



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        _SCREAMING_SNAKE_CASE		(       _lowerCAmelCase			,			unittest.TestCase ):
   a_							:      Dict						= UnCLIPImageVariationPipeline
   a_							:      Any						= IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''guidance_scale'''}
   a_							:      Optional[int]						= IMAGE_VARIATION_BATCH_PARAMS

   a_							:      Optional[Any]						= [
       '''generator''',
       '''return_dict''',
       '''decoder_num_inference_steps''',
       '''super_res_num_inference_steps''',
   ]
   a_							:      Optional[int]						= False





   @property
   def 	A__      (self):
       '''simple docstring'''







       return 3_2





   @property
   def 	A__      (self):
       '''simple docstring'''







       return 3_2





   @property
   def 	A__      (self):
       '''simple docstring'''







       return self.time_input_dim





   @property
   def 	A__      (self):
       '''simple docstring'''







       return self.time_input_dim * 4





   @property
   def 	A__      (self):
       '''simple docstring'''







       return 1_0_0





   @property
   def 	A__      (self):
       '''simple docstring'''







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





   @property
   def 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(0)
       __UpperCAmelCase			 =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 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(0)
       __UpperCAmelCase			 =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=3_2    ,  intermediate_size=3_7    ,  patch_size=1    ,  )
       return CLIPVisionModelWithProjection(UpperCAmelCase)





   @property
   def 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(0)

       __UpperCAmelCase			 ={
           '''clip_embeddings_dim''': self.text_embedder_hidden_size,
           '''time_embed_dim''': self.time_embed_dim,
           '''cross_attention_dim''': self.cross_attention_dim,
       }

       __UpperCAmelCase			 =UnCLIPTextProjModel(**UpperCAmelCase)
       return model





   @property
   def 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(0)

       __UpperCAmelCase			 ={
           '''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''',
       }

       __UpperCAmelCase			 =UNetaDConditionModel(**UpperCAmelCase)
       return model





   @property
   def 	A__      (self):
       '''simple docstring'''







       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 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(0)

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





   @property
   def 	A__      (self):
       '''simple docstring'''







       torch.manual_seed(1)

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





   def 	A__      (self):
       '''simple docstring'''







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

       __UpperCAmelCase			 =UnCLIPScheduler(
           variance_type='''learned_range'''    ,  prediction_type='''epsilon'''    ,  num_train_timesteps=1_0_0_0    ,  )

       __UpperCAmelCase			 =UnCLIPScheduler(
           variance_type='''fixed_small_log'''    ,  prediction_type='''epsilon'''    ,  num_train_timesteps=1_0_0_0    ,  )

       __UpperCAmelCase			 =CLIPImageProcessor(crop_size=3_2    ,  size=3_2)

       __UpperCAmelCase			 =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):
       '''simple docstring'''







       __UpperCAmelCase			 =floats_tensor((1, 3, 3_2, 3_2)    ,  rng=random.Random(UpperCAmelCase)).to(UpperCAmelCase)
       if str(UpperCAmelCase).startswith('''mps'''):
           __UpperCAmelCase			 =torch.manual_seed(UpperCAmelCase)
       else:
           __UpperCAmelCase			 =torch.Generator(device=UpperCAmelCase).manual_seed(UpperCAmelCase)

       if pil_image:
           __UpperCAmelCase			 =input_image * 0.5 + 0.5
           __UpperCAmelCase			 =input_image.clamp(0    ,  1)
           __UpperCAmelCase			 =input_image.cpu().permute(0    ,  2    ,  3    ,  1).float().numpy()
           __UpperCAmelCase			 =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):
       '''simple docstring'''







       __UpperCAmelCase			 ='''cpu'''

       __UpperCAmelCase			 =self.get_dummy_components()

       __UpperCAmelCase			 =self.pipeline_class(**UpperCAmelCase)
       __UpperCAmelCase			 =pipe.to(UpperCAmelCase)

       pipe.set_progress_bar_config(disable=UpperCAmelCase)

