import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 		a__						(					A__    ,		unittest.TestCase					):
					A	       =							KandinskyVaaPriorPipeline
					A	       =							['prompt']
					A	       =							['prompt', 'negative_prompt']
					A	       =							[
					    'num_images_per_prompt',
					    'generator',
					    'num_inference_steps',
					    'latents',
					    'negative_prompt',
					    'guidance_scale',
					    'output_type',
					    'return_dict',
					]
					A	       =							False

					@property
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							return 32

					@property
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							return 32

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return self.time_input_dim

					@property
					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							return self.time_input_dim * 4

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return 100

					@property
					def 				__UpperCamelCase   (    self      :       int    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )
							return tokenizer

					@property
					def 				__UpperCamelCase   (    self      :       List[str]    ):







							"""simple docstring"""






							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		str				      =    CLIPTextConfig(
							    bos_token_id=0,eos_token_id=2,hidden_size=self.text_embedder_hidden_size,projection_dim=self.text_embedder_hidden_size,intermediate_size=37,layer_norm_eps=1E-05,num_attention_heads=4,num_hidden_layers=5,pad_token_id=1,vocab_size=1000,)
							return CLIPTextModelWithProjection(_A    )

					@property
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							torch.manual_seed(0    )

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    {
							    "num_attention_heads": 2,
							    "attention_head_dim": 12,
							    "embedding_dim": self.text_embedder_hidden_size,
							    "num_layers": 1,
							}

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    PriorTransformer(**_A    )
							# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
							SCREAMING_SNAKE_CASE_  :		Dict				      =    nn.Parameter(torch.ones(model.clip_std.shape    )    )
							return model

					@property
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							torch.manual_seed(0    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    CLIPVisionConfig(
							    hidden_size=self.text_embedder_hidden_size,image_size=224,projection_dim=self.text_embedder_hidden_size,intermediate_size=37,num_attention_heads=4,num_channels=3,num_hidden_layers=5,patch_size=14,)

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    CLIPVisionModelWithProjection(_A    )
							return model

					@property
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    CLIPImageProcessor(
							    crop_size=224,do_center_crop=_A,do_normalize=_A,do_resize=_A,image_mean=[0.48145466, 0.4578275, 0.40821073],image_std=[0.26862954, 0.26130258, 0.27577711],resample=3,size=224,)

							return image_processor

					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    self.dummy_prior
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    self.dummy_image_encoder
							SCREAMING_SNAKE_CASE_  :		Any				      =    self.dummy_text_encoder
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.dummy_tokenizer
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    self.dummy_image_processor

							SCREAMING_SNAKE_CASE_  :		Any				      =    UnCLIPScheduler(
							    variance_type="fixed_small_log",prediction_type="sample",num_train_timesteps=1000,clip_sample=_A,clip_sample_range=10.0,)

							SCREAMING_SNAKE_CASE_  :		str				      =    {
							    "prior": prior,
							    "image_encoder": image_encoder,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "scheduler": scheduler,
							    "image_processor": image_processor,
							}

							return components

					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       int,_A      :       Dict=0    ):







							"""simple docstring"""






							if str(_A    ).startswith("mps"    ):
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    torch.manual_seed(_A    )
							else:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    torch.Generator(device=_A    ).manual_seed(_A    )
							SCREAMING_SNAKE_CASE_  :		str				      =    {
							    "prompt": "horse",
							    "generator": generator,
							    "guidance_scale": 4.0,
							    "num_inference_steps": 2,
							    "output_type": "np",
							}
							return inputs

					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    "cpu"

							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    self.get_dummy_components()

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    self.pipeline_class(**_A    )
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    pipe.to(_A    )

							pipe.set_progress_bar_config(disable=_A    )

							SCREAMING_SNAKE_CASE_  :		List[str]				      =    pipe(**self.get_dummy_inputs(_A    )    )
							SCREAMING_SNAKE_CASE_  :		int				      =    output.image_embeds

							SCREAMING_SNAKE_CASE_  :		Any				      =    pipe(
							    **self.get_dummy_inputs(_A    ),return_dict=_A,)[0]

							SCREAMING_SNAKE_CASE_  :		Any				      =    image[0, -10:]
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    image_from_tuple[0, -10:]

							assert image.shape == (1, 32)

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    np.array(
							    [-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156]    )

							assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2

					@skip_mps
					def 				__UpperCamelCase   (    self      :       Any    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    torch_device == "cpu"
							SCREAMING_SNAKE_CASE_  :		str				      =    True
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    False

							self._test_inference_batch_single_identical(
							    test_max_difference=_A,relax_max_difference=_A,test_mean_pixel_difference=_A,)






					@skip_mps
					def 				__UpperCamelCase   (    self      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[str]				      =    torch_device == "cpu"
							SCREAMING_SNAKE_CASE_  :		Dict				      =    False

							self._test_attention_slicing_forward_pass(
							    test_max_difference=_A,test_mean_pixel_difference=_A,)



import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


a__				=		logging.get_logger(__name__)



def    __UpperCAmelCase (	__a      :			np.ndarray		,__a      :			Union[int, Iterable[int]]		,__a      :			bool		,__a      :			int	)			->     Tuple[int, int]:

