






'''simple docstring'''






from typing import Dict, Iterable, 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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A    	=   logging.get_logger(__name__)




class a_    (    UpperCAmelCase_							):
   _snake_case        =							['pixel_values']







   def __init__(self					,   __a = True					,   __a = None					,   __a = PILImageResampling.BICUBIC					,   __a = True					,   __a = None					,   __a = True					,   __a = 1 / 2_5_5					,   __a = True					,   __a = IMAGENET_DEFAULT_MEAN					,   __a = IMAGENET_DEFAULT_STD					,   **__a					,   )			->      None:
      """simple docstring"""






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

      __snake_case				:      Any     							= do_resize
      __snake_case				:      List[Any]     							= size
      __snake_case				:      Tuple     							= resample
      __snake_case				:      Union[str, Any]     							= do_center_crop
      __snake_case				:      List[str]     							= crop_size
      __snake_case				:      Any     							= do_rescale
      __snake_case				:      Any     							= rescale_factor
      __snake_case				:      Any     							= do_normalize
      __snake_case				:      Dict     							= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      __snake_case				:      Optional[int]     							= image_std if image_std is not None else IMAGENET_DEFAULT_STD







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = PILImageResampling.BICUBIC					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= get_size_dict(_lowercase					,   default_to_square=_lowercase)
      # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
      if "shortest_edge" in size:
         __snake_case				:      Any     							= int((2_5_6 / 2_2_4) * size['shortest_edge'])
         __snake_case				:      Dict     							= get_resize_output_image_size(_lowercase					,   size=_lowercase					,   default_to_square=_lowercase)
         __snake_case				:      str     							= {'height': output_size[0], 'width': output_size[1]}
      if "height" not in size_dict or "width" not in size_dict:
         raise ValueError(
             F"""Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}""")
      return resize(
          _lowercase					,   size=(size_dict['height'], size_dict['width'])					,   resample=_lowercase					,   data_format=_lowercase					,   **_lowercase)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= get_size_dict(_lowercase)
      if "height" not in size or "width" not in size:
         raise ValueError(F"""Size dict must have keys 'height' and 'width'. Got {size.keys()}""")
      return center_crop(_lowercase					,   size=(size['height'], size['width'])					,   data_format=_lowercase					,   **_lowercase)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a					,   __a					,   __a = None					,   **__a					,   )			->      np.ndarray:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = None					,   __a = ChannelDimension.FIRST					,   **__a					,   )			->      BatchFeature:
      """simple docstring"""






      __snake_case				:      int     							= do_resize if do_resize is not None else self.do_resize
      __snake_case				:      Optional[int]     							= resample if resample is not None else self.resample
      __snake_case				:      Optional[int]     							= do_center_crop if do_center_crop is not None else self.do_center_crop
      __snake_case				:      int     							= do_rescale if do_rescale is not None else self.do_rescale
      __snake_case				:      List[Any]     							= rescale_factor if rescale_factor is not None else self.rescale_factor
      __snake_case				:      str     							= do_normalize if do_normalize is not None else self.do_normalize
      __snake_case				:      Dict     							= image_mean if image_mean is not None else self.image_mean
      __snake_case				:      str     							= image_std if image_std is not None else self.image_std

      __snake_case				:      List[str]     							= size if size is not None else self.size
      __snake_case				:      str     							= get_size_dict(_lowercase					,   default_to_square=_lowercase)
      __snake_case				:      Optional[Any]     							= crop_size if crop_size is not None else self.crop_size
      __snake_case				:      Optional[Any]     							= get_size_dict(_lowercase					,   param_name='crop_size')

      __snake_case				:      Union[str, Any]     							= 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.')

      # All transformations expect numpy arrays.
      __snake_case				:      Union[str, Any]     							= [to_numpy_array(_lowercase) for image in images]

      if do_resize:
         __snake_case				:      Dict     							= [self.resize(_lowercase					,   _lowercase					,   _lowercase) for image in images]

      if do_center_crop:
         __snake_case				:      Optional[int]     							= [self.center_crop(_lowercase					,   _lowercase) for image in images]

      if do_rescale:
         __snake_case				:      Optional[Any]     							= [self.rescale(_lowercase					,   _lowercase) for image in images]

      if do_normalize:
         __snake_case				:      Dict     							= [self.normalize(_lowercase					,   _lowercase					,   _lowercase) for image in images]

      __snake_case				:      Tuple     							= [to_channel_dimension_format(_lowercase					,   _lowercase) for image in images]

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







'''simple docstring'''






import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
      import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin




@require_torch
class a_    (    unittest.TestCase		,      UpperCamelCase_							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= load_tool('text-to-speech')
      self.tool.setup()







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Dict     							= self.tool('hey')
      __snake_case				:      List[Any]     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Any     							= self.tool('hey')
      __snake_case				:      Any     							= result.to_raw()
      self.assertTrue(
          torch.allclose(
              resulting_tensor[:3]					,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485])					,   ))