

"""simple docstring"""





import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def  __lowerCAmelCase				( self    :       Union[str, Any]	):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					@property
					def  __lowerCAmelCase				( self    :       Tuple	):
						lowerCAmelCase__			:		List[str]						       =			1
						lowerCAmelCase__			:		Union[str, Any]						       =			3
						lowerCAmelCase__			:		str						       =			(3_2, 3_2)

						lowerCAmelCase__			:		Any						       =			floats_tensor((batch_size, num_channels) + sizes     ,rng=random.Random(0	)	).to(lowercase_	)
						return image







					@property
					def  __lowerCAmelCase				( self    :       Optional[int]	):
						torch.manual_seed(0	)
						lowerCAmelCase__			:		int						       =			UNetaDConditionModel(
						    block_out_channels=(3_2, 6_4)     ,layers_per_block=2     ,sample_size=3_2     ,in_channels=4     ,out_channels=4     ,down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')     ,up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')     ,cross_attention_dim=3_2     ,)
						return model







					@property
					def  __lowerCAmelCase				( self    :       List[Any]	):
						torch.manual_seed(0	)
						lowerCAmelCase__			:		Union[str, Any]						       =			AutoencoderKL(
						    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=4     ,)
						return model







					@property
					def  __lowerCAmelCase				( self    :       List[Any]	):
						torch.manual_seed(0	)
						lowerCAmelCase__			:		Any						       =			CLIPTextConfig(
						    bos_token_id=0     ,eos_token_id=2     ,hidden_size=3_2     ,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  __lowerCAmelCase				( self    :       Dict	):
						def extract(*lowercase_    :       Tuple     ,**lowercase_    :       Optional[Any]	):







							class        SCREAMING_SNAKE_CASE      :






												"""simple docstring"""



												def __init__( self    :       int	):
													lowerCAmelCase__			:		List[Any]						       =			torch.ones([0]	)







												def  __lowerCAmelCase				( self    :       int     ,lowercase_    :       Optional[Any]	):
													self.pixel_values.to(lowercase_	)
													return self

							return Out()

						return extract







					def  __lowerCAmelCase				( self    :       Union[str, Any]	):
						lowerCAmelCase__			:		str						       =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__			:		int						       =			self.dummy_cond_unet
						lowerCAmelCase__			:		Optional[Any]						       =			DDIMScheduler(
						    beta_start=0.0_0085     ,beta_end=0.012     ,beta_schedule='''scaled_linear'''     ,clip_sample=lowercase_     ,set_alpha_to_one=lowercase_     ,)

						lowerCAmelCase__			:		str						       =			self.dummy_vae
						lowerCAmelCase__			:		List[str]						       =			self.dummy_text_encoder
						lowerCAmelCase__			:		List[Any]						       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

						# make sure here that pndm scheduler skips prk
						lowerCAmelCase__			:		str						       =			StableDiffusionPipeline(
						    unet=lowercase_     ,scheduler=lowercase_     ,vae=lowercase_     ,text_encoder=lowercase_     ,tokenizer=lowercase_     ,safety_checker=lowercase_     ,feature_extractor=self.dummy_extractor     ,)
						lowerCAmelCase__			:		Tuple						       =			sd_pipe.to(lowercase_	)
						sd_pipe.set_progress_bar_config(disable=lowercase_	)

						lowerCAmelCase__			:		Optional[int]						       =			'''A painting of a squirrel eating a burger'''

						lowerCAmelCase__			:		Tuple						       =			torch.Generator(device=lowercase_	).manual_seed(0	)
						lowerCAmelCase__			:		List[Any]						       =			sd_pipe([prompt]     ,generator=lowercase_     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type='''np'''	)
						lowerCAmelCase__			:		Tuple						       =			output.images

						lowerCAmelCase__			:		Optional[Any]						       =			torch.Generator(device=lowercase_	).manual_seed(0	)
						lowerCAmelCase__			:		Tuple						       =			sd_pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type='''np'''     ,return_dict=lowercase_     ,)[0]

						lowerCAmelCase__			:		Tuple						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		Optional[Any]						       =			image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						lowerCAmelCase__			:		int						       =			np.array([0.5753, 0.6114, 0.5001, 0.5034, 0.5470, 0.4729, 0.4971, 0.4867, 0.4867]	)

