

'''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,
    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


lowerCAmelCase__      						=			logging.get_logger(__name__)


if is_vision_available():
 import PIL






class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[int]           =    ['pixel_values']






  def __init__(   self     :  List[str]      ,lowercase__     :  bool = True      ,lowercase__     :  Dict[str, int] = None      ,lowercase__     :  PILImageResampling = PILImageResampling.BICUBIC      ,lowercase__     :  bool = True      ,lowercase__     :  Dict[str, int] = None      ,lowercase__     :  bool = True      ,lowercase__     :  Union[int, float] = 1 / 2_5_5      ,lowercase__     :  bool = True      ,lowercase__     :  Optional[Union[float, List[float]]] = None      ,lowercase__     :  Optional[Union[float, List[float]]] = None      ,lowercase__     :  bool = True      ,**lowercase__     :  str      ,):
   super().__init__(**lowercase__    )
   __lowercase													=				size if size is not None else {'''shortest_edge''': 2_2_4}
   __lowercase													=				get_size_dict(lowercase__      ,default_to_square=lowercase__    )
   __lowercase													=				crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
   __lowercase													=				get_size_dict(lowercase__      ,default_to_square=lowercase__      ,param_name='''crop_size'''    )

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






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]      ,lowercase__     :  np.ndarray      ,lowercase__     :  Dict[str, int]      ,lowercase__     :  PILImageResampling = PILImageResampling.BICUBIC      ,lowercase__     :  Optional[Union[str, ChannelDimension]] = None      ,**lowercase__     :  Union[str, Any]      ,):
   __lowercase													=				get_size_dict(lowercase__      ,default_to_square=lowercase__    )
   if "shortest_edge" not in size:
    raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"    )
   __lowercase													=				get_resize_output_image_size(lowercase__      ,size=size['''shortest_edge''']      ,default_to_square=lowercase__    )
   return resize(lowercase__      ,size=lowercase__      ,resample=lowercase__      ,data_format=lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  np.ndarray      ,lowercase__     :  Dict[str, int]      ,lowercase__     :  Optional[Union[str, ChannelDimension]] = None      ,**lowercase__     :  List[str]      ,):
   __lowercase													=				get_size_dict(lowercase__    )
   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(lowercase__      ,size=(size['''height'''], size['''width'''])      ,data_format=lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  np.ndarray      ,lowercase__     :  Union[int, float]      ,lowercase__     :  Optional[Union[str, ChannelDimension]] = None      ,**lowercase__     :  int      ,):
   return rescale(lowercase__      ,scale=lowercase__      ,data_format=lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  np.ndarray      ,lowercase__     :  Union[float, List[float]]      ,lowercase__     :  Union[float, List[float]]      ,lowercase__     :  Optional[Union[str, ChannelDimension]] = None      ,**lowercase__     :  str      ,):
   return normalize(lowercase__      ,mean=lowercase__      ,std=lowercase__      ,data_format=lowercase__      ,**lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]      ,lowercase__     :  ImageInput      ,lowercase__     :  bool = None      ,lowercase__     :  Dict[str, int] = None      ,lowercase__     :  PILImageResampling = None      ,lowercase__     :  bool = None      ,lowercase__     :  int = None      ,lowercase__     :  bool = None      ,lowercase__     :  float = None      ,lowercase__     :  bool = None      ,lowercase__     :  Optional[Union[float, List[float]]] = None      ,lowercase__     :  Optional[Union[float, List[float]]] = None      ,lowercase__     :  bool = None      ,lowercase__     :  Optional[Union[str, TensorType]] = None      ,lowercase__     :  Optional[ChannelDimension] = ChannelDimension.FIRST      ,**lowercase__     :  str      ,):
   __lowercase													=				do_resize if do_resize is not None else self.do_resize
   __lowercase													=				size if size is not None else self.size
   __lowercase													=				get_size_dict(lowercase__      ,param_name='''size'''      ,default_to_square=lowercase__    )
   __lowercase													=				resample if resample is not None else self.resample
   __lowercase													=				do_center_crop if do_center_crop is not None else self.do_center_crop
   __lowercase													=				crop_size if crop_size is not None else self.crop_size
   __lowercase													=				get_size_dict(lowercase__      ,param_name='''crop_size'''      ,default_to_square=lowercase__    )
   __lowercase													=				do_rescale if do_rescale is not None else self.do_rescale
   __lowercase													=				rescale_factor if rescale_factor is not None else self.rescale_factor
   __lowercase													=				do_normalize if do_normalize is not None else self.do_normalize
   __lowercase													=				image_mean if image_mean is not None else self.image_mean
   __lowercase													=				image_std if image_std is not None else self.image_std
   __lowercase													=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   __lowercase													=				make_list_of_images(lowercase__    )

   if not valid_images(lowercase__    ):
    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:
    __lowercase													=				[convert_to_rgb(lowercase__    ) for image in images]

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

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

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

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

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

   __lowercase													=				[to_channel_dimension_format(lowercase__      ,lowercase__    ) for image in images]

   __lowercase													=				{'''pixel_values''': images}
   return BatchFeature(data=lowercase__      ,tensor_type=lowercase__    )




'''simple docstring'''






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				TaConfig.from_json_file(A__   )
 print(F"Building PyTorch model from configuration: {config}"   )
 __lowercase													=				TaForConditionalGeneration(A__   )

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(A__  ,       A__  ,       A__   )

 # Save pytorch-model
 print(F"Save PyTorch model to {pytorch_dump_path}"   )
 model.save_pretrained(A__   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

