


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				List[str]	    =    (DDIMParallelScheduler,)
							__UpperCAmelCase       :				Tuple	    =    (("eta", 0.0), ("num_inference_steps", 50))

							def        __snake_case   (				self			:    int       ,      **lowerCamelCase			:    Optional[Any]							)      ->       Optional[Any]:
														__snake_case						:     Any									=  {
														    "num_train_timesteps": 1000,
														    "beta_start": 0.00_01,
														    "beta_end": 0.02,
														    "beta_schedule": "linear",
														    "clip_sample": True,
														}

														config.update(**lowerCamelCase							)
														return config

							def        __snake_case   (				self			:    Optional[Any]       ,      **lowerCamelCase			:    List[str]							)      ->       Any:
														__snake_case						:     Optional[int]									=  self.scheduler_classes[0]
														__snake_case						:     Optional[int]									=  self.get_scheduler_config(**lowerCamelCase							)
														__snake_case						:     Optional[int]									=  scheduler_class(**lowerCamelCase							)

														__snake_case					,     __snake_case						:     List[Any]									=  10, 0.0

														__snake_case						:     Union[str, Any]									=  self.dummy_model()
														__snake_case						:     Union[str, Any]									=  self.dummy_sample_deter

														scheduler.set_timesteps(lowerCamelCase							)

														for t in scheduler.timesteps:
																					__snake_case						:     List[Any]									=  model(lowerCamelCase       ,      lowerCamelCase							)
																					__snake_case						:     List[str]									=  scheduler.step(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							).prev_sample

														return sample

							def        __snake_case   (				self			:    Tuple							)      ->       Dict:
														for timesteps in [100, 500, 1000]:
																					self.check_over_configs(num_train_timesteps=lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       int:
														for steps_offset in [0, 1]:
																					self.check_over_configs(steps_offset=lowerCamelCase							)

														__snake_case						:     Optional[int]									=  self.scheduler_classes[0]
														__snake_case						:     Optional[Any]									=  self.get_scheduler_config(steps_offset=1							)
														__snake_case						:     List[str]									=  scheduler_class(**lowerCamelCase							)
														scheduler.set_timesteps(5							)
														assert torch.equal(scheduler.timesteps       ,      torch.LongTensor([801, 601, 401, 201, 1]							)							)

							def        __snake_case   (				self			:    List[Any]							)      ->       int:
														for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]       ,      [0.0_02, 0.02, 0.2, 2]							):
																					self.check_over_configs(beta_start=lowerCamelCase       ,      beta_end=lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       str:
														for schedule in ["linear", "squaredcos_cap_v2"]:
																					self.check_over_configs(beta_schedule=lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       List[str]:
														for prediction_type in ["epsilon", "v_prediction"]:
																					self.check_over_configs(prediction_type=lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[Any]							)      ->       List[Any]:
														for clip_sample in [True, False]:
																					self.check_over_configs(clip_sample=lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       List[Any]:
														for timestep_spacing in ["trailing", "leading"]:
																					self.check_over_configs(timestep_spacing=lowerCamelCase							)

							def        __snake_case   (				self			:    Tuple							)      ->       Any:
														for rescale_betas_zero_snr in [True, False]:
																					self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       List[str]:
														self.check_over_configs(thresholding=lowerCamelCase							)
														for threshold in [0.5, 1.0, 2.0]:
																					for prediction_type in ["epsilon", "v_prediction"]:
																												self.check_over_configs(
																												    thresholding=lowerCamelCase       ,      prediction_type=lowerCamelCase       ,      sample_max_value=lowerCamelCase       ,      )

							def        __snake_case   (				self			:    int							)      ->       Union[str, Any]:
														for t in [1, 10, 49]:
																					self.check_over_forward(time_step=lowerCamelCase							)

							def        __snake_case   (				self			:    int							)      ->       List[Any]:
														for t, num_inference_steps in zip([1, 10, 50]       ,      [10, 50, 500]							):
																					self.check_over_forward(time_step=lowerCamelCase       ,      num_inference_steps=lowerCamelCase							)

							def        __snake_case   (				self			:    Dict							)      ->       str:
														for t, eta in zip([1, 10, 49]       ,      [0.0, 0.5, 1.0]							):
																					self.check_over_forward(time_step=lowerCamelCase       ,      eta=lowerCamelCase							)

