


import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    UnCLIPImageVariationPipeline
       snake_case      =    IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
       snake_case      =    IMAGE_VARIATION_BATCH_PARAMS

       snake_case      =    [
           "generator",
           "return_dict",
           "decoder_num_inference_steps",
           "super_res_num_inference_steps",
       ]
       snake_case      =    False



       @property
       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         return 32



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




         '''simple docstring'''





         return 32



       @property
       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         return self.time_input_dim



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




         '''simple docstring'''





         return self.time_input_dim * 4



       @property
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         return 100



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




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)
         return tokenizer



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




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				Tuple		     =					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(_SCREAMING_SNAKE_CASE		)



       @property
       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         torch.manual_seed(0		)
         A_      :				Dict		     =					CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size				,       projection_dim=self.text_embedder_hidden_size				,       num_hidden_layers=5				,       num_attention_heads=4				,       image_size=32				,       intermediate_size=37				,       patch_size=1				,       )
         return CLIPVisionModelWithProjection(_SCREAMING_SNAKE_CASE		)



       @property
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         torch.manual_seed(0		)

         A_      :				List[Any]		     =					{
             '''clip_embeddings_dim''': self.text_embedder_hidden_size,
             '''time_embed_dim''': self.time_embed_dim,
             '''cross_attention_dim''': self.cross_attention_dim,
         }

         A_      :				Tuple		     =					UnCLIPTextProjModel(**_SCREAMING_SNAKE_CASE		)
         return model



       @property
       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         torch.manual_seed(0		)

         A_      :				List[str]		     =					{
             '''sample_size''': 32,
             # RGB in channels
             '''in_channels''': 3,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 6,
             '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
             '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
             '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
             '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
             '''layers_per_block''': 1,
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': '''identity''',
         }

         A_      :				int		     =					UNetaDConditionModel(**_SCREAMING_SNAKE_CASE		)
         return model



       @property
       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         return {
             "sample_size": 64,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }



       @property
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         torch.manual_seed(0		)

         A_      :				Union[str, Any]		     =					UNetaDModel(**self.dummy_super_res_kwargs		)
         return model



       @property
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         torch.manual_seed(1		)

         A_      :				Dict		     =					UNetaDModel(**self.dummy_super_res_kwargs		)
         return model



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




         '''simple docstring'''





         A_      :				List[str]		     =					self.dummy_decoder
         A_      :				List[str]		     =					self.dummy_text_proj
         A_      :				Optional[Any]		     =					self.dummy_text_encoder
         A_      :				Any		     =					self.dummy_tokenizer
         A_      :				Optional[Any]		     =					self.dummy_super_res_first
         A_      :				Union[str, Any]		     =					self.dummy_super_res_last

         A_      :				str		     =					UnCLIPScheduler(
             variance_type='''learned_range'''				,       prediction_type='''epsilon'''				,       num_train_timesteps=1000				,       )

         A_      :				Optional[Any]		     =					UnCLIPScheduler(
             variance_type='''fixed_small_log'''				,       prediction_type='''epsilon'''				,       num_train_timesteps=1000				,       )

         A_      :				Optional[int]		     =					CLIPImageProcessor(crop_size=32				,       size=32		)

         A_      :				Optional[Any]		     =					self.dummy_image_encoder

         return {
             "decoder": decoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_proj": text_proj,
             "feature_extractor": feature_extractor,
             "image_encoder": image_encoder,
             "super_res_first": super_res_first,
             "super_res_last": super_res_last,
             "decoder_scheduler": decoder_scheduler,
             "super_res_scheduler": super_res_scheduler,
         }



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=True		)->Optional[Any]:




         '''simple docstring'''





         A_      :				int		     =					floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_SCREAMING_SNAKE_CASE		)		).to(_SCREAMING_SNAKE_CASE		)
         if str(_SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
           A_      :				Dict		     =					torch.manual_seed(_SCREAMING_SNAKE_CASE		)
         else:
           A_      :				List[str]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(_SCREAMING_SNAKE_CASE		)

         if pil_image:
           A_      :				str		     =					input_image * 0.5 + 0.5
           A_      :				int		     =					input_image.clamp(0				,       1		)
           A_      :				Any		     =					input_image.cpu().permute(0				,       2				,       3				,       1		).float().numpy()
           A_      :				Any		     =					DiffusionPipeline.numpy_to_pil(_SCREAMING_SNAKE_CASE		)[0]

