"""simple docstring"""


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMParallelScheduler,
    DDPMParallelScheduler,
    StableDiffusionParadigmsPipeline,
    UNetaDConditionModel,
)
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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 					a					(				A_   ,		A_   ,		unittest.TestCase							):
  A_ :				Optional[Any] 			=							StableDiffusionParadigmsPipeline
  A_ :				Optional[int] 			=							TEXT_TO_IMAGE_PARAMS
  A_ :				int 			=							TEXT_TO_IMAGE_BATCH_PARAMS
  A_ :				Union[str, Any] 			=							TEXT_TO_IMAGE_IMAGE_PARAMS
  A_ :				List[Any] 			=							TEXT_TO_IMAGE_IMAGE_PARAMS



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> Dict:
      torch.manual_seed(0     )
      __a			      =						UNetaDConditionModel(
          block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=4						,  out_channels=4						,  down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")						,  up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")						,  cross_attention_dim=32						,  attention_head_dim=(2, 4)						,  use_linear_projection=lowerCamelCase_						,  )
      __a			      =						DDIMParallelScheduler(
          beta_start=0.0_00_85						,  beta_end=0.0_12						,  beta_schedule="""scaled_linear"""						,  clip_sample=lowerCamelCase_						,  set_alpha_to_one=lowerCamelCase_						,  )
      torch.manual_seed(0     )
      __a			      =						AutoencoderKL(
          block_out_channels=[32, 64]						,  in_channels=3						,  out_channels=3						,  down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,  up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,  latent_channels=4						,  sample_size=1_28						,  )
      torch.manual_seed(0     )
      __a			      =						CLIPTextConfig(
          bos_token_id=0						,  eos_token_id=2						,  hidden_size=32						,  intermediate_size=37						,  layer_norm_eps=1E-05						,  num_attention_heads=4						,  num_hidden_layers=5						,  pad_token_id=1						,  vocab_size=10_00						,  hidden_act="""gelu"""						,  projection_dim=5_12						,  )
      __a			      =						CLIPTextModel(lowerCamelCase_     )
      __a			      =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

      __a			      =						{
          """unet""": unet,
          """scheduler""": scheduler,
          """vae""": vae,
          """text_encoder""": text_encoder,
          """tokenizer""": tokenizer,
          """safety_checker""": None,
          """feature_extractor""": None,
      }
      return components



  def        lowerCAmelCase_				(		self   :    Optional[Any]						,  lowerCamelCase_   :    Optional[Any]						,  lowerCamelCase_   :    Any=0     )      -> Optional[Any]:
      if str(lowerCamelCase_     ).startswith("""mps"""     ):
          __a			      =						torch.manual_seed(lowerCamelCase_     )
      else:
          __a			      =						torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
      __a			      =						{
          """prompt""": """a photograph of an astronaut riding a horse""",
          """generator""": generator,
          """num_inference_steps""": 10,
          """guidance_scale""": 6.0,
          """output_type""": """numpy""",
          """parallel""": 3,
          """debug""": True,
      }
      return inputs



  def        lowerCAmelCase_				(		self   :    List[str]     )      -> List[Any]:
      __a			      =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __a			      =						self.get_dummy_components()
      __a			      =						StableDiffusionParadigmsPipeline(**lowerCamelCase_     )
      __a			      =						sd_pipe.to(lowerCamelCase_     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase_     )

      __a			      =						self.get_dummy_inputs(lowerCamelCase_     )
      __a			      =						sd_pipe(**lowerCamelCase_     ).images
      __a			      =						image[0, -3:, -3:, -1]
      assert image.shape == (1, 64, 64, 3)

      __a			      =						np.array([0.47_73, 0.54_17, 0.47_23, 0.49_25, 0.56_31, 0.47_52, 0.52_40, 0.49_35, 0.50_23]     )

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



  def        lowerCAmelCase_				(		self   :    List[Any]     )      -> Any:
      __a			      =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __a			      =						self.get_dummy_components()
      torch.manual_seed(0     )
      __a			      =						DDPMParallelScheduler()
      torch.manual_seed(0     )
      __a			      =						StableDiffusionParadigmsPipeline(**lowerCamelCase_     )
      __a			      =						sd_pipe.to(lowerCamelCase_     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase_     )

      __a			      =						self.get_dummy_inputs(lowerCamelCase_     )
      __a			      =						sd_pipe(**lowerCamelCase_     ).images
      __a			      =						image[0, -3:, -3:, -1]
      assert image.shape == (1, 64, 64, 3)

