

'''simple docstring'''

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 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  UnCLIPImageVariationPipeline
     UpperCAmelCase_						:     Optional[int]  		=  IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
     UpperCAmelCase_						:     Dict  		=  IMAGE_VARIATION_BATCH_PARAMS

     UpperCAmelCase_						:     Dict  		=  [
         """generator""",
         """return_dict""",
         """decoder_num_inference_steps""",
         """super_res_num_inference_steps""",
     ]
     UpperCAmelCase_						:     Any  		=  False


     @property
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     Dict:
      return 32


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     Any:
      return 32


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     Dict:
      return self.time_input_dim


     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Optional[int]:
      return self.time_input_dim * 4


     @property
     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[int]:
      return 100


     @property
     def        UpperCAmelCase_	(		self			:					int	)						->     Tuple:
      UpperCAmelCase	:     List[Any]						 =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
      return tokenizer


     @property
     def        UpperCAmelCase_	(		self			:					Dict	)						->     Any:
      torch.manual_seed(0	)
      UpperCAmelCase	:     str						 =			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=1_000					,      )
      return CLIPTextModelWithProjection(lowercase_	)


     @property
     def        UpperCAmelCase_	(		self			:					Dict	)						->     int:
      torch.manual_seed(0	)
      UpperCAmelCase	:     Optional[Any]						 =			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(lowercase_	)


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

      UpperCAmelCase	:     Optional[Any]						 =			{
          'clip_embeddings_dim': self.text_embedder_hidden_size,
          'time_embed_dim': self.time_embed_dim,
          'cross_attention_dim': self.cross_attention_dim,
      }

      UpperCAmelCase	:     str						 =			UnCLIPTextProjModel(**lowercase_	)
      return model


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     int:
      torch.manual_seed(0	)

      UpperCAmelCase	:     int						 =			{
          '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',
      }

      UpperCAmelCase	:     Optional[Any]						 =			UNetaDConditionModel(**lowercase_	)
      return model


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

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


     @property
     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Any:
      # seeded differently to get different unet than `self.dummy_super_res_first`
      torch.manual_seed(1	)

      UpperCAmelCase	:     List[str]						 =			UNetaDModel(**self.dummy_super_res_kwargs	)
      return model


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

      UpperCAmelCase	:     List[str]						 =			UnCLIPScheduler(
          variance_type='learned_range'					,      prediction_type='epsilon'					,      num_train_timesteps=1_000					,      )

      UpperCAmelCase	:     int						 =			UnCLIPScheduler(
          variance_type='fixed_small_log'					,      prediction_type='epsilon'					,      num_train_timesteps=1_000					,      )

      UpperCAmelCase	:     Optional[Any]						 =			CLIPImageProcessor(crop_size=32					,      size=32	)

      UpperCAmelCase	:     Union[str, 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        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					int					,      lowercase_			:					List[str]=0					,      lowercase_			:					Dict=True	)						->     Tuple:
      UpperCAmelCase	:     str						 =			floats_tensor((1, 3, 32, 32)					,      rng=random.Random(lowercase_	)	).to(lowercase_	)
      if str(lowercase_	).startswith('mps'	):
       UpperCAmelCase	:     int						 =			torch.manual_seed(lowercase_	)
      else:
       UpperCAmelCase	:     int						 =			torch.Generator(device=lowercase_	).manual_seed(lowercase_	)

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

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


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Tuple:
      UpperCAmelCase	:     Dict						 =			'cpu'

      UpperCAmelCase	:     Optional[Any]						 =			self.get_dummy_components()

      UpperCAmelCase	:     List[str]						 =			self.pipeline_class(**lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			pipe.to(lowercase_	)

      pipe.set_progress_bar_config(disable=lowercase_	)

      UpperCAmelCase	:     int						 =			self.get_dummy_inputs(lowercase_					,      pil_image=lowercase_	)

      UpperCAmelCase	:     List[Any]						 =			pipe(**lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			output.images

      UpperCAmelCase	:     Optional[Any]						 =			self.get_dummy_inputs(lowercase_					,      pil_image=lowercase_	)

      UpperCAmelCase	:     int						 =			pipe(
          **lowercase_					,      return_dict=lowercase_					,      )[0]

      UpperCAmelCase	:     Optional[int]						 =			image[0, -3:, -3:, -1]
      UpperCAmelCase	:     List[str]						 =			image_from_tuple[0, -3:, -3:, -1]

