






import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class  _lowercase   (   __A						,				__A						,				__A						,				unittest.TestCase						):
      _a							:						Optional[Any]										=							AltDiffusionPipeline
      _a							:						int										=							TEXT_TO_IMAGE_PARAMS
      _a							:						Tuple										=							TEXT_TO_IMAGE_BATCH_PARAMS
      _a							:						str										=							TEXT_TO_IMAGE_IMAGE_PARAMS
      _a							:						Union[str, Any]										=							TEXT_TO_IMAGE_IMAGE_PARAMS
      def 						lowercase__					(     self   ):
       torch.manual_seed(0   )
       snake_case__  :    Union[str, Any]							       =UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)				,			layers_per_block=2				,			sample_size=3_2				,			in_channels=4				,			out_channels=4				,			down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,			up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,			cross_attention_dim=3_2				,			)
       snake_case__  :    Dict							       =DDIMScheduler(
           beta_start=0.00085				,			beta_end=0.012				,			beta_schedule="""scaled_linear"""				,			clip_sample=UpperCamelCase__				,			set_alpha_to_one=UpperCamelCase__				,			)
       torch.manual_seed(0   )
       snake_case__  :    int							       =AutoencoderKL(
           block_out_channels=[3_2, 6_4]				,			in_channels=3				,			out_channels=3				,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]				,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]				,			latent_channels=4				,			)

       # TODO: address the non-deterministic text encoder (fails for save-load tests)
       # torch.manual_seed(0)
       # text_encoder_config = RobertaSeriesConfig(
       #     hidden_size=32,
       #     project_dim=32,
       #     intermediate_size=37,
       #     layer_norm_eps=1e-05,
       #     num_attention_heads=4,
       #     num_hidden_layers=5,
       #     vocab_size=5002,
       # )
       # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

       torch.manual_seed(0   )
       snake_case__  :    Tuple							       =CLIPTextConfig(
           bos_token_id=0				,			eos_token_id=2				,			hidden_size=3_2				,			projection_dim=3_2				,			intermediate_size=3_7				,			layer_norm_eps=1e-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			pad_token_id=1				,			vocab_size=5_0_0_2				,			)
       snake_case__  :    List[str]							       =CLIPTextModel(UpperCamelCase__   )

       snake_case__  :    Any							       =XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""   )
       snake_case__  :    List[Any]							       =7_7

       snake_case__  :    List[str]							       ={
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components
      def 						lowercase__					(     self				,			a				,			a=0   ):
       if str(UpperCamelCase__   ).startswith("""mps"""   ):
        snake_case__  :    List[Any]							       =torch.manual_seed(UpperCamelCase__   )
       else:
        snake_case__  :    Union[str, Any]							       =torch.Generator(device=UpperCamelCase__   ).manual_seed(UpperCamelCase__   )
       snake_case__  :    int							       ={
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs
      def 						lowercase__					(     self   ):
       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3   )
      def 						lowercase__					(     self   ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Dict							       ='cpu'  # ensure determinism for the device-dependent torch.Generator

       snake_case__  :    int							       =self.get_dummy_components()
       torch.manual_seed(0   )
       snake_case__  :    Any							       =RobertaSeriesConfig(
           hidden_size=3_2				,			project_dim=3_2				,			intermediate_size=3_7				,			layer_norm_eps=1e-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			vocab_size=5_0_0_2				,			)
       # TODO: remove after fixing the non-deterministic text encoder
       snake_case__  :    int							       =RobertaSeriesModelWithTransformation(UpperCamelCase__   )
       snake_case__  :    str							       =text_encoder

       snake_case__  :    Union[str, Any]							       =AltDiffusionPipeline(**UpperCamelCase__   )
       snake_case__  :    int							       =alt_pipe.to(UpperCamelCase__   )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

       snake_case__  :    Optional[Any]							       =self.get_dummy_inputs(UpperCamelCase__   )
       snake_case__  :    Optional[int]							       ='A photo of an astronaut'
       snake_case__  :    str							       =alt_pipe(**UpperCamelCase__   )
       snake_case__  :    int							       =output.images
       snake_case__  :    Dict							       =image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)
       snake_case__  :    Dict							       =np.array(
           [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2



      def 						lowercase__					(     self   ):
       snake_case__  :    int							       ='cpu'  # ensure determinism for the device-dependent torch.Generator

       snake_case__  :    Any							       =self.get_dummy_components()
       snake_case__  :    List[str]							       =PNDMScheduler(skip_prk_steps=UpperCamelCase__   )
       torch.manual_seed(0   )
       snake_case__  :    Optional[int]							       =RobertaSeriesConfig(
           hidden_size=3_2				,			project_dim=3_2				,			intermediate_size=3_7				,			layer_norm_eps=1e-05				,			num_attention_heads=4				,			num_hidden_layers=5				,			vocab_size=5_0_0_2				,			)
       # TODO: remove after fixing the non-deterministic text encoder
       snake_case__  :    str							       =RobertaSeriesModelWithTransformation(UpperCamelCase__   )
       snake_case__  :    str							       =text_encoder
       snake_case__  :    Any							       =AltDiffusionPipeline(**UpperCamelCase__   )
       snake_case__  :    Optional[int]							       =alt_pipe.to(UpperCamelCase__   )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

       snake_case__  :    str							       =self.get_dummy_inputs(UpperCamelCase__   )
       snake_case__  :    Tuple							       =alt_pipe(**UpperCamelCase__   )
       snake_case__  :    Optional[Any]							       =output.images
       snake_case__  :    Dict							       =image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)
       snake_case__  :    Dict							       =np.array(
           [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







