





import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      ["image_processor", "tokenizer"]
 lowercase_		=      "BlipImageProcessor"
 lowercase_		=      "AutoTokenizer"

 def __init__(self    :       str	,			UpperCAmelCase_    :       str	,			UpperCAmelCase_    :       Any	,			UpperCAmelCase_    :       Union[str, Any])	->List[str]:



   '''simple docstring'''



   super().__init__(UpperCAmelCase_	,			UpperCAmelCase_)

   # add QFormer tokenizer
   lowerCamelCase__:				List[str]        =qformer_tokenizer

 def __call__(self    :       Any	,			UpperCAmelCase_    :       ImageInput = None	,			UpperCAmelCase_    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Union[bool, str, PaddingStrategy] = False	,			UpperCAmelCase_    :       Union[bool, str, TruncationStrategy] = None	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       int = 0	,			UpperCAmelCase_    :       Optional[int] = None	,			UpperCAmelCase_    :       Optional[bool] = None	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = False	,			UpperCAmelCase_    :       bool = True	,			UpperCAmelCase_    :       Optional[Union[str, TensorType]] = None	,			**UpperCAmelCase_    :       Union[str, Any]	,			)	->BatchFeature:



   '''simple docstring'''



   if images is None and text is None:
     raise ValueError("You have to specify at least images or text.")

   lowerCamelCase__:				List[str]        =BatchFeature()

   if text is not None:
     lowerCamelCase__:				Optional[int]        =self.tokenizer(
         text=UpperCAmelCase_	,			add_special_tokens=UpperCAmelCase_	,			padding=UpperCAmelCase_	,			truncation=UpperCAmelCase_	,			max_length=UpperCAmelCase_	,			stride=UpperCAmelCase_	,			pad_to_multiple_of=UpperCAmelCase_	,			return_attention_mask=UpperCAmelCase_	,			return_overflowing_tokens=UpperCAmelCase_	,			return_special_tokens_mask=UpperCAmelCase_	,			return_offsets_mapping=UpperCAmelCase_	,			return_token_type_ids=UpperCAmelCase_	,			return_length=UpperCAmelCase_	,			verbose=UpperCAmelCase_	,			return_tensors=UpperCAmelCase_	,			**UpperCAmelCase_	,			)
     encoding.update(UpperCAmelCase_)
     lowerCamelCase__:				Optional[int]        =self.qformer_tokenizer(
         text=UpperCAmelCase_	,			add_special_tokens=UpperCAmelCase_	,			padding=UpperCAmelCase_	,			truncation=UpperCAmelCase_	,			max_length=UpperCAmelCase_	,			stride=UpperCAmelCase_	,			pad_to_multiple_of=UpperCAmelCase_	,			return_attention_mask=UpperCAmelCase_	,			return_overflowing_tokens=UpperCAmelCase_	,			return_special_tokens_mask=UpperCAmelCase_	,			return_offsets_mapping=UpperCAmelCase_	,			return_token_type_ids=UpperCAmelCase_	,			return_length=UpperCAmelCase_	,			verbose=UpperCAmelCase_	,			return_tensors=UpperCAmelCase_	,			**UpperCAmelCase_	,			)
     lowerCamelCase__:				Optional[Any]        =qformer_text_encoding.pop("input_ids")
     lowerCamelCase__:				Tuple        =qformer_text_encoding.pop("attention_mask")

   if images is not None:
     lowerCamelCase__:				str        =self.image_processor(UpperCAmelCase_	,			return_tensors=UpperCAmelCase_)
     encoding.update(UpperCAmelCase_)

   return encoding

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple	,			*UpperCAmelCase_    :       Dict	,			**UpperCAmelCase_    :       Any)	->Optional[int]:



   '''simple docstring'''



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

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			*UpperCAmelCase_    :       List[Any]	,			**UpperCAmelCase_    :       Tuple)	->Optional[Any]:



   '''simple docstring'''



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

 @property
 # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.tokenizer.model_input_names
   lowerCamelCase__:				Dict        =self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))

 def 							SCREAMING_SNAKE_CASE_ (self    :       Any	,			UpperCAmelCase_    :       List[Any]	,			**UpperCAmelCase_    :       List[str])	->Dict:



