


import gc
import random
import unittest

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

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

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


enable_full_determinism()







class lowerCAmelCase_						(			lowerCamelCase__ ,						unittest.TestCase	):

 '''simple docstring'''

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

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



 @property
 def 						UpperCamelCase__					(     self ):
       return 32



 @property
 def 						UpperCamelCase__					(     self ):
       return 32



 @property
 def 						UpperCamelCase__					(     self ):
       return self.time_input_dim



 @property
 def 						UpperCamelCase__					(     self ):
       return self.time_input_dim * 4



 @property
 def 						UpperCamelCase__					(     self ):
       return 1_00



 @property
 def 						UpperCamelCase__					(     self ):
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
       return tokenizer



 @property
 def 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self ):
       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 ):
       torch.manual_seed(0 )

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



 @property
 def 						UpperCamelCase__					(     self ):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1 )

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



 def 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase=0		,				_UpperCAmelCase=True ):
       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 						UpperCamelCase__					(     self ):
       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(
           [
               0.9_997,
               0.0_002,
               0.9_997,
               0.9_997,
               0.9_969,
               0.0_023,
               0.9_997,
               0.9_969,
               0.9_970,
           ] )

       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 ):
       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([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971] )

       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 ):
       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(
           [
               0.9_997,
               0.9_989,
               0.0_008,
               0.0_021,
               0.9_960,
               0.0_018,
               0.0_014,
               0.0_002,
               0.9_933,
           ] )

       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 ):
       snake_case_	=				torch.device('''cpu''' )







       class lowerCAmelCase_						:

        '''simple docstring'''

        __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 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self ):
       return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def 						UpperCamelCase__					(     self ):
       return super().test_save_load_local()





 @skip_mps
 def 						UpperCamelCase__					(     self ):
       return super().test_save_load_optional_components()







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

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def 						UpperCamelCase__					(     self ):
       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 )



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  list[int]:




      """simple docstring"""

      snake_case_	=				int(SCREAMING_SNAKE_CASE )

      # Initialize Result
      snake_case_	=				[]

      # Traverse through all denomination
      for denomination in reversed(SCREAMING_SNAKE_CASE ):
            # Find denominations
            while int(SCREAMING_SNAKE_CASE ) >= int(SCREAMING_SNAKE_CASE ):
                  total_value -= int(SCREAMING_SNAKE_CASE )
                  answer.append(SCREAMING_SNAKE_CASE )  # Append the "answers" array

      return answer


# Driver Code
if __name__ == "__main__":
   UpperCAmelCase      =   []
   UpperCAmelCase      =   """0"""

   if (
       input("""Do you want to enter your denominations ? (yY/n): """).strip().lower()
       == "y"
   ):
      UpperCAmelCase      =   int(input("""Enter the number of denominations you want to add: """).strip())

      for i in range(0, n):
         denominations.append(int(input(f'''Denomination {i}: ''').strip()))
      UpperCAmelCase      =   input("""Enter the change you want to make in Indian Currency: """).strip()
   else:
      # All denominations of Indian Currency if user does not enter
      UpperCAmelCase      =   [1, 2, 5, 10, 20, 50, 100, 500, 2000]
      UpperCAmelCase      =   input("""Enter the change you want to make: """).strip()

   if int(value) == 0 or int(value) < 0:
      print("""The total value cannot be zero or negative.""")

   else:
      print(f'''Following is minimal change for {value}: ''')
      UpperCAmelCase      =   find_minimum_change(denominations, value)
      # Print result
      for i in range(len(answer)):
         print(answer[i], end=""" """)