




"""simple docstring"""

import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class      A__   (   lowercase_  ):
	A_				:					str      =   (HeunDiscreteScheduler,)
	A_				:					str      =   1_0




	def 				a_		( self	:						str   ,     **lowercase	:						Any						)       ->			Optional[int]:
						__snake_case        =						{
						    """num_train_timesteps""": 1_100,
						    """beta_start""": 0.00_01,
						    """beta_end""": 0.02,
						    """beta_schedule""": """linear""",
						}

						config.update(**lowercase						)
						return config




	def 				a_		( self	:						Tuple						)       ->			List[Any]:
						for timesteps in [10, 50, 100, 1_000]:
											self.check_over_configs(num_train_timesteps=lowercase						)




	def 				a_		( self	:						int						)       ->			str:
						for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]   ,     [0.00_02, 0.0_02, 0.02]						):
											self.check_over_configs(beta_start=lowercase   ,     beta_end=lowercase						)




	def 				a_		( self	:						Optional[int]						)       ->			List[Any]:
						for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=lowercase						)




	def 				a_		( self	:						int						)       ->			Optional[Any]:
						for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=lowercase   ,     clip_sample=lowercase						)




	def 				a_		( self	:						List[str]						)       ->			Optional[int]:
						for prediction_type in ["epsilon", "v_prediction", "sample"]:
											self.check_over_configs(prediction_type=lowercase						)




	def 				a_		( self	:						Dict						)       ->			int:
						__snake_case        =						self.scheduler_classes[0]
						__snake_case        =						self.get_scheduler_config()
						__snake_case        =						scheduler_class(**lowercase						)

						scheduler.set_timesteps(self.num_inference_steps						)

						__snake_case        =						self.dummy_model()
						__snake_case        =						self.dummy_sample_deter * scheduler.init_noise_sigma
						__snake_case        =						sample.to(lowercase						)

						for i, t in enumerate(scheduler.timesteps						):
											__snake_case        =						scheduler.scale_model_input(lowercase   ,     lowercase						)

											__snake_case        =						model(lowercase   ,     lowercase						)

											__snake_case        =						scheduler.step(lowercase   ,     lowercase   ,     lowercase						)
											__snake_case        =						output.prev_sample

						__snake_case        =						torch.sum(torch.abs(lowercase						)						)
						__snake_case        =						torch.mean(torch.abs(lowercase						)						)

						if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.12_33						) < 1e-2
											assert abs(result_mean.item() - 0.00_02						) < 1e-3
						else:
											# CUDA
											assert abs(result_sum.item() - 0.12_33						) < 1e-2
											assert abs(result_mean.item() - 0.00_02						) < 1e-3




	def 				a_		( self	:						List[str]						)       ->			Optional[Any]:
						__snake_case        =						self.scheduler_classes[0]
						__snake_case        =						self.get_scheduler_config(prediction_type="""v_prediction"""						)
						__snake_case        =						scheduler_class(**lowercase						)

						scheduler.set_timesteps(self.num_inference_steps						)

						__snake_case        =						self.dummy_model()
						__snake_case        =						self.dummy_sample_deter * scheduler.init_noise_sigma
						__snake_case        =						sample.to(lowercase						)

						for i, t in enumerate(scheduler.timesteps						):
											__snake_case        =						scheduler.scale_model_input(lowercase   ,     lowercase						)

											__snake_case        =						model(lowercase   ,     lowercase						)

											__snake_case        =						scheduler.step(lowercase   ,     lowercase   ,     lowercase						)
											__snake_case        =						output.prev_sample

						__snake_case        =						torch.sum(torch.abs(lowercase						)						)
						__snake_case        =						torch.mean(torch.abs(lowercase						)						)

						if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934e-07						) < 1e-2
											assert abs(result_mean.item() - 6.1112e-10						) < 1e-3
						else:
											# CUDA
											assert abs(result_sum.item() - 4.693428650170972e-07						) < 1e-2
											assert abs(result_mean.item() - 0.00_02						) < 1e-3




	def 				a_		( self	:						int						)       ->			Union[str, Any]:
						__snake_case        =						self.scheduler_classes[0]
						__snake_case        =						self.get_scheduler_config()
						__snake_case        =						scheduler_class(**lowercase						)

						scheduler.set_timesteps(self.num_inference_steps   ,     device=lowercase						)

						__snake_case        =						self.dummy_model()
						__snake_case        =						self.dummy_sample_deter.to(lowercase						) * scheduler.init_noise_sigma

						for t in scheduler.timesteps:
											__snake_case        =						scheduler.scale_model_input(lowercase   ,     lowercase						)

											__snake_case        =						model(lowercase   ,     lowercase						)

											__snake_case        =						scheduler.step(lowercase   ,     lowercase   ,     lowercase						)
											__snake_case        =						output.prev_sample