					"""simple docstring"""

					def constraint_to_multiple_of(__a      :			List[str]		,__a      :			Dict		,__a      :			Any=0		,__a      :			int=None	):
										_a	:    Dict         =		round(val / multiple	) * multiple

										if max_val is not None and x > max_val:
															_a	:    Any         =		math.floor(val / multiple	) * multiple

										if x < min_val:
															_a	:    Dict         =		math.ceil(val / multiple	) * multiple

										return x

					_a	:    Union[str, Any]         =		(output_size, output_size) if isinstance(__a		,__a	) else output_size

					_a      ,					_a	:    List[Any]         =		get_image_size(__a	)
					_a      ,					_a	:    Any         =		output_size

					# determine new height and width
					_a	:    Union[str, Any]         =		output_height / input_height
					_a	:    Tuple         =		output_width / input_width

					if keep_aspect_ratio:
										# scale as little as possible
										if abs(1 - scale_width	) < abs(1 - scale_height	):
															# fit width
															_a	:    Optional[Any]         =		scale_width
										else:
															# fit height
															_a	:    Tuple         =		scale_height

					_a	:    Optional[Any]         =		constraint_to_multiple_of(scale_height * input_height		,multiple=__a	)
					_a	:    int         =		constraint_to_multiple_of(scale_width * input_width		,multiple=__a	)

					return (new_height, new_width)







class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    ["pixel_values"]


							def __init__(			self       ,							_a = True       ,							_a = None       ,							_a = PILImageResampling.BILINEAR       ,							_a = False       ,							_a = 1       ,							_a = True       ,							_a = 1 / 2_5_5       ,							_a = True       ,							_a = None       ,							_a = None       ,							**_a       ,							)    ->    None:
												super().__init__(**_a       )
												_a	:    Optional[int]         =		size if size is not None else {'''height''': 3_8_4, '''width''': 3_8_4}
												_a	:    Optional[Any]         =		get_size_dict(_a       )
												_a	:    Any         =		do_resize
												_a	:    Dict         =		size
												_a	:    str         =		keep_aspect_ratio
												_a	:    Any         =		ensure_multiple_of
												_a	:    Optional[Any]         =		resample
												_a	:    List[Any]         =		do_rescale
												_a	:    int         =		rescale_factor
												_a	:    Any         =		do_normalize
												_a	:    Union[str, 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 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = False       ,							_a = 1       ,							_a = PILImageResampling.BICUBIC       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												_a	:    str         =		get_size_dict(_a       )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""       )
												_a	:    Optional[Any]         =		get_resize_output_image_size(
												    _a       ,							output_size=(size['''height'''], size['''width'''])       ,							keep_aspect_ratio=_a       ,							multiple=_a       ,							)
												return resize(_a       ,							size=_a       ,							resample=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    int:
												return rescale(_a       ,							scale=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a = None       ,							**_a       ,							)    ->    np.ndarray:
												return normalize(_a       ,							mean=_a       ,							std=_a       ,							data_format=_a       ,							**_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a = None       ,							_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:
												_a	:    Optional[int]         =		do_resize if do_resize is not None else self.do_resize
												_a	:    Union[str, Any]         =		size if size is not None else self.size
												_a	:    str         =		get_size_dict(_a       )
												_a	:    List[Any]         =		keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
												_a	:    Optional[Any]         =		ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
												_a	:    str         =		resample if resample is not None else self.resample
												_a	:    str         =		do_rescale if do_rescale is not None else self.do_rescale
												_a	:    Union[str, Any]         =		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	:    Tuple         =		image_std if image_std is not None else self.image_std

												_a	:    Dict         =		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_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.
												_a	:    Dict         =		[to_numpy_array(_a       ) for image in images]

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

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

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

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

												_a	:    Tuple         =		{'''pixel_values''': images}
												return BatchFeature(data=_a       ,							tensor_type=_a       )




							def 				__lowercase				(			self       ,							_a       ,							_a = None       )    ->    Any:
												_a	:    Optional[Any]         =		outputs.logits

												# Resize logits and compute semantic segmentation maps
												if target_sizes is not None:
																	if len(_a       ) != len(_a       ):
																						raise ValueError(
																						    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''       )

																	if is_torch_tensor(_a       ):
																						_a	:    List[Any]         =		target_sizes.numpy()

																	_a	:    str         =		[]

																	for idx in range(len(_a       )       ):
																						_a	:    str         =		torch.nn.functional.interpolate(
																						    logits[idx].unsqueeze(dim=0       )       ,							size=target_sizes[idx]       ,							mode='''bilinear'''       ,							align_corners=_a       )
																						_a	:    Union[str, Any]         =		resized_logits[0].argmax(dim=0       )
																						semantic_segmentation.append(_a       )
												else:
																	_a	:    Tuple         =		logits.argmax(dim=1       )
																	_a	:    Union[str, Any]         =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]       )]

												return semantic_segmentation



