


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


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


if is_vision_available():
   import PIL



class 							a       (      _UpperCAmelCase ):
     UpperCAmelCase__			:	Any       =						["pixel_values"]





     def __init__(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE_       :					PILImageResampling = PILImageResampling.BILINEAR     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					bool = True     ,						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_       :					Any     ,						):
        super().__init__(**SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Optional[Any]	 =						size if size is not None else {"""height""": 2560, """width""": 1920}
        if isinstance(SCREAMING_SNAKE_CASE_     ,						(tuple, list)				):
           # The previous feature extractor size parameter was in (width, height) format
           __lowerCamelCase:					List[Any]	 =						size[::-1]
        __lowerCamelCase:					Optional[Any]	 =						get_size_dict(SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					int	 =						do_resize
        __lowerCamelCase:					Tuple	 =						size
        __lowerCamelCase:					Union[str, Any]	 =						resample
        __lowerCamelCase:					Union[str, Any]	 =						do_thumbnail
        __lowerCamelCase:					List[Any]	 =						do_align_long_axis
        __lowerCamelCase:					Any	 =						do_pad
        __lowerCamelCase:					Optional[int]	 =						do_rescale
        __lowerCamelCase:					str	 =						rescale_factor
        __lowerCamelCase:					List[Any]	 =						do_normalize
        __lowerCamelCase:					str	 =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __lowerCamelCase:					List[Any]	 =						image_std if image_std is not None else IMAGENET_STANDARD_STD





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]     ,						SCREAMING_SNAKE_CASE_       :					np.ndarray     ,						SCREAMING_SNAKE_CASE_       :					Dict[str, int]     ,						SCREAMING_SNAKE_CASE_       :					Optional[Union[str, ChannelDimension]] = None				):
        __lowerCamelCase ,  __lowerCamelCase:					Dict	 =						get_image_size(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase ,  __lowerCamelCase:					Dict	 =						size["""height"""], size["""width"""]

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
           __lowerCamelCase:					Tuple	 =						np.rotaa(SCREAMING_SNAKE_CASE_     ,						3				)

        if data_format is not None:
           __lowerCamelCase:					Dict	 =						to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)

        return image





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[Any]     ,						SCREAMING_SNAKE_CASE_       :					np.ndarray     ,						SCREAMING_SNAKE_CASE_       :					Dict[str, int]     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					Optional[Union[str, ChannelDimension]] = None     ,						):
        __lowerCamelCase ,  __lowerCamelCase:					List[Any]	 =						size["""height"""], size["""width"""]
        __lowerCamelCase ,  __lowerCamelCase:					Optional[Any]	 =						get_image_size(SCREAMING_SNAKE_CASE_				)

        __lowerCamelCase:					Tuple	 =						output_width - input_width
        __lowerCamelCase:					str	 =						output_height - input_height

        if random_padding:
           __lowerCamelCase:					List[str]	 =						np.random.randint(low=0     ,						high=delta_height + 1				)
           __lowerCamelCase:					List[str]	 =						np.random.randint(low=0     ,						high=delta_width + 1				)
        else:
           __lowerCamelCase:					Optional[Any]	 =						delta_height // 2
           __lowerCamelCase:					Tuple	 =						delta_width // 2

        __lowerCamelCase:					List[str]	 =						delta_height - pad_top
        __lowerCamelCase:					List[str]	 =						delta_width - pad_left

        __lowerCamelCase:					Optional[Any]	 =						((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_     ,						data_format=SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						*SCREAMING_SNAKE_CASE_       :					Tuple     ,						**SCREAMING_SNAKE_CASE_       :					str				):
        logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""				)
        return self.pad_image(*SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					int     ,						SCREAMING_SNAKE_CASE_       :					np.ndarray     ,						SCREAMING_SNAKE_CASE_       :					Dict[str, int]     ,						SCREAMING_SNAKE_CASE_       :					PILImageResampling = PILImageResampling.BICUBIC     ,						SCREAMING_SNAKE_CASE_       :					Optional[Union[str, ChannelDimension]] = None     ,						**SCREAMING_SNAKE_CASE_       :					Tuple     ,						):
        __lowerCamelCase ,  __lowerCamelCase:					Optional[Any]	 =						get_image_size(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase ,  __lowerCamelCase:					Dict	 =						size["""height"""], size["""width"""]

        # We always resize to the smallest of either the input or output size.
        __lowerCamelCase:					Dict	 =						min(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Dict	 =						min(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				)

        if height == input_height and width == input_width:
           return image

        if input_height > input_width:
           __lowerCamelCase:					Any	 =						int(input_width * height / input_height				)
        elif input_width > input_height:
           __lowerCamelCase:					Any	 =						int(input_height * width / input_width				)

        return resize(
            SCREAMING_SNAKE_CASE_     ,						size=(height, width)     ,						resample=SCREAMING_SNAKE_CASE_     ,						reducing_gap=2.0     ,						data_format=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					np.ndarray     ,						SCREAMING_SNAKE_CASE_       :					Dict[str, int]     ,						SCREAMING_SNAKE_CASE_       :					PILImageResampling = PILImageResampling.BICUBIC     ,						SCREAMING_SNAKE_CASE_       :					Optional[Union[str, ChannelDimension]] = None     ,						**SCREAMING_SNAKE_CASE_       :					Union[str, Any]     ,						):
        __lowerCamelCase:					List[Any]	 =						get_size_dict(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Optional[int]	 =						min(size["""height"""]     ,						size["""width"""]				)
        __lowerCamelCase:					Optional[Any]	 =						get_resize_output_image_size(SCREAMING_SNAKE_CASE_     ,						size=SCREAMING_SNAKE_CASE_     ,						default_to_square=SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Optional[int]	 =						resize(SCREAMING_SNAKE_CASE_     ,						size=SCREAMING_SNAKE_CASE_     ,						resample=SCREAMING_SNAKE_CASE_     ,						data_format=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_				)
        return resized_image





