

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     __snake_case    (				snake_case__			,	unittest.TestCase  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							UnCLIPImageVariationPipeline
       __SCREAMING_SNAKE_CASE				    =							IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
       __SCREAMING_SNAKE_CASE				    =							IMAGE_VARIATION_BATCH_PARAMS

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


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






              """simple docstring"""


              return 32


       @property
       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              return 32


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






              """simple docstring"""


              return self.time_input_dim


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






              """simple docstring"""


              return self.time_input_dim * 4


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return 1_00


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


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


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              torch.manual_seed(0   )
              __snake_case       	=							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=10_00      , )
              return CLIPTextModelWithProjection(_UpperCamelCase   )


       @property
       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              torch.manual_seed(0   )
              __snake_case       	=							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(_UpperCamelCase   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              torch.manual_seed(0   )

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

              __snake_case       	=							UnCLIPTextProjModel(**_UpperCamelCase   )
              return model


       @property
       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              torch.manual_seed(0   )

              __snake_case       	=							{
                  """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""",
              }

              __snake_case       	=							UNetaDConditionModel(**_UpperCamelCase   )
              return model


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






              """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 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              torch.manual_seed(0   )

              __snake_case       	=							UNetaDModel(**self.dummy_super_res_kwargs   )
              return model


       @property
       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              torch.manual_seed(1   )

              __snake_case       	=							UNetaDModel(**self.dummy_super_res_kwargs   )
              return model


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






              """simple docstring"""


              __snake_case       	=							self.dummy_decoder
              __snake_case       	=							self.dummy_text_proj
              __snake_case       	=							self.dummy_text_encoder
              __snake_case       	=							self.dummy_tokenizer
              __snake_case       	=							self.dummy_super_res_first
              __snake_case       	=							self.dummy_super_res_last

              __snake_case       	=							UnCLIPScheduler(
                  variance_type="""learned_range"""      , prediction_type="""epsilon"""      , num_train_timesteps=10_00      , )

              __snake_case       	=							UnCLIPScheduler(
                  variance_type="""fixed_small_log"""      , prediction_type="""epsilon"""      , num_train_timesteps=10_00      , )

              __snake_case       	=							CLIPImageProcessor(crop_size=32      , size=32   )

              __snake_case       	=							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 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=0      , _UpperCamelCase=True   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							floats_tensor((1, 3, 32, 32)      , rng=random.Random(_UpperCamelCase   )   ).to(_UpperCamelCase   )
              if str(_UpperCamelCase   ).startswith("""mps"""   ):
                     __snake_case       	=							torch.manual_seed(_UpperCamelCase   )
              else:
                     __snake_case       	=							torch.Generator(device=_UpperCamelCase   ).manual_seed(_UpperCamelCase   )

              if pil_image:
                     __snake_case       	=							input_image * 0.5 + 0.5
                     __snake_case       	=							input_image.clamp(0      , 1   )
                     __snake_case       	=							input_image.cpu().permute(0      , 2      , 3      , 1   ).float().numpy()
                     __snake_case       	=							DiffusionPipeline.numpy_to_pil(_UpperCamelCase   )[0]

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


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							"""cpu"""

              __snake_case       	=							self.get_dummy_components()

              __snake_case       	=							self.pipeline_class(**_UpperCamelCase   )
              __snake_case       	=							pipe.to(_UpperCamelCase   )

              pipe.set_progress_bar_config(disable=_UpperCamelCase   )

              __snake_case       	=							self.get_dummy_inputs(_UpperCamelCase      , pil_image=_UpperCamelCase   )

              __snake_case       	=							pipe(**_UpperCamelCase   )
              __snake_case       	=							output.images

              __snake_case       	=							self.get_dummy_inputs(_UpperCamelCase      , pil_image=_UpperCamelCase   )

              __snake_case       	=							pipe(
                  **_UpperCamelCase      , return_dict=_UpperCamelCase      , )[0]

              __snake_case       	=							image[0, -3:, -3:, -1]
              __snake_case       	=							image_from_tuple[0, -3:, -3:, -1]

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

              __snake_case       	=							np.array(
                  [
                      0.9997,
                      0.0002,
                      0.9997,
                      0.9997,
                      0.9969,
                      0.0023,
                      0.9997,
                      0.9969,
                      0.9970,
                  ]   )

              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 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							"""cpu"""

              __snake_case       	=							self.get_dummy_components()

              __snake_case       	=							self.pipeline_class(**_UpperCamelCase   )
              __snake_case       	=							pipe.to(_UpperCamelCase   )

              pipe.set_progress_bar_config(disable=_UpperCamelCase   )

              __snake_case       	=							self.get_dummy_inputs(_UpperCamelCase      , pil_image=_UpperCamelCase   )

              __snake_case       	=							pipe(**_UpperCamelCase   )
              __snake_case       	=							output.images

              __snake_case       	=							self.get_dummy_inputs(_UpperCamelCase      , pil_image=_UpperCamelCase   )

              __snake_case       	=							pipe(
                  **_UpperCamelCase      , return_dict=_UpperCamelCase      , )[0]

              __snake_case       	=							image[0, -3:, -3:, -1]
              __snake_case       	=							image_from_tuple[0, -3:, -3:, -1]

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

              __snake_case       	=							np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]   )

              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 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							"""cpu"""

              __snake_case       	=							self.get_dummy_components()

              __snake_case       	=							self.pipeline_class(**_UpperCamelCase   )
              __snake_case       	=							pipe.to(_UpperCamelCase   )

              pipe.set_progress_bar_config(disable=_UpperCamelCase   )