						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  __lowerCAmelCase				( self    :       Optional[Any]	):
						lowerCAmelCase__			:		List[Any]						       =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
						lowerCAmelCase__			:		str						       =			self.dummy_cond_unet
						lowerCAmelCase__			:		int						       =			PNDMScheduler(skip_prk_steps=lowercase_	)
						lowerCAmelCase__			:		Tuple						       =			self.dummy_vae
						lowerCAmelCase__			:		Any						       =			self.dummy_text_encoder
						lowerCAmelCase__			:		Union[str, Any]						       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

						# make sure here that pndm scheduler skips prk
						lowerCAmelCase__			:		Any						       =			StableDiffusionPipeline(
						    unet=lowercase_     ,scheduler=lowercase_     ,vae=lowercase_     ,text_encoder=lowercase_     ,tokenizer=lowercase_     ,safety_checker=lowercase_     ,feature_extractor=self.dummy_extractor     ,)
						lowerCAmelCase__			:		Union[str, Any]						       =			sd_pipe.to(lowercase_	)
						sd_pipe.set_progress_bar_config(disable=lowercase_	)

						lowerCAmelCase__			:		str						       =			'''A painting of a squirrel eating a burger'''
						lowerCAmelCase__			:		List[str]						       =			torch.Generator(device=lowercase_	).manual_seed(0	)
						lowerCAmelCase__			:		Optional[int]						       =			sd_pipe([prompt]     ,generator=lowercase_     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type='''np'''	)

						lowerCAmelCase__			:		str						       =			output.images

						lowerCAmelCase__			:		List[str]						       =			torch.Generator(device=lowercase_	).manual_seed(0	)
						lowerCAmelCase__			:		str						       =			sd_pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=6.0     ,num_inference_steps=2     ,output_type='''np'''     ,return_dict=lowercase_     ,)[0]

						lowerCAmelCase__			:		Dict						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		Optional[int]						       =			image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 6_4, 6_4, 3)
						lowerCAmelCase__			:		int						       =			np.array([0.5122, 0.5712, 0.4825, 0.5053, 0.5646, 0.4769, 0.5179, 0.4894, 0.4994]	)

						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  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Optional[Any]						       =			StableDiffusionPipeline.from_pretrained(
						    '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''     ,safety_checker=lowercase_	)
						assert isinstance(lowercase_     ,lowercase_	)
						assert isinstance(pipe.scheduler     ,lowercase_	)
						assert pipe.safety_checker is None

						lowerCAmelCase__			:		Dict						       =			pipe('''example prompt'''     ,num_inference_steps=2	).images[0]
						assert image is not None

						# check that there's no error when saving a pipeline with one of the models being None
						with tempfile.TemporaryDirectory() as tmpdirname:
							pipe.save_pretrained(lowercase_	)
							lowerCAmelCase__			:		Optional[int]						       =			StableDiffusionPipeline.from_pretrained(lowercase_	)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						lowerCAmelCase__			:		Tuple						       =			pipe('''example prompt'''     ,num_inference_steps=2	).images[0]
						assert image is not None







					@unittest.skipIf(torch_device != '''cuda'''     ,'''This test requires a GPU'''	)
					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		str						       =			self.dummy_cond_unet
						lowerCAmelCase__			:		List[str]						       =			PNDMScheduler(skip_prk_steps=lowercase_	)
						lowerCAmelCase__			:		List[str]						       =			self.dummy_vae
						lowerCAmelCase__			:		int						       =			self.dummy_text_encoder
						lowerCAmelCase__			:		List[Any]						       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	)

						# put models in fp16
						lowerCAmelCase__			:		Optional[int]						       =			unet.half()
						lowerCAmelCase__			:		List[Any]						       =			vae.half()
						lowerCAmelCase__			:		Union[str, Any]						       =			bert.half()

						# make sure here that pndm scheduler skips prk
						lowerCAmelCase__			:		List[str]						       =			StableDiffusionPipeline(
						    unet=lowercase_     ,scheduler=lowercase_     ,vae=lowercase_     ,text_encoder=lowercase_     ,tokenizer=lowercase_     ,safety_checker=lowercase_     ,feature_extractor=self.dummy_extractor     ,)
						lowerCAmelCase__			:		Optional[Any]						       =			sd_pipe.to(lowercase_	)
						sd_pipe.set_progress_bar_config(disable=lowercase_	)

						lowerCAmelCase__			:		Dict						       =			'''A painting of a squirrel eating a burger'''
						lowerCAmelCase__			:		Union[str, Any]						       =			sd_pipe([prompt]     ,num_inference_steps=2     ,output_type='''np'''	).images

						assert image.shape == (1, 6_4, 6_4, 3)







