






'''simple docstring'''





import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


__lowerCamelCase	:		int										=   re.compile(r"""\s+""")







def 			A__							(      _a  :					int	):





 '''simple docstring'''







 return {"hash": hashlib.mda(re.sub(_a		,  """"""		,  example["""content"""]	).encode("""utf-8"""	)	).hexdigest()}







def 			A__							(      _a  :					List[str]	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =[len(_a	) for line in example["""content"""].splitlines()]
 return {"line_mean": np.mean(_a	), "line_max": max(_a	)}







def 			A__							(      _a  :					Dict	):





 '''simple docstring'''







 snake_case__  :    int							       =np.mean([c.isalnum() for c in example["""content"""]]	)
 return {"alpha_frac": alpha_frac}







def 			A__							(      _a  :					Optional[int]		,  _a  :					List[Any]	):





 '''simple docstring'''







 if example["hash"] in uniques:
  uniques.remove(example["""hash"""]	)
  return True
 else:
  return False







def 			A__							(      _a  :					Dict		,  _a  :					Dict=5	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =["""auto-generated""", """autogenerated""", """automatically generated"""]
 snake_case__  :    Any							       =example["""content"""].splitlines()
 for _, line in zip(range(_a	)		,  _a	):
  for keyword in keywords:
   if keyword in line.lower():
    return {"autogenerated": True}
 else:
  return {"autogenerated": False}







def 			A__							(      _a  :					Tuple		,  _a  :					str=5		,  _a  :					Union[str, Any]=0.0_5	):





 '''simple docstring'''







 snake_case__  :    Optional[int]							       =["""unit tests""", """test file""", """configuration file"""]
 snake_case__  :    Tuple							       =example["""content"""].splitlines()
 snake_case__  :    List[str]							       =0
 snake_case__  :    Dict							       =0
 # first test
 for _, line in zip(range(_a	)		,  _a	):
  for keyword in keywords:
   if keyword in line.lower():
    return {"config_or_test": True}
    # second test
 snake_case__  :    Optional[Any]							       =example["""content"""].count("""\n"""	)
 snake_case__  :    Any							       =int(coeff * nlines	)
 for line in lines:
  count_config += line.lower().count("""config"""	)
  count_test += line.lower().count("""test"""	)
  if count_config > threshold or count_test > threshold:
   return {"config_or_test": True}
 return {"config_or_test": False}







def 			A__							(      _a  :					Any	):





 '''simple docstring'''







 snake_case__  :    Any							       =["""def """, """class """, """for """, """while """]
 snake_case__  :    Any							       =example["""content"""].splitlines()
 for line in lines:
  for keyword in keywords:
   if keyword in line.lower():
    return {"has_no_keywords": False}
 return {"has_no_keywords": True}







def 			A__							(      _a  :					Tuple		,  _a  :					List[Any]=4	):





 '''simple docstring'''







 snake_case__  :    int							       =example["""content"""].splitlines()
 snake_case__  :    Any							       =0
 for line in lines:
  counter += line.lower().count("""="""	)
  if counter > minimum:
   return {"has_few_assignments": False}
 return {"has_few_assignments": True}







def 			A__							(      _a  :					Optional[Any]	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =tokenizer(example["""content"""]		,  truncation=_a	)["""input_ids"""]
 snake_case__  :    str							       =len(example["""content"""]	) / len(_a	)
 return {"ratio": ratio}







def 			A__							(      _a  :					Union[str, Any]	):





 '''simple docstring'''







 snake_case__  :    Tuple							       ={}
 results.update(get_hash(_a	)	)
 results.update(line_stats(_a	)	)
 results.update(alpha_stats(_a	)	)
 results.update(char_token_ratio(_a	)	)
 results.update(is_autogenerated(_a	)	)
 results.update(is_config_or_test(_a	)	)
 results.update(has_no_keywords(_a	)	)
 results.update(has_few_assignments(_a	)	)
 return results







def 			A__							(      _a  :					Union[str, Any]		,  _a  :					Tuple		,  _a  :					List[Any]	):





