'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


__SCREAMING_SNAKE_CASE       :     Optional[int]     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     int     =  {
    '''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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[int]						=						"roformer"




      def __init__(       self						:				List[str]						,					A						:				List[Any]=50000						,					A						:				Dict=None						,					A						:				Tuple=768						,					A						:				Optional[Any]=12						,					A						:				Tuple=12						,					A						:				Dict=3072						,					A						:				List[Any]="gelu"						,					A						:				Optional[Any]=0.1						,					A						:				str=0.1						,					A						:				int=1536						,					A						:				Optional[int]=2						,					A						:				List[str]=0.02						,					A						:				List[str]=1E-12						,					A						:				Optional[int]=0						,					A						:				List[Any]=False						,					A						:				List[str]=True						,					**A						:				Dict						,					):
             super().__init__(pad_token_id=A						,					**A       )

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

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      @property
      def      _A	(       self						:				Tuple       ):


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



import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


_lowerCamelCase       :				int       =			False




class    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	def  A							(  self	:    Union[str, Any]			,  lowercase	:    Optional[int]=32	):


				'''simple docstring'''


				set_seed(0	)
				_snake_case		=			UNetaDModel(sample_size=lowercase			,  in_channels=3			,  out_channels=3	)
				_snake_case		=			torch.optim.SGD(model.parameters()			,  lr=0.0001	)
				return model, optimizer





	@slow
	def  A							(  self	:    List[str]	):


				'''simple docstring'''


				_snake_case		=			'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				_snake_case		=			DDPMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )
				_snake_case		=			DDIMScheduler(
				    num_train_timesteps=1_000			,  beta_start=0.0001			,  beta_end=0.02			,  beta_schedule='linear'			,  clip_sample=lowercase			,  )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).clip(-1			,  1	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randn((4, 3, 32, 32)	).to(lowercase	) for _ in range(4	)]
				_snake_case		=			[torch.randint(0			,  1_000			,  (4,)	).long().to(lowercase	) for _ in range(4	)]

				# train with a DDPM scheduler
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddpm_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				_snake_case	,					_snake_case		=			self.get_model_optimizer(resolution=32	)
				model.train().to(lowercase	)
				for i in range(4	):
							optimizer.zero_grad()
							_snake_case		=			ddim_scheduler.add_noise(clean_images[i]			,  noise[i]			,  timesteps[i]	)
							_snake_case		=			model(lowercase			,  timesteps[i]	).sample
							_snake_case		=			torch.nn.functional.mse_loss(lowercase			,  noise[i]	)
							loss.backward()
							optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)
				self.assertTrue(torch.allclose(lowercase			,  lowercase			,  atol=1E-5	)	)