
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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__lowerCamelCase							=       logging.get_logger(__name__)


if is_vision_available():
   import PIL






class   UpperCAmelCase		(						A_ ):
   A__					:		str						     =     ["pixel_values"]



   def __init__(self		:					List[str]    ,						snake_case__		:					bool = True    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						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__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					bool = True    ,						**snake_case__		:					List[Any]    ,						)		->	None:


          '''simple docstring'''


          super().__init__(**snake_case__ )
          snake_case       :   Any							=      size if size is not None else {"shortest_edge": 2_24}
          snake_case       :   str							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__ )
          snake_case       :   Dict							=      crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
          snake_case       :   Optional[int]							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__    ,						param_name="crop_size" )

          snake_case       :   List[str]							=      do_resize
          snake_case       :   List[str]							=      size
          snake_case       :   Optional[int]							=      resample
          snake_case       :   List[str]							=      do_center_crop
          snake_case       :   List[Any]							=      crop_size
          snake_case       :   Any							=      do_rescale
          snake_case       :   Union[str, Any]							=      rescale_factor
          snake_case       :   Dict							=      do_normalize
          snake_case       :   Tuple							=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          snake_case       :   Union[str, Any]							=      image_std if image_std is not None else OPENAI_CLIP_STD
          snake_case       :   Union[str, Any]							=      do_convert_rgb



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					PILImageResampling = PILImageResampling.BICUBIC    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   List[str]							=      get_size_dict(snake_case__    ,						default_to_square=snake_case__ )
          if "shortest_edge" not in size:
                 raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
          snake_case       :   Dict							=      get_resize_output_image_size(snake_case__    ,						size=size["shortest_edge"]    ,						default_to_square=snake_case__ )
          return resize(snake_case__    ,						size=snake_case__    ,						resample=snake_case__    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Dict[str, int]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[Any]    ,						)		->	np.ndarray:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      get_size_dict(snake_case__ )
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
          return center_crop(snake_case__    ,						size=(size["height"], size["width"])    ,						data_format=snake_case__    ,						**snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					np.ndarray    ,						snake_case__		:					Union[int, float]    ,						snake_case__		:					Optional[Union[str, ChannelDimension]] = None    ,						**snake_case__		:					Optional[int]    ,						)		->	Any:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						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__		:					Optional[int]    ,						)		->	np.ndarray:


          '''simple docstring'''


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



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					ImageInput    ,						snake_case__		:					bool = None    ,						snake_case__		:					Dict[str, int] = None    ,						snake_case__		:					PILImageResampling = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					int = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					float = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					Optional[Union[float, List[float]]] = None    ,						snake_case__		:					bool = None    ,						snake_case__		:					Optional[Union[str, TensorType]] = None    ,						snake_case__		:					Optional[ChannelDimension] = ChannelDimension.FIRST    ,						**snake_case__		:					Any    ,						)		->	PIL.Image.Image:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      do_resize if do_resize is not None else self.do_resize
          snake_case       :   Union[str, Any]							=      size if size is not None else self.size
          snake_case       :   List[str]							=      get_size_dict(snake_case__    ,						param_name="size"    ,						default_to_square=snake_case__ )
          snake_case       :   Any							=      resample if resample is not None else self.resample
          snake_case       :   Dict							=      do_center_crop if do_center_crop is not None else self.do_center_crop
          snake_case       :   List[Any]							=      crop_size if crop_size is not None else self.crop_size
          snake_case       :   Optional[int]							=      get_size_dict(snake_case__    ,						param_name="crop_size"    ,						default_to_square=snake_case__ )
          snake_case       :   Any							=      do_rescale if do_rescale is not None else self.do_rescale
          snake_case       :   str							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          snake_case       :   List[str]							=      do_normalize if do_normalize is not None else self.do_normalize
          snake_case       :   Union[str, Any]							=      image_mean if image_mean is not None else self.image_mean
          snake_case       :   str							=      image_std if image_std is not None else self.image_std
          snake_case       :   Union[str, Any]							=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          snake_case       :   int							=      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_resize and size is None:
                 raise ValueError("Size 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." )

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
                 snake_case       :   Union[str, Any]							=      [convert_to_rgb(snake_case__ ) for image in images]

          # All transformations expect numpy arrays.
          snake_case       :   Optional[int]							=      [to_numpy_array(snake_case__ ) for image in images]

          if do_resize:
                 snake_case       :   Optional[Any]							=      [self.resize(image=snake_case__    ,						size=snake_case__    ,						resample=snake_case__ ) for image in images]

          if do_center_crop:
                 snake_case       :   Tuple							=      [self.center_crop(image=snake_case__    ,						size=snake_case__ ) for image in images]

