



'''simple docstring'''






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCamelCase__:     int										=    logging.get_logger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= ["""pixel_values"""]







	def __init__(		self    :  Optional[int]	,    __snake_case    :  bool = True	,    __snake_case    :  Union[int, float] = 1 / 255	,    __snake_case    :  bool = True	,    __snake_case    :  int = 8	,    **__snake_case    :  str	,    )    ->	None:
								super().__init__(**__snake_case	)

								UpperCAmelCase				: Any											=	do_rescale
								UpperCAmelCase				: List[Any]											=	rescale_factor
								UpperCAmelCase				: Optional[int]											=	do_pad
								UpperCAmelCase				: Union[str, Any]											=	pad_size







	def   A			(		self    :  Any	,    __snake_case    :  np.ndarray	,    __snake_case    :  float	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	,    **__snake_case    :  Tuple	)    ->	np.ndarray:
								return rescale(__snake_case	,    scale=__snake_case	,    data_format=__snake_case	,    **__snake_case	)







	def   A			(		self    :  int	,    __snake_case    :  np.ndarray	,    __snake_case    :  int	,    __snake_case    :  Optional[Union[str, ChannelDimension]] = None	)    ->	List[str]:
								UpperCAmelCase							,	UpperCAmelCase				: Tuple											=	get_image_size(__snake_case	)
								UpperCAmelCase				: Tuple											=	(old_height // size + 1) * size - old_height
								UpperCAmelCase				: List[str]											=	(old_width // size + 1) * size - old_width

								return pad(__snake_case	,    ((0, pad_height), (0, pad_width))	,    mode='''symmetric'''	,    data_format=__snake_case	)







	def   A			(		self    :  List[Any]	,    __snake_case    :  ImageInput	,    __snake_case    :  Optional[bool] = None	,    __snake_case    :  Optional[float] = None	,    __snake_case    :  Optional[bool] = None	,    __snake_case    :  Optional[int] = None	,    __snake_case    :  Optional[Union[str, TensorType]] = None	,    __snake_case    :  Union[str, ChannelDimension] = ChannelDimension.FIRST	,    **__snake_case    :  Dict	,    )    ->	str:
								UpperCAmelCase				: List[str]											=	do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase				: List[str]											=	rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase				: str											=	do_pad if do_pad is not None else self.do_pad
								UpperCAmelCase				: Union[str, Any]											=	pad_size if pad_size is not None else self.pad_size

								UpperCAmelCase				: List[Any]											=	make_list_of_images(__snake_case	)

								if not valid_images(__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_rescale and rescale_factor is None:
															raise ValueError('''Rescale factor must be specified if do_rescale is True.'''	)

								# All transformations expect numpy arrays.
								UpperCAmelCase				: List[Any]											=	[to_numpy_array(__snake_case	) for image in images]

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

								if do_pad:
															UpperCAmelCase				: int											=	[self.pad(__snake_case	,    size=__snake_case	) for image in images]

								UpperCAmelCase				: int											=	[to_channel_dimension_format(__snake_case	,    __snake_case	) for image in images]

								UpperCAmelCase				: str											=	{'''pixel_values''': images}
								return BatchFeature(data=__snake_case	,    tensor_type=__snake_case	)










'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch

@require_torch
@require_optimum
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

															self.assertFalse(
															    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
															with self.assertRaises(__snake_case	):
																						model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





