



import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     unittest.TestCase			):
     lowerCAmelCase       =      UnCLIPPipeline
     lowerCAmelCase       =      TEXT_TO_IMAGE_PARAMS - {
         """negative_prompt""",
         """height""",
         """width""",
         """negative_prompt_embeds""",
         """guidance_scale""",
         """prompt_embeds""",
         """cross_attention_kwargs""",
     }
     lowerCAmelCase       =      TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCAmelCase       =      [
         """generator""",
         """return_dict""",
         """prior_num_inference_steps""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     lowerCAmelCase       =      False





     @property
     def 						__snake_case     (							self			:				List[str]					)->Optional[Any]:
      return 3_2





     @property
     def 						__snake_case     (							self			:				List[Any]					)->List[Any]:
      return 3_2





     @property
     def 						__snake_case     (							self			:				List[str]					)->Dict:
      return self.time_input_dim





     @property
     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      return self.time_input_dim * 4





     @property
     def 						__snake_case     (							self			:				Optional[Any]					)->Any:
      return 1_0_0





     @property
     def 						__snake_case     (							self			:				int					)->Any:
      __SCREAMING_SNAKE_CASE							:     Any							=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
      return tokenizer





     @property
     def 						__snake_case     (							self			:				List[Any]					)->Any:
      torch.manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     Any							=						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=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 CLIPTextModelWithProjection(UpperCamelCase					)





     @property
     def 						__snake_case     (							self			:				Tuple					)->List[str]:
      torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     List[str]							=						{
          "num_attention_heads": 2,
          "attention_head_dim": 1_2,
          "embedding_dim": self.text_embedder_hidden_size,
          "num_layers": 1,
      }

      __SCREAMING_SNAKE_CASE							:     Tuple							=						PriorTransformer(**UpperCamelCase					)
      return model





     @property
     def 						__snake_case     (							self			:				Optional[int]					)->List[Any]:
      torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     Dict							=						{
          "clip_embeddings_dim": self.text_embedder_hidden_size,
          "time_embed_dim": self.time_embed_dim,
          "cross_attention_dim": self.cross_attention_dim,
      }

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						UnCLIPTextProjModel(**UpperCamelCase					)
      return model





     @property
     def 						__snake_case     (							self			:				Tuple					)->List[Any]:
      torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						{
          "sample_size": 3_2,
          # 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",
      }

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						UNetaDConditionModel(**UpperCamelCase					)
      return model





     @property
     def 						__snake_case     (							self			:				str					)->Optional[Any]:
      return {
          "sample_size": 6_4,
          "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			:				Any					)->Dict:
      torch.manual_seed(0					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						UNetaDModel(**self.dummy_super_res_kwargs					)
      return model





     @property
     def 						__snake_case     (							self			:				Dict					)->int:
      # seeded differently to get different unet than `self.dummy_super_res_first`
      torch.manual_seed(1					)

      __SCREAMING_SNAKE_CASE							:     Any							=						UNetaDModel(**self.dummy_super_res_kwargs					)
      return model





     def 						__snake_case     (							self			:				Union[str, Any]					)->str:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.dummy_prior
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.dummy_decoder
      __SCREAMING_SNAKE_CASE							:     int							=						self.dummy_text_proj
      __SCREAMING_SNAKE_CASE							:     str							=						self.dummy_text_encoder
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.dummy_tokenizer
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.dummy_super_res_first
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.dummy_super_res_last

      __SCREAMING_SNAKE_CASE							:     Dict							=						UnCLIPScheduler(
          variance_type="fixed_small_log"  ,	prediction_type="sample"  ,	num_train_timesteps=1_0_0_0  ,	clip_sample_range=5.0  ,	)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						UnCLIPScheduler(
          variance_type="learned_range"  ,	prediction_type="epsilon"  ,	num_train_timesteps=1_0_0_0  ,	)

      __SCREAMING_SNAKE_CASE							:     str							=						UnCLIPScheduler(
          variance_type="fixed_small_log"  ,	prediction_type="epsilon"  ,	num_train_timesteps=1_0_0_0  ,	)

