"""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    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		UnCLIPImageVariationPipeline
      a__        =		IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
      a__        =		IMAGE_VARIATION_BATCH_PARAMS

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



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





        '''simple docstring'''





        return 32



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





        '''simple docstring'''





        return 32



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





        '''simple docstring'''





        return self.time_input_dim



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





        '''simple docstring'''





        return self.time_input_dim * 4



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





        '''simple docstring'''





        return 1_00



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





        '''simple docstring'''





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



      @property
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     Optional[int]										=	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(lowercase)



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





        '''simple docstring'''





        torch.manual_seed(0)
        a__:     List[str]										=	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   lowerCamelCase_   (						self)	->			Any:





        '''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__:     List[Any]										=	UnCLIPTextProjModel(**lowercase)
        return model



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





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     Any										=	{
            '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__:     Optional[Any]										=	UNetaDConditionModel(**lowercase)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Dict:





        '''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   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        torch.manual_seed(0)

        a__:     int										=	UNetaDModel(**self.dummy_super_res_kwargs)
        return model



      @property
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        torch.manual_seed(1)

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



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





        '''simple docstring'''





        a__:     List[str]										=	self.dummy_decoder
        a__:     str										=	self.dummy_text_proj
        a__:     Dict										=	self.dummy_text_encoder
        a__:     Dict										=	self.dummy_tokenizer
        a__:     Optional[int]										=	self.dummy_super_res_first
        a__:     Dict										=	self.dummy_super_res_last

        a__:     Tuple										=	UnCLIPScheduler(
            variance_type='learned_range'    ,			prediction_type='epsilon'    ,			num_train_timesteps=10_00    ,			)

        a__:     Dict										=	UnCLIPScheduler(
            variance_type='fixed_small_log'    ,			prediction_type='epsilon'    ,			num_train_timesteps=10_00    ,			)

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

        a__:     int										=	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   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase=0    ,			lowercase=True)	->			Optional[int]:





        '''simple docstring'''





        a__:     Any										=	floats_tensor((1, 3, 32, 32)    ,			rng=random.Random(lowercase)).to(lowercase)
        if str(lowercase).startswith('mps'):
          a__:     Dict										=	torch.manual_seed(lowercase)
        else:
          a__:     str										=	torch.Generator(device=lowercase).manual_seed(lowercase)

        if pil_image:
          a__:     List[Any]										=	input_image * 0.5 + 0.5
          a__:     Any										=	input_image.clamp(0    ,			1)
          a__:     Tuple										=	input_image.cpu().permute(0    ,			2    ,			3    ,			1).float().numpy()
          a__:     Tuple										=	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   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	'cpu'

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

        a__:     Dict										=	self.pipeline_class(**lowercase)
        a__:     str										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[Any]										=	self.get_dummy_inputs(lowercase    ,			pil_image=lowercase)

        a__:     str										=	pipe(**lowercase)
        a__:     str										=	output.images

        a__:     List[str]										=	self.get_dummy_inputs(lowercase    ,			pil_image=lowercase)

        a__:     Optional[int]										=	pipe(
            **lowercase    ,			return_dict=lowercase    ,			)[0]

        a__:     Optional[Any]										=	image[0, -3:, -3:, -1]
        a__:     Union[str, Any]										=	image_from_tuple[0, -3:, -3:, -1]

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

        a__:     List[str]										=	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   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     str										=	'cpu'

        a__:     int										=	self.get_dummy_components()

        a__:     Union[str, Any]										=	self.pipeline_class(**lowercase)
        a__:     int										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     int										=	self.get_dummy_inputs(lowercase    ,			pil_image=lowercase)

        a__:     str										=	pipe(**lowercase)
        a__:     Dict										=	output.images

        a__:     List[str]										=	self.get_dummy_inputs(lowercase    ,			pil_image=lowercase)

        a__:     Any										=	pipe(
            **lowercase    ,			return_dict=lowercase    ,			)[0]

        a__:     str										=	image[0, -3:, -3:, -1]
        a__:     Optional[Any]										=	image_from_tuple[0, -3:, -3:, -1]

