




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np






class     __lowerCAmelCase :







			def UpperCamelCase			(       self    :			Union[str, Any]      ):




									"""simple docstring"""


									torch.manual_seed(0      )
									_UpperCAmelCase													=     TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

									torch.manual_seed(0      )
									_UpperCAmelCase													=     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

									torch.manual_seed(0      )
									_UpperCAmelCase													=     UNetaDConditionModel(
									    sample_size=32    ,    layers_per_block=1    ,    block_out_channels=[32, 64]    ,    down_block_types=[
									        "ResnetDownsampleBlock2D",
									        "SimpleCrossAttnDownBlock2D",
									    ]    ,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"    ,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]    ,    in_channels=3    ,    out_channels=6    ,    cross_attention_dim=32    ,    encoder_hid_dim=32    ,    attention_head_dim=8    ,    addition_embed_type="text"    ,    addition_embed_type_num_heads=2    ,    cross_attention_norm="group_norm"    ,    resnet_time_scale_shift="scale_shift"    ,    act_fn="gelu"    ,    )
									unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

									torch.manual_seed(0      )
									_UpperCAmelCase													=     DDPMScheduler(
									    num_train_timesteps=1_000    ,    beta_schedule="squaredcos_cap_v2"    ,    beta_start=0.0_001    ,    beta_end=0.02    ,    thresholding=_UpperCamelCase    ,    dynamic_thresholding_ratio=0.95    ,    sample_max_value=1.0    ,    prediction_type="epsilon"    ,    variance_type="learned_range"    ,    )

									torch.manual_seed(0      )
									_UpperCAmelCase													=     IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}







			def UpperCamelCase			(       self    :			int      ):




									"""simple docstring"""


									torch.manual_seed(0      )
									_UpperCAmelCase													=     TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"      )

									torch.manual_seed(0      )
									_UpperCAmelCase													=     AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"      )

									torch.manual_seed(0      )
									_UpperCAmelCase													=     UNetaDConditionModel(
									    sample_size=32    ,    layers_per_block=[1, 2]    ,    block_out_channels=[32, 64]    ,    down_block_types=[
									        "ResnetDownsampleBlock2D",
									        "SimpleCrossAttnDownBlock2D",
									    ]    ,    mid_block_type="UNetMidBlock2DSimpleCrossAttn"    ,    up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]    ,    in_channels=6    ,    out_channels=6    ,    cross_attention_dim=32    ,    encoder_hid_dim=32    ,    attention_head_dim=8    ,    addition_embed_type="text"    ,    addition_embed_type_num_heads=2    ,    cross_attention_norm="group_norm"    ,    resnet_time_scale_shift="scale_shift"    ,    act_fn="gelu"    ,    class_embed_type="timestep"    ,    mid_block_scale_factor=1.414    ,    time_embedding_act_fn="gelu"    ,    time_embedding_dim=32    ,    )
									unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

									torch.manual_seed(0      )
									_UpperCAmelCase													=     DDPMScheduler(
									    num_train_timesteps=1_000    ,    beta_schedule="squaredcos_cap_v2"    ,    beta_start=0.0_001    ,    beta_end=0.02    ,    thresholding=_UpperCamelCase    ,    dynamic_thresholding_ratio=0.95    ,    sample_max_value=1.0    ,    prediction_type="epsilon"    ,    variance_type="learned_range"    ,    )

									torch.manual_seed(0      )
									_UpperCAmelCase													=     DDPMScheduler(
									    num_train_timesteps=1_000    ,    beta_schedule="squaredcos_cap_v2"    ,    beta_start=0.0_001    ,    beta_end=0.02    ,    )

									torch.manual_seed(0      )
									_UpperCAmelCase													=     IFWatermarker()

									return {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "image_noising_scheduler": image_noising_scheduler,
									    "watermarker": watermarker,
									    "safety_checker": None,
									    "feature_extractor": None,
									}







			def UpperCamelCase			(       self    :			List[Any]      ):




									"""simple docstring"""


									_UpperCAmelCase													=     self.get_dummy_components()
									_UpperCAmelCase													=     self.pipeline_class(**_UpperCamelCase      )
									pipe.to(_UpperCamelCase      )
									pipe.set_progress_bar_config(disable=_UpperCamelCase      )

									_UpperCAmelCase													=     self.get_dummy_inputs(_UpperCamelCase      )

									_UpperCAmelCase													=     inputs["prompt"]
									_UpperCAmelCase													=     inputs["generator"]
									_UpperCAmelCase													=     inputs["num_inference_steps"]
									_UpperCAmelCase													=     inputs["output_type"]

									if "image" in inputs:
															_UpperCAmelCase													=     inputs["image"]
									else:
															_UpperCAmelCase													=     None

									if "mask_image" in inputs:
															_UpperCAmelCase													=     inputs["mask_image"]
									else:
															_UpperCAmelCase													=     None

									if "original_image" in inputs:
															_UpperCAmelCase													=     inputs["original_image"]
									else:
															_UpperCAmelCase													=     None

