






"""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


A_			: Union[str, Any]				=			logging.get_logger(__name__)


if is_vision_available():
      import PIL



class 		a_      (	snake_case_ ):







      '''simple docstring'''

      lowerCamelCase__       :				Tuple =	['pixel_values']




      def __init__(self,     lowerCamelCase_ = True,     lowerCamelCase_ = None,     lowerCamelCase_ = PILImageResampling.BICUBIC,     lowerCamelCase_ = True,     lowerCamelCase_ = None,     lowerCamelCase_ = True,     lowerCamelCase_ = 1 / 2_5_5,     lowerCamelCase_ = True,     lowerCamelCase_ = None,     lowerCamelCase_ = None,     lowerCamelCase_ = True,     **lowerCamelCase_,     ):

       '''simple docstring'''



       super().__init__(**lowerCamelCase_					)
       lowerCamelCase__     :			Dict     				=						size if size is not None else {'shortest_edge': 2_2_4}
       lowerCamelCase__     :			Any     				=						get_size_dict(lowerCamelCase_,     default_to_square=lowerCamelCase_					)
       lowerCamelCase__     :			List[Any]     				=						crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
       lowerCamelCase__     :			int     				=						get_size_dict(lowerCamelCase_					)

       lowerCamelCase__     :			int     				=						do_resize
       lowerCamelCase__     :			int     				=						size
       lowerCamelCase__     :			Union[str, Any]     				=						resample
       lowerCamelCase__     :			Optional[Any]     				=						do_center_crop
       lowerCamelCase__     :			Tuple     				=						crop_size
       lowerCamelCase__     :			str     				=						do_rescale
       lowerCamelCase__     :			List[Any]     				=						rescale_factor
       lowerCamelCase__     :			Optional[Any]     				=						do_normalize
       lowerCamelCase__     :			Dict     				=						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 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_ = PILImageResampling.BICUBIC,     lowerCamelCase_ = None,     **lowerCamelCase_,     ):

       '''simple docstring'''



       lowerCamelCase__     :			List[Any]     				=						get_size_dict(lowerCamelCase_,     default_to_square=lowerCamelCase_					)
       lowerCamelCase__     :			Any     				=						get_resize_output_image_size(
           lowerCamelCase_,     size=(size['height'], size['width']),     default_to_square=lowerCamelCase_					)
       return resize(lowerCamelCase_,     size=lowerCamelCase_,     resample=lowerCamelCase_,     data_format=lowerCamelCase_,     **lowerCamelCase_					)




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_ = None,     **lowerCamelCase_,     ):

       '''simple docstring'''



       lowerCamelCase__     :			List[Any]     				=						get_size_dict(lowerCamelCase_					)
       return center_crop(lowerCamelCase_,     size=(size['height'], size['width']),     data_format=lowerCamelCase_,     **lowerCamelCase_					)




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_ = None,     **lowerCamelCase_,     ):

       '''simple docstring'''



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




      def 			a__							(self,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_,     lowerCamelCase_ = None,     **lowerCamelCase_,     ):

       '''simple docstring'''



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





      def 			a__							(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_,     ):

       '''simple docstring'''



       lowerCamelCase__     :			Any     				=						do_resize if do_resize is not None else self.do_resize
       lowerCamelCase__     :			Optional[int]     				=						size if size is not None else self.size
       lowerCamelCase__     :			List[str]     				=						get_size_dict(lowerCamelCase_,     default_to_square=lowerCamelCase_					)
       lowerCamelCase__     :			Any     				=						resample if resample is not None else self.resample
       lowerCamelCase__     :			Tuple     				=						do_center_crop if do_center_crop is not None else self.do_center_crop
       lowerCamelCase__     :			Dict     				=						crop_size if crop_size is not None else self.crop_size
       lowerCamelCase__     :			List[Any]     				=						get_size_dict(lowerCamelCase_					)
       lowerCamelCase__     :			Tuple     				=						do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase__     :			Union[str, Any]     				=						rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase__     :			Tuple     				=						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__     :			Optional[Any]     				=						image_std if image_std is not None else self.image_std
       lowerCamelCase__     :			List[str]     				=						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       lowerCamelCase__     :			Optional[Any]     				=						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__     :			Any     				=						[convert_to_rgb(lowerCamelCase_					) for image in images]

       # All transformations expect numpy arrays.
       lowerCamelCase__     :			Dict     				=						[to_numpy_array(lowerCamelCase_					) for image in images]

       if do_resize:
        lowerCamelCase__     :			Union[str, Any]     				=						[self.resize(image=lowerCamelCase_,     size=lowerCamelCase_,     resample=lowerCamelCase_					) for image in images]

       if do_center_crop:
        lowerCamelCase__     :			int     				=						[self.center_crop(image=lowerCamelCase_,     size=lowerCamelCase_					) for image in images]

       if do_rescale:
        lowerCamelCase__     :			List[str]     				=						[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__     :			str     				=						[to_channel_dimension_format(lowerCamelCase_,     lowerCamelCase_					) for image in images]

       lowerCamelCase__     :			Optional[Any]     				=						{'pixel_values': images}
       return BatchFeature(data=lowerCamelCase_,     tensor_type=lowerCamelCase_					)








"""simple docstring"""





import argparse

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


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

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

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


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)