      __SCREAMING_SNAKE_CASE							:     str							=						{
          "prior": prior,
          "decoder": decoder,
          "text_proj": text_proj,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "super_res_first": super_res_first,
          "super_res_last": super_res_last,
          "prior_scheduler": prior_scheduler,
          "decoder_scheduler": decoder_scheduler,
          "super_res_scheduler": super_res_scheduler,
      }

      return components





     def 						__snake_case     (							self			:				Tuple  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Tuple=0					)->List[str]:
      if str(UpperCamelCase					).startswith("mps"					):
       __SCREAMING_SNAKE_CASE							:     Any							=						torch.manual_seed(UpperCamelCase					)
      else:
       __SCREAMING_SNAKE_CASE							:     Any							=						torch.Generator(device=UpperCamelCase					).manual_seed(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						{
          "prompt": "horse",
          "generator": generator,
          "prior_num_inference_steps": 2,
          "decoder_num_inference_steps": 2,
          "super_res_num_inference_steps": 2,
          "output_type": "numpy",
      }
      return inputs





     def 						__snake_case     (							self			:				Dict					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     int							=						"cpu"

      __SCREAMING_SNAKE_CASE							:     str							=						self.get_dummy_components()

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.pipeline_class(**UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						pipe.to(UpperCamelCase					)

      pipe.set_progress_bar_config(disable=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						pipe(**self.get_dummy_inputs(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.images

      __SCREAMING_SNAKE_CASE							:     str							=						pipe(
          **self.get_dummy_inputs(UpperCamelCase					)  ,	return_dict=UpperCamelCase  ,	)[0]

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

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

      __SCREAMING_SNAKE_CASE							:     Tuple							=						np.array(
          [
              0.9_9_9_7,
              0.9_9_8_8,
              0.0_0_2_8,
              0.9_9_9_7,
              0.9_9_8_4,
              0.9_9_6_5,
              0.0_0_2_9,
              0.9_9_8_6,
              0.0_0_2_5,
          ]					)

      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			:				Tuple					)->str:
      __SCREAMING_SNAKE_CASE							:     str							=						torch.device("cpu"					)

      class    _SCREAMING_SNAKE_CASE						:
           lowerCAmelCase       =      1






      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.get_dummy_components()

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.pipeline_class(**UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						pipe.to(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						components["prior"]
      __SCREAMING_SNAKE_CASE							:     str							=						components["decoder"]
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						components["super_res_first"]
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						components["tokenizer"]
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						components["text_encoder"]

      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.Generator(device=UpperCamelCase					).manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     int							=						prior.dtype
      __SCREAMING_SNAKE_CASE							:     Dict							=						1

      __SCREAMING_SNAKE_CASE							:     Tuple							=						(batch_size, prior.config.embedding_dim)
      __SCREAMING_SNAKE_CASE							:     Dict							=						pipe.prepare_latents(
          UpperCamelCase  ,	dtype=UpperCamelCase  ,	device=UpperCamelCase  ,	generator=UpperCamelCase  ,	latents=UpperCamelCase  ,	scheduler=DummyScheduler()					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						pipe.prepare_latents(
          UpperCamelCase  ,	dtype=UpperCamelCase  ,	device=UpperCamelCase  ,	generator=UpperCamelCase  ,	latents=UpperCamelCase  ,	scheduler=DummyScheduler()					)

      __SCREAMING_SNAKE_CASE							:     int							=						(
          batch_size,
          super_res_first.config.in_channels // 2,
          super_res_first.config.sample_size,
          super_res_first.config.sample_size,
      )
      __SCREAMING_SNAKE_CASE							:     Dict							=						pipe.prepare_latents(
          UpperCamelCase  ,	dtype=UpperCamelCase  ,	device=UpperCamelCase  ,	generator=UpperCamelCase  ,	latents=UpperCamelCase  ,	scheduler=DummyScheduler()					)

      pipe.set_progress_bar_config(disable=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						"this is a prompt example"

