


"""simple docstring"""






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      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Optional[int]:

					"""simple docstring"""



					return f"gaussian_noise_s={seed}_shape={'_'.join([str(SCREAMING_SNAKE_CASE__   ) for s in shape]   )}.npy"
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					super().tearDown()
					gc.collect()
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=(4, 4, 64, 64)					,				SCREAMING_SNAKE_CASE__=False   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	jnp.bfloataa if fpaa else jnp.floataa
					_UpperCamelCase					:Dict					     =	jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )   )					,				dtype=SCREAMING_SNAKE_CASE__   )
					return image
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__="CompVis/stable-diffusion-v1-4"   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Any					     =	jnp.bfloataa if fpaa else jnp.floataa
					_UpperCamelCase					:Any					     =	'''bf16''' if fpaa else None

					_UpperCamelCase     ,      _UpperCamelCase					:str					     =	FlaxUNetaDConditionModel.from_pretrained(
					    SCREAMING_SNAKE_CASE__					,				subfolder='''unet'''					,				dtype=SCREAMING_SNAKE_CASE__					,				revision=SCREAMING_SNAKE_CASE__   )
					return model, params
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=(4, 77, 7_68)					,				SCREAMING_SNAKE_CASE__=False   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	jnp.bfloataa if fpaa else jnp.floataa
					_UpperCamelCase					:Any					     =	jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )   )					,				dtype=SCREAMING_SNAKE_CASE__   )
					return hidden_states
		@parameterized.expand(
		    [
		        # fmt: off
		        [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
		        [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
		        [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
		        [3, 10_00, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
		        # fmt: on
		    ]   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	self.get_unet_model(model_id='''CompVis/stable-diffusion-v1-4'''					,				fpaa=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	self.get_latents(SCREAMING_SNAKE_CASE__					,				fpaa=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE__					,				fpaa=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:List[Any]					     =	model.apply(
					    {'''params''': params}					,				SCREAMING_SNAKE_CASE__					,				jnp.array(SCREAMING_SNAKE_CASE__					,				dtype=jnp.intaa   )					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__					,				).sample

					assert sample.shape == latents.shape

					_UpperCamelCase					:List[Any]					     =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())   )					,				dtype=jnp.floataa   )
					_UpperCamelCase					:List[Any]					     =	jnp.array(SCREAMING_SNAKE_CASE__					,				dtype=jnp.floataa   )

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





		@parameterized.expand(
		    [
		        # fmt: off
		        [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
		        [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
		        [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
		        [3, 10_00, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
		        # fmt: on
		    ]   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:Optional[int]					     =	self.get_unet_model(model_id='''stabilityai/stable-diffusion-2'''					,				fpaa=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[Any]					     =	self.get_latents(SCREAMING_SNAKE_CASE__					,				shape=(4, 4, 96, 96)					,				fpaa=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Dict					     =	self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE__					,				shape=(4, 77, 10_24)					,				fpaa=SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Optional[Any]					     =	model.apply(
					    {'''params''': params}					,				SCREAMING_SNAKE_CASE__					,				jnp.array(SCREAMING_SNAKE_CASE__					,				dtype=jnp.intaa   )					,				encoder_hidden_states=SCREAMING_SNAKE_CASE__					,				).sample

					assert sample.shape == latents.shape

					_UpperCamelCase					:Dict					     =	jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())   )					,				dtype=jnp.floataa   )
					_UpperCamelCase					:Tuple					     =	jnp.array(SCREAMING_SNAKE_CASE__					,				dtype=jnp.floataa   )

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



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,
    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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowerCamelCase__						=				logging.get_logger(__name__)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			Dict						=		["pixel_values"]

						def __init__(      self							,				a = True							,				a = None							,				a = None							,				a = PILImageResampling.BILINEAR							,				a = True							,				a = 1 / 2_55							,				a = True							,				a = None							,				a = None							,				**a							,				)       ->   None:


								'''simple docstring'''

								super().__init__(**a					)
								_UpperCamelCase         =						size if size is not None else {"""shortest_edge""": 3_84}
								_UpperCamelCase         =						get_size_dict(a							,				default_to_square=a					)

								_UpperCamelCase         =						do_resize
								_UpperCamelCase         =						size
								# Default value set here for backwards compatibility where the value in config is None
								_UpperCamelCase         =						crop_pct if crop_pct is not None else 2_24 / 2_56
								_UpperCamelCase         =						resample
								_UpperCamelCase         =						do_rescale
								_UpperCamelCase         =						rescale_factor
								_UpperCamelCase         =						do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_UpperCamelCase         =						image_std if image_std is not None else IMAGENET_STANDARD_STD

						def 			A_					(      self							,				a							,				a							,				a							,				a = PILImageResampling.BICUBIC							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a							,				default_to_square=a					)
								if "shortest_edge" not in size:
										raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'					)
								_UpperCamelCase         =						size["""shortest_edge"""]

								if shortest_edge < 3_84:
										# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
										_UpperCamelCase         =						int(shortest_edge / crop_pct					)
										_UpperCamelCase         =						get_resize_output_image_size(a							,				size=a							,				default_to_square=a					)
										_UpperCamelCase         =						resize(image=a							,				size=a							,				resample=a							,				data_format=a							,				**a					)
										# then crop to (shortest_edge, shortest_edge)
										return center_crop(image=a							,				size=(shortest_edge, shortest_edge)							,				data_format=a							,				**a					)
								else:
										# warping (no cropping) when evaluated at 384 or larger
										return resize(
										    a							,				size=(shortest_edge, shortest_edge)							,				resample=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   Optional[int]:


								'''simple docstring'''

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

						def 			A_					(      self							,				a							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

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







						def 			A_					(      self							,				a							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = ChannelDimension.FIRST							,				**a							,				)       ->   PIL.Image.Image:


								'''simple docstring'''

								_UpperCamelCase         =						do_resize if do_resize is not None else self.do_resize
								_UpperCamelCase         =						crop_pct if crop_pct is not None else self.crop_pct
								_UpperCamelCase         =						resample if resample is not None else self.resample
								_UpperCamelCase         =						do_rescale if do_rescale is not None else self.do_rescale
								_UpperCamelCase         =						rescale_factor if rescale_factor is not None else self.rescale_factor
								_UpperCamelCase         =						do_normalize if do_normalize is not None else self.do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else self.image_mean
								_UpperCamelCase         =						image_std if image_std is not None else self.image_std

								_UpperCamelCase         =						size if size is not None else self.size
								_UpperCamelCase         =						get_size_dict(a							,				default_to_square=a					)

								_UpperCamelCase         =						make_list_of_images(a					)

								if not valid_images(a					):
										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 or resample is None:
										raise ValueError("""Size and resample must be specified if do_resize is True."""					)

								if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
										raise ValueError("""crop_pct must be specified if size < 384."""					)

								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."""					)

								# All transformations expect numpy arrays.
								_UpperCamelCase         =						[to_numpy_array(a					) for image in images]

								if do_resize:
										_UpperCamelCase         =						[self.resize(image=a							,				size=a							,				crop_pct=a							,				resample=a					) for image in images]

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

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

								_UpperCamelCase         =						[to_channel_dimension_format(a							,				a					) for image in images]

								_UpperCamelCase         =						{"""pixel_values""": images}
								return BatchFeature(data=a							,				tensor_type=a					)