@nightly
@require_torch_gpu
class        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def  __lowerCAmelCase				( self    :       Optional[int]	):
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







					def  __lowerCAmelCase				( self    :       List[Any]	):
						lowerCAmelCase__			:		int						       =			'''cuda'''
						lowerCAmelCase__			:		Optional[int]						       =			StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	)
						lowerCAmelCase__			:		Tuple						       =			pipe.to(lowercase_	)
						pipe.set_progress_bar_config(disable=lowercase_	)

						lowerCAmelCase__			:		Optional[int]						       =			'''a photo of a cat'''
						lowerCAmelCase__			:		Dict						       =			{
						    '''editing_prompt''': ['''sunglasses'''],
						    '''reverse_editing_direction''': [False],
						    '''edit_warmup_steps''': 1_0,
						    '''edit_guidance_scale''': 6,
						    '''edit_threshold''': 0.95,
						    '''edit_momentum_scale''': 0.5,
						    '''edit_mom_beta''': 0.6,
						}

						lowerCAmelCase__			:		int						       =			3
						lowerCAmelCase__			:		List[str]						       =			7

						# no sega enabled
						lowerCAmelCase__			:		List[Any]						       =			torch.Generator(lowercase_	)
						generator.manual_seed(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=lowercase_     ,num_inference_steps=5_0     ,output_type='''np'''     ,width=5_1_2     ,height=5_1_2     ,)

						lowerCAmelCase__			:		Dict						       =			output.images
						lowerCAmelCase__			:		Optional[int]						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		str						       =			[
						    0.3467_3113,
						    0.3849_2733,
						    0.3759_7352,
						    0.3408_6335,
						    0.3565_0748,
						    0.3557_9205,
						    0.338_4763,
						    0.3434_0236,
						    0.357_3271,
						]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=lowercase_     ,num_inference_steps=5_0     ,output_type='''np'''     ,width=5_1_2     ,height=5_1_2     ,**lowercase_     ,)

						lowerCAmelCase__			:		Optional[Any]						       =			output.images
						lowerCAmelCase__			:		Optional[Any]						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		Optional[Any]						       =			[
						    0.4188_7826,
						    0.3772_8766,
						    0.3013_8272,
						    0.4141_6335,
						    0.4166_4985,
						    0.3628_3392,
						    0.3619_1246,
						    0.4336_4465,
						    0.4300_1732,
						]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







					def  __lowerCAmelCase				( self    :       Optional[Any]	):
						lowerCAmelCase__			:		Optional[Any]						       =			'''cuda'''
						lowerCAmelCase__			:		Tuple						       =			StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	)
						lowerCAmelCase__			:		Tuple						       =			pipe.to(lowercase_	)
						pipe.set_progress_bar_config(disable=lowercase_	)

						lowerCAmelCase__			:		Any						       =			'''an image of a crowded boulevard, realistic, 4k'''
						lowerCAmelCase__			:		List[str]						       =			{
						    '''editing_prompt''': '''crowd, crowded, people''',
						    '''reverse_editing_direction''': True,
						    '''edit_warmup_steps''': 1_0,
						    '''edit_guidance_scale''': 8.3,
						    '''edit_threshold''': 0.9,
						    '''edit_momentum_scale''': 0.5,
						    '''edit_mom_beta''': 0.6,
						}

						lowerCAmelCase__			:		Optional[int]						       =			9
						lowerCAmelCase__			:		Tuple						       =			7

						# no sega enabled
						lowerCAmelCase__			:		List[Any]						       =			torch.Generator(lowercase_	)
						generator.manual_seed(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=lowercase_     ,num_inference_steps=5_0     ,output_type='''np'''     ,width=5_1_2     ,height=5_1_2     ,)

						lowerCAmelCase__			:		Dict						       =			output.images
						lowerCAmelCase__			:		str						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		int						       =			[
						    0.4349_7998,
						    0.9181_4065,
						    0.754_0739,
						    0.5558_0205,
						    0.846_7265,
						    0.538_9691,
						    0.6257_4506,
						    0.5889_7763,
						    0.5092_6757,
						]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(lowercase_	)
						lowerCAmelCase__			:		List[str]						       =			pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=lowercase_     ,num_inference_steps=5_0     ,output_type='''np'''     ,width=5_1_2     ,height=5_1_2     ,**lowercase_     ,)