						__snake_case        =						torch.sum(torch.abs(lowercase						)						)
						__snake_case        =						torch.mean(torch.abs(lowercase						)						)

						if str(lowercase						).startswith("""cpu"""						):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.12_33						) < 1e-2
											assert abs(result_mean.item() - 0.00_02						) < 1e-3
						elif str(lowercase						).startswith("""mps"""						):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.00_02						) < 1e-2
						else:
											# CUDA
											assert abs(result_sum.item() - 0.12_33						) < 1e-2
											assert abs(result_mean.item() - 0.00_02						) < 1e-3




	def 				a_		( self	:						int						)       ->			Tuple:
						__snake_case        =						self.scheduler_classes[0]
						__snake_case        =						self.get_scheduler_config()
						__snake_case        =						scheduler_class(**lowercase   ,     use_karras_sigmas=lowercase						)

						scheduler.set_timesteps(self.num_inference_steps   ,     device=lowercase						)

						__snake_case        =						self.dummy_model()
						__snake_case        =						self.dummy_sample_deter.to(lowercase						) * scheduler.init_noise_sigma
						__snake_case        =						sample.to(lowercase						)

						for t in scheduler.timesteps:
											__snake_case        =						scheduler.scale_model_input(lowercase   ,     lowercase						)

											__snake_case        =						model(lowercase   ,     lowercase						)

											__snake_case        =						scheduler.step(lowercase   ,     lowercase   ,     lowercase						)
											__snake_case        =						output.prev_sample

						__snake_case        =						torch.sum(torch.abs(lowercase						)						)
						__snake_case        =						torch.mean(torch.abs(lowercase						)						)

						assert abs(result_sum.item() - 0.0_00_15						) < 1e-2
						assert abs(result_mean.item() - 1.9869554535034695e-07						) < 1e-2








"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__			:     List[Any]			       =				logging.get_logger(__name__)

UpperCamelCase__			:     List[str]			       =				{}
class      A__   (   lowercase_  ):
	A_				:					Dict      =   """fsmt_decoder"""




	def __init__( self	:						List[Any]   ,     lowercase	:						Any=0   ,     lowercase	:						Dict=0						)       ->			str:
						super().__init__()
						__snake_case        =						vocab_size
						__snake_case        =						bos_token_id

class      A__   (   lowercase_  ):
	A_				:					Union[str, Any]      =   """fsmt"""
	A_				:					int      =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}




	def __init__( self	:						Optional[Any]   ,     lowercase	:						Any=["en", "de"]   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Optional[int]=42_024   ,     lowercase	:						Tuple="relu"   ,     lowercase	:						Tuple=1_024   ,     lowercase	:						Union[str, Any]=200   ,     lowercase	:						List[str]=1_024   ,     lowercase	:						Tuple=4_096   ,     lowercase	:						int=12   ,     lowercase	:						Any=16   ,     lowercase	:						List[Any]=0.0   ,     lowercase	:						str=4_096   ,     lowercase	:						str=12   ,     lowercase	:						Optional[int]=16   ,     lowercase	:						Dict=0.0   ,     lowercase	:						Any=0.0   ,     lowercase	:						Union[str, Any]=0.1   ,     lowercase	:						Tuple=0.0   ,     lowercase	:						List[str]=0.02   ,     lowercase	:						int=2   ,     lowercase	:						Any=True   ,     lowercase	:						int=True   ,     lowercase	:						int=False   ,     lowercase	:						List[str]=5   ,     lowercase	:						List[Any]=1.0   ,     lowercase	:						Union[str, Any]=False   ,     lowercase	:						Optional[Any]=True   ,     lowercase	:						int=1   ,     lowercase	:						Any=0   ,     lowercase	:						Optional[Any]=2   ,     lowercase	:						Any=2   ,     **lowercase	:						List[Any]   ,     )       ->			Union[str, Any]:
						__snake_case        =						langs
						__snake_case        =						src_vocab_size
						__snake_case        =						tgt_vocab_size
						__snake_case        =						d_model  # encoder_embed_dim and decoder_embed_dim

						__snake_case        =						encoder_ffn_dim
						__snake_case        =						__snake_case        =						encoder_layers
						__snake_case        =						encoder_attention_heads
						__snake_case        =						encoder_layerdrop
						__snake_case        =						decoder_layerdrop
						__snake_case        =						decoder_ffn_dim
						__snake_case        =						decoder_layers
						__snake_case        =						decoder_attention_heads
						__snake_case        =						max_position_embeddings
						__snake_case        =						init_std  # Normal(0, this parameter)
						__snake_case        =						activation_function

						__snake_case        =						DecoderConfig(vocab_size=lowercase   ,     bos_token_id=lowercase						)
						if "decoder" in common_kwargs:
											del common_kwargs["decoder"]

						__snake_case        =						scale_embedding  # scale factor will be sqrt(d_model) if True

						# 3 Types of Dropout
						__snake_case        =						attention_dropout
						__snake_case        =						activation_dropout
						__snake_case        =						dropout

						__snake_case        =						use_cache
						super().__init__(
						    pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     decoder_start_token_id=lowercase   ,     is_encoder_decoder=lowercase   ,     tie_word_embeddings=lowercase   ,     forced_eos_token_id=lowercase   ,     max_length=lowercase   ,     num_beams=lowercase   ,     length_penalty=lowercase   ,     early_stopping=lowercase   ,     **lowercase   ,     )




	def 				a_		( self	:						Union[str, Any]						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.decoder.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