							def        __snake_case   (				self			:    Any							)      ->       Any:
														__snake_case						:     Any									=  self.scheduler_classes[0]
														__snake_case						:     Dict									=  self.get_scheduler_config()
														__snake_case						:     Optional[int]									=  scheduler_class(**lowerCamelCase							)

														assert torch.sum(torch.abs(scheduler._get_variance(0       ,      0							) - 0.0							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(420       ,      400							) - 0.1_47_71							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(980       ,      960							) - 0.3_24_60							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(0       ,      0							) - 0.0							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(487       ,      486							) - 0.0_09_79							)							) < 1E-5
														assert torch.sum(torch.abs(scheduler._get_variance(999       ,      998							) - 0.02							)							) < 1E-5

							def        __snake_case   (				self			:    Dict							)      ->       List[Any]:
														__snake_case						:     Any									=  self.scheduler_classes[0]
														__snake_case						:     Dict									=  self.get_scheduler_config()
														__snake_case						:     Optional[int]									=  scheduler_class(**lowerCamelCase							)

														__snake_case					,     __snake_case						:     str									=  10, 0.0
														scheduler.set_timesteps(lowerCamelCase							)

														__snake_case						:     Dict									=  self.dummy_model()
														__snake_case						:     List[Any]									=  self.dummy_sample_deter
														__snake_case						:     int									=  self.dummy_sample_deter + 0.1
														__snake_case						:     str									=  self.dummy_sample_deter - 0.1

														__snake_case						:     Dict									=  samplea.shape[0]
														__snake_case						:     str									=  torch.stack([samplea, samplea, samplea]       ,      dim=0							)
														__snake_case						:     int									=  torch.arange(lowerCamelCase							)[0:3, None].repeat(1       ,      lowerCamelCase							)

														__snake_case						:     Union[str, Any]									=  model(samples.flatten(0       ,      1							)       ,      timesteps.flatten(0       ,      1							)							)
														__snake_case						:     Any									=  scheduler.batch_step_no_noise(lowerCamelCase       ,      timesteps.flatten(0       ,      1							)       ,      samples.flatten(0       ,      1							)       ,      lowerCamelCase							)

														__snake_case						:     Optional[Any]									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     List[Any]									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 11_47.79_04							) < 1E-2
														assert abs(result_mean.item() - 0.49_82							) < 1E-3

							def        __snake_case   (				self			:    str							)      ->       Optional[int]:
														__snake_case						:     Union[str, Any]									=  self.full_loop()

														__snake_case						:     Dict									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     Dict									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 1_72.00_67							) < 1E-2
														assert abs(result_mean.item() - 0.22_39_67							) < 1E-3

							def        __snake_case   (				self			:    str							)      ->       Dict:
														__snake_case						:     Any									=  self.full_loop(prediction_type="v_prediction"							)

														__snake_case						:     Optional[int]									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     int									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 52.53_02							) < 1E-2
														assert abs(result_mean.item() - 0.06_84							) < 1E-3

							def        __snake_case   (				self			:    Dict							)      ->       Tuple:
														# We specify different beta, so that the first alpha is 0.99
														__snake_case						:     str									=  self.full_loop(set_alpha_to_one=lowerCamelCase       ,      beta_start=0.01							)
														__snake_case						:     Dict									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     Dict									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 1_49.82_95							) < 1E-2
														assert abs(result_mean.item() - 0.19_51							) < 1E-3






							def        __snake_case   (				self			:    Optional[Any]							)      ->       List[Any]:
														# We specify different beta, so that the first alpha is 0.99
														__snake_case						:     Union[str, Any]									=  self.full_loop(set_alpha_to_one=lowerCamelCase       ,      beta_start=0.01							)
														__snake_case						:     Optional[Any]									=  torch.sum(torch.abs(lowerCamelCase							)							)
														__snake_case						:     Dict									=  torch.mean(torch.abs(lowerCamelCase							)							)

														assert abs(result_sum.item() - 1_49.07_84							) < 1E-2
														assert abs(result_mean.item() - 0.19_41							) < 1E-3




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

				if self.task == "multiple-choice":
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							{0: "batch", 1: "choice", 2: "sequence"}
				else:
					SCREAMING_SNAKE_CASE :   str					  =							{0: "batch", 1: "sequence"}
				SCREAMING_SNAKE_CASE :   List[Any]					  =							{0: "batch", 1: "sequence"}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				        ("token_type_ids", dynamic_axis),
				    ]						)