       __UpperCAmelCase			 =self.get_dummy_inputs(UpperCAmelCase    ,  pil_image=UpperCAmelCase)

       __UpperCAmelCase			 =pipe(**UpperCAmelCase)
       __UpperCAmelCase			 =output.images

       __UpperCAmelCase			 =self.get_dummy_inputs(UpperCAmelCase    ,  pil_image=UpperCAmelCase)

       __UpperCAmelCase			 =pipe(
           **UpperCAmelCase    ,  return_dict=UpperCAmelCase    ,  )[0]

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

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

       __UpperCAmelCase			 =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):
       '''simple docstring'''







       __UpperCAmelCase			 ='''cpu'''

       __UpperCAmelCase			 =self.get_dummy_components()

       __UpperCAmelCase			 =self.pipeline_class(**UpperCAmelCase)
       __UpperCAmelCase			 =pipe.to(UpperCAmelCase)

       pipe.set_progress_bar_config(disable=UpperCAmelCase)

       __UpperCAmelCase			 =self.get_dummy_inputs(UpperCAmelCase    ,  pil_image=UpperCAmelCase)

       __UpperCAmelCase			 =pipe(**UpperCAmelCase)
       __UpperCAmelCase			 =output.images

       __UpperCAmelCase			 =self.get_dummy_inputs(UpperCAmelCase    ,  pil_image=UpperCAmelCase)

       __UpperCAmelCase			 =pipe(
           **UpperCAmelCase    ,  return_dict=UpperCAmelCase    ,  )[0]

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

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

       __UpperCAmelCase			 =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):
       '''simple docstring'''







       __UpperCAmelCase			 ='''cpu'''

       __UpperCAmelCase			 =self.get_dummy_components()

       __UpperCAmelCase			 =self.pipeline_class(**UpperCAmelCase)
       __UpperCAmelCase			 =pipe.to(UpperCAmelCase)

       pipe.set_progress_bar_config(disable=UpperCAmelCase)

       __UpperCAmelCase			 =self.get_dummy_inputs(UpperCAmelCase    ,  pil_image=UpperCAmelCase)
       __UpperCAmelCase			 =[
           pipeline_inputs['''image'''],
           pipeline_inputs['''image'''],
       ]

       __UpperCAmelCase			 =pipe(**UpperCAmelCase)
       __UpperCAmelCase			 =output.images

       __UpperCAmelCase			 =self.get_dummy_inputs(UpperCAmelCase    ,  pil_image=UpperCAmelCase)
       __UpperCAmelCase			 =[
           tuple_pipeline_inputs['''image'''],
           tuple_pipeline_inputs['''image'''],
       ]

       __UpperCAmelCase			 =pipe(
           **UpperCAmelCase    ,  return_dict=UpperCAmelCase    ,  )[0]

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

       assert image.shape == (2, 6_4, 6_4, 3)

       __UpperCAmelCase			 =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):
       '''simple docstring'''







       __UpperCAmelCase			 =torch.device('''cpu''')



       class        _SCREAMING_SNAKE_CASE		:
          a_							:      str						= 1







       __UpperCAmelCase			 =self.get_dummy_components()

       __UpperCAmelCase			 =self.pipeline_class(**UpperCAmelCase)
       __UpperCAmelCase			 =pipe.to(UpperCAmelCase)

       pipe.set_progress_bar_config(disable=UpperCAmelCase)

       __UpperCAmelCase			 =torch.Generator(device=UpperCAmelCase).manual_seed(0)
       __UpperCAmelCase			 =pipe.decoder.dtype
       __UpperCAmelCase			 =1

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

       __UpperCAmelCase			 =(
           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			 =pipe.prepare_latents(
           UpperCAmelCase    ,  dtype=UpperCAmelCase    ,  device=UpperCAmelCase    ,  generator=UpperCAmelCase    ,  latents=UpperCAmelCase    ,  scheduler=DummyScheduler())

       __UpperCAmelCase			 =self.get_dummy_inputs(UpperCAmelCase    ,  pil_image=UpperCAmelCase)

       __UpperCAmelCase			 =pipe(
           **UpperCAmelCase    ,  decoder_latents=UpperCAmelCase    ,  super_res_latents=UpperCAmelCase).images

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

       __UpperCAmelCase			 =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):
       '''simple docstring'''







       __UpperCAmelCase			 =torch_device == '''cpu'''

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

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





   @skip_mps
   def 	A__      (self):
       '''simple docstring'''







