
"""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 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


A_						       =				logging.get_logger(__name__)




class    __lowerCamelCase     (   UpperCamelCase_ ):
				a__:			Dict									=						['pixel_values']




				def __init__(							self       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	UpperCAmelCase = PILImageResampling.BILINEAR       ,	UpperCAmelCase = True       ,	UpperCAmelCase = 1 / 255       ,	UpperCAmelCase = True       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							super().__init__(**__UpperCamelCase      )
							lowerCamelCase_ 		=     size if size is not None else {'''height''': 224, '''width''': 224}
							lowerCamelCase_ 		=     get_size_dict(__UpperCamelCase      )
							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 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = PILImageResampling.BILINEAR       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     get_size_dict(__UpperCamelCase      )
							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(
							    __UpperCamelCase       ,	size=(size['''height'''], size['''width'''])       ,	resample=__UpperCamelCase       ,	data_format=__UpperCamelCase       ,	**__UpperCamelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase      ):
							return rescale(__UpperCamelCase       ,	scale=__UpperCamelCase       ,	data_format=__UpperCamelCase       ,	**__UpperCamelCase      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	**UpperCAmelCase       ,	):
							return normalize(__UpperCamelCase       ,	mean=__UpperCamelCase       ,	std=__UpperCamelCase       ,	data_format=__UpperCamelCase       ,	**__UpperCamelCase      )





				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = None       ,	UpperCAmelCase = ChannelDimension.FIRST       ,	**UpperCAmelCase       ,	):
							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(__UpperCamelCase      )

							lowerCamelCase_ 		=     make_list_of_images(__UpperCamelCase      )

							if not valid_images(__UpperCamelCase      ):
										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(__UpperCamelCase      ) for image in images]

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

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

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

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

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





"""simple docstring"""


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils




class    __lowerCamelCase     (   unittest.TestCase ):




				@patch('''diffusers.utils.hub_utils.get_full_repo_name'''      )
				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase      ):
							lowerCamelCase_ 		=     '''full_repo_name'''
							with TemporaryDirectory() as tmpdir:
										# Dummy args values
										lowerCamelCase_ 		=     Mock()
										lowerCamelCase_ 		=     tmpdir
										lowerCamelCase_ 		=     0
										lowerCamelCase_ 		=     '''hub_token'''
										lowerCamelCase_ 		=     '''dataset_name'''
										lowerCamelCase_ 		=     0.0_1
										lowerCamelCase_ 		=     10_0000
										lowerCamelCase_ 		=     1_0000
										lowerCamelCase_ 		=     0.0_1
										lowerCamelCase_ 		=     0.0_2
										lowerCamelCase_ 		=     0.0_3
										lowerCamelCase_ 		=     0.0_0_0_5
										lowerCamelCase_ 		=     0.0_0_0_0_0_1
										lowerCamelCase_ 		=     1
										lowerCamelCase_ 		=     10
										lowerCamelCase_ 		=     0.0_0_1
										lowerCamelCase_ 		=     0.1
										lowerCamelCase_ 		=     0.2
										lowerCamelCase_ 		=     True

										# Model card mush be rendered and saved
										diffusers.utils.hub_utils.create_model_card(UpperCAmelCase       ,	model_name='''model_name'''      )
										self.assertTrue((Path(UpperCAmelCase      ) / '''README.md''').is_file()      )



