




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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase								=     logging.get_logger(__name__)





class  __lowerCamelCase       (    UpperCamelCase__    ):




    """simple docstring"""

    snake_case__					=      ["pixel_values"]


    def __init__(       self     :     int   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	SCREAMING_SNAKE_CASE__     :     Optional[Dict[str, int]] = None   ,	SCREAMING_SNAKE_CASE__     :     PILImageResampling = PILImageResampling.BILINEAR   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	SCREAMING_SNAKE_CASE__     :     Union[int, float] = 1 / 255   ,	SCREAMING_SNAKE_CASE__     :     bool = True   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[float, List[float]]] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[float, List[float]]] = None   ,	**SCREAMING_SNAKE_CASE__     :     int   ,	)      ->       None:
         super().__init__(**SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= size if size is not None else {"height": 224, "width": 224}
         lowerCAmelCase__		= get_size_dict(SCREAMING_SNAKE_CASE__   )
         lowerCAmelCase__		= do_resize
         lowerCAmelCase__		= do_rescale
         lowerCAmelCase__		= do_normalize
         lowerCAmelCase__		= size
         lowerCAmelCase__		= resample
         lowerCAmelCase__		= rescale_factor
         lowerCAmelCase__		= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         lowerCAmelCase__		= image_std if image_std is not None else IMAGENET_STANDARD_STD


    def    a      (       self     :     Optional[Any]   ,	SCREAMING_SNAKE_CASE__     :     np.ndarray   ,	SCREAMING_SNAKE_CASE__     :     Dict[str, int]   ,	SCREAMING_SNAKE_CASE__     :     PILImageResampling = PILImageResampling.BILINEAR   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE__     :     Optional[int]   ,	)      ->       np.ndarray:
         lowerCAmelCase__		= get_size_dict(SCREAMING_SNAKE_CASE__   )
         if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'   )
         return resize(
             SCREAMING_SNAKE_CASE__   ,	size=(size["height"], size["width"])   ,	resample=SCREAMING_SNAKE_CASE__   ,	data_format=SCREAMING_SNAKE_CASE__   ,	**SCREAMING_SNAKE_CASE__   )


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


    def    a      (       self     :     List[Any]   ,	SCREAMING_SNAKE_CASE__     :     np.ndarray   ,	SCREAMING_SNAKE_CASE__     :     Union[float, List[float]]   ,	SCREAMING_SNAKE_CASE__     :     Union[float, List[float]]   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[str, ChannelDimension]] = None   ,	**SCREAMING_SNAKE_CASE__     :     Dict   ,	)      ->       np.ndarray:
         return normalize(SCREAMING_SNAKE_CASE__   ,	mean=SCREAMING_SNAKE_CASE__   ,	std=SCREAMING_SNAKE_CASE__   ,	data_format=SCREAMING_SNAKE_CASE__   ,	**SCREAMING_SNAKE_CASE__   )


    def    a      (       self     :     Optional[int]   ,	SCREAMING_SNAKE_CASE__     :     ImageInput   ,	SCREAMING_SNAKE_CASE__     :     Optional[bool] = None   ,	SCREAMING_SNAKE_CASE__     :     Dict[str, int] = None   ,	SCREAMING_SNAKE_CASE__     :     PILImageResampling = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[bool] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[float] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[bool] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[float, List[float]]] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[float, List[float]]] = None   ,	SCREAMING_SNAKE_CASE__     :     Optional[Union[str, TensorType]] = None   ,	SCREAMING_SNAKE_CASE__     :     Union[str, ChannelDimension] = ChannelDimension.FIRST   ,	**SCREAMING_SNAKE_CASE__     :     Dict   ,	)      ->       Any:
         lowerCAmelCase__		= do_resize if do_resize is not None else self.do_resize
         lowerCAmelCase__		= do_rescale if do_rescale is not None else self.do_rescale
         lowerCAmelCase__		= do_normalize if do_normalize is not None else self.do_normalize
         lowerCAmelCase__		= resample if resample is not None else self.resample
         lowerCAmelCase__		= rescale_factor if rescale_factor is not None else self.rescale_factor
         lowerCAmelCase__		= image_mean if image_mean is not None else self.image_mean
         lowerCAmelCase__		= image_std if image_std is not None else self.image_std

         lowerCAmelCase__		= size if size is not None else self.size
         lowerCAmelCase__		= get_size_dict(SCREAMING_SNAKE_CASE__   )

         lowerCAmelCase__		= make_list_of_images(SCREAMING_SNAKE_CASE__   )

         if not valid_images(SCREAMING_SNAKE_CASE__   ):
              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_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."   )

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

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

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

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

         lowerCAmelCase__		= [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,	SCREAMING_SNAKE_CASE__   ) for image in images]

         lowerCAmelCase__		= {"pixel_values": images}
         return BatchFeature(data=SCREAMING_SNAKE_CASE__   ,	tensor_type=SCREAMING_SNAKE_CASE__   )






import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()



def 							_A   (						lowerCAmelCase_     :					Optional[Any]   ,				lowerCAmelCase_     :					Tuple			):
     """simple docstring"""

     lowerCAmelCase__		= CanineConfig()
     lowerCAmelCase__		= CanineModel(lowerCAmelCase_			)
     model.eval()

     print(F'Building PyTorch model from configuration: {config}'			)

     # Load weights from tf checkpoint
     load_tf_weights_in_canine(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_			)

     # Save pytorch-model (weights and configuration)
     print(F'Save PyTorch model to {pytorch_dump_path}'			)
     model.save_pretrained(lowerCAmelCase_			)

     # Save tokenizer files
     lowerCAmelCase__		= CanineTokenizer()
     print(F'Save tokenizer files to {pytorch_dump_path}'			)
     tokenizer.save_pretrained(lowerCAmelCase_			)


if __name__ == "__main__":
     UpperCamelCase								=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     UpperCamelCase								=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)