      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.Generator(device=UpperCamelCase					).manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						pipe(
          [prompt]  ,	generator=UpperCamelCase  ,	prior_num_inference_steps=2  ,	decoder_num_inference_steps=2  ,	super_res_num_inference_steps=2  ,	prior_latents=UpperCamelCase  ,	decoder_latents=UpperCamelCase  ,	super_res_latents=UpperCamelCase  ,	output_type="np"  ,	)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						output.images

      __SCREAMING_SNAKE_CASE							:     List[str]							=						tokenizer(
          UpperCamelCase  ,	padding="max_length"  ,	max_length=tokenizer.model_max_length  ,	return_tensors="pt"  ,	)
      __SCREAMING_SNAKE_CASE							:     Dict							=						text_encoder(text_inputs.input_ids					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						text_inputs.attention_mask

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.Generator(device=UpperCamelCase					).manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						pipe(
          generator=UpperCamelCase  ,	prior_num_inference_steps=2  ,	decoder_num_inference_steps=2  ,	super_res_num_inference_steps=2  ,	prior_latents=UpperCamelCase  ,	decoder_latents=UpperCamelCase  ,	super_res_latents=UpperCamelCase  ,	text_model_output=UpperCamelCase  ,	text_attention_mask=UpperCamelCase  ,	output_type="np"  ,	)[0]

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





     @skip_mps
     def 						__snake_case     (							self			:				Dict					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch_device == "cpu"

      self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase  ,	expected_max_diff=0.0_1					)





     @skip_mps
     def 						__snake_case     (							self			:				Union[str, Any]					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch_device == "cpu"
      __SCREAMING_SNAKE_CASE							:     Dict							=						True
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						[
          "prior_num_inference_steps",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

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





     def 						__snake_case     (							self			:				List[str]					)->Any:
      __SCREAMING_SNAKE_CASE							:     Dict							=						[
          "prior_num_inference_steps",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]

      if torch_device == "mps":
       # TODO: MPS errors with larger batch sizes
       __SCREAMING_SNAKE_CASE							:     int							=						[2, 3]
       self._test_inference_batch_consistent(
           batch_sizes=UpperCamelCase  ,	additional_params_copy_to_batched_inputs=UpperCamelCase  ,	)
      else:
       self._test_inference_batch_consistent(
           additional_params_copy_to_batched_inputs=UpperCamelCase					)





     @skip_mps
     def 						__snake_case     (							self			:				str					)->List[Any]:
      return super().test_dict_tuple_outputs_equivalent()





     @skip_mps
     def 						__snake_case     (							self			:				Tuple					)->str:
      return super().test_save_load_local()






     @skip_mps
     def 						__snake_case     (							self			:				Dict					)->List[Any]:
      return super().test_save_load_optional_components()






@nightly
class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def 						__snake_case     (							self			:				Dict					)->Any:
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()






     def 						__snake_case     (							self			:				Optional[int]					)->Any:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/unclip/karlo_v1_alpha_horse_cpu.npy"					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"					)
      pipeline.set_progress_bar_config(disable=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Any							=						torch.manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     int							=						pipeline(
          "horse"  ,	num_images_per_prompt=1  ,	generator=UpperCamelCase  ,	output_type="np"  ,	)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						output.images[0]

      assert image.shape == (2_5_6, 2_5_6, 3)
      assert np.abs(expected_image - image					).max() < 1E-1






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





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





     def 						__snake_case     (							self			:				Tuple					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/unclip/karlo_v1_alpha_horse_fp16.npy"					)

      __SCREAMING_SNAKE_CASE							:     int							=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"  ,	torch_dtype=torch.floataa					)
      __SCREAMING_SNAKE_CASE							:     int							=						pipeline.to(UpperCamelCase					)
      pipeline.set_progress_bar_config(disable=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						torch.Generator(device="cpu"					).manual_seed(0					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						pipeline(
          "horse"  ,	generator=UpperCamelCase  ,	output_type="np"  ,	)