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

        a__:     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   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Optional[int]										=	'cpu'

        a__:     List[str]										=	self.get_dummy_components()

        a__:     Dict										=	self.pipeline_class(**lowercase)
        a__:     Any										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Optional[Any]										=	self.get_dummy_inputs(lowercase    ,			pil_image=lowercase)
        a__:     str										=	[
            pipeline_inputs['image'],
            pipeline_inputs['image'],
        ]

        a__:     Any										=	pipe(**lowercase)
        a__:     str										=	output.images

        a__:     Union[str, Any]										=	self.get_dummy_inputs(lowercase    ,			pil_image=lowercase)
        a__:     Optional[Any]										=	[
            tuple_pipeline_inputs['image'],
            tuple_pipeline_inputs['image'],
        ]

        a__:     Tuple										=	pipe(
            **lowercase    ,			return_dict=lowercase    ,			)[0]

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

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

        a__:     str										=	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   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Optional[Any]										=	torch.device('cpu')




        class    __snake_case       :
              a__        =		1







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

        a__:     List[str]										=	self.pipeline_class(**lowercase)
        a__:     List[Any]										=	pipe.to(lowercase)

        pipe.set_progress_bar_config(disable=lowercase)

        a__:     Tuple										=	torch.Generator(device=lowercase).manual_seed(0)
        a__:     Any										=	pipe.decoder.dtype
        a__:     Dict										=	1

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

        a__:     str										=	(
            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__:     Tuple										=	pipe.prepare_latents(
            lowercase    ,			dtype=lowercase    ,			device=lowercase    ,			generator=lowercase    ,			latents=lowercase    ,			scheduler=DummyScheduler())

        a__:     Dict										=	self.get_dummy_inputs(lowercase    ,			pil_image=lowercase)

        a__:     Optional[Any]										=	pipe(
            **lowercase    ,			decoder_latents=lowercase    ,			super_res_latents=lowercase).images

        a__:     int										=	self.get_dummy_inputs(lowercase    ,			pil_image=lowercase)
        # Don't pass image, instead pass embedding
        a__:     Tuple										=	pipeline_inputs.pop('image')
        a__:     int										=	pipe.image_encoder(lowercase).image_embeds

        a__:     Union[str, Any]										=	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   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     List[Any]										=	torch_device == 'cpu'

        # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
        a__:     Any										=	1e-2

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



      @skip_mps
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Any										=	torch_device == 'cpu'
        a__:     Any										=	True
        a__:     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   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     int										=	[
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
          # TODO: MPS errors with larger batch sizes
          a__:     Union[str, Any]										=	[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   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





        return super().test_dict_tuple_outputs_equivalent()



      @skip_mps
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        return super().test_save_load_local()



      @skip_mps
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        return super().test_save_load_optional_components()







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



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





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



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





        '''simple docstring'''





        a__:     int										=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png')
        a__:     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__:     Dict										=	pipeline.to(lowercase)
        pipeline.set_progress_bar_config(disable=lowercase)

        a__:     Dict										=	torch.Generator(device='cpu').manual_seed(0)
        a__:     Union[str, Any]										=	pipeline(
            lowercase    ,			generator=lowercase    ,			output_type='np'    ,			)

        a__:     int										=	output.images[0]

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

        assert_mean_pixel_difference(lowercase    ,			lowercase    ,			15)



"""simple docstring"""
from math import pow, sqrt







def 						__a   (	*_SCREAMING_SNAKE_CASE   )	->bool:
  a__:     Union[str, Any]										=	len(_SCREAMING_SNAKE_CASE   ) > 0 and all(value > 0.0 for value in values   )
  return result







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError('Input Error: Molar mass values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(effusion_rate * sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(effusion_rate / sqrt(molar_mass_a / molar_mass_a   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(molar_mass / pow(effusion_rate_a / effusion_rate_a		,     2   )		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )	->float | ValueError:
  return (
      round(pow(effusion_rate_a / effusion_rate_a		,     2   ) / molar_mass		,     6   )
      if validate(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
      else ValueError(
          'Input Error: Molar mass and effusion rate values must greater than 0.'   )
  )