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

      UpperCAmelCase	:     List[Any]						 =			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        UpperCAmelCase_	(		self			:					Tuple	)						->     Optional[int]:
      UpperCAmelCase	:     str						 =			'cpu'

      UpperCAmelCase	:     str						 =			self.get_dummy_components()

      UpperCAmelCase	:     Dict						 =			self.pipeline_class(**lowercase_	)
      UpperCAmelCase	:     int						 =			pipe.to(lowercase_	)

      pipe.set_progress_bar_config(disable=lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			self.get_dummy_inputs(lowercase_					,      pil_image=lowercase_	)

      UpperCAmelCase	:     str						 =			pipe(**lowercase_	)
      UpperCAmelCase	:     List[Any]						 =			output.images

      UpperCAmelCase	:     Dict						 =			self.get_dummy_inputs(lowercase_					,      pil_image=lowercase_	)

      UpperCAmelCase	:     int						 =			pipe(
          **lowercase_					,      return_dict=lowercase_					,      )[0]

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

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

      UpperCAmelCase	:     str						 =			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        UpperCAmelCase_	(		self			:					int	)						->     Any:
      UpperCAmelCase	:     List[str]						 =			'cpu'

      UpperCAmelCase	:     Dict						 =			self.get_dummy_components()

      UpperCAmelCase	:     int						 =			self.pipeline_class(**lowercase_	)
      UpperCAmelCase	:     str						 =			pipe.to(lowercase_	)

      pipe.set_progress_bar_config(disable=lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			self.get_dummy_inputs(lowercase_					,      pil_image=lowercase_	)
      UpperCAmelCase	:     int						 =			[
          pipeline_inputs['image'],
          pipeline_inputs['image'],
      ]

      UpperCAmelCase	:     List[str]						 =			pipe(**lowercase_	)
      UpperCAmelCase	:     Tuple						 =			output.images

      UpperCAmelCase	:     Tuple						 =			self.get_dummy_inputs(lowercase_					,      pil_image=lowercase_	)
      UpperCAmelCase	:     List[str]						 =			[
          tuple_pipeline_inputs['image'],
          tuple_pipeline_inputs['image'],
      ]

      UpperCAmelCase	:     str						 =			pipe(
          **lowercase_					,      return_dict=lowercase_					,      )[0]

      UpperCAmelCase	:     List[str]						 =			image[0, -3:, -3:, -1]
      UpperCAmelCase	:     Tuple						 =			image_from_tuple[0, -3:, -3:, -1]

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

      UpperCAmelCase	:     Dict						 =			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        UpperCAmelCase_	(		self			:					Tuple	)						->     int:
      UpperCAmelCase	:     List[Any]						 =			torch.device('cpu'	)




      class 	A_			:






           '''simple docstring'''



           UpperCAmelCase_						:     Optional[int]  		=  1



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

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

      pipe.set_progress_bar_config(disable=lowercase_	)

      UpperCAmelCase	:     Optional[Any]						 =			torch.Generator(device=lowercase_	).manual_seed(0	)
      UpperCAmelCase	:     Tuple						 =			pipe.decoder.dtype
      UpperCAmelCase	:     str						 =			1

      UpperCAmelCase	:     List[str]						 =			(
          batch_size,
          pipe.decoder.config.in_channels,
          pipe.decoder.config.sample_size,
          pipe.decoder.config.sample_size,
      )
      UpperCAmelCase	:     Union[str, Any]						 =			pipe.prepare_latents(
          lowercase_					,      dtype=lowercase_					,      device=lowercase_					,      generator=lowercase_					,      latents=lowercase_					,      scheduler=DummyScheduler()	)

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

      UpperCAmelCase	:     Union[str, Any]						 =			self.get_dummy_inputs(lowercase_					,      pil_image=lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			pipe(
          **lowercase_					,      decoder_latents=lowercase_					,      super_res_latents=lowercase_	).images

      UpperCAmelCase	:     int						 =			self.get_dummy_inputs(lowercase_					,      pil_image=lowercase_	)
      # Don't pass image, instead pass embedding
      UpperCAmelCase	:     Optional[int]						 =			pipeline_inputs.pop('image'	)
      UpperCAmelCase	:     List[str]						 =			pipe.image_encoder(lowercase_	).image_embeds

      UpperCAmelCase	:     List[str]						 =			pipe(
          **lowercase_					,      decoder_latents=lowercase_					,      super_res_latents=lowercase_					,      image_embeddings=lowercase_					,      ).images