      __SCREAMING_SNAKE_CASE							:     Any							=						output.images[0]

      assert image.shape == (2_5_6, 2_5_6, 3)

      assert_mean_pixel_difference(UpperCamelCase  ,	UpperCamelCase					)






     def 						__snake_case     (							self			:				Tuple					)->List[Any]:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      __SCREAMING_SNAKE_CASE							:     List[Any]							=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"  ,	torch_dtype=torch.floataa					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						pipe.to(UpperCamelCase					)
      pipe.set_progress_bar_config(disable=UpperCamelCase					)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      __SCREAMING_SNAKE_CASE							:     Any							=						pipe(
          "horse"  ,	num_images_per_prompt=1  ,	prior_num_inference_steps=2  ,	decoder_num_inference_steps=2  ,	super_res_num_inference_steps=2  ,	output_type="np"  ,	)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.cuda.max_memory_allocated()
      # make sure that less than 7 GB is allocated
      assert mem_bytes < 7 * 1_0**9








'''simple docstring'''







import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DeiTImageProcessor






class 					lowerCAmelCase__  (    unittest.TestCase	):






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=7							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=18							,				__SCREAMING_SNAKE_CASE=30							,				__SCREAMING_SNAKE_CASE=4_00							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				__SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,				):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			size if size is not None else {'''height''': 20, '''width''': 20}
											lowercase_      :	List[Any]						=			crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

											lowercase_      :	List[str]						=			parent
											lowercase_      :	Optional[Any]						=			batch_size
											lowercase_      :	int						=			num_channels
											lowercase_      :	List[str]						=			image_size
											lowercase_      :	Optional[Any]						=			min_resolution
											lowercase_      :	Tuple						=			max_resolution
											lowercase_      :	Union[str, Any]						=			do_resize
											lowercase_      :	List[str]						=			size
											lowercase_      :	Tuple						=			do_center_crop
											lowercase_      :	Dict						=			crop_size
											lowercase_      :	int						=			do_normalize
											lowercase_      :	Optional[int]						=			image_mean
											lowercase_      :	List[str]						=			image_std






					def       _snake_case       (  self						):


											"""simple docstring"""





											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											    "do_normalize": self.do_normalize,
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											}







@require_torch
@require_vision
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	DeiTImageProcessor if is_vision_available() else None
					lowerCAmelCase_	  =	True






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			DeiTImageProcessingTester(self						)






					@property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	str						=			self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_resize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''size'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_center_crop'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''center_crop'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''do_normalize'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_mean'''						)						)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE							,				'''image_std'''						)						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size							,				{'''height''': 20, '''width''': 20}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 18, '''width''': 18}						)

											lowercase_      :	List[str]						=			self.image_processing_class.from_dict(self.image_processor_dict							,				size=42							,				crop_size=84						)
											self.assertEqual(image_processor.size							,				{'''height''': 42, '''width''': 42}						)
											self.assertEqual(image_processor.crop_size							,				{'''height''': 84, '''width''': 84}						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Tuple						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											lowercase_      :	Optional[int]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				Image.Image						)

											# Test not batched input
											lowercase_      :	List[Any]						=			image_processing(image_inputs[0]							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)

											# Test batched
											lowercase_      :	List[str]						=			image_processing(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											lowercase_      :	Tuple						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				numpify=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				np.ndarray						)

											# Test not batched input
											lowercase_      :	Dict						=			image_processing(image_inputs[0]							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)

											# Test batched
											lowercase_      :	Union[str, Any]						=			image_processing(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											lowercase_      :	List[Any]						=			prepare_image_inputs(self.image_processor_tester							,				equal_resolution=__SCREAMING_SNAKE_CASE							,				torchify=__SCREAMING_SNAKE_CASE						)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE							,				torch.Tensor						)

											# Test not batched input
											lowercase_      :	List[str]						=			image_processing(image_inputs[0]							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)

											# Test batched
											lowercase_      :	Optional[int]						=			image_processing(__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).pixel_values
											self.assertEqual(
											    encoded_images.shape							,				(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )							,				)



