


"""simple docstring"""


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class   _a      (      SCREAMING_SNAKE_CASE_		,		unittest.TestCase ):
    a_    :				int             =    DiTPipeline
    a_    :				Optional[int]             =    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
    a_    :				Tuple             =    PipelineTesterMixin.required_optional_params - {
        "latents",
        "num_images_per_prompt",
        "callback",
        "callback_steps",
    }
    a_    :				Tuple             =    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
    a_    :				str             =    False
    def      _UpperCamelCase       ( self							:					Dict				):
          torch.manual_seed(0				)
          lowerCamelCase__           =		TransformeraDModel(
              sample_size=16							,					num_layers=2							,					patch_size=4							,					attention_head_dim=8							,					num_attention_heads=2							,					in_channels=4							,					out_channels=8							,					attention_bias=UpperCamelCase__							,					activation_fn='gelu-approximate'							,					num_embeds_ada_norm=10_00							,					norm_type='ada_norm_zero'							,					norm_elementwise_affine=UpperCamelCase__							,					)
          lowerCamelCase__           =		AutoencoderKL()
          lowerCamelCase__           =		DDIMScheduler()
          lowerCamelCase__           =		{'transformer': transformer.eval(), 'vae': vae.eval(), 'scheduler': scheduler}
          return components
    def      _UpperCamelCase       ( self							:					int							,					SCREAMING_SNAKE_CASE__							:					Dict							,					SCREAMING_SNAKE_CASE__							:					str=0				):





          if str(UpperCamelCase__				).startswith('mps'				):
                lowerCamelCase__           =		torch.manual_seed(UpperCamelCase__				)
          else:
                lowerCamelCase__           =		torch.Generator(device=UpperCamelCase__				).manual_seed(UpperCamelCase__				)
          lowerCamelCase__           =		{
              'class_labels': [1],
              'generator': generator,
              'num_inference_steps': 2,
              'output_type': 'numpy',
          }
          return inputs
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		'cpu'

          lowerCamelCase__           =		self.get_dummy_components()
          lowerCamelCase__           =		self.pipeline_class(**UpperCamelCase__				)
          pipe.to(UpperCamelCase__				)
          pipe.set_progress_bar_config(disable=UpperCamelCase__				)

          lowerCamelCase__           =		self.get_dummy_inputs(UpperCamelCase__				)
          lowerCamelCase__           =		pipe(**UpperCamelCase__				).images
          lowerCamelCase__           =		image[0, -3:, -3:, -1]

          self.assertEqual(image.shape							,					(1, 16, 16, 3)				)
          lowerCamelCase__           =		np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]				)
          lowerCamelCase__           =		np.abs(image_slice.flatten() - expected_slice				).max()
          self.assertLessEqual(UpperCamelCase__							,					1e-3				)
    def      _UpperCamelCase       ( self							:					List[Any]				):
          self._test_inference_batch_single_identical(relax_max_difference=UpperCamelCase__							,					expected_max_diff=1e-3				)


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()							,					reason='XFormers attention is only available with CUDA and `xformers` installed'							,					)
    def      _UpperCamelCase       ( self							:					int				):
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3				)





@require_torch_gpu
@slow
class   _a      (      unittest.TestCase ):
    def      _UpperCamelCase       ( self							:					str				):
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()
    def      _UpperCamelCase       ( self							:					Tuple				):
          lowerCamelCase__           =		torch.manual_seed(0				)

          lowerCamelCase__           =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-256'				)
          pipe.to('cuda'				)

          lowerCamelCase__           =		['vase', 'umbrella', 'white shark', 'white wolf']
          lowerCamelCase__           =		pipe.get_label_ids(UpperCamelCase__				)

          lowerCamelCase__           =		pipe(UpperCamelCase__							,					generator=UpperCamelCase__							,					num_inference_steps=40							,					output_type='np'				).images

          for word, image in zip(UpperCamelCase__							,					UpperCamelCase__				):
                lowerCamelCase__           =		load_numpy(
                    F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'				)
                assert np.abs((expected_image - image).max()				) < 1e-2


    def      _UpperCamelCase       ( self							:					int				):
          lowerCamelCase__           =		DiTPipeline.from_pretrained('facebook/DiT-XL-2-512'				)
          lowerCamelCase__           =		DPMSolverMultistepScheduler.from_config(pipe.scheduler.config				)
          pipe.to('cuda'				)

          lowerCamelCase__           =		['vase', 'umbrella']
          lowerCamelCase__           =		pipe.get_label_ids(UpperCamelCase__				)

          lowerCamelCase__           =		torch.manual_seed(0				)
          lowerCamelCase__           =		pipe(UpperCamelCase__							,					generator=UpperCamelCase__							,					num_inference_steps=25							,					output_type='np'				).images

          for word, image in zip(UpperCamelCase__							,					UpperCamelCase__				):
                lowerCamelCase__           =		load_numpy(
                    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                    F'/dit/{word}_512.npy'				)

                assert np.abs((expected_image - image).max()				) < 1e-1







"""simple docstring"""


def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations(_a:     int							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            return sum(count_of_possible_combinations(target - item							) for item in array							)

      return count_of_possible_combinations(_a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      def count_of_possible_combinations_with_dp_array(
          _a:     int		,   _a:     list[int]							) -> int:
            if target < 0:
                  return 0
            if target == 0:
                  return 1
            if dp_array[target] != -1:
                  return dp_array[target]
            lowerCamelCase__           =		sum(
                count_of_possible_combinations_with_dp_array(target - item		,   _a							)
                for item in array							)
            lowerCamelCase__           =		answer
            return answer

      lowerCamelCase__           =		[-1] * (target + 1)
      return count_of_possible_combinations_with_dp_array(_a		,   _a							)



def   snake_case						(				_a:     int		,   _a:     list[int]		,   _a:     int							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		[0] * (target + 1)
      lowerCamelCase__           =		1

      for i in range(1		,   target + 1							):
            for j in range(_a							):
                  if i - array[j] >= 0:
                        dp_array[i] += dp_array[i - array[j]]

      return dp_array[target]


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   _snake_case          =							3
   _snake_case          =							5
   _snake_case          =							[1, 2, 5]
   print(combination_sum_iv(n, array, target))