      __a			      =						np.array([0.35_73, 0.44_20, 0.49_60, 0.47_99, 0.37_96, 0.38_79, 0.48_19, 0.43_65, 0.44_68]     )

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



  def        lowerCAmelCase_				(		self   :    Optional[Any]     )      -> List[Any]:
      super().test_inference_batch_consistent(batch_sizes=[1, 2]     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> List[Any]:
      super().test_inference_batch_single_identical(batch_size=2						,  expected_max_diff=3E-3     )



  def        lowerCAmelCase_				(		self   :    Dict     )      -> str:
      __a			      =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
      __a			      =						self.get_dummy_components()
      __a			      =						StableDiffusionParadigmsPipeline(**lowerCamelCase_     )
      __a			      =						sd_pipe.to(lowerCamelCase_     )
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase_     )

      __a			      =						self.get_dummy_inputs(lowerCamelCase_     )
      __a			      =						"""french fries"""
      __a			      =						sd_pipe(**lowerCamelCase_						,  negative_prompt=lowerCamelCase_     )
      __a			      =						output.images
      __a			      =						image[0, -3:, -3:, -1]

      assert image.shape == (1, 64, 64, 3)

      __a			      =						np.array([0.47_71, 0.54_20, 0.46_83, 0.49_18, 0.56_36, 0.47_25, 0.52_30, 0.49_23, 0.50_15]     )

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






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



  def        lowerCAmelCase_				(		self   :    str     )      -> Any:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



  def        lowerCAmelCase_				(		self   :    Union[str, Any]						,  lowerCamelCase_   :    List[str]=0     )      -> Union[str, Any]:
      __a			      =						torch.Generator(device=lowerCamelCase_     ).manual_seed(lowerCamelCase_     )
      __a			      =						{
          """prompt""": """a photograph of an astronaut riding a horse""",
          """generator""": generator,
          """num_inference_steps""": 10,
          """guidance_scale""": 7.5,
          """output_type""": """numpy""",
          """parallel""": 3,
          """debug""": True,
      }
      return inputs



  def        lowerCAmelCase_				(		self   :    Optional[int]     )      -> Optional[Any]:
      __a			      =						"""stabilityai/stable-diffusion-2-base"""
      __a			      =						DDIMParallelScheduler.from_pretrained(lowerCamelCase_						,  subfolder="""scheduler"""     )
      __a			      =						StableDiffusionParadigmsPipeline.from_pretrained(lowerCamelCase_						,  scheduler=lowerCamelCase_						,  safety_checker=lowerCamelCase_     )
      pipe.to(lowerCamelCase_     )
      pipe.set_progress_bar_config(disable=lowerCamelCase_     )
      pipe.enable_attention_slicing()

      __a			      =						self.get_inputs()
      __a			      =						pipe(**lowerCamelCase_     ).images
      __a			      =						image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 5_12, 5_12, 3)

      __a			      =						np.array([0.96_22, 0.96_02, 0.97_48, 0.95_91, 0.96_30, 0.96_91, 0.96_61, 0.96_31, 0.97_41]     )

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

"""simple docstring"""


def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Tuple						):
    __a			      =						len(_lowerCAmelCase						)
    # Create a stack and push index of fist element to it
    __a			      =						[]
    st.append(0						)

    # Span value of first element is always 1
    __a			      =						1

    # Calculate span values for rest of the elements
    for i in range(1				, _lowerCAmelCase						):
        # Pop elements from stack while stack is not
        # empty and top of stack is smaller than price[i]
        while len(_lowerCAmelCase						) > 0 and price[st[0]] <= price[i]:
            st.pop()

        # If stack becomes empty, then price[i] is greater
        # than all elements on left of it, i.e. price[0],
        # price[1], ..price[i-1]. Else the price[i]  is
        # greater than elements after top of stack
        __a			      =						i + 1 if len(_lowerCAmelCase						) <= 0 else (i - st[0])

        # Push this element to stack
        st.append(_lowerCAmelCase						)

def 						UpperCamelCase				(	_lowerCAmelCase   :							Dict				, _lowerCAmelCase   :							Optional[Any]						):
    for i in range(0				, _lowerCAmelCase						):
        print(arr[i]				, end=""" """						)


# Driver program to test above function
__A       			=      [10, 4, 5, 90, 1_20, 80]
__A       			=      [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)






print_array(S, len(price))
