""token_type_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """token_type_embeddings""" )
								else:
										raise ValueError(F'Unknown embedding layer with name {full_name}' )
								trace.append("""weight""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """weight""" )
						elif m_name == "_attention_layer":
								# self-attention layer
								trace.extend(["""attention""", """self"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """self""" )
						elif m_name == "_attention_layer_norm":
								# output attention norm
								trace.extend(["""attention""", """output""", """LayerNorm"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """LayerNorm""" )
						elif m_name == "_attention_output_dense":
								# output attention dense
								trace.extend(["""attention""", """output""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_dense":
								# output dense
								trace.extend(["""output""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_layer_norm":
								# output dense
								trace.extend(["""output""", """LayerNorm"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """LayerNorm""" )
						elif m_name == "_key_dense":
								# attention key
								trace.append("""key""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """key""" )
						elif m_name == "_query_dense":
								# attention query
								trace.append("""query""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """query""" )
						elif m_name == "_value_dense":
								# attention value
								trace.append("""value""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """value""" )
						elif m_name == "_intermediate_dense":
								# attention intermediate dense
								trace.extend(["""intermediate""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """intermediate""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_layer_norm":
								# output layer norm
								trace.append("""output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
						# weights & biases
						elif m_name in ["bias", "beta"]:
								trace.append("""bias""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """bias""" )
						elif m_name in ["kernel", "gamma"]:
								trace.append("""weight""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """weight""" )
						else:
								logger.warning(F'Ignored {m_name}' )
        # for certain layers reshape is necessary
				_UpperCamelCase						    =	""".""".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 ):
						_UpperCamelCase						    =	array.reshape(pointer.data.shape )
				if "kernel" in full_name:
						_UpperCamelCase						    =	array.transpose()
				if pointer.shape == array.shape:
						_UpperCamelCase						    =	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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		logger.info(F'Loading model based on config from {config_path}...' )
		_UpperCamelCase						    =	BertConfig.from_json_file(lowerCAmelCase )
		_UpperCamelCase						    =	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__":
		lowerCamelCase__	=							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).",
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



