
"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
_a								=						"""
Human: <<task>>

Assistant: """


_a								=						"""huggingface-tools/default-prompts"""
_a								=						{"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case="run"      )				->						Any:


					"""simple docstring"""





					if prompt_or_repo_id is None:
										_UpperCamelCase									=    DEFAULT_PROMPTS_REPO

					# prompt is considered a repo ID when it does not contain any kind of space
					if re.search('''\\s''',	__snake_case      ) is not None:
										return prompt_or_repo_id

					_UpperCamelCase									=    cached_file(
					    __snake_case,	PROMPT_FILES[mode],	repo_type='''dataset''',	user_agent={'''agent''': agent_name}      )
					with open(__snake_case,	'''r''',	encoding='''utf-8'''      ) as f:
										return f.read()








"""simple docstring"""





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DDPMScheduler,)



		def 		UpperCAmelCase				(   self							,						**__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 10_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''variance_type''': '''fixed_small''',
							    '''clip_sample''': True,
							}

							config.update(**__a)
							return config



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for timesteps in [1, 5, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]							,						[0.002, 0.02, 0.2, 2]):
												self.check_over_configs(beta_start=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							for variance in ["fixed_small", "fixed_large", "other"]:
												self.check_over_configs(variance_type=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							self.check_over_configs(thresholding=__a)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "sample", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=__a							,						prediction_type=__a							,						sample_max_value=__a							,						)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
												self.check_over_configs(prediction_type=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							for t in [0, 5_00, 9_99]:
												self.check_over_forward(time_step=__a)



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0979)) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.02)) < 1e-5



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter
							_UpperCamelCase									=    torch.manual_seed(0)

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a							,						generator=__a).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 258.9606) < 1e-2
							assert abs(result_mean.item() - 0.3372) < 1e-3



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    len(__a)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter
							_UpperCamelCase									=    torch.manual_seed(0)

							for t in reversed(range(__a)):
												# 1. predict noise residual
												_UpperCamelCase									=    model(__a							,						__a)

												# 2. predict previous mean of sample x_t-1
												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a							,						generator=__a).prev_sample

												# if t > 0:
												#     noise = self.dummy_sample_deter
												#     variance = scheduler.get_variance(t) ** (0.5) * noise
												#
												# sample = pred_prev_sample + variance
												_UpperCamelCase									=    pred_prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							assert abs(result_sum.item() - 202.0296) < 1e-2
							assert abs(result_mean.item() - 0.2631) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]

							scheduler.set_timesteps(timesteps=__a)

							_UpperCamelCase									=    scheduler.timesteps

							for i, timestep in enumerate(__a):
												if i == len(__a) - 1:
																	_UpperCamelCase									=    -1
												else:
																	_UpperCamelCase									=    timesteps[i + 1]

												_UpperCamelCase									=    scheduler.previous_timestep(__a)
												_UpperCamelCase									=    prev_t.item()

												self.assertEqual(__a							,						__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 51, 0]

							with self.assertRaises(__a							,						msg='''`custom_timesteps` must be in descending order.'''):
												scheduler.set_timesteps(timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [1_00, 87, 50, 1, 0]
							_UpperCamelCase									=    len(__a)

							with self.assertRaises(__a							,						msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
												scheduler.set_timesteps(num_inference_steps=__a							,						timesteps=__a)




		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							_UpperCamelCase									=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    __a							,						msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,						):
												scheduler.set_timesteps(timesteps=__a)