 '''simple docstring'''







 if not check_uniques(_a		,  _a	):
  return False
 elif example["autogenerated"]:
  return False
 elif example["line_max"] > args.line_max:
  return False
 elif example["line_mean"] > args.line_mean:
  return False
 elif example["alpha_frac"] < args.alpha_frac:
  return False
 elif example["ratio"] < args.min_token_ratio:
  return False
 elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
  return False
 elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
  return False
 elif example["has_few_assignments"]:
  return False
 else:
  return True







def 			A__							(      _a  :					Any	):





 '''simple docstring'''







 with open(_a		,  """rb"""	) as f_in:
  with gzip.open(str(_a	) + """.gz"""		,  """wb"""		,  compresslevel=6	) as f_out:
   shutil.copyfileobj(_a		,  _a	)
 os.unlink(_a	)


# Settings
__lowerCamelCase	:		Optional[Any]										=   HfArgumentParser(PreprocessingArguments)
__lowerCamelCase	:		Optional[int]										=   parser.parse_args()
if args.num_workers is None:
       __lowerCamelCase	:		List[str]										=   multiprocessing.cpu_count()
__lowerCamelCase	:		List[str]										=   AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
__lowerCamelCase	:		int										=   time.time()
__lowerCamelCase	:		List[Any]										=   load_dataset(args.dataset_name, split="""train""")
print(F"Time to load dataset: {time.time()-t_start:.2f}")

# Run preprocessing
__lowerCamelCase	:		Optional[Any]										=   time.time()
__lowerCamelCase	:		Tuple										=   ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")

# Deduplicate hashes
__lowerCamelCase	:		Any										=   set(ds.unique("""hash"""))
__lowerCamelCase	:		Tuple										=   len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

# Deduplicate data and apply heuristics
__lowerCamelCase	:		str										=   time.time()
__lowerCamelCase	:		Dict										=   ds.filter(filter, fn_kwargs={"""uniques""": uniques, """args""": args})
print(F"Time to filter dataset: {time.time()-t_start:.2f}")
print(F"Size of filtered dataset: {len(ds_filter)}")

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
       __lowerCamelCase	:		int										=   time.time()
       __lowerCamelCase			,				__lowerCamelCase	:		Dict										=   deduplicate_dataset(ds_filter, args.jaccard_threshold)
       print(F"Time to deduplicate dataset: {time.time()-t_start:.2f}")
       print(F"Size of deduplicate dataset: {len(ds_filter)}")

# Save data in batches of samples_per_file
__lowerCamelCase	:		Union[str, Any]										=   Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
       with open(output_dir / """duplicate_clusters.json""", """w""") as f:
              json.dump(duplicate_clusters, f)

__lowerCamelCase	:		str										=   output_dir / """data"""
data_dir.mkdir(exist_ok=True)

__lowerCamelCase	:		Dict										=   time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
       __lowerCamelCase	:		Union[str, Any]										=   str(data_dir / F"file-{file_number+1:012}.json")
       __lowerCamelCase	:		Optional[int]										=   min(len(ds_filter), index + args.samples_per_file)
       ds_filter.select(list(range(index, end_index))).to_json(file_path)
       compress_file(file_path)

print(F"Time to save dataset: {time.time()-t_start:.2f}")













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  _lowercase   (   _A						,				unittest.TestCase						):
      _a							:						Optional[Any]										=							UnCLIPImageVariationPipeline
      _a							:						Optional[int]										=							IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      _a							:						Optional[Any]										=							IMAGE_VARIATION_BATCH_PARAMS

      _a							:						str										=							[
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      _a							:						Any										=							False
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return 3_2
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim
      @property
      def 						lowercase__					(     self   ):
       return self.time_input_dim * 4
      @property
      def 						lowercase__					(     self   ):
       return 1_0_0
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
       return tokenizer
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    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=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(a   )
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    Optional[int]							       =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(a   )
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

