'''simple docstring'''




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


__SCREAMING_SNAKE_CASE       :     int     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     str     =  {
    '''google/realm-cc-news-pretrained-embedder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-encoder''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-scorer''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/config.json'''
    ),
    '''google/realm-cc-news-pretrained-openqa''': (
        '''https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/config.json'''
    ),
    '''google/realm-orqa-nq-openqa''': '''https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-nq-reader''': '''https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/config.json''',
    '''google/realm-orqa-wq-openqa''': '''https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/config.json''',
    '''google/realm-orqa-wq-reader''': '''https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/config.json''',
    # See all REALM models at https://huggingface.co/models?filter=realm
}

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

      __UpperCamelCase:  Tuple						=						"realm"




      def __init__(       self						:				int						,					A						:				Tuple=30522						,					A						:				Any=768						,					A						:				str=128						,					A						:				Dict=12						,					A						:				Union[str, Any]=12						,					A						:				int=8						,					A						:				Union[str, Any]=3072						,					A						:				List[str]="gelu_new"						,					A						:				int=0.1						,					A						:				List[Any]=0.1						,					A						:				Optional[int]=512						,					A						:				Optional[int]=2						,					A						:				Union[str, Any]=0.02						,					A						:				Any=1E-12						,					A						:				Optional[Any]=256						,					A						:				Any=10						,					A						:				List[Any]=1E-3						,					A						:				Optional[int]=5						,					A						:				List[str]=320						,					A						:				List[Any]=13353718						,					A						:				List[str]=5000						,					A						:				int=1						,					A						:				Optional[int]=0						,					A						:				List[str]=2						,					**A						:				Union[str, Any]						,					):
             super().__init__(pad_token_id=A						,					bos_token_id=A						,					eos_token_id=A						,					**A       )

             # Common config
             _UpperCAmelCase     :  Optional[Any]							      =    vocab_size
             _UpperCAmelCase     :  str							      =    max_position_embeddings
             _UpperCAmelCase     :  List[str]							      =    hidden_size
             _UpperCAmelCase     :  Optional[Any]							      =    retriever_proj_size
             _UpperCAmelCase     :  List[Any]							      =    num_hidden_layers
             _UpperCAmelCase     :  Any							      =    num_attention_heads
             _UpperCAmelCase     :  List[Any]							      =    num_candidates
             _UpperCAmelCase     :  Optional[int]							      =    intermediate_size
             _UpperCAmelCase     :  Tuple							      =    hidden_act
             _UpperCAmelCase     :  Dict							      =    hidden_dropout_prob
             _UpperCAmelCase     :  Tuple							      =    attention_probs_dropout_prob
             _UpperCAmelCase     :  Any							      =    initializer_range
             _UpperCAmelCase     :  Dict							      =    type_vocab_size
             _UpperCAmelCase     :  int							      =    layer_norm_eps

             # Reader config
             _UpperCAmelCase     :  Optional[int]							      =    span_hidden_size
             _UpperCAmelCase     :  List[str]							      =    max_span_width
             _UpperCAmelCase     :  List[Any]							      =    reader_layer_norm_eps
             _UpperCAmelCase     :  int							      =    reader_beam_size
             _UpperCAmelCase     :  Dict							      =    reader_seq_len

             # Retrieval config
             _UpperCAmelCase     :  Dict							      =    num_block_records
             _UpperCAmelCase     :  List[str]							      =    searcher_beam_size



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	)	)