						lowerCAmelCase__			:		List[Any]						       =			output.images
						lowerCAmelCase__			:		List[Any]						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		Any						       =			[
						    0.308_9719,
						    0.3050_0144,
						    0.2901_6042,
						    0.3063_0964,
						    0.32_5687,
						    0.2941_9225,
						    0.290_8091,
						    0.2872_3598,
						    0.2769_6294,
						]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







					def  __lowerCAmelCase				( self    :       str	):
						lowerCAmelCase__			:		List[Any]						       =			'''cuda'''
						lowerCAmelCase__			:		Tuple						       =			StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''	)
						lowerCAmelCase__			:		str						       =			pipe.to(lowercase_	)
						pipe.set_progress_bar_config(disable=lowercase_	)

						lowerCAmelCase__			:		List[str]						       =			'''a castle next to a river'''
						lowerCAmelCase__			:		Any						       =			{
						    '''editing_prompt''': ['''boat on a river, boat''', '''monet, impression, sunrise'''],
						    '''reverse_editing_direction''': False,
						    '''edit_warmup_steps''': [1_5, 1_8],
						    '''edit_guidance_scale''': 6,
						    '''edit_threshold''': [0.9, 0.8],
						    '''edit_momentum_scale''': 0.5,
						    '''edit_mom_beta''': 0.6,
						}

						lowerCAmelCase__			:		List[Any]						       =			4_8
						lowerCAmelCase__			:		Any						       =			7

						# no sega enabled
						lowerCAmelCase__			:		Union[str, Any]						       =			torch.Generator(lowercase_	)
						generator.manual_seed(lowercase_	)
						lowerCAmelCase__			:		int						       =			pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=lowercase_     ,num_inference_steps=5_0     ,output_type='''np'''     ,width=5_1_2     ,height=5_1_2     ,)

						lowerCAmelCase__			:		Tuple						       =			output.images
						lowerCAmelCase__			:		Optional[int]						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		str						       =			[
						    0.7516_3555,
						    0.7603_7145,
						    0.6_1785,
						    0.918_9673,
						    0.862_7701,
						    0.8518_9694,
						    0.851_2813,
						    0.8701_2076,
						    0.831_2857,
						]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(lowercase_	)
						lowerCAmelCase__			:		List[Any]						       =			pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=lowercase_     ,num_inference_steps=5_0     ,output_type='''np'''     ,width=5_1_2     ,height=5_1_2     ,**lowercase_     ,)

						lowerCAmelCase__			:		Any						       =			output.images
						lowerCAmelCase__			:		Union[str, Any]						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		List[Any]						       =			[
						    0.7355_3365,
						    0.753_7271,
						    0.7434_1905,
						    0.6648_0356,
						    0.647_2925,
						    0.6303_9416,
						    0.6481_2905,
						    0.674_9717,
						    0.651_7102,
						]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







					def  __lowerCAmelCase				( self    :       Union[str, Any]	):
						lowerCAmelCase__			:		Optional[int]						       =			'''cuda'''
						lowerCAmelCase__			:		List[Any]						       =			StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''     ,torch_dtype=torch.floataa	)
						lowerCAmelCase__			:		List[Any]						       =			pipe.to(lowercase_	)
						pipe.set_progress_bar_config(disable=lowercase_	)

						lowerCAmelCase__			:		int						       =			'''a photo of a cat'''
						lowerCAmelCase__			:		Any						       =			{
						    '''editing_prompt''': ['''sunglasses'''],
						    '''reverse_editing_direction''': [False],
						    '''edit_warmup_steps''': 1_0,
						    '''edit_guidance_scale''': 6,
						    '''edit_threshold''': 0.95,
						    '''edit_momentum_scale''': 0.5,
						    '''edit_mom_beta''': 0.6,
						}

						lowerCAmelCase__			:		Optional[Any]						       =			3
						lowerCAmelCase__			:		Any						       =			7

						# no sega enabled
						lowerCAmelCase__			:		Dict						       =			torch.Generator(lowercase_	)
						generator.manual_seed(lowercase_	)
						lowerCAmelCase__			:		List[str]						       =			pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=lowercase_     ,num_inference_steps=5_0     ,output_type='''np'''     ,width=5_1_2     ,height=5_1_2     ,)

						lowerCAmelCase__			:		List[Any]						       =			output.images
						lowerCAmelCase__			:		Tuple						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		Tuple						       =			[
						    0.3488_7695,
						    0.387_6953,
						    0.375,
						    0.3442_3828,
						    0.358_1543,
						    0.3571_7773,
						    0.338_3789,
						    0.3457_0312,
						    0.35_9375,
						]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(lowercase_	)
						lowerCAmelCase__			:		str						       =			pipe(
						    [prompt]     ,generator=lowercase_     ,guidance_scale=lowercase_     ,num_inference_steps=5_0     ,output_type='''np'''     ,width=5_1_2     ,height=5_1_2     ,**lowercase_     ,)