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }



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




         '''simple docstring'''





         A_      :				int		     =					'''cpu'''

         A_      :				int		     =					self.get_dummy_components()

         A_      :				Any		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					pipe.to(_SCREAMING_SNAKE_CASE		)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				,       pil_image=_SCREAMING_SNAKE_CASE		)

         A_      :				Union[str, Any]		     =					pipe(**_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					output.images

         A_      :				Tuple		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				,       pil_image=_SCREAMING_SNAKE_CASE		)

         A_      :				Tuple		     =					pipe(
             **_SCREAMING_SNAKE_CASE				,       return_dict=_SCREAMING_SNAKE_CASE				,       )[0]

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

         assert image.shape == (1, 64, 64, 3)

         A_      :				Any		     =					np.array(
             [
                 0.9_9_9_7,
                 0.0_0_0_2,
                 0.9_9_9_7,
                 0.9_9_9_7,
                 0.9_9_6_9,
                 0.0_0_2_3,
                 0.9_9_9_7,
                 0.9_9_6_9,
                 0.9_9_7_0,
             ]		)

         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        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				int		     =					'''cpu'''

         A_      :				Optional[int]		     =					self.get_dummy_components()

         A_      :				Any		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[Any]		     =					pipe.to(_SCREAMING_SNAKE_CASE		)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Union[str, Any]		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				,       pil_image=_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					pipe(**_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					output.images

         A_      :				Tuple		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				,       pil_image=_SCREAMING_SNAKE_CASE		)

         A_      :				Tuple		     =					pipe(
             **_SCREAMING_SNAKE_CASE				,       return_dict=_SCREAMING_SNAKE_CASE				,       )[0]

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

         assert image.shape == (1, 64, 64, 3)

         A_      :				Dict		     =					np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]		)

         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        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					'''cpu'''

         A_      :				Any		     =					self.get_dummy_components()

         A_      :				Union[str, Any]		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         A_      :				Any		     =					pipe.to(_SCREAMING_SNAKE_CASE		)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				,       pil_image=_SCREAMING_SNAKE_CASE		)
         A_      :				Union[str, Any]		     =					[
             pipeline_inputs['''image'''],
             pipeline_inputs['''image'''],
         ]

         A_      :				str		     =					pipe(**_SCREAMING_SNAKE_CASE		)
         A_      :				str		     =					output.images

         A_      :				Tuple		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				,       pil_image=_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					[
             tuple_pipeline_inputs['''image'''],
             tuple_pipeline_inputs['''image'''],
         ]

         A_      :				List[str]		     =					pipe(
             **_SCREAMING_SNAKE_CASE				,       return_dict=_SCREAMING_SNAKE_CASE				,       )[0]

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

         assert image.shape == (2, 64, 64, 3)

         A_      :				Tuple		     =					np.array(
             [
                 0.9_9_9_7,
                 0.9_9_8_9,
                 0.0_0_0_8,
                 0.0_0_2_1,
                 0.9_9_6_0,
                 0.0_0_1_8,
                 0.0_0_1_4,
                 0.0_0_0_2,
                 0.9_9_3_3,
             ]		)

         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        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_      :				Dict		     =					torch.device('''cpu'''		)






         class   _lowerCamelCase :




                """simple docstring"""
                snake_case      =    1




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

         A_      :				str		     =					self.pipeline_class(**_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					pipe.to(_SCREAMING_SNAKE_CASE		)

         pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				List[Any]		     =					torch.Generator(device=_SCREAMING_SNAKE_CASE		).manual_seed(0		)
         A_      :				Union[str, Any]		     =					pipe.decoder.dtype
         A_      :				int		     =					1

