





'''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,
    get_resize_output_image_size,
    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, is_vision_available, logging


if is_vision_available():
    import PIL


a__							:							Dict =logging.get_logger(__name__)

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=["pixel_values"]






 def __init__(       self       :							int							,	__A       :							bool = True							,	__A       :							Dict[str, int] = None							,	__A       :							float = None							,	__A       :							PILImageResampling = PILImageResampling.BILINEAR							,	__A       :							bool = True							,	__A       :							Union[int, float] = 1 / 2_5_5							,	__A       :							bool = True							,	__A       :							Optional[Union[float, List[float]]] = None							,	__A       :							Optional[Union[float, List[float]]] = None							,	**__A       :							Tuple							,	):
        super().__init__(**__A	)
        __UpperCamelCase       =  size if size is not None else {'shortest_edge': 3_8_4}
        __UpperCamelCase       =  get_size_dict(__A							,	default_to_square=__A	)

        __UpperCamelCase       =  do_resize
        __UpperCamelCase       =  size
        # Default value set here for backwards compatibility where the value in config is None
        __UpperCamelCase       =  crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
        __UpperCamelCase       =  resample
        __UpperCamelCase       =  do_rescale
        __UpperCamelCase       =  rescale_factor
        __UpperCamelCase       =  do_normalize
        __UpperCamelCase       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __UpperCamelCase       =  image_std if image_std is not None else IMAGENET_STANDARD_STD






 def        _lowerCamelCase					(       self       :							int							,	__A       :							np.ndarray							,	__A       :							Dict[str, int]							,	__A       :							float							,	__A       :							PILImageResampling = PILImageResampling.BICUBIC							,	__A       :							Optional[Union[str, ChannelDimension]] = None							,	**__A       :							str							,	):
        __UpperCamelCase       =  get_size_dict(__A							,	default_to_square=__A	)
        if "shortest_edge" not in size:
               raise ValueError(f'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''	)
        __UpperCamelCase       =  size['shortest_edge']

        if shortest_edge < 3_8_4:
               # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
               __UpperCamelCase       =  int(shortest_edge / crop_pct	)
               __UpperCamelCase       =  get_resize_output_image_size(__A							,	size=__A							,	default_to_square=__A	)
               __UpperCamelCase       =  resize(image=__A							,	size=__A							,	resample=__A							,	data_format=__A							,	**__A	)
               # then crop to (shortest_edge, shortest_edge)
               return center_crop(image=__A							,	size=(shortest_edge, shortest_edge)							,	data_format=__A							,	**__A	)
        else:
               # warping (no cropping) when evaluated at 384 or larger
               return resize(
                   __A							,	size=(shortest_edge, shortest_edge)							,	resample=__A							,	data_format=__A							,	**__A	)






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






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






 def        _lowerCamelCase					(       self       :							str							,	__A       :							ImageInput							,	__A       :							bool = None							,	__A       :							Dict[str, int] = None							,	__A       :							float = None							,	__A       :							PILImageResampling = None							,	__A       :							bool = None							,	__A       :							float = None							,	__A       :							bool = None							,	__A       :							Optional[Union[float, List[float]]] = None							,	__A       :							Optional[Union[float, List[float]]] = None							,	__A       :							Optional[Union[str, TensorType]] = None							,	__A       :							ChannelDimension = ChannelDimension.FIRST							,	**__A       :							List[str]							,	):
        __UpperCamelCase       =  do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase       =  crop_pct if crop_pct is not None else self.crop_pct
        __UpperCamelCase       =  resample if resample is not None else self.resample
        __UpperCamelCase       =  do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase       =  rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase       =  do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase       =  image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase       =  image_std if image_std is not None else self.image_std

        __UpperCamelCase       =  size if size is not None else self.size
        __UpperCamelCase       =  get_size_dict(__A							,	default_to_square=__A	)

        __UpperCamelCase       =  make_list_of_images(__A	)

        if not valid_images(__A	):
               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 or resample is None:
               raise ValueError('Size and resample must be specified if do_resize is True.'	)

        if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
               raise ValueError('crop_pct must be specified if size < 384.'	)

        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.'	)

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

        if do_resize:
               __UpperCamelCase       =  [self.resize(image=__A							,	size=__A							,	crop_pct=__A							,	resample=__A	) for image in images]

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

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

        __UpperCamelCase       =  [to_channel_dimension_format(__A							,	__A	) for image in images]

        __UpperCamelCase       =  {'pixel_values': images}
        return BatchFeature(data=__A							,	tensor_type=__A	)







'''simple docstring'''
# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


a__							:							Tuple ='''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
    import torch

    print('''Torch version:''', torch.__version__)
    print('''Cuda available:''', torch.cuda.is_available())
    print('''Cuda version:''', torch.version.cuda)
    print('''CuDNN version:''', torch.backends.cudnn.version())
    print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
    print('''Torch version:''', None)

try:
    import transformers

    print('''transformers version:''', transformers.__version__)
except ImportError:
    print('''transformers version:''', None)
