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


__magic_name__            =  logging.get_logger(__name__)

__magic_name__            =  {
    '''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 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		List[Any]    	=     'realm'
      def __init__(			self		,	lowerCamelCase=3_05_22		,	lowerCamelCase=7_68		,	lowerCamelCase=1_28		,	lowerCamelCase=12		,	lowerCamelCase=12		,	lowerCamelCase=8		,	lowerCamelCase=30_72		,	lowerCamelCase="gelu_new"		,	lowerCamelCase=0.1		,	lowerCamelCase=0.1		,	lowerCamelCase=5_12		,	lowerCamelCase=2		,	lowerCamelCase=0.0_2		,	lowerCamelCase=1e-12		,	lowerCamelCase=2_56		,	lowerCamelCase=10		,	lowerCamelCase=1e-3		,	lowerCamelCase=5		,	lowerCamelCase=3_20		,	lowerCamelCase=13_35_37_18		,	lowerCamelCase=50_00		,	lowerCamelCase=1		,	lowerCamelCase=0		,	lowerCamelCase=2		,	**lowerCamelCase		,	):
          super().__init__(pad_token_id=lowerCamelCase		,	bos_token_id=lowerCamelCase		,	eos_token_id=lowerCamelCase		,	**lowerCamelCase )

          # Common config
          snake_case__						=				vocab_size
          snake_case__						=				max_position_embeddings
          snake_case__						=				hidden_size
          snake_case__						=				retriever_proj_size
          snake_case__						=				num_hidden_layers
          snake_case__						=				num_attention_heads
          snake_case__						=				num_candidates
          snake_case__						=				intermediate_size
          snake_case__						=				hidden_act
          snake_case__						=				hidden_dropout_prob
          snake_case__						=				attention_probs_dropout_prob
          snake_case__						=				initializer_range
          snake_case__						=				type_vocab_size
          snake_case__						=				layer_norm_eps

          # Reader config
          snake_case__						=				span_hidden_size
          snake_case__						=				max_span_width
          snake_case__						=				reader_layer_norm_eps
          snake_case__						=				reader_beam_size
          snake_case__						=				reader_seq_len

          # Retrieval config
          snake_case__						=				num_block_records
          snake_case__						=				searcher_beam_size





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		Any    	=     (KDPMaDiscreteScheduler,)
      _A						:		Dict    	=     10
      def A_			(			self		,	**lowerCamelCase ):
          snake_case__						=				{
              "num_train_timesteps": 11_00,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
          }

          config.update(**lowerCamelCase )
          return config
      def A_			(			self ):
          for timesteps in [10, 50, 1_00, 10_00]:
              self.check_over_configs(num_train_timesteps=lowerCamelCase )
      def A_			(			self ):
          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,	[0.0_0_0_2, 0.0_0_2, 0.0_2] ):
              self.check_over_configs(beta_start=lowerCamelCase		,	beta_end=lowerCamelCase )
      def A_			(			self ):
          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=lowerCamelCase )
      def A_			(			self ):
          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=lowerCamelCase )
      def A_			(			self ):
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config(prediction_type="v_prediction" )
          snake_case__						=				scheduler_class(**lowerCamelCase )

          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(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 4.69_34e-07 ) < 1e-2
              assert abs(result_mean.item() - 6.11_12e-10 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07 ) < 1e-2
              assert abs(result_mean.item() - 0.0_0_0_2 ) < 1e-3
      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

          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(lowerCamelCase )

          for i, t in enumerate(scheduler.timesteps ):
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if torch_device in ["cpu", "mps"]:
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



      def A_			(			self ):
          if torch_device == "mps":
              return
          snake_case__						=				self.scheduler_classes[0]
          snake_case__						=				self.get_scheduler_config()
          snake_case__						=				scheduler_class(**lowerCamelCase )

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

          snake_case__						=				self.dummy_model()
          snake_case__						=				self.dummy_sample_deter.to(lowerCamelCase ) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
              snake_case__						=				scheduler.scale_model_input(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				model(lowerCamelCase		,	lowerCamelCase )

              snake_case__						=				scheduler.step(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )
              snake_case__						=				output.prev_sample

          snake_case__						=				torch.sum(torch.abs(lowerCamelCase ) )
          snake_case__						=				torch.mean(torch.abs(lowerCamelCase ) )

          if str(lowerCamelCase ).startswith("cpu" ):
              # The following sum varies between 148 and 156 on mps. Why?
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3
          else:
              # CUDA
              assert abs(result_sum.item() - 2_0.4_1_2_5 ) < 1e-2
              assert abs(result_mean.item() - 0.0_2_6_6 ) < 1e-3



