




def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     snake_case					=       len(UpperCamelCase_       )
     # Create a stack and push index of fist element to it
     snake_case					=       []
     st.append(0       )

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

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

          # Push this element to stack
          st.append(UpperCamelCase_       )
def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):



     """simple docstring"""






     for i in range(0							,UpperCamelCase_       ):
          print(arr[i]							,end=''' '''       )


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

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

print_array(S, len(price))








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__      (					snake_case__ ,      snake_case__ ,      unittest.TestCase			):







    """simple docstring"""

    __magic_name__    						=	StableDiffusionParadigmsPipeline
    __magic_name__    						=	TEXT_TO_IMAGE_PARAMS
    __magic_name__    						=	TEXT_TO_IMAGE_BATCH_PARAMS
    __magic_name__    						=	TEXT_TO_IMAGE_IMAGE_PARAMS
    __magic_name__    						=	TEXT_TO_IMAGE_IMAGE_PARAMS
    def 	a_    ( self					):
         torch.manual_seed(0					)
         snake_case					=       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    ,      attention_head_dim=(2, 4)    ,      use_linear_projection=__snake_case    ,      )
         snake_case					=       DDIMParallelScheduler(
             beta_start=0.0_0085    ,      beta_end=0.012    ,      beta_schedule='''scaled_linear'''    ,      clip_sample=__snake_case    ,      set_alpha_to_one=__snake_case    ,      )
         torch.manual_seed(0					)
         snake_case					=       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    ,      sample_size=1_2_8    ,      )
         torch.manual_seed(0					)
         snake_case					=       CLIPTextConfig(
             bos_token_id=0    ,      eos_token_id=2    ,      hidden_size=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=1_0_0_0    ,      hidden_act='''gelu'''    ,      projection_dim=5_1_2    ,      )
         snake_case					=       CLIPTextModel(__snake_case					)
         snake_case					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

         snake_case					=       {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components
    def 	a_    ( self    ,      __snake_case    ,      __snake_case=0					):
         if str(__snake_case					).startswith('''mps'''					):
              snake_case					=       torch.manual_seed(__snake_case					)
         else:
              snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
         snake_case					=       {
             '''prompt''': '''a photograph of an astronaut riding a horse''',
             '''generator''': generator,
             '''num_inference_steps''': 1_0,
             '''guidance_scale''': 6.0,
             '''output_type''': '''numpy''',
             '''parallel''': 3,
             '''debug''': True,
         }
         return inputs
    def 	a_    ( self					):
         snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         snake_case					=       self.get_dummy_components()
         snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
         snake_case					=       sd_pipe.to(__snake_case					)
         sd_pipe.set_progress_bar_config(disable=__snake_case					)

         snake_case					=       self.get_dummy_inputs(__snake_case					)
         snake_case					=       sd_pipe(**__snake_case					).images
         snake_case					=       image[0, -3:, -3:, -1]
         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=       np.array([0.4773, 0.5417, 0.4723, 0.4925, 0.5631, 0.4752, 0.5240, 0.4935, 0.5023]					)

         assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
    def 	a_    ( self					):
         snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         snake_case					=       self.get_dummy_components()
         torch.manual_seed(0					)
         snake_case					=       DDPMParallelScheduler()
         torch.manual_seed(0					)
         snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
         snake_case					=       sd_pipe.to(__snake_case					)
         sd_pipe.set_progress_bar_config(disable=__snake_case					)

         snake_case					=       self.get_dummy_inputs(__snake_case					)
         snake_case					=       sd_pipe(**__snake_case					).images
         snake_case					=       image[0, -3:, -3:, -1]
         assert image.shape == (1, 6_4, 6_4, 3)

         snake_case					=       np.array([0.3573, 0.4420, 0.4960, 0.4799, 0.3796, 0.3879, 0.4819, 0.4365, 0.4468]					)

         assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
    def 	a_    ( self					):
         super().test_inference_batch_consistent(batch_sizes=[1, 2]					)
    def 	a_    ( self					):
         super().test_inference_batch_single_identical(batch_size=2    ,      expected_max_diff=3E-3					)




    def 	a_    ( self					):
         snake_case					=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         snake_case					=       self.get_dummy_components()
         snake_case					=       StableDiffusionParadigmsPipeline(**__snake_case					)
         snake_case					=       sd_pipe.to(__snake_case					)
         sd_pipe.set_progress_bar_config(disable=__snake_case					)

         snake_case					=       self.get_dummy_inputs(__snake_case					)
         snake_case					=       '''french fries'''
         snake_case					=       sd_pipe(**__snake_case    ,      negative_prompt=__snake_case					)
         snake_case					=       output.images
         snake_case					=       image[0, -3:, -3:, -1]

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

         snake_case					=       np.array([0.4771, 0.5420, 0.4683, 0.4918, 0.5636, 0.4725, 0.5230, 0.4923, 0.5015]					)

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






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







    """simple docstring"""

    def 	a_    ( self					):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
    def 	a_    ( self    ,      __snake_case=0					):
         snake_case					=       torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
         snake_case					=       {
             '''prompt''': '''a photograph of an astronaut riding a horse''',
             '''generator''': generator,
             '''num_inference_steps''': 1_0,
             '''guidance_scale''': 7.5,
             '''output_type''': '''numpy''',
             '''parallel''': 3,
             '''debug''': True,
         }
         return inputs




    def 	a_    ( self					):
         snake_case					=       '''stabilityai/stable-diffusion-2-base'''
         snake_case					=       DDIMParallelScheduler.from_pretrained(__snake_case    ,      subfolder='''scheduler'''					)
         snake_case					=       StableDiffusionParadigmsPipeline.from_pretrained(__snake_case    ,      scheduler=__snake_case    ,      safety_checker=__snake_case					)
         pipe.to(__snake_case					)
         pipe.set_progress_bar_config(disable=__snake_case					)
         pipe.enable_attention_slicing()

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

         assert image.shape == (1, 5_1_2, 5_1_2, 3)

         snake_case					=       np.array([0.9622, 0.9602, 0.9748, 0.9591, 0.9630, 0.9691, 0.9661, 0.9631, 0.9741]					)

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




