






'''simple docstring'''

def 				snake_case       (  a_				:			Optional[Any]						)							->					List[Any]:





      """simple docstring"""

      if collection == []:
            return []

      # get some information about the collection
      UpperCamelCase_					: Optional[Any]            =				len(a_						)
      UpperCamelCase_					: int            =				max(a_						)
      UpperCamelCase_					: Tuple            =				min(a_						)

      # create the counting array
      UpperCamelCase_					: Any            =				coll_max + 1 - coll_min
      UpperCamelCase_					: Optional[int]            =				[0] * counting_arr_length

      # count how much a number appears in the collection
      for number in collection:
            counting_arr[number - coll_min] += 1

      # sum each position with it's predecessors. now, counting_arr[i] tells
      # us how many elements <= i has in the collection
      for i in range(1     , a_						):
            UpperCamelCase_					: int            =				counting_arr[i] + counting_arr[i - 1]

      # create the output collection
      UpperCamelCase_					: Any            =				[0] * coll_len

      # place the elements in the output, respecting the original order (stable
      # sort) from end to begin, updating counting_arr
      for i in reversed(range(0     , a_						)						):
            UpperCamelCase_					: List[Any]            =				collection[i]
            counting_arr[collection[i] - coll_min] -= 1

      return ordered



def 				snake_case       (  a_				:			List[Any]						)							->					Optional[Any]:





      """simple docstring"""

      return "".join([chr(a_						) for i in counting_sort([ord(a_						) for c in string]						)]						)


if __name__ == "__main__":
      # Test string sort
      assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"

      UpperCamelCase      					=input("Enter numbers separated by a comma:\n").strip()
      UpperCamelCase      					=[int(item) for item in user_input.split(",")]
      print(counting_sort(unsorted))














'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


UpperCamelCase      					=False




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 12
 @property
 def 	_UpperCAmelCase							(				self			):
       return 32
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Union[str, Any]            =				VQModel(
           block_out_channels=[32, 64]      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]      ,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]      ,  latent_channels=3      ,  num_vq_embeddings=self.num_embed      ,  vq_embed_dim=3      ,  )
       return model
 @property
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: str            =				CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
       return tokenizer
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: Any            =				CLIPTextConfig(
           bos_token_id=0      ,  eos_token_id=2      ,  hidden_size=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 CLIPTextModel(__lowerCAmelCase			)
 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)

       UpperCamelCase_					: Optional[Any]            =				12
       UpperCamelCase_					: Optional[int]            =				12

       UpperCamelCase_					: Optional[Any]            =				{
           """attention_bias""": True,
           """cross_attention_dim""": 32,
           """attention_head_dim""": height * width,
           """num_attention_heads""": 1,
           """num_vector_embeds""": self.num_embed,
           """num_embeds_ada_norm""": self.num_embeds_ada_norm,
           """norm_num_groups""": 32,
           """sample_size""": width,
           """activation_fn""": """geglu-approximate""",
       }

       UpperCamelCase_					: Optional[Any]            =				TransformeraDModel(**__lowerCAmelCase			)
       return model
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				"""cpu"""

       UpperCamelCase_					: List[str]            =				self.dummy_vqvae
       UpperCamelCase_					: str            =				self.dummy_text_encoder
       UpperCamelCase_					: Optional[Any]            =				self.dummy_tokenizer
       UpperCamelCase_					: Optional[int]            =				self.dummy_transformer
       UpperCamelCase_					: Optional[Any]            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: Union[str, Any]            =				LearnedClassifierFreeSamplingEmbeddings(learnable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: Union[str, Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Optional[int]            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: Union[str, Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[Any]            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: Dict            =				output.images

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Dict            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

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

       assert image.shape == (1, 24, 24, 3)

       UpperCamelCase_					: List[str]            =				np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]			)

       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			):
       UpperCamelCase_					: int            =				"""cpu"""

       UpperCamelCase_					: List[Any]            =				self.dummy_vqvae
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_text_encoder
       UpperCamelCase_					: int            =				self.dummy_tokenizer
       UpperCamelCase_					: Tuple            =				self.dummy_transformer
       UpperCamelCase_					: Tuple            =				VQDiffusionScheduler(self.num_embed			)
       UpperCamelCase_					: List[Any]            =				LearnedClassifierFreeSamplingEmbeddings(
           learnable=__lowerCAmelCase      ,  hidden_size=self.text_embedder_hidden_size      ,  length=tokenizer.model_max_length			)

       UpperCamelCase_					: Optional[Any]            =				VQDiffusionPipeline(
           vqvae=__lowerCAmelCase      ,  text_encoder=__lowerCAmelCase      ,  tokenizer=__lowerCAmelCase      ,  transformer=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase      ,  learned_classifier_free_sampling_embeddings=__lowerCAmelCase      ,  )
       UpperCamelCase_					: List[Any]            =				pipe.to(__lowerCAmelCase			)
       pipe.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				"""teddy bear playing in the pool"""

       UpperCamelCase_					: List[str]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pipe([prompt]      ,  generator=__lowerCAmelCase      ,  num_inference_steps=2      ,  output_type="""np"""			)
       UpperCamelCase_					: List[str]            =				output.images

       UpperCamelCase_					: Optional[int]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: List[str]            =				pipe(
           [prompt]      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  return_dict=__lowerCAmelCase      ,  num_inference_steps=2			)[0]

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

       assert image.shape == (1, 24, 24, 3)

       UpperCamelCase_					: List[Any]            =				np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 2.0
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






@slow
@require_torch_gpu
class      A     (   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			):
       UpperCamelCase_					: List[Any]            =				load_numpy(
           """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
           """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""			)

       UpperCamelCase_					: Optional[int]            =				VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""			)
       UpperCamelCase_					: str            =				pipeline.to(__lowerCAmelCase			)
       pipeline.set_progress_bar_config(disable=__lowerCAmelCase			)

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       UpperCamelCase_					: List[Any]            =				torch.Generator(device=__lowerCAmelCase			).manual_seed(0			)
       UpperCamelCase_					: str            =				pipeline(
           """teddy bear playing in the pool"""      ,  num_images_per_prompt=1      ,  generator=__lowerCAmelCase      ,  output_type="""np"""      ,  )

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

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image			).max() < 2.0