   '''simple docstring'''



   if os.path.isfile(UpperCAmelCase_):
     raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file""")
   os.makedirs(UpperCAmelCase_	,			exist_ok=UpperCAmelCase_)
   lowerCamelCase__:				Dict        =os.path.join(UpperCAmelCase_	,			"qformer_tokenizer")
   self.qformer_tokenizer.save_pretrained(UpperCAmelCase_)
   return super().save_pretrained(UpperCAmelCase_	,			**UpperCAmelCase_)







 @classmethod
 def 							SCREAMING_SNAKE_CASE_ (cls    :       Union[str, Any]	,			UpperCAmelCase_    :       List[str]	,			**UpperCAmelCase_    :       Tuple)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				Union[str, Any]        =AutoTokenizer.from_pretrained(UpperCAmelCase_	,			subfolder="qformer_tokenizer")
   lowerCamelCase__:				Optional[Any]        =cls._get_arguments_from_pretrained(UpperCAmelCase_	,			**UpperCAmelCase_)
   args.append(UpperCAmelCase_)
   return cls(*UpperCAmelCase_)








import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel





def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		List[str]:







  """simple docstring"""





  lowerCamelCase__:				int        =("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

  lowerCamelCase__:				Any        =(
      ("layer.", "layer_"),
      ("word_embeddings.weight", "word_embeddings"),
      ("position_embeddings.weight", "position_embeddings"),
      ("token_type_embeddings.weight", "token_type_embeddings"),
      (".", "/"),
      ("LayerNorm/weight", "LayerNorm/gamma"),
      ("LayerNorm/bias", "LayerNorm/beta"),
      ("weight", "kernel"),
  )

  if not os.path.isdir(__a		):
    os.makedirs(__a		)

  lowerCamelCase__:				Tuple        =model.state_dict()

  def to_tf_var_name(__a		):
    for patt, repl in iter(__a		):
      lowerCamelCase__:				Tuple        =name.replace(__a     ,   __a		)
    return F"""bert/{name}"""

  def create_tf_var(__a     ,   __a     ,   __a		):
    lowerCamelCase__:				Union[str, Any]        =tf.dtypes.as_dtype(tensor.dtype		)
    lowerCamelCase__:				Any        =tf.get_variable(dtype=__a     ,   shape=tensor.shape     ,   name=__a     ,   initializer=tf.zeros_initializer()		)
    session.run(tf.variables_initializer([tf_var]		)		)
    session.run(__a		)
    return tf_var

  tf.reset_default_graph()
  with tf.Session() as session:
    for var_name in state_dict:
      lowerCamelCase__:				Tuple        =to_tf_var_name(__a		)
      lowerCamelCase__:				str        =state_dict[var_name].numpy()
      if any(x in var_name for x in tensors_to_transpose		):
        lowerCamelCase__:				List[Any]        =torch_tensor.T
      lowerCamelCase__:				Optional[int]        =create_tf_var(tensor=__a     ,   name=__a     ,   session=__a		)
      tf.keras.backend.set_value(__a     ,   __a		)
      lowerCamelCase__:				Union[str, Any]        =session.run(__a		)
      print(F"""Successfully created {tf_name}: {np.allclose(__a     ,   __a		)}"""		)

    lowerCamelCase__:				List[Any]        =tf.train.Saver(tf.trainable_variables()		)
    saver.save(__a     ,   os.path.join(__a     ,   model_name.replace("-"     ,   "_"		) + ".ckpt"		)		)





def 					lowerCAmelCase_			(	__a=None		)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =argparse.ArgumentParser()
  parser.add_argument("--model_name"     ,   type=__a     ,   required=__a     ,   help="model name e.g. bert-base-uncased"		)
  parser.add_argument(
      "--cache_dir"     ,   type=__a     ,   default=__a     ,   required=__a     ,   help="Directory containing pytorch model"		)
  parser.add_argument("--pytorch_model_path"     ,   type=__a     ,   required=__a     ,   help="/path/to/<pytorch-model-name>.bin"		)
  parser.add_argument("--tf_cache_dir"     ,   type=__a     ,   required=__a     ,   help="Directory in which to save tensorflow model"		)
  lowerCamelCase__:				Optional[Any]        =parser.parse_args(__a		)

  lowerCamelCase__:				Dict        =BertModel.from_pretrained(
      pretrained_model_name_or_path=args.model_name     ,   state_dict=torch.load(args.pytorch_model_path		)     ,   cache_dir=args.cache_dir     ,   )

  convert_pytorch_checkpoint_to_tf(model=__a     ,   ckpt_dir=args.tf_cache_dir     ,   model_name=args.model_name		)


if __name__ == "__main__":
     main()

