

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


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  np.ndarray  ,      lowerCAmelCase:  Union[int, Iterable[int]]  ,      lowerCAmelCase:  bool  ,      lowerCAmelCase:  int )   ->       Tuple[int, int]:
				def constraint_to_multiple_of(lowerCAmelCase:  Any  ,      lowerCAmelCase:  Optional[int]  ,      lowerCAmelCase:  Any=0  ,      lowerCAmelCase:  Any=None ):
								_UpperCAmelCase       :		Any 		=    round(val / multiple ) * multiple

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

								if x < min_val:
												_UpperCAmelCase       :		Tuple 		=    math.ceil(val / multiple ) * multiple

								return x

				_UpperCAmelCase       :		Any 		=    (output_size, output_size) if isinstance(lowerCAmelCase  ,      lowerCAmelCase ) else output_size

				_UpperCAmelCase		,							_UpperCAmelCase       :		List[str] 		=    get_image_size(lowerCAmelCase )
				_UpperCAmelCase		,							_UpperCAmelCase       :		Optional[int] 		=    output_size

				# determine new height and width
				_UpperCAmelCase       :		Optional[Any] 		=    output_height / input_height
				_UpperCAmelCase       :		List[str] 		=    output_width / input_width

				if keep_aspect_ratio:
								# scale as little as possible
								if abs(1 - scale_width ) < abs(1 - scale_height ):
												# fit width
												_UpperCAmelCase       :		int 		=    scale_width
								else:
												# fit height
												_UpperCAmelCase       :		Union[str, Any] 		=    scale_height

				_UpperCAmelCase       :		int 		=    constraint_to_multiple_of(scale_height * input_height  ,      multiple=lowerCAmelCase )
				_UpperCAmelCase       :		List[str] 		=    constraint_to_multiple_of(scale_width * input_width  ,      multiple=lowerCAmelCase )

				return (new_height, new_width)





class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["pixel_values"]


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







										'''simple docstring'''


										super().__init__(**A_	)
										_UpperCAmelCase       :		Optional[Any] 		=    size if size is not None else {"height": 384, "width": 384}
										_UpperCAmelCase       :		Tuple 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		Union[str, Any] 		=    do_resize
										_UpperCAmelCase       :		int 		=    size
										_UpperCAmelCase       :		int 		=    keep_aspect_ratio
										_UpperCAmelCase       :		Dict 		=    ensure_multiple_of
										_UpperCAmelCase       :		Tuple 		=    resample
										_UpperCAmelCase       :		int 		=    do_rescale
										_UpperCAmelCase       :		Optional[Any] 		=    rescale_factor
										_UpperCAmelCase       :		Dict 		=    do_normalize
										_UpperCAmelCase       :		str 		=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										_UpperCAmelCase       :		Dict 		=    image_std if image_std is not None else IMAGENET_STANDARD_STD


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = False						,					A_ = 1						,					A_ = PILImageResampling.BICUBIC						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		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()}'	)
										_UpperCAmelCase       :		str 		=    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        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       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_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase       :		str 		=    size if size is not None else self.size
										_UpperCAmelCase       :		str 		=    get_size_dict(A_	)
										_UpperCAmelCase       :		int 		=    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
										_UpperCAmelCase       :		Tuple 		=    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
										_UpperCAmelCase       :		int 		=    resample if resample is not None else self.resample
										_UpperCAmelCase       :		Union[str, Any] 		=    do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase       :		Optional[Any] 		=    rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase       :		Any 		=    do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase       :		Union[str, Any] 		=    image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase       :		Optional[int] 		=    image_std if image_std is not None else self.image_std

										_UpperCAmelCase       :		int 		=    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.
										_UpperCAmelCase       :		List[str] 		=    [to_numpy_array(A_	) for image in images]

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

										if do_rescale:
														_UpperCAmelCase       :		List[Any] 		=    [self.rescale(image=A_						,					scale=A_	) for image in images]

										if do_normalize:
														_UpperCAmelCase       :		Any 		=    [self.normalize(image=A_						,					mean=A_						,					std=A_	) for image in images]

										_UpperCAmelCase       :		Optional[int] 		=    [to_channel_dimension_format(A_						,					A_	) for image in images]

										_UpperCAmelCase       :		Optional[int] 		=    {"pixel_values": images}
										return BatchFeature(data=A_						,					tensor_type=A_	)





						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    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_	):
																		_UpperCAmelCase       :		Tuple 		=    target_sizes.numpy()