              __snake_case       	=							self.get_dummy_inputs(_UpperCamelCase      , pil_image=_UpperCamelCase   )
              __snake_case       	=							[
                  pipeline_inputs["""image"""],
                  pipeline_inputs["""image"""],
              ]

              __snake_case       	=							pipe(**_UpperCamelCase   )
              __snake_case       	=							output.images

              __snake_case       	=							self.get_dummy_inputs(_UpperCamelCase      , pil_image=_UpperCamelCase   )
              __snake_case       	=							[
                  tuple_pipeline_inputs["""image"""],
                  tuple_pipeline_inputs["""image"""],
              ]

              __snake_case       	=							pipe(
                  **_UpperCamelCase      , return_dict=_UpperCamelCase      , )[0]

              __snake_case       	=							image[0, -3:, -3:, -1]
              __snake_case       	=							image_from_tuple[0, -3:, -3:, -1]

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

              __snake_case       	=							np.array(
                  [
                      0.9997,
                      0.9989,
                      0.0008,
                      0.0021,
                      0.9960,
                      0.0018,
                      0.0014,
                      0.0002,
                      0.9933,
                  ]   )

              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 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							torch.device("""cpu"""   )

              class     __snake_case    :
                     """simple docstring"""







                     __SCREAMING_SNAKE_CASE				    =							1






              __snake_case       	=							self.get_dummy_components()

              __snake_case       	=							self.pipeline_class(**_UpperCamelCase   )
              __snake_case       	=							pipe.to(_UpperCamelCase   )

              pipe.set_progress_bar_config(disable=_UpperCamelCase   )

              __snake_case       	=							torch.Generator(device=_UpperCamelCase   ).manual_seed(0   )
              __snake_case       	=							pipe.decoder.dtype
              __snake_case       	=							1

              __snake_case       	=							(
                  batch_size,
                  pipe.decoder.config.in_channels,
                  pipe.decoder.config.sample_size,
                  pipe.decoder.config.sample_size,
              )
              __snake_case       	=							pipe.prepare_latents(
                  _UpperCamelCase      , dtype=_UpperCamelCase      , device=_UpperCamelCase      , generator=_UpperCamelCase      , latents=_UpperCamelCase      , scheduler=DummyScheduler()   )

              __snake_case       	=							(
                  batch_size,
                  pipe.super_res_first.config.in_channels // 2,
                  pipe.super_res_first.config.sample_size,
                  pipe.super_res_first.config.sample_size,
              )
              __snake_case       	=							pipe.prepare_latents(
                  _UpperCamelCase      , dtype=_UpperCamelCase      , device=_UpperCamelCase      , generator=_UpperCamelCase      , latents=_UpperCamelCase      , scheduler=DummyScheduler()   )

              __snake_case       	=							self.get_dummy_inputs(_UpperCamelCase      , pil_image=_UpperCamelCase   )

              __snake_case       	=							pipe(
                  **_UpperCamelCase      , decoder_latents=_UpperCamelCase      , super_res_latents=_UpperCamelCase   ).images

              __snake_case       	=							self.get_dummy_inputs(_UpperCamelCase      , pil_image=_UpperCamelCase   )
              # Don't pass image, instead pass embedding
              __snake_case       	=							pipeline_inputs.pop("""image"""   )
              __snake_case       	=							pipe.image_encoder(_UpperCamelCase   ).image_embeds

              __snake_case       	=							pipe(
                  **_UpperCamelCase      , decoder_latents=_UpperCamelCase      , super_res_latents=_UpperCamelCase      , image_embeddings=_UpperCamelCase      , ).images

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


       @skip_mps
       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							torch_device == """cpu"""

              # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
              __snake_case       	=							1E-2

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


       @skip_mps
       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							torch_device == """cpu"""
              __snake_case       	=							True
              __snake_case       	=							[
                  """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 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							[
                  """decoder_num_inference_steps""",
                  """super_res_num_inference_steps""",
              ]

              if torch_device == "mps":
                     # TODO: MPS errors with larger batch sizes
                     __snake_case       	=							[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 			a		(		self   )      ->							int:






              """simple docstring"""


              return super().test_dict_tuple_outputs_equivalent()


       @skip_mps
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return super().test_save_load_local()






       @skip_mps
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class     __snake_case    (				unittest.TestCase  ):
       """simple docstring"""







       def 			a		(		self   )      ->							Any:






              """simple docstring"""


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






       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							load_image(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""   )
              __snake_case       	=							load_numpy(
                  """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                  """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""   )

              __snake_case       	=							UnCLIPImageVariationPipeline.from_pretrained(
                  """kakaobrain/karlo-v1-alpha-image-variations"""      , torch_dtype=torch.floataa   )
              __snake_case       	=							pipeline.to(_UpperCamelCase   )
              pipeline.set_progress_bar_config(disable=_UpperCamelCase   )

              __snake_case       	=							torch.Generator(device="""cpu"""   ).manual_seed(0   )
              __snake_case       	=							pipeline(
                  _UpperCamelCase      , generator=_UpperCamelCase      , output_type="""np"""      , )

              __snake_case       	=							output.images[0]

              assert image.shape == (2_56, 2_56, 3)

              assert_mean_pixel_difference(_UpperCamelCase      , _UpperCamelCase      , 15   )




import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


UpperCamelCase__		 =     logging.get_logger(__name__)

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def __init__(		self      , *_UpperCamelCase      , **_UpperCamelCase   )      ->							None:






              """simple docstring"""


              warnings.warn(
                  """The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
                  """ use YolosImageProcessor instead."""      , _UpperCamelCase      , )
              super().__init__(*_UpperCamelCase      , **_UpperCamelCase   )