@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   ):
       # make sure here that pndm scheduler skips prk
       snake_case__  :    int							       =AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""				,			safety_checker=UpperCamelCase__   )
       snake_case__  :    str							       =alt_pipe.to(UpperCamelCase__   )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

       snake_case__  :    List[Any]							       ='A painting of a squirrel eating a burger'
       snake_case__  :    str							       =torch.manual_seed(0   )
       snake_case__  :    int							       =alt_pipe([prompt]				,			generator=UpperCamelCase__				,			guidance_scale=6.0				,			num_inference_steps=2_0				,			output_type="""np"""   )

       snake_case__  :    List[str]							       =output.images

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

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       snake_case__  :    List[Any]							       =np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2



      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""				,			subfolder="""scheduler"""   )

       snake_case__  :    List[str]							       =AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""				,			scheduler=UpperCamelCase__				,			safety_checker=UpperCamelCase__   )
       snake_case__  :    int							       =alt_pipe.to(UpperCamelCase__   )
       alt_pipe.set_progress_bar_config(disable=UpperCamelCase__   )

       snake_case__  :    List[Any]							       ='A painting of a squirrel eating a burger'
       snake_case__  :    Optional[int]							       =torch.manual_seed(0   )

       snake_case__  :    Optional[Any]							       =alt_pipe([prompt]				,			generator=UpperCamelCase__				,			num_inference_steps=2				,			output_type="""numpy"""   )
       snake_case__  :    int							       =output.images

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

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       snake_case__  :    Optional[Any]							       =np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]   )

       assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2













import math







def 			A__							(      _a  :					list		,  _a  :					int = 0		,  _a  :					int = 0	):





 '''simple docstring'''







 snake_case__  :    Tuple							       =end or len(_a	)
 for i in range(_a		,  _a	):
  snake_case__  :    int							       =i
  snake_case__  :    int							       =array[i]
  while temp_index != start and temp_index_value < array[temp_index - 1]:
   snake_case__  :    Dict							       =array[temp_index - 1]
   temp_index -= 1
  snake_case__  :    Dict							       =temp_index_value
 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int	):  # Max Heap





 '''simple docstring'''







 snake_case__  :    Tuple							       =index
 snake_case__  :    Dict							       =2 * index + 1  # Left Node
 snake_case__  :    Union[str, Any]							       =2 * index + 2  # Right Node

 if left_index < heap_size and array[largest] < array[left_index]:
  snake_case__  :    Dict							       =left_index

 if right_index < heap_size and array[largest] < array[right_index]:
  snake_case__  :    List[Any]							       =right_index

 if largest != index:
  snake_case__       ,   snake_case__  :    List[Any]							       =array[largest], array[index]
  heapify(_a		,  _a		,  _a	)







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 snake_case__  :    List[str]							       =len(_a	)

 for i in range(n // 2		,  -1		,  -1	):
  heapify(_a		,  _a		,  _a	)

 for i in range(n - 1		,  0		,  -1	):
  snake_case__       ,   snake_case__  :    Tuple							       =array[0], array[i]
  heapify(_a		,  0		,  _a	)

 return array







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 if (array[first_index] > array[middle_index]) != (
     array[first_index] > array[last_index]
 ):
  return array[first_index]
 elif (array[middle_index] > array[first_index]) != (
     array[middle_index] > array[last_index]
 ):
  return array[middle_index]
 else:
  return array[last_index]







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 snake_case__  :    Dict							       =low
 snake_case__  :    Union[str, Any]							       =high
 while True:
  while array[i] < pivot:
   i += 1
  j -= 1
  while pivot < array[j]:
   j -= 1
  if i >= j:
   return i
  snake_case__       ,   snake_case__  :    List[Any]							       =array[j], array[i]
  i += 1







def 			A__							(      _a  :					list	):





 '''simple docstring'''







 if len(_a	) == 0:
  return array
 snake_case__  :    Optional[int]							       =2 * math.ceil(math.loga(len(_a	)	)	)
 snake_case__  :    Tuple							       =16
 return intro_sort(_a		,  0		,  len(_a	)		,  _a		,  _a	)







def 			A__							(      _a  :					list		,  _a  :					int		,  _a  :					int		,  _a  :					int		,  _a  :					int	):





 '''simple docstring'''







 while end - start > size_threshold:
  if max_depth == 0:
   return heap_sort(_a	)
  max_depth -= 1
  snake_case__  :    Dict							       =median_of_a(_a		,  _a		,  start + ((end - start) // 2) + 1		,  end - 1	)
  snake_case__  :    List[str]							       =partition(_a		,  _a		,  _a		,  _a	)
  intro_sort(_a		,  _a		,  _a		,  _a		,  _a	)
  snake_case__  :    Union[str, Any]							       =p
 return insertion_sort(_a		,  _a		,  _a	)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       __lowerCamelCase	:		Optional[int]										=   input("""Enter numbers separated by a comma : """).strip()
       __lowerCamelCase	:		Dict										=   [float(item) for item in user_input.split(""",""")]
       print(sort(unsorted))