         A_      :				List[Any]		     =					(
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         A_      :				int		     =					pipe.prepare_latents(
             _SCREAMING_SNAKE_CASE				,       dtype=_SCREAMING_SNAKE_CASE				,       device=_SCREAMING_SNAKE_CASE				,       generator=_SCREAMING_SNAKE_CASE				,       latents=_SCREAMING_SNAKE_CASE				,       scheduler=DummyScheduler()		)

         A_      :				Optional[int]		     =					(
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         A_      :				Optional[int]		     =					pipe.prepare_latents(
             _SCREAMING_SNAKE_CASE				,       dtype=_SCREAMING_SNAKE_CASE				,       device=_SCREAMING_SNAKE_CASE				,       generator=_SCREAMING_SNAKE_CASE				,       latents=_SCREAMING_SNAKE_CASE				,       scheduler=DummyScheduler()		)

         A_      :				Optional[int]		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				,       pil_image=_SCREAMING_SNAKE_CASE		)

         A_      :				List[str]		     =					pipe(
             **_SCREAMING_SNAKE_CASE				,       decoder_latents=_SCREAMING_SNAKE_CASE				,       super_res_latents=_SCREAMING_SNAKE_CASE		).images

         A_      :				Dict		     =					self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				,       pil_image=_SCREAMING_SNAKE_CASE		)
         # Don't pass image, instead pass embedding
         A_      :				str		     =					pipeline_inputs.pop('''image'''		)
         A_      :				List[str]		     =					pipe.image_encoder(_SCREAMING_SNAKE_CASE		).image_embeds

         A_      :				List[Any]		     =					pipe(
             **_SCREAMING_SNAKE_CASE				,       decoder_latents=_SCREAMING_SNAKE_CASE				,       super_res_latents=_SCREAMING_SNAKE_CASE				,       image_embeddings=_SCREAMING_SNAKE_CASE				,       ).images

         # make sure passing text embeddings manually is identical
         assert np.abs(img_out_a - img_out_a		).max() < 1e-4



       @skip_mps
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				Optional[Any]		     =					torch_device == '''cpu'''

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         A_      :				Union[str, Any]		     =					1e-2

         self._test_attention_slicing_forward_pass(
             test_max_difference=_SCREAMING_SNAKE_CASE				,       expected_max_diff=_SCREAMING_SNAKE_CASE		)



       @skip_mps
       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				str		     =					torch_device == '''cpu'''
         A_      :				Tuple		     =					True
         A_      :				int		     =					[
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=_SCREAMING_SNAKE_CASE				,       relax_max_difference=_SCREAMING_SNAKE_CASE				,       additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE				,       )



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




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					[
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         if torch_device == "mps":
           # TODO: MPS errors with larger batch sizes
           A_      :				Optional[Any]		     =					[2, 3]
           self._test_inference_batch_consistent(
               batch_sizes=_SCREAMING_SNAKE_CASE				,       additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE				,       )
         else:
           self._test_inference_batch_consistent(
               additional_params_copy_to_batched_inputs=_SCREAMING_SNAKE_CASE		)



       @skip_mps
       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         return super().test_dict_tuple_outputs_equivalent()



       @skip_mps
       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         return super().test_save_load_local()







       @skip_mps
       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         return super().test_save_load_optional_components()






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




       """simple docstring"""



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




         '''simple docstring'''





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







       def        _snake_case       (      self		)->Any:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''		)
         A_      :				Optional[int]		     =					load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''		)

         A_      :				Optional[Any]		     =					UnCLIPImageVariationPipeline.from_pretrained(
             '''kakaobrain/karlo-v1-alpha-image-variations'''				,       torch_dtype=torch.floataa		)
         A_      :				Tuple		     =					pipeline.to(_SCREAMING_SNAKE_CASE		)
         pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE		)

         A_      :				List[str]		     =					torch.Generator(device='''cpu'''		).manual_seed(0		)
         A_      :				Dict		     =					pipeline(
             _SCREAMING_SNAKE_CASE				,       generator=_SCREAMING_SNAKE_CASE				,       output_type='''np'''				,       )

         A_      :				Tuple		     =					output.images[0]

         assert image.shape == (256, 256, 3)

         assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       15		)








