



"""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 (
    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       :					str      					=					logging.get_logger(__name__)



class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       ['''pixel_values''']





		def __init__(					self							,    _lowercase = True							,    _lowercase = None							,    _lowercase = None							,    _lowercase = PILImageResampling.BILINEAR							,    _lowercase = True							,    _lowercase = 1 / 2_5_5							,    _lowercase = True							,    _lowercase = None							,    _lowercase = None							,    **_lowercase							,    ) ->						None:







								'''simple docstring'''

								super().__init__(**_lowercase   )
								snake_case_   : int						=			size if size is not None else {"""shortest_edge""": 3_8_4}
								snake_case_   : Optional[Any]						=			get_size_dict(_lowercase							,    default_to_square=_lowercase   )

								snake_case_   : Any						=			do_resize
								snake_case_   : int						=			size
								# Default value set here for backwards compatibility where the value in config is None
								snake_case_   : Any						=			crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								snake_case_   : Optional[Any]						=			resample
								snake_case_   : List[str]						=			do_rescale
								snake_case_   : List[Any]						=			rescale_factor
								snake_case_   : List[Any]						=			do_normalize
								snake_case_   : Dict						=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								snake_case_   : Union[str, Any]						=			image_std if image_std is not None else IMAGENET_STANDARD_STD





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







								'''simple docstring'''

								snake_case_   : Any						=			get_size_dict(_lowercase							,    default_to_square=_lowercase   )
								if "shortest_edge" not in size:
														raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'   )
								snake_case_   : List[Any]						=			size["""shortest_edge"""]

								if shortest_edge < 3_8_4:
														# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
														snake_case_   : Any						=			int(shortest_edge / crop_pct   )
														snake_case_   : Optional[int]						=			get_resize_output_image_size(_lowercase							,    size=_lowercase							,    default_to_square=_lowercase   )
														snake_case_   : Union[str, Any]						=			resize(image=_lowercase							,    size=_lowercase							,    resample=_lowercase							,    data_format=_lowercase							,    **_lowercase   )
														# then crop to (shortest_edge, shortest_edge)
														return center_crop(image=_lowercase							,    size=(shortest_edge, shortest_edge)							,    data_format=_lowercase							,    **_lowercase   )
								else:
														# warping (no cropping) when evaluated at 384 or larger
														return resize(
														    _lowercase							,    size=(shortest_edge, shortest_edge)							,    resample=_lowercase							,    data_format=_lowercase							,    **_lowercase   )





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase = None							,    **_lowercase							,    ) ->						Union[str, Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase							,    _lowercase							,    _lowercase = None							,    **_lowercase							,    ) ->						np.ndarray:







								'''simple docstring'''

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





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







								'''simple docstring'''

								snake_case_   : Optional[int]						=			do_resize if do_resize is not None else self.do_resize
								snake_case_   : List[str]						=			crop_pct if crop_pct is not None else self.crop_pct
								snake_case_   : str						=			resample if resample is not None else self.resample
								snake_case_   : int						=			do_rescale if do_rescale is not None else self.do_rescale
								snake_case_   : Optional[Any]						=			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_   : Optional[int]						=			image_mean if image_mean is not None else self.image_mean
								snake_case_   : Dict						=			image_std if image_std is not None else self.image_std

								snake_case_   : int						=			size if size is not None else self.size
								snake_case_   : int						=			get_size_dict(_lowercase							,    default_to_square=_lowercase   )

								snake_case_   : str						=			make_list_of_images(_lowercase   )

								if not valid_images(_lowercase   ):
														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_8_4 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.
								snake_case_   : str						=			[to_numpy_array(_lowercase   ) for image in images]

								if do_resize:
														snake_case_   : Tuple						=			[self.resize(image=_lowercase							,    size=_lowercase							,    crop_pct=_lowercase							,    resample=_lowercase   ) for image in images]

								if do_rescale:
														snake_case_   : int						=			[self.rescale(image=_lowercase							,    scale=_lowercase   ) for image in images]

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

								snake_case_   : List[str]						=			[to_channel_dimension_format(_lowercase							,    _lowercase   ) for image in images]

								snake_case_   : int						=			{"""pixel_values""": images}
								return BatchFeature(data=_lowercase							,    tensor_type=_lowercase   )






"""simple docstring"""





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


__lowerCAmelCase       :					Optional[Any]      					=					False



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

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





		@property
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								return 1_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								return 1_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

								return 3_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						Optional[Any]:







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : List[Any]						=			VQModel(
								    block_out_channels=[3_2, 6_4]							,    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   ) ->						List[Any]:







								'''simple docstring'''

								snake_case_   : int						=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
								return tokenizer





		@property
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : Dict						=			CLIPTextConfig(
								    bos_token_id=0							,    eos_token_id=2							,    hidden_size=self.text_embedder_hidden_size							,    intermediate_size=3_7							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=1_0_0_0							,    )
								return CLIPTextModel(_lowercase   )





		@property
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								torch.manual_seed(0   )

								snake_case_   : Union[str, Any]						=			1_2
								snake_case_   : Tuple						=			1_2

								snake_case_   : Tuple						=			{
								    """attention_bias""": True,
								    """cross_attention_dim""": 3_2,
								    """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""": 3_2,
								    """sample_size""": width,
								    """activation_fn""": """geglu-approximate""",
								}

								snake_case_   : Optional[Any]						=			TransformeraDModel(**_lowercase   )
								return model





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : str						=			"""cpu"""

								snake_case_   : List[str]						=			self.dummy_vqvae
								snake_case_   : Any						=			self.dummy_text_encoder
								snake_case_   : Tuple						=			self.dummy_tokenizer
								snake_case_   : int						=			self.dummy_transformer
								snake_case_   : int						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : Dict						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase   )

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

								snake_case_   : List[Any]						=			"""teddy bear playing in the pool"""

								snake_case_   : Dict						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : List[Any]						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Optional[int]						=			output.images

								snake_case_   : List[Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Dict						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

								snake_case_   : List[Any]						=			image[0, -3:, -3:, -1]
								snake_case_   : Any						=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : Dict						=			np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]   )

								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   ) ->						str:







								'''simple docstring'''

								snake_case_   : int						=			"""cpu"""

								snake_case_   : List[Any]						=			self.dummy_vqvae
								snake_case_   : Optional[int]						=			self.dummy_text_encoder
								snake_case_   : List[Any]						=			self.dummy_tokenizer
								snake_case_   : Union[str, Any]						=			self.dummy_transformer
								snake_case_   : str						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(
								    learnable=_lowercase							,    hidden_size=self.text_embedder_hidden_size							,    length=tokenizer.model_max_length   )

								snake_case_   : Union[str, Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : Any						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : Tuple						=			"""teddy bear playing in the pool"""

								snake_case_   : str						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Tuple						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Dict						=			output.images

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

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

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : int						=			np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]   )

								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    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

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

								snake_case_   : str						=			VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""   )
								snake_case_   : Optional[Any]						=			pipeline.to(_lowercase   )
								pipeline.set_progress_bar_config(disable=_lowercase   )

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

								snake_case_   : Union[str, Any]						=			output.images[0]

								assert image.shape == (2_5_6, 2_5_6, 3)
								assert np.abs(expected_image - image   ).max() < 2.0