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

       snake_case__  :    Optional[int]							       =UnCLIPTextProjModel(**a   )
       return model
      @property
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    List[str]							       ={
           """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""",
       }

       snake_case__  :    List[str]							       =UNetaDConditionModel(**a   )
       return model
      @property
      def 						lowercase__					(     self   ):
       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 						lowercase__					(     self   ):
       torch.manual_seed(0   )

       snake_case__  :    Union[str, Any]							       =UNetaDModel(**self.dummy_super_res_kwargs   )
       return model
      @property
      def 						lowercase__					(     self   ):
       # seeded differently to get different unet than `self.dummy_super_res_first`
       torch.manual_seed(1   )

       snake_case__  :    int							       =UNetaDModel(**self.dummy_super_res_kwargs   )
       return model
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.dummy_decoder
       snake_case__  :    Any							       =self.dummy_text_proj
       snake_case__  :    Optional[int]							       =self.dummy_text_encoder
       snake_case__  :    Optional[Any]							       =self.dummy_tokenizer
       snake_case__  :    List[str]							       =self.dummy_super_res_first
       snake_case__  :    str							       =self.dummy_super_res_last

       snake_case__  :    List[Any]							       =UnCLIPScheduler(
           variance_type="""learned_range"""				,			prediction_type="""epsilon"""				,			num_train_timesteps=1_0_0_0				,			)

       snake_case__  :    Any							       =UnCLIPScheduler(
           variance_type="""fixed_small_log"""				,			prediction_type="""epsilon"""				,			num_train_timesteps=1_0_0_0				,			)

       snake_case__  :    str							       =CLIPImageProcessor(crop_size=3_2				,			size=3_2   )

       snake_case__  :    List[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 						lowercase__					(     self				,			a				,			a=0				,			a=True   ):
       snake_case__  :    str							       =floats_tensor((1, 3, 3_2, 3_2)				,			rng=random.Random(a   )   ).to(a   )
       if str(a   ).startswith("""mps"""   ):
        snake_case__  :    str							       =torch.manual_seed(a   )
       else:
        snake_case__  :    List[Any]							       =torch.Generator(device=a   ).manual_seed(a   )

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

       return {
           "image": input_image,
           "generator": generator,
           "decoder_num_inference_steps": 2,
           "super_res_num_inference_steps": 2,
           "output_type": "np",
       }
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       ="""cpu"""

       snake_case__  :    Dict							       =self.get_dummy_components()

       snake_case__  :    Tuple							       =self.pipeline_class(**a   )
       snake_case__  :    Optional[Any]							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Optional[int]							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(**a   )
       snake_case__  :    List[str]							       =output.images

       snake_case__  :    Any							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[Any]							       =pipe(
           **a				,			return_dict=a				,			)[0]

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

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

       snake_case__  :    Optional[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 						lowercase__					(     self   ):
       snake_case__  :    Dict							       ="""cpu"""

       snake_case__  :    int							       =self.get_dummy_components()

       snake_case__  :    Any							       =self.pipeline_class(**a   )
       snake_case__  :    Tuple							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    int							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    Any							       =pipe(**a   )
       snake_case__  :    Optional[int]							       =output.images

       snake_case__  :    int							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(
           **a				,			return_dict=a				,			)[0]

       snake_case__  :    List[str]							       =image[0, -3:, -3:, -1]
       snake_case__  :    List[Any]							       =image_from_tuple[0, -3:, -3:, -1]

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

       snake_case__  :    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 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       ="""cpu"""

       snake_case__  :    Dict							       =self.get_dummy_components()

       snake_case__  :    Tuple							       =self.pipeline_class(**a   )
       snake_case__  :    List[Any]							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    Tuple							       =self.get_dummy_inputs(a				,			pil_image=a   )
       snake_case__  :    Any							       =[
           pipeline_inputs["""image"""],
           pipeline_inputs["""image"""],
       ]

       snake_case__  :    Tuple							       =pipe(**a   )
       snake_case__  :    str							       =output.images

       snake_case__  :    Tuple							       =self.get_dummy_inputs(a				,			pil_image=a   )
       snake_case__  :    str							       =[
           tuple_pipeline_inputs["""image"""],
           tuple_pipeline_inputs["""image"""],
       ]

       snake_case__  :    Union[str, Any]							       =pipe(
           **a				,			return_dict=a				,			)[0]

       snake_case__  :    List[Any]							       =image[0, -3:, -3:, -1]
       snake_case__  :    Tuple							       =image_from_tuple[0, -3:, -3:, -1]

