





'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
      import PIL

A__				:   List[str]      =logging.get_logger(__name__)
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      if isinstance(lowerCAmelCase							,	(list, tuple)  ) and isinstance(videos[0]							,	(list, tuple)  ) and is_valid_image(videos[0][0]  ):
            return videos

      elif isinstance(lowerCAmelCase							,	(list, tuple)  ) and is_valid_image(videos[0]  ):
            return [videos]

      elif is_valid_image(lowerCAmelCase  ):
            return [[videos]]

      raise ValueError(f"Could not make batched video from {videos}"  )





class  UpperCAmelCase     (			snake_case_   ):
      _lowercase:			Any						     =   ['''pixel_values''']




      def __init__(			self			: Tuple			,			__snake_case			: bool = True			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: PILImageResampling = PILImageResampling.BILINEAR			,			__snake_case			: bool = True			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: bool = True			,			__snake_case			: Union[int, float] = 1 / 2_55			,			__snake_case			: bool = True			,			__snake_case			: bool = True			,			__snake_case			: Optional[Union[float, List[float]]] = None			,			__snake_case			: Optional[Union[float, List[float]]] = None			,			**__snake_case			: str			,			)				->      None:
            super().__init__(**__snake_case  )
            _lowerCAmelCase							     =							size if size is not None else {"""shortest_edge""": 2_56}
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			default_to_square=__snake_case  )
            _lowerCAmelCase							     =							crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			param_name="""crop_size"""  )

            _lowerCAmelCase							     =							do_resize
            _lowerCAmelCase							     =							size
            _lowerCAmelCase							     =							do_center_crop
            _lowerCAmelCase							     =							crop_size
            _lowerCAmelCase							     =							resample
            _lowerCAmelCase							     =							do_rescale
            _lowerCAmelCase							     =							rescale_factor
            _lowerCAmelCase							     =							offset
            _lowerCAmelCase							     =							do_normalize
            _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 				lowercase__    (			self			: int			,			__snake_case			: np.ndarray			,			__snake_case			: Dict[str, int]			,			__snake_case			: PILImageResampling = PILImageResampling.BILINEAR			,			__snake_case			: Optional[Union[str, ChannelDimension]] = None			,			**__snake_case			: Optional[Any]			,			)				->      np.ndarray:
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			default_to_square=__snake_case  )
            if "shortest_edge" in size:
                  _lowerCAmelCase							     =							get_resize_output_image_size(__snake_case			,			size["""shortest_edge"""]			,			default_to_square=__snake_case  )
            elif "height" in size and "width" in size:
                  _lowerCAmelCase							     =							(size["""height"""], size["""width"""])
            else:
                  raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"  )
            return resize(__snake_case			,			size=__snake_case			,			resample=__snake_case			,			data_format=__snake_case			,			**__snake_case  )




      def 				lowercase__    (			self			: Union[str, Any]			,			__snake_case			: np.ndarray			,			__snake_case			: Dict[str, int]			,			__snake_case			: Optional[Union[str, ChannelDimension]] = None			,			**__snake_case			: List[Any]			,			)				->      np.ndarray:
            _lowerCAmelCase							     =							get_size_dict(__snake_case  )
            if "height" not in size or "width" not in size:
                  raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"  )
            return center_crop(__snake_case			,			size=(size["""height"""], size["""width"""])			,			data_format=__snake_case			,			**__snake_case  )




      def 				lowercase__    (			self			: Union[str, Any]			,			__snake_case			: np.ndarray			,			__snake_case			: Union[int, float]			,			__snake_case			: bool = True			,			__snake_case			: Optional[Union[str, ChannelDimension]] = None			,			**__snake_case			: Optional[Any]			,			)				->      Dict:
            _lowerCAmelCase							     =							image.astype(np.floataa  )
            if offset:
                  _lowerCAmelCase							     =							image - (scale / 2)
            return rescale(__snake_case			,			scale=__snake_case			,			data_format=__snake_case			,			**__snake_case  )




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




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: ImageInput			,			__snake_case			: bool = None			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: PILImageResampling = None			,			__snake_case			: bool = None			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: bool = None			,			__snake_case			: float = None			,			__snake_case			: bool = None			,			__snake_case			: bool = None			,			__snake_case			: Optional[Union[float, List[float]]] = None			,			__snake_case			: Optional[Union[float, List[float]]] = None			,			__snake_case			: Optional[ChannelDimension] = ChannelDimension.FIRST			,			)				->      np.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_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."""  )

            if offset and not do_rescale:
                  raise ValueError("""For offset, do_rescale must also be set to True."""  )