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





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]     ,						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     ,						):
        return normalize(SCREAMING_SNAKE_CASE_     ,						mean=SCREAMING_SNAKE_CASE_     ,						std=SCREAMING_SNAKE_CASE_     ,						data_format=SCREAMING_SNAKE_CASE_     ,						**SCREAMING_SNAKE_CASE_				)







     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]     ,						SCREAMING_SNAKE_CASE_       :					ImageInput     ,						SCREAMING_SNAKE_CASE_       :					bool = None     ,						SCREAMING_SNAKE_CASE_       :					Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE_       :					PILImageResampling = None     ,						SCREAMING_SNAKE_CASE_       :					bool = None     ,						SCREAMING_SNAKE_CASE_       :					bool = None     ,						SCREAMING_SNAKE_CASE_       :					bool = None     ,						SCREAMING_SNAKE_CASE_       :					bool = False     ,						SCREAMING_SNAKE_CASE_       :					bool = None     ,						SCREAMING_SNAKE_CASE_       :					float = None     ,						SCREAMING_SNAKE_CASE_       :					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_       :					Optional[ChannelDimension] = ChannelDimension.FIRST     ,						**SCREAMING_SNAKE_CASE_       :					List[Any]     ,						):
        __lowerCamelCase:					int	 =						do_resize if do_resize is not None else self.do_resize
        __lowerCamelCase:					Optional[Any]	 =						size if size is not None else self.size
        if isinstance(SCREAMING_SNAKE_CASE_     ,						(tuple, list)				):
           # Previous feature extractor had size in (width, height) format
           __lowerCamelCase:					Optional[Any]	 =						size[::-1]
        __lowerCamelCase:					Any	 =						get_size_dict(SCREAMING_SNAKE_CASE_				)
        __lowerCamelCase:					Tuple	 =						resample if resample is not None else self.resample
        __lowerCamelCase:					Dict	 =						do_thumbnail if do_thumbnail is not None else self.do_thumbnail
        __lowerCamelCase:					Union[str, Any]	 =						do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
        __lowerCamelCase:					Tuple	 =						do_pad if do_pad is not None else self.do_pad
        __lowerCamelCase:					Union[str, Any]	 =						do_rescale if do_rescale is not None else self.do_rescale
        __lowerCamelCase:					List[Any]	 =						rescale_factor if rescale_factor is not None else self.rescale_factor
        __lowerCamelCase:					Optional[int]	 =						do_normalize if do_normalize is not None else self.do_normalize
        __lowerCamelCase:					Optional[int]	 =						image_mean if image_mean is not None else self.image_mean
        __lowerCamelCase:					int	 =						image_std if image_std is not None else self.image_std

        __lowerCamelCase:					Optional[Any]	 =						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."""				)

        if do_pad and size is None:
           raise ValueError("""Size must be specified if do_pad 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."""				)

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

        if do_align_long_axis:
           __lowerCamelCase:					Optional[int]	 =						[self.align_long_axis(SCREAMING_SNAKE_CASE_     ,						size=SCREAMING_SNAKE_CASE_				) for image in images]

        if do_resize:
           __lowerCamelCase:					Optional[Any]	 =						[self.resize(image=SCREAMING_SNAKE_CASE_     ,						size=SCREAMING_SNAKE_CASE_     ,						resample=SCREAMING_SNAKE_CASE_				) for image in images]

        if do_thumbnail:
           __lowerCamelCase:					List[Any]	 =						[self.thumbnail(image=SCREAMING_SNAKE_CASE_     ,						size=SCREAMING_SNAKE_CASE_				) for image in images]

        if do_pad:
           __lowerCamelCase:					List[str]	 =						[self.pad_image(image=SCREAMING_SNAKE_CASE_     ,						size=SCREAMING_SNAKE_CASE_     ,						random_padding=SCREAMING_SNAKE_CASE_				) for image in images]

        if do_rescale:
           __lowerCamelCase:					Dict	 =						[self.rescale(image=SCREAMING_SNAKE_CASE_     ,						scale=SCREAMING_SNAKE_CASE_				) for image in images]

        if do_normalize:
           __lowerCamelCase:					Union[str, Any]	 =						[self.normalize(image=SCREAMING_SNAKE_CASE_     ,						mean=SCREAMING_SNAKE_CASE_     ,						std=SCREAMING_SNAKE_CASE_				) for image in images]

        __lowerCamelCase:					Optional[Any]	 =						[to_channel_dimension_format(SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_				) for image in images]

        __lowerCamelCase:					Union[str, Any]	 =						{"""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      __lowerCAmelCase    (				snake_case						:     Union[str, Any]    ,   snake_case						:     Optional[int]			)							->				List[Any]:
   # Initialize PyTorch model
   __lowerCamelCase:					str	 =						CanineConfig()
   __lowerCamelCase:					Union[str, Any]	 =						CanineModel(snake_case			)
   model.eval()

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

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

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

   # Save tokenizer files
   __lowerCamelCase:					Optional[int]	 =						CanineTokenizer()
   print(f'Save tokenizer files to {pytorch_dump_path}'			)
   tokenizer.save_pretrained(snake_case			)


if __name__ == "__main__":
   _A		:					int          =							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.''',
   )
   _A		:					int          =							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