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


     @skip_mps
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     str:
      UpperCAmelCase	:     Any						 =			torch_device == 'cpu'

      # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
      UpperCAmelCase	:     Optional[Any]						 =			1E-2

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


     @skip_mps
     def        UpperCAmelCase_	(		self			:					Any	)						->     List[Any]:
      UpperCAmelCase	:     Optional[int]						 =			torch_device == 'cpu'
      UpperCAmelCase	:     str						 =			True
      UpperCAmelCase	:     List[str]						 =			[
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]

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


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     List[str]:
      UpperCAmelCase	:     Union[str, Any]						 =			[
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]

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


     @skip_mps
     def        UpperCAmelCase_	(		self			:					Dict	)						->     Optional[Any]:
      return super().test_dict_tuple_outputs_equivalent()


     @skip_mps
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Tuple:
      return super().test_save_load_local()




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




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






     '''simple docstring'''



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




     def        UpperCAmelCase_	(		self			:					Tuple	)						->     Any:
      UpperCAmelCase	:     int						 =			load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'	)
      UpperCAmelCase	:     int						 =			load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'	)

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

      UpperCAmelCase	:     Any						 =			torch.Generator(device='cpu'	).manual_seed(0	)
      UpperCAmelCase	:     str						 =			pipeline(
          lowercase_					,      generator=lowercase_					,      output_type='np'					,      )

      UpperCAmelCase	:     int						 =			output.images[0]

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

      assert_mean_pixel_difference(lowercase_					,      lowercase_					,      15	)







'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


lowercase__         =      re.compile(r"([A-Z]+)([A-Z][a-z])")
lowercase__         =      re.compile(r"([a-z\d])([A-Z])")

lowercase__         =      re.compile(r"(?<!_)_(?!_)")
lowercase__         =      re.compile(r"(_{2,})")

lowercase__         =      r"^\w+(\.\w+)*$"

lowercase__         =      r"<>:/\|?*"




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     List[str]						 =			_uppercase_uppercase_re.sub(R'\1_\2'  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			_lowercase_uppercase_re.sub(R'\1_\2'  ,	UpperCAmelCase_     )
 return name.lower()




def 				UpperCamelCase(					UpperCAmelCase_     ):
 UpperCAmelCase	:     List[str]						 =			_single_underscore_re.split(UpperCAmelCase_     )
 UpperCAmelCase	:     Optional[Any]						 =			[_multiple_underscores_re.split(UpperCAmelCase_     ) for n in name]
 return "".join(n.capitalize() for n in itertools.chain.from_iterable(UpperCAmelCase_     ) if n != ''     )




def 				UpperCamelCase(					UpperCAmelCase_     ):
 if os.path.basename(UpperCAmelCase_     ) != name:
  raise ValueError(F"""Should be a dataset name, not a path: {name}"""     )
 return camelcase_to_snakecase(UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if os.path.basename(UpperCAmelCase_     ) != name:
  raise ValueError(F"""Should be a dataset name, not a path: {name}"""     )
 if not re.match(_split_re  ,	UpperCAmelCase_     ):
  raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'."""     )
 return F"""{filename_prefix_for_name(UpperCAmelCase_     )}-{split}"""




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None     ):
 UpperCAmelCase	:     Dict						 =			filename_prefix_for_split(UpperCAmelCase_  ,	UpperCAmelCase_     )
 if filetype_suffix:
  prefix += F""".{filetype_suffix}"""
 UpperCAmelCase	:     Optional[int]						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return F"""{filepath}*"""




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=None  ,	UpperCAmelCase_=None     ):
 UpperCAmelCase	:     Optional[int]						 =			filename_prefix_for_split(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     str						 =			os.path.join(UpperCAmelCase_  ,	UpperCAmelCase_     )

 if shard_lengths:
  UpperCAmelCase	:     List[str]						 =			len(UpperCAmelCase_     )
  UpperCAmelCase	:     List[str]						 =			[F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(UpperCAmelCase_     )]
  if filetype_suffix:
   UpperCAmelCase	:     Dict						 =			[filename + F""".{filetype_suffix}""" for filename in filenames]
  return filenames
 else:
  UpperCAmelCase	:     Optional[Any]						 =			prefix
  if filetype_suffix:
   filename += F""".{filetype_suffix}"""
  return [filename]




