'''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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


a		:  Dict   			=			logging.get_logger(__name__)


if is_vision_available():
  import PIL




class     UpperCamelCase					(  snake_case__					):
  a    :Tuple      					=				['pixel_values']



  def __init__( self		,    __lowerCAmelCase = True		,    __lowerCAmelCase = None		,    __lowerCAmelCase = PILImageResampling.BILINEAR		,    __lowerCAmelCase = True		,    __lowerCAmelCase = False		,    __lowerCAmelCase = True		,    __lowerCAmelCase = True		,    __lowerCAmelCase = 1 / 255		,    __lowerCAmelCase = True		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      super().__init__(**__lowerCAmelCase   )

      _a							:       Any       					=			size if size is not None else {"""height""": 2560, """width""": 1920}
      if isinstance(__lowerCAmelCase		,    (tuple, list)   ):
          # The previous feature extractor size parameter was in (width, height) format
          _a							:       int       					=			size[::-1]
      _a							:       Dict       					=			get_size_dict(__lowerCAmelCase   )

      _a							:       Any       					=			do_resize
      _a							:       int       					=			size
      _a							:       int       					=			resample
      _a							:       int       					=			do_thumbnail
      _a							:       str       					=			do_align_long_axis
      _a							:       Dict       					=			do_pad
      _a							:       Any       					=			do_rescale
      _a							:       Any       					=			rescale_factor
      _a							:       Any       					=			do_normalize
      _a							:       List[Any]       					=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _a							:       List[str]       					=			image_std if image_std is not None else IMAGENET_STANDARD_STD



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = None   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			get_image_size(__lowerCAmelCase   )
      _a							:       List[Any]       					=			size["""height"""], size["""width"""]

      if (output_width < output_height and input_width > input_height) or (
          output_width > output_height and input_width < input_height
      ):
          _a							:       Tuple       					=			np.rotaa(__lowerCAmelCase		,    3   )

      if data_format is not None:
          _a							:       Any       					=			to_channel_dimension_format(__lowerCAmelCase		,    __lowerCAmelCase   )

      return image



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = False		,    __lowerCAmelCase = None		,    ):







      '''simple docstring'''


      _a							:       str       					=			size["""height"""], size["""width"""]
      _a							:       Tuple       					=			get_image_size(__lowerCAmelCase   )

      _a							:       Dict       					=			output_width - input_width
      _a							:       Any       					=			output_height - input_height

      if random_padding:
          _a							:       List[str]       					=			np.random.randint(low=0		,    high=delta_height + 1   )
          _a							:       Any       					=			np.random.randint(low=0		,    high=delta_width + 1   )
      else:
          _a							:       Optional[Any]       					=			delta_height // 2
          _a							:       Tuple       					=			delta_width // 2

      _a							:       Union[str, Any]       					=			delta_height - pad_top
      _a							:       Any       					=			delta_width - pad_left

      _a							:       List[Any]       					=			((pad_top, pad_bottom), (pad_left, pad_right))
      return pad(__lowerCAmelCase		,    __lowerCAmelCase		,    data_format=__lowerCAmelCase   )



  def 						a_       ( self		,    *__lowerCAmelCase		,    **__lowerCAmelCase   ):







      '''simple docstring'''


      logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""   )
      return self.pad_image(*__lowerCAmelCase		,    **__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = PILImageResampling.BICUBIC		,    __lowerCAmelCase = None		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       Optional[Any]       					=			get_image_size(__lowerCAmelCase   )
      _a							:       Any       					=			size["""height"""], size["""width"""]

      # We always resize to the smallest of either the input or output size.
      _a							:       int       					=			min(__lowerCAmelCase		,    __lowerCAmelCase   )
      _a							:       str       					=			min(__lowerCAmelCase		,    __lowerCAmelCase   )

      if height == input_height and width == input_width:
          return image

      if input_height > input_width:
          _a							:       Any       					=			int(input_width * height / input_height   )
      elif input_width > input_height:
          _a							:       Tuple       					=			int(input_height * width / input_width   )

      return resize(
          __lowerCAmelCase		,    size=(height, width)		,    resample=__lowerCAmelCase		,    reducing_gap=2.0		,    data_format=__lowerCAmelCase		,    **__lowerCAmelCase   )