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

          if do_normalize:
                 snake_case       :   int							=      [self.normalize(image=snake_case__    ,						mean=snake_case__    ,						std=snake_case__ ) for image in images]

          snake_case       :   str							=      [to_channel_dimension_format(snake_case__    ,						snake_case__ ) for image in images]

          snake_case       :   Optional[Any]							=      {"pixel_values": images}
          return BatchFeature(data=snake_case__    ,						tensor_type=snake_case__ )







import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
   import jax
   import jax.numpy as jnp






@slow
@require_flax
class   UpperCAmelCase		(						unittest.TestCase ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[str] )		->	List[str]:


          '''simple docstring'''


          return f"""gaussian_noise_s={seed}_shape={'_'.join([str(snake_case__ ) for s in shape] )}.npy"""



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple )		->	int:


          '''simple docstring'''


          super().tearDown()
          gc.collect()



   def 				_SCREAMING_SNAKE_CASE   (self		:					str    ,						snake_case__		:					Optional[Any]=0    ,						snake_case__		:					Any=(4, 4, 64, 64)    ,						snake_case__		:					List[Any]=False )		->	int:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Optional[int]							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return image



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Tuple=False    ,						snake_case__		:					List[Any]="CompVis/stable-diffusion-v1-4" )		->	List[Any]:


          '''simple docstring'''


          snake_case       :   List[str]							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   str							=      "bf16" if fpaa else None

          snake_case				,					snake_case       :   Optional[int]							=      FlaxUNetaDConditionModel.from_pretrained(
              snake_case__    ,						subfolder="unet"    ,						dtype=snake_case__    ,						revision=snake_case__ )
          return model, params



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str]    ,						snake_case__		:					Union[str, Any]=0    ,						snake_case__		:					Union[str, Any]=(4, 77, 7_68)    ,						snake_case__		:					Dict=False )		->	List[str]:


          '''simple docstring'''


          snake_case       :   Any							=      jnp.bfloataa if fpaa else jnp.floataa
          snake_case       :   Any							=      jnp.array(load_hf_numpy(self.get_file_format(snake_case__    ,						snake_case__ ) )    ,						dtype=snake_case__ )
          return hidden_states



   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
           [17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
           [8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
           [3, 10_00, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					int    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					Dict )		->	List[str]:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[str]							=      self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4"    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_latents(snake_case__    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_encoder_hidden_states(snake_case__    ,						fpaa=snake_case__ )

          snake_case       :   Dict							=      model.apply(
              {"params": params}    ,						snake_case__    ,						jnp.array(snake_case__    ,						dtype=jnp.intaa )    ,						encoder_hidden_states=snake_case__    ,						).sample

          assert sample.shape == latents.shape

          snake_case       :   Optional[Any]							=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )    ,						dtype=jnp.floataa )
          snake_case       :   Optional[int]							=      jnp.array(snake_case__    ,						dtype=jnp.floataa )

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
          assert jnp.allclose(snake_case__    ,						snake_case__    ,						atol=1e-2 )



   @parameterized.expand(
       [
           # fmt: off
           [83, 4, [0.1514, 0.0807, 0.1624, 0.1016, -0.1896, 0.0263, 0.0677, 0.2310]],
           [17, 0.55, [0.1164, -0.0216, 0.0170, 0.1589, -0.3120, 0.1005, -0.0581, -0.1458]],
           [8, 0.89, [-0.1758, -0.0169, 0.1004, -0.1411, 0.1312, 0.1103, -0.1996, 0.2139]],
           [3, 10_00, [0.1214, 0.0352, -0.0731, -0.1562, -0.0994, -0.0906, -0.2340, -0.0539]],
           # fmt: on
       ] )
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int]    ,						snake_case__		:					Optional[int]    ,						snake_case__		:					str    ,						snake_case__		:					Tuple )		->	str:


          '''simple docstring'''


          snake_case				,					snake_case       :   List[Any]							=      self.get_unet_model(model_id="stabilityai/stable-diffusion-2"    ,						fpaa=snake_case__ )
          snake_case       :   List[str]							=      self.get_latents(snake_case__    ,						shape=(4, 4, 96, 96)    ,						fpaa=snake_case__ )
          snake_case       :   Union[str, Any]							=      self.get_encoder_hidden_states(snake_case__    ,						shape=(4, 77, 10_24)    ,						fpaa=snake_case__ )

          snake_case       :   Optional[int]							=      model.apply(
              {"params": params}    ,						snake_case__    ,						jnp.array(snake_case__    ,						dtype=jnp.intaa )    ,						encoder_hidden_states=snake_case__    ,						).sample

          assert sample.shape == latents.shape

          snake_case       :   int							=      jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten()) )    ,						dtype=jnp.floataa )
          snake_case       :   Dict							=      jnp.array(snake_case__    ,						dtype=jnp.floataa )

          # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
          assert jnp.allclose(snake_case__    ,						snake_case__    ,						atol=1e-2 )





