



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__A   			=logging.get_logger(__name__)

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			UpperCamelCase									=       ["""pixel_values"""]




			def __init__(   self							: Union[str, Any]       ,       a_							: bool = True       ,       a_							: int = 32       ,       a_							: List[Any]=PILImageResampling.BILINEAR       ,       a_							: bool = True       ,       **a_							: Tuple       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	do_resize
					__UpperCAmelCase    :     List[Any]													=	do_rescale
					__UpperCAmelCase    :     str													=	size_divisor
					__UpperCAmelCase    :     str													=	resample
					super().__init__(**a_ )




			def  snake_case__     (   self							: int       ,       a_							: np.ndarray       ,       a_							: int       ,       a_							: Any       ,       a_							: Optional[ChannelDimension] = None       ,       **a_							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase							,					__UpperCAmelCase    :     List[Any]													=	get_image_size(a_ )
					# Rounds the height and width down to the closest multiple of size_divisor
					__UpperCAmelCase    :     str													=	height // size_divisor * size_divisor
					__UpperCAmelCase    :     Dict													=	width // size_divisor * size_divisor
					__UpperCAmelCase    :     str													=	resize(a_       ,       (new_h, new_w)       ,       resample=a_       ,       data_format=a_       ,       **a_ )
					return image




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: np.ndarray       ,       a_							: float       ,       a_							: Optional[ChannelDimension] = None       ,       **a_							: Union[str, Any] ):
					'''simple docstring'''





					return rescale(image=a_       ,       scale=a_       ,       data_format=a_       ,       **a_ )




			def  snake_case__     (   self							: str       ,       a_							: Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,       a_							: Optional[bool] = None       ,       a_							: Optional[int] = None       ,       a_							: Optional[Any]=None       ,       a_							: Optional[bool] = None       ,       a_							: Optional[Union[TensorType, str]] = None       ,       a_							: ChannelDimension = ChannelDimension.FIRST       ,       **a_							: List[Any]       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	do_resize if do_resize is not None else self.do_resize
					__UpperCAmelCase    :     Optional[Any]													=	do_rescale if do_rescale is not None else self.do_rescale
					__UpperCAmelCase    :     Any													=	size_divisor if size_divisor is not None else self.size_divisor
					__UpperCAmelCase    :     Union[str, Any]													=	resample if resample is not None else self.resample

					if do_resize and size_divisor is None:
							raise ValueError('''size_divisor is required for resizing''' )

					__UpperCAmelCase    :     str													=	make_list_of_images(a_ )

					if not valid_images(a_ ):
							raise ValueError('''Invalid image(s)''' )

					# All transformations expect numpy arrays.
					__UpperCAmelCase    :     Any													=	[to_numpy_array(a_ ) for img in images]

					if do_resize:
							__UpperCAmelCase    :     Any													=	[self.resize(a_       ,       size_divisor=a_       ,       resample=a_ ) for image in images]

					if do_rescale:
							__UpperCAmelCase    :     Union[str, Any]													=	[self.rescale(a_       ,       scale=1 / 2_55 ) for image in images]

					__UpperCAmelCase    :     Optional[Any]													=	[to_channel_dimension_format(a_       ,       a_ ) for image in images]

					__UpperCAmelCase    :     Union[str, Any]													=	{'''pixel_values''': images}
					return BatchFeature(data=a_       ,       tensor_type=a_ )










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 		UpperCAmelCase__			(			unittest.TestCase						,__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	load_tool('''text-to-speech''' )
					self.tool.setup()




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     List[Any]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     Union[str, Any]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )




			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					torch.manual_seed(0 )
					__UpperCAmelCase    :     Optional[int]													=	self.tool('''hey''' )
					__UpperCAmelCase    :     List[str]													=	result.to_raw()
					self.assertTrue(
					    torch.allclose(
					        resulting_tensor[:3]       ,       torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5] )       ,       ) )