														_UpperCAmelCase       :		List[Any] 		=    []

														for idx in range(len(A_	)	):
																		_UpperCAmelCase       :		Any 		=    torch.nn.functional.interpolate(
																		    logits[idx].unsqueeze(dim=0	)						,					size=target_sizes[idx]						,					mode="bilinear"						,					align_corners=A_	)
																		_UpperCAmelCase       :		Optional[int] 		=    resized_logits[0].argmax(dim=0	)
																		semantic_segmentation.append(A_	)
										else:
														_UpperCAmelCase       :		List[str] 		=    logits.argmax(dim=1	)
														_UpperCAmelCase       :		List[Any] 		=    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]	)]

										return semantic_segmentation





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


SCREAMING_SNAKE_CASE_        =      False

class 				a  (	unittest.TestCase				):


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 12


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										return 32


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		Optional[int] 		=    VQModel(
										    block_out_channels=[32, 64]						,					in_channels=3						,					out_channels=3						,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]						,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]						,					latent_channels=3						,					num_vq_embeddings=self.num_embed						,					vq_embed_dim=3						,					)
										return model


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)
										_UpperCAmelCase       :		int 		=    CLIPTextConfig(
										    bos_token_id=0						,					eos_token_id=2						,					hidden_size=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 CLIPTextModel(A_	)


						@property
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										torch.manual_seed(0	)

										_UpperCAmelCase       :		Optional[int] 		=    12
										_UpperCAmelCase       :		Union[str, Any] 		=    12

										_UpperCAmelCase       :		str 		=    {
										    "attention_bias": True,
										    "cross_attention_dim": 32,
										    "attention_head_dim": height * width,
										    "num_attention_heads": 1,
										    "num_vector_embeds": self.num_embed,
										    "num_embeds_ada_norm": self.num_embeds_ada_norm,
										    "norm_num_groups": 32,
										    "sample_size": width,
										    "activation_fn": "geglu-approximate",
										}

										_UpperCAmelCase       :		Any 		=    TransformeraDModel(**A_	)
										return model


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    "cpu"

										_UpperCAmelCase       :		List[str] 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Any 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		Optional[int] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Union[str, Any] 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Union[str, Any] 		=    LearnedClassifierFreeSamplingEmbeddings(learnable=A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Optional[int] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Union[str, Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		List[Any] 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		List[Any] 		=    output.images

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Optional[Any] 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		int 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		List[str] 		=    np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]	)

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





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    "cpu"

										_UpperCAmelCase       :		Tuple 		=    self.dummy_vqvae
										_UpperCAmelCase       :		Optional[Any] 		=    self.dummy_text_encoder
										_UpperCAmelCase       :		List[str] 		=    self.dummy_tokenizer
										_UpperCAmelCase       :		List[Any] 		=    self.dummy_transformer
										_UpperCAmelCase       :		Dict 		=    VQDiffusionScheduler(self.num_embed	)
										_UpperCAmelCase       :		Dict 		=    LearnedClassifierFreeSamplingEmbeddings(
										    learnable=A_						,					hidden_size=self.text_embedder_hidden_size						,					length=tokenizer.model_max_length	)

										_UpperCAmelCase       :		Optional[int] 		=    VQDiffusionPipeline(
										    vqvae=A_						,					text_encoder=A_						,					tokenizer=A_						,					transformer=A_						,					scheduler=A_						,					learned_classifier_free_sampling_embeddings=A_						,					)
										_UpperCAmelCase       :		Union[str, Any] 		=    pipe.to(A_	)
										pipe.set_progress_bar_config(disable=A_	)

										_UpperCAmelCase       :		int 		=    "teddy bear playing in the pool"

										_UpperCAmelCase       :		Dict 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe([prompt]						,					generator=A_						,					num_inference_steps=2						,					output_type="np"	)
										_UpperCAmelCase       :		Union[str, Any] 		=    output.images

										_UpperCAmelCase       :		Optional[Any] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		int 		=    pipe(
										    [prompt]						,					generator=A_						,					output_type="np"						,					return_dict=A_						,					num_inference_steps=2	)[0]

										_UpperCAmelCase       :		Any 		=    image[0, -3:, -3:, -1]
										_UpperCAmelCase       :		Optional[int] 		=    image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 24, 24, 3)

										_UpperCAmelCase       :		Union[str, Any] 		=    np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()





						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"	)

										_UpperCAmelCase       :		Dict 		=    VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"	)
										_UpperCAmelCase       :		Optional[int] 		=    pipeline.to(A_	)
										pipeline.set_progress_bar_config(disable=A_	)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										_UpperCAmelCase       :		Optional[int] 		=    torch.Generator(device=A_	).manual_seed(0	)
										_UpperCAmelCase       :		Optional[Any] 		=    pipeline(
										    "teddy bear playing in the pool"						,					num_images_per_prompt=1						,					generator=A_						,					output_type="np"						,					)

										_UpperCAmelCase       :		str 		=    output.images[0]

										assert image.shape == (256, 256, 3)
										assert np.abs(expected_image - image	).max() < 2.0