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = PILImageResampling.BICUBIC		,    __lowerCAmelCase = None		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       List[Any]       					=			get_size_dict(__lowerCAmelCase   )
      _a							:       Optional[int]       					=			min(size["""height"""]		,    size["""width"""]   )
      _a							:       Optional[int]       					=			get_resize_output_image_size(__lowerCAmelCase		,    size=__lowerCAmelCase		,    default_to_square=__lowerCAmelCase   )
      _a							:       List[Any]       					=			resize(__lowerCAmelCase		,    size=__lowerCAmelCase		,    resample=__lowerCAmelCase		,    data_format=__lowerCAmelCase		,    **__lowerCAmelCase   )
      return resized_image



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = None		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


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



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase = None		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


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



  def 						a_       ( self		,    __lowerCAmelCase		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = False		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = None		,    __lowerCAmelCase = ChannelDimension.FIRST		,    **__lowerCAmelCase		,    ):







      '''simple docstring'''


      _a							:       Dict       					=			do_resize if do_resize is not None else self.do_resize
      _a							:       Optional[int]       					=			size if size is not None else self.size
      if isinstance(__lowerCAmelCase		,    (tuple, list)   ):
          # Previous feature extractor had size in (width, height) format
          _a							:       Any       					=			size[::-1]
      _a							:       int       					=			get_size_dict(__lowerCAmelCase   )
      _a							:       Optional[Any]       					=			resample if resample is not None else self.resample
      _a							:       int       					=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
      _a							:       Union[str, Any]       					=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
      _a							:       Optional[int]       					=			do_pad if do_pad is not None else self.do_pad
      _a							:       List[Any]       					=			do_rescale if do_rescale is not None else self.do_rescale
      _a							:       int       					=			rescale_factor if rescale_factor is not None else self.rescale_factor
      _a							:       List[Any]       					=			do_normalize if do_normalize is not None else self.do_normalize
      _a							:       str       					=			image_mean if image_mean is not None else self.image_mean
      _a							:       Any       					=			image_std if image_std is not None else self.image_std

      _a							:       Dict       					=			make_list_of_images(__lowerCAmelCase   )

      if not valid_images(__lowerCAmelCase   ):
          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."""   )

      if do_pad and size is None:
          raise ValueError("""Size must be specified if do_pad 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.
      _a							:       Any       					=			[to_numpy_array(__lowerCAmelCase   ) for image in images]

      if do_align_long_axis:
          _a							:       List[str]       					=			[self.align_long_axis(__lowerCAmelCase		,    size=__lowerCAmelCase   ) for image in images]

      if do_resize:
          _a							:       int       					=			[self.resize(image=__lowerCAmelCase		,    size=__lowerCAmelCase		,    resample=__lowerCAmelCase   ) for image in images]

      if do_thumbnail:
          _a							:       List[str]       					=			[self.thumbnail(image=__lowerCAmelCase		,    size=__lowerCAmelCase   ) for image in images]

      if do_pad:
          _a							:       str       					=			[self.pad_image(image=__lowerCAmelCase		,    size=__lowerCAmelCase		,    random_padding=__lowerCAmelCase   ) for image in images]

      if do_rescale:
          _a							:       Optional[Any]       					=			[self.rescale(image=__lowerCAmelCase		,    scale=__lowerCAmelCase   ) for image in images]

      if do_normalize:
          _a							:       List[str]       					=			[self.normalize(image=__lowerCAmelCase		,    mean=__lowerCAmelCase		,    std=__lowerCAmelCase   ) for image in images]

      _a							:       List[str]       					=			[to_channel_dimension_format(__lowerCAmelCase		,    __lowerCAmelCase   ) for image in images]

      _a							:       Dict       					=			{"""pixel_values""": images}
      return BatchFeature(data=__lowerCAmelCase		,    tensor_type=__lowerCAmelCase   )








'''simple docstring'''





import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class     UpperCamelCase					(  unittest.TestCase					):



  def 						a_       ( self   ):







      '''simple docstring'''


      _a							:       List[Any]       					=			"""runwayml/stable-diffusion-v1-5"""
      _a							:       Tuple       					=			TextToVideoZeroPipeline.from_pretrained(__lowerCAmelCase		,    torch_dtype=torch.floataa   ).to("""cuda"""   )
      _a							:       Dict       					=			DDIMScheduler.from_config(pipe.scheduler.config   )
      _a							:       int       					=			torch.Generator(device="""cuda"""   ).manual_seed(0   )

      _a							:       Union[str, Any]       					=			"""A bear is playing a guitar on Times Square"""
      _a							:       List[Any]       					=			pipe(prompt=__lowerCAmelCase		,    generator=__lowerCAmelCase   ).images

      _a							:       Any       					=			load_pt(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt"""   )

      assert_mean_pixel_difference(__lowerCAmelCase		,    __lowerCAmelCase   )