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

       snake_case__  :    int							       =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 						lowercase__					(     self   ):
       snake_case__  :    Optional[int]							       =torch.device("""cpu"""   )







       class  _lowercase   :
             _a							:						Tuple										=							1




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

       snake_case__  :    List[str]							       =self.pipeline_class(**a   )
       snake_case__  :    int							       =pipe.to(a   )

       pipe.set_progress_bar_config(disable=a   )

       snake_case__  :    List[Any]							       =torch.Generator(device=a   ).manual_seed(0   )
       snake_case__  :    List[Any]							       =pipe.decoder.dtype
       snake_case__  :    str							       =1

       snake_case__  :    Dict							       =(
           batch_size,
           pipe.decoder.config.in_channels,
           pipe.decoder.config.sample_size,
           pipe.decoder.config.sample_size,
       )
       snake_case__  :    List[Any]							       =pipe.prepare_latents(
           a				,			dtype=a				,			device=a				,			generator=a				,			latents=a				,			scheduler=DummyScheduler()   )

       snake_case__  :    Optional[int]							       =(
           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__  :    Dict							       =pipe.prepare_latents(
           a				,			dtype=a				,			device=a				,			generator=a				,			latents=a				,			scheduler=DummyScheduler()   )

       snake_case__  :    Union[str, Any]							       =self.get_dummy_inputs(a				,			pil_image=a   )

       snake_case__  :    List[str]							       =pipe(
           **a				,			decoder_latents=a				,			super_res_latents=a   ).images

       snake_case__  :    Any							       =self.get_dummy_inputs(a				,			pil_image=a   )
       # Don't pass image, instead pass embedding
       snake_case__  :    Optional[Any]							       =pipeline_inputs.pop("""image"""   )
       snake_case__  :    Optional[int]							       =pipe.image_encoder(a   ).image_embeds

       snake_case__  :    Dict							       =pipe(
           **a				,			decoder_latents=a				,			super_res_latents=a				,			image_embeddings=a				,			).images

       # make sure passing text embeddings manually is identical
       assert np.abs(img_out_a - img_out_a   ).max() < 1e-4
      @skip_mps
      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =torch_device == """cpu"""

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

       self._test_attention_slicing_forward_pass(
           test_max_difference=a				,			expected_max_diff=a   )
      @skip_mps
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =torch_device == """cpu"""
       snake_case__  :    List[Any]							       =True
       snake_case__  :    List[Any]							       =[
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]

       self._test_inference_batch_single_identical(
           test_max_difference=a				,			relax_max_difference=a				,			additional_params_copy_to_batched_inputs=a				,			)
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       =[
           """decoder_num_inference_steps""",
           """super_res_num_inference_steps""",
       ]

       if torch_device == "mps":
        # TODO: MPS errors with larger batch sizes
        snake_case__  :    List[Any]							       =[2, 3]
        self._test_inference_batch_consistent(
            batch_sizes=a				,			additional_params_copy_to_batched_inputs=a				,			)
       else:
        self._test_inference_batch_consistent(
            additional_params_copy_to_batched_inputs=a   )
      @skip_mps
      def 						lowercase__					(     self   ):
       return super().test_dict_tuple_outputs_equivalent()
      @skip_mps
      def 						lowercase__					(     self   ):
       return super().test_save_load_local()



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







@slow
@require_torch_gpu
class  _lowercase   (   unittest.TestCase						):
      def 						lowercase__					(     self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



      def 						lowercase__					(     self   ):
       snake_case__  :    str							       =load_image(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"""   )
       snake_case__  :    Union[str, Any]							       =load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/unclip/karlo_v1_alpha_cat_variation_fp16.npy"""   )

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

       snake_case__  :    Optional[Any]							       =torch.Generator(device="""cpu"""   ).manual_seed(0   )
       snake_case__  :    Tuple							       =pipeline(
           a				,			generator=a				,			output_type="""np"""				,			)

       snake_case__  :    Union[str, Any]							       =output.images[0]

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

       assert_mean_pixel_difference(a				,			a				,			1_5   )