						lowerCAmelCase__			:		List[str]						       =			output.images
						lowerCAmelCase__			:		int						       =			image[0, -3:, -3:, -1]
						lowerCAmelCase__			:		Any						       =			[
						    0.4228_5156,
						    0.3691_4062,
						    0.2907_7148,
						    0.4204_1016,
						    0.4191_8945,
						    0.3549_8047,
						    0.361_8164,
						    0.442_3828,
						    0.4311_5234,
						]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)

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







"""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 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, logging


__UpperCamelCase						:   List[Any]							   =	logging.get_logger(__name__)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= ["pixel_values"]







					def __init__( self    :       Tuple     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Dict[str, int]] = None     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       bool = True     ,lowercase_    :       Union[int, float] = 1 / 2_5_5     ,lowercase_    :       bool = True     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,**lowercase_    :       Tuple     ,):
						super().__init__(**lowercase_	)
						lowerCAmelCase__			:		Any						       =			size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__			:		Tuple						       =			get_size_dict(lowercase_	)
						lowerCAmelCase__			:		Tuple						       =			do_resize
						lowerCAmelCase__			:		Tuple						       =			do_rescale
						lowerCAmelCase__			:		Union[str, Any]						       =			do_normalize
						lowerCAmelCase__			:		Optional[int]						       =			size
						lowerCAmelCase__			:		List[Any]						       =			resample
						lowerCAmelCase__			:		int						       =			rescale_factor
						lowerCAmelCase__			:		int						       =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__			:		Any						       =			image_std if image_std is not None else IMAGENET_STANDARD_STD







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       Dict[str, int]     ,lowercase_    :       PILImageResampling = PILImageResampling.BILINEAR     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       str     ,):
						lowerCAmelCase__			:		Union[str, Any]						       =			get_size_dict(lowercase_	)
						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()}'	)
						return resize(
						    lowercase_     ,size=(size['''height'''], size['''width'''])     ,resample=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       np.ndarray     ,lowercase_    :       float     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       Tuple	):
						return rescale(lowercase_     ,scale=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       Dict     ,lowercase_    :       np.ndarray     ,lowercase_    :       Union[float, List[float]]     ,lowercase_    :       Union[float, List[float]]     ,lowercase_    :       Optional[Union[str, ChannelDimension]] = None     ,**lowercase_    :       Union[str, Any]     ,):
						return normalize(lowercase_     ,mean=lowercase_     ,std=lowercase_     ,data_format=lowercase_     ,**lowercase_	)







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       ImageInput     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Dict[str, int] = None     ,lowercase_    :       PILImageResampling = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[float] = None     ,lowercase_    :       Optional[bool] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[float, List[float]]] = None     ,lowercase_    :       Optional[Union[str, TensorType]] = None     ,lowercase_    :       Union[str, ChannelDimension] = ChannelDimension.FIRST     ,**lowercase_    :       List[Any]     ,):
						lowerCAmelCase__			:		Optional[int]						       =			do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__			:		Any						       =			do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__			:		Dict						       =			do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__			:		Dict						       =			resample if resample is not None else self.resample
						lowerCAmelCase__			:		Dict						       =			rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__			:		Optional[Any]						       =			image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__			:		str						       =			image_std if image_std is not None else self.image_std

						lowerCAmelCase__			:		Any						       =			size if size is not None else self.size
						lowerCAmelCase__			:		Dict						       =			get_size_dict(lowercase_	)

						lowerCAmelCase__			:		Union[str, Any]						       =			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:
							raise ValueError('''Size 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.'''	)

						# All transformations expect numpy arrays.
						lowerCAmelCase__			:		Dict						       =			[to_numpy_array(lowercase_	) for image in images]

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

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

						if do_normalize:
							lowerCAmelCase__			:		Dict						       =			[self.normalize(image=lowercase_     ,mean=lowercase_     ,std=lowercase_	) for image in images]

						lowerCAmelCase__			:		Tuple						       =			[to_channel_dimension_format(lowercase_     ,lowercase_	) for image in images]

						lowerCAmelCase__			:		int						       =			{'''pixel_values''': images}
						return BatchFeature(data=lowercase_     ,tensor_type=lowercase_	)