       __UpperCAmelCase			 =torch_device == '''cpu'''
       __UpperCAmelCase			 =True
       __UpperCAmelCase			 =[
           '''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):
       '''simple docstring'''







       __UpperCAmelCase			 =[
           '''decoder_num_inference_steps''',
           '''super_res_num_inference_steps''',
       ]

       if torch_device == "mps":
           # TODO: MPS errors with larger batch sizes
           __UpperCAmelCase			 =[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):
       '''simple docstring'''







       return super().test_dict_tuple_outputs_equivalent()





   @skip_mps
   def 	A__      (self):
       '''simple docstring'''







       return super().test_save_load_local()






   @skip_mps
   def 	A__      (self):
       '''simple docstring'''







       return super().test_save_load_optional_components()







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





   def 	A__      (self):
       '''simple docstring'''







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






   def 	A__      (self):
       '''simple docstring'''







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

       __UpperCAmelCase			 =UnCLIPImageVariationPipeline.from_pretrained(
           '''kakaobrain/karlo-v1-alpha-image-variations'''    ,  torch_dtype=torch.floataa)
       __UpperCAmelCase			 =pipeline.to(UpperCAmelCase)
       pipeline.set_progress_bar_config(disable=UpperCAmelCase)

       __UpperCAmelCase			 =torch.Generator(device='''cpu''').manual_seed(0)
       __UpperCAmelCase			 =pipeline(
           UpperCAmelCase    ,  generator=UpperCAmelCase    ,  output_type='''np'''    ,  )

       __UpperCAmelCase			 =output.images[0]

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

       assert_mean_pixel_difference(UpperCAmelCase    ,  UpperCAmelCase    ,  1_5)











'''simple docstring'''



from __future__ import annotations

from typing import TypedDict
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       =42
      __a	       =42







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      list[str]:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )

   return [s[i:] + s[:i] for i in range(len(SCREAMING_SNAKE_CASE__     )     )]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      BWTTransformDict:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter s type must be str.'     )
   if not s:
      raise ValueError('The parameter s must not be empty.'     )

   __a					  =     all_rotations(SCREAMING_SNAKE_CASE__     )
   rotations.sort()  # sort the list of rotations in alphabetically order
   # make a string composed of the last char of each rotation
   __a					  =     {
       "bwt_string": "".join([word[-1] for word in rotations]     ),
       "idx_original_string": rotations.index(SCREAMING_SNAKE_CASE__     ),
   }
   return response



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						int     )	->      str:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     ):
      raise TypeError('The parameter bwt_string type must be str.'     )
   if not bwt_string:
      raise ValueError('The parameter bwt_string must not be empty.'     )
   try:
      __a					  =     int(SCREAMING_SNAKE_CASE__     )
   except ValueError:
      raise TypeError(
          'The parameter idx_original_string type must be int or passive'
          ' of cast to int.'     )
   if idx_original_string < 0:
      raise ValueError('The parameter idx_original_string must not be lower than 0.'     )
   if idx_original_string >= len(SCREAMING_SNAKE_CASE__     ):
      raise ValueError(
          'The parameter idx_original_string must be lower than' ' len(bwt_string).'     )

   __a					  =     [''] * len(SCREAMING_SNAKE_CASE__     )
   for _ in range(len(SCREAMING_SNAKE_CASE__     )     ):
      for i in range(len(SCREAMING_SNAKE_CASE__     )     ):
         __a					  =     bwt_string[i] + ordered_rotations[i]
      ordered_rotations.sort()
   return ordered_rotations[idx_original_string]


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   """Provide a string that I will generate its BWT transform: """
 __UpperCamelCase   :							Dict         =   input(entry_msg).strip()
 __UpperCamelCase   :							Optional[int]         =   bwt_transform(s)
 print(
     f"""Burrows Wheeler transform for string '{s}' results """
     f"""in '{result["bwt_string"]}'"""
 )
 __UpperCamelCase   :							Dict         =   reverse_bwt(result["""bwt_string"""], result["""idx_original_string"""])
 print(
     f"""Reversing Burrows Wheeler transform for entry '{result["bwt_string"]}' """
     f"""we get original string '{original_string}'"""
 )