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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCamelCase								=	logging.get_logger(__name__)


if is_vision_available():
     import PIL






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    ["pixel_values"]



       def __init__(      self				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = 1 / 255				,       _SCREAMING_SNAKE_CASE = True				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = None				,       _SCREAMING_SNAKE_CASE = True				,       **_SCREAMING_SNAKE_CASE				,       )->None:




         '''simple docstring'''





         super().__init__(**_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					size if size is not None else {'''shortest_edge''': 224}
         A_      :				Any		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				Tuple		     =					crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
         A_      :				Dict		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''		)

         A_      :				str		     =					do_resize
         A_      :				Tuple		     =					size
         A_      :				Optional[Any]		     =					resample
         A_      :				Tuple		     =					do_center_crop
         A_      :				List[Any]		     =					crop_size
         A_      :				Optional[int]		     =					do_rescale
         A_      :				Tuple		     =					rescale_factor
         A_      :				Any		     =					do_normalize
         A_      :				int		     =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         A_      :				Any		     =					image_std if image_std is not None else OPENAI_CLIP_STD
         A_      :				Any		     =					do_convert_rgb



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




         '''simple docstring'''





         A_      :				Dict		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         if "shortest_edge" not in size:
           raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''		)
         A_      :				Any		     =					get_resize_output_image_size(_SCREAMING_SNAKE_CASE				,       size=size['''shortest_edge''']				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         return resize(_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





         A_      :				str		     =					get_size_dict(_SCREAMING_SNAKE_CASE		)
         if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''		)
         return center_crop(_SCREAMING_SNAKE_CASE				,       size=(size['''height'''], size['''width'''])				,       data_format=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->int:




         '''simple docstring'''





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



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE = None				,       **_SCREAMING_SNAKE_CASE				,       )->np.ndarray:




         '''simple docstring'''





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







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




         '''simple docstring'''





         A_      :				Optional[int]		     =					do_resize if do_resize is not None else self.do_resize
         A_      :				int		     =					size if size is not None else self.size
         A_      :				Optional[int]		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				List[Any]		     =					resample if resample is not None else self.resample
         A_      :				Any		     =					do_center_crop if do_center_crop is not None else self.do_center_crop
         A_      :				List[str]		     =					crop_size if crop_size is not None else self.crop_size
         A_      :				int		     =					get_size_dict(_SCREAMING_SNAKE_CASE				,       param_name='''crop_size'''				,       default_to_square=_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					do_rescale if do_rescale is not None else self.do_rescale
         A_      :				int		     =					rescale_factor if rescale_factor is not None else self.rescale_factor
         A_      :				int		     =					do_normalize if do_normalize is not None else self.do_normalize
         A_      :				Tuple		     =					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_      :				List[str]		     =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         A_      :				Optional[int]		     =					make_list_of_images(_SCREAMING_SNAKE_CASE		)

         if not valid_images(_SCREAMING_SNAKE_CASE		):
           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_center_crop and crop_size is None:
           raise ValueError('''Crop size must be specified if do_center_crop 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.'''		)

         # PIL RGBA images are converted to RGB
         if do_convert_rgb:
           A_      :				List[str]		     =					[convert_to_rgb(_SCREAMING_SNAKE_CASE		) for image in images]

         # All transformations expect numpy arrays.
         A_      :				int		     =					[to_numpy_array(_SCREAMING_SNAKE_CASE		) for image in images]

         if do_resize:
           A_      :				Tuple		     =					[self.resize(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE				,       resample=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_center_crop:
           A_      :				Union[str, Any]		     =					[self.center_crop(image=_SCREAMING_SNAKE_CASE				,       size=_SCREAMING_SNAKE_CASE		) for image in images]

         if do_rescale:
           A_      :				Tuple		     =					[self.rescale(image=_SCREAMING_SNAKE_CASE				,       scale=_SCREAMING_SNAKE_CASE		) for image in images]

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

         A_      :				str		     =					[to_channel_dimension_format(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		) for image in images]

         A_      :				Optional[Any]		     =					{'''pixel_values''': images}
         return BatchFeature(data=_SCREAMING_SNAKE_CASE				,       tensor_type=_SCREAMING_SNAKE_CASE		)