									_UpperCAmelCase							, _UpperCAmelCase													=     pipe.encode_prompt(_UpperCamelCase      )

									# inputs with prompt converted to embeddings
									_UpperCAmelCase													=     {
									    "prompt_embeds": prompt_embeds,
									    "negative_prompt_embeds": negative_prompt_embeds,
									    "generator": generator,
									    "num_inference_steps": num_inference_steps,
									    "output_type": output_type,
									}

									if image is not None:
															_UpperCAmelCase													=     image

									if mask_image is not None:
															_UpperCAmelCase													=     mask_image

									if original_image is not None:
															_UpperCAmelCase													=     original_image

									# set all optional components to None
									for optional_component in pipe._optional_components:
															setattr(_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase      )

									_UpperCAmelCase													=     pipe(**_UpperCamelCase      )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(_UpperCamelCase      )
															_UpperCAmelCase													=     self.pipeline_class.from_pretrained(_UpperCamelCase      )
															pipe_loaded.to(_UpperCamelCase      )
															pipe_loaded.set_progress_bar_config(disable=_UpperCamelCase      )

									pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

									for optional_component in pipe._optional_components:
															self.assertTrue(
															    getattr(_UpperCamelCase    ,    _UpperCamelCase      ) is None    ,    F"""`{optional_component}` did not stay set to None after loading."""    ,    )

									_UpperCAmelCase													=     self.get_dummy_inputs(_UpperCamelCase      )

									_UpperCAmelCase													=     inputs["generator"]
									_UpperCAmelCase													=     inputs["num_inference_steps"]
									_UpperCAmelCase													=     inputs["output_type"]

									# inputs with prompt converted to embeddings
									_UpperCAmelCase													=     {
									    "prompt_embeds": prompt_embeds,
									    "negative_prompt_embeds": negative_prompt_embeds,
									    "generator": generator,
									    "num_inference_steps": num_inference_steps,
									    "output_type": output_type,
									}

									if image is not None:
															_UpperCAmelCase													=     image

									if mask_image is not None:
															_UpperCAmelCase													=     mask_image

									if original_image is not None:
															_UpperCAmelCase													=     original_image

									_UpperCAmelCase													=     pipe_loaded(**_UpperCamelCase      )[0]

									_UpperCAmelCase													=     np.abs(to_np(_UpperCamelCase      ) - to_np(_UpperCamelCase      )      ).max()
									self.assertLess(_UpperCamelCase    ,    1e-4      )







			def UpperCamelCase			(       self    :			Optional[Any]      ):




									"""simple docstring"""


									_UpperCAmelCase													=     self.get_dummy_components()
									_UpperCAmelCase													=     self.pipeline_class(**_UpperCamelCase      )
									pipe.to(_UpperCamelCase      )
									pipe.set_progress_bar_config(disable=_UpperCamelCase      )

									_UpperCAmelCase													=     self.get_dummy_inputs(_UpperCamelCase      )
									_UpperCAmelCase													=     pipe(**_UpperCamelCase      )[0]

									with tempfile.TemporaryDirectory() as tmpdir:
															pipe.save_pretrained(_UpperCamelCase      )
															_UpperCAmelCase													=     self.pipeline_class.from_pretrained(_UpperCamelCase      )
															pipe_loaded.to(_UpperCamelCase      )
															pipe_loaded.set_progress_bar_config(disable=_UpperCamelCase      )

									pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()      )  # For reproducibility tests

									_UpperCAmelCase													=     self.get_dummy_inputs(_UpperCamelCase      )
									_UpperCAmelCase													=     pipe_loaded(**_UpperCamelCase      )[0]

									_UpperCAmelCase													=     np.abs(to_np(_UpperCamelCase      ) - to_np(_UpperCamelCase      )      ).max()
									self.assertLess(_UpperCamelCase    ,    1e-4      )











def _a				(	lowerCamelCase:   int = 2_00 )	->						int:







      '''simple docstring'''






      __A          =				[1, 2, 5, 10, 20, 50, 1_00, 2_00]
      __A          =				[0] * (pence + 1)
      __A          =				1  # base case: 1 way to make 0 pence

      for coin in coins:
            for i in range(lowerCamelCase					,				pence + 1					,				1 ):
                  number_of_ways[i] += number_of_ways[i - coin]
      return number_of_ways[pence]


if __name__ == "__main__":
       assert solution(200) == 73682