            # All transformations expect numpy arrays.
            _lowerCAmelCase							     =							to_numpy_array(__snake_case  )

            if do_resize:
                  _lowerCAmelCase							     =							self.resize(image=__snake_case			,			size=__snake_case			,			resample=__snake_case  )

            if do_center_crop:
                  _lowerCAmelCase							     =							self.center_crop(__snake_case			,			size=__snake_case  )

            if do_rescale:
                  _lowerCAmelCase							     =							self.rescale(image=__snake_case			,			scale=__snake_case			,			offset=__snake_case  )

            if do_normalize:
                  _lowerCAmelCase							     =							self.normalize(image=__snake_case			,			mean=__snake_case			,			std=__snake_case  )

            _lowerCAmelCase							     =							to_channel_dimension_format(__snake_case			,			__snake_case  )
            return image




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: ImageInput			,			__snake_case			: bool = None			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: PILImageResampling = None			,			__snake_case			: bool = None			,			__snake_case			: Dict[str, int] = None			,			__snake_case			: bool = None			,			__snake_case			: float = None			,			__snake_case			: bool = None			,			__snake_case			: bool = None			,			__snake_case			: Optional[Union[float, List[float]]] = None			,			__snake_case			: Optional[Union[float, List[float]]] = None			,			__snake_case			: Optional[Union[str, TensorType]] = None			,			__snake_case			: ChannelDimension = ChannelDimension.FIRST			,			**__snake_case			: List[str]			,			)				->      PIL.Image.Image:
            _lowerCAmelCase							     =							do_resize if do_resize is not None else self.do_resize
            _lowerCAmelCase							     =							resample if resample is not None else self.resample
            _lowerCAmelCase							     =							do_center_crop if do_center_crop is not None else self.do_center_crop
            _lowerCAmelCase							     =							do_rescale if do_rescale is not None else self.do_rescale
            _lowerCAmelCase							     =							rescale_factor if rescale_factor is not None else self.rescale_factor
            _lowerCAmelCase							     =							offset if offset is not None else self.offset
            _lowerCAmelCase							     =							do_normalize if do_normalize is not None else self.do_normalize
            _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(__snake_case			,			default_to_square=__snake_case  )
            _lowerCAmelCase							     =							crop_size if crop_size is not None else self.crop_size
            _lowerCAmelCase							     =							get_size_dict(__snake_case			,			param_name="""crop_size"""  )

            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."""  )

            _lowerCAmelCase							     =							make_batched(__snake_case  )

            _lowerCAmelCase							     =							[
                [
                    self._preprocess_image(
                        image=__snake_case			,			do_resize=__snake_case			,			size=__snake_case			,			resample=__snake_case			,			do_center_crop=__snake_case			,			crop_size=__snake_case			,			do_rescale=__snake_case			,			rescale_factor=__snake_case			,			offset=__snake_case			,			do_normalize=__snake_case			,			image_mean=__snake_case			,			image_std=__snake_case			,			data_format=__snake_case			,			)
                    for img in video
                ]
                for video in videos
            ]

            _lowerCAmelCase							     =							{"""pixel_values""": videos}
            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 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:Optional[Any]											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:List[Any]											=							AutoTokenizer.from_pretrained(__UpperCAmelCase      )
         lowerCAmelCase__			:str											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:Any											=							tokenizer('This is me'    , return_tensors='pt'      )

         lowerCAmelCase__			:Dict											=							model.to_bettertransformer()

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

         lowerCAmelCase__			:Optional[Any]											=							model.generate(**__UpperCAmelCase      )

         lowerCAmelCase__			:List[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(__UpperCAmelCase      )

           lowerCAmelCase__			:Any											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

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

           lowerCAmelCase__			:Union[str, Any]											=							model_reloaded.generate(**__UpperCAmelCase      )
           self.assertTrue(torch.allclose(__UpperCAmelCase    , __UpperCAmelCase      )      )






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:int											=							'hf-internal-testing/tiny-random-t5'
         lowerCAmelCase__			:Union[str, Any]											=							AutoModelForSeqaSeqLM.from_pretrained(__UpperCAmelCase      )

         lowerCAmelCase__			:str											=							model.to_bettertransformer()

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

           lowerCAmelCase__			:Optional[int]											=							model.reverse_bettertransformer()
           model.save_pretrained(__UpperCAmelCase      )
