

'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


lowercase__            =     logging.get_logger(__name__)

lowercase__            =     {
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      List[str]    						=    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
     UpperCAmelCase_		:      Any    						=    """mobilebert"""


     def __init__(						self					:	int	,     lowercase_					:	Any=30_522	,     lowercase_					:	Tuple=512	,     lowercase_					:	str=24	,     lowercase_					:	str=4	,     lowercase_					:	str=512	,     lowercase_					:	Any="relu"	,     lowercase_					:	Optional[Any]=0.0	,     lowercase_					:	List[str]=0.1	,     lowercase_					:	Optional[int]=512	,     lowercase_					:	Dict=2	,     lowercase_					:	Union[str, Any]=0.02	,     lowercase_					:	Optional[int]=1E-12	,     lowercase_					:	List[Any]=0	,     lowercase_					:	Optional[Any]=128	,     lowercase_					:	int=True	,     lowercase_					:	Optional[Any]=True	,     lowercase_					:	List[Any]=128	,     lowercase_					:	List[str]=False	,     lowercase_					:	Optional[int]=True	,     lowercase_					:	Optional[Any]=4	,     lowercase_					:	Union[str, Any]="no_norm"	,     lowercase_					:	Optional[int]=True	,     lowercase_					:	int=None	,     **lowercase_					:	Union[str, Any]	,     )		->      List[Any]:
      super().__init__(pad_token_id=lowercase_	,     **lowercase_			)

      UpperCAmelCase			:      List[str]		   =					vocab_size
      UpperCAmelCase			:      str		   =					hidden_size
      UpperCAmelCase			:      str		   =					num_hidden_layers
      UpperCAmelCase			:      Dict		   =					num_attention_heads
      UpperCAmelCase			:      Optional[Any]		   =					hidden_act
      UpperCAmelCase			:      str		   =					intermediate_size
      UpperCAmelCase			:      Any		   =					hidden_dropout_prob
      UpperCAmelCase			:      List[str]		   =					attention_probs_dropout_prob
      UpperCAmelCase			:      Optional[Any]		   =					max_position_embeddings
      UpperCAmelCase			:      str		   =					type_vocab_size
      UpperCAmelCase			:      List[Any]		   =					initializer_range
      UpperCAmelCase			:      Union[str, Any]		   =					layer_norm_eps
      UpperCAmelCase			:      Dict		   =					embedding_size
      UpperCAmelCase			:      Optional[Any]		   =					trigram_input
      UpperCAmelCase			:      Dict		   =					use_bottleneck
      UpperCAmelCase			:      str		   =					intra_bottleneck_size
      UpperCAmelCase			:      List[Any]		   =					use_bottleneck_attention
      UpperCAmelCase			:      str		   =					key_query_shared_bottleneck
      UpperCAmelCase			:      Optional[int]		   =					num_feedforward_networks
      UpperCAmelCase			:      Dict		   =					normalization_type
      UpperCAmelCase			:      Union[str, Any]		   =					classifier_activation

      if self.use_bottleneck:
       UpperCAmelCase			:      List[Any]		   =					intra_bottleneck_size
      else:
       UpperCAmelCase			:      Tuple		   =					hidden_size

      UpperCAmelCase			:      int		   =					classifier_dropout




class 			A_					(							_snake_case				):






     '''simple docstring'''



     @property
     def   UpperCAmelCase_			(						self					:	List[str]			)		->      Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase			:      Any		   =					{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase			:      Dict		   =					{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('input_ids', dynamic_axis),
              ('attention_mask', dynamic_axis),
              ('token_type_ids', dynamic_axis),
          ]			)







'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 			A_					(							_snake_case				):






     '''simple docstring'''



     UpperCAmelCase_		:      Dict    						=    (LMSDiscreteScheduler,)
     UpperCAmelCase_		:      Any    						=    10


     def   UpperCAmelCase_			(						self					:	str	,     **lowercase_					:	List[str]			)		->      str:
      UpperCAmelCase			:      Optional[Any]		   =					{
          'num_train_timesteps': 1_100,
          'beta_start': 0.0001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**lowercase_			)
      return config


     def   UpperCAmelCase_			(						self					:	Optional[int]			)		->      Tuple:
      for timesteps in [10, 50, 100, 1_000]:
       self.check_over_configs(num_train_timesteps=lowercase_			)


     def   UpperCAmelCase_			(						self					:	int			)		->      Tuple:
      for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]	,     [0.0002, 0.002, 0.02]			):
       self.check_over_configs(beta_start=lowercase_	,     beta_end=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Dict			)		->      Union[str, Any]:
      for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Tuple			)		->      List[Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=lowercase_			)


     def   UpperCAmelCase_			(						self					:	str			)		->      Union[str, Any]:
      for t in [0, 500, 800]:
       self.check_over_forward(time_step=lowercase_			)


     def   UpperCAmelCase_			(						self					:	Union[str, Any]			)		->      List[str]:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Tuple		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Any		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      str		   =					self.dummy_model()
      UpperCAmelCase			:      str		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Optional[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Union[str, Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Optional[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Optional[Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3


     def   UpperCAmelCase_			(						self					:	str			)		->      Dict:
      UpperCAmelCase			:      Any		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Dict		   =					self.get_scheduler_config(prediction_type='v_prediction'			)
      UpperCAmelCase			:      Optional[int]		   =					scheduler_class(**lowercase_			)

      scheduler.set_timesteps(self.num_inference_steps			)

      UpperCAmelCase			:      Optional[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      List[str]		   =					self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      List[Any]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Dict		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[str]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 0.0017			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3


     def   UpperCAmelCase_			(						self					:	Optional[Any]			)		->      Dict:
      UpperCAmelCase			:      Dict		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Optional[int]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      List[str]		   =					scheduler_class(**lowercase_			)

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

      UpperCAmelCase			:      Tuple		   =					self.dummy_model()
      UpperCAmelCase			:      Tuple		   =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase			:      Any		   =					sample.to(lowercase_			)

      for i, t in enumerate(scheduler.timesteps			):
       UpperCAmelCase			:      Dict		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Optional[Any]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      Tuple		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      List[Any]		   =					output.prev_sample

      UpperCAmelCase			:      Union[str, Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Union[str, Any]		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 1006.388			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3




     def   UpperCAmelCase_			(						self					:	Dict			)		->      Optional[Any]:
      UpperCAmelCase			:      int		   =					self.scheduler_classes[0]
      UpperCAmelCase			:      Union[str, Any]		   =					self.get_scheduler_config()
      UpperCAmelCase			:      Optional[Any]		   =					scheduler_class(**lowercase_	,     use_karras_sigmas=lowercase_			)

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

      UpperCAmelCase			:      List[Any]		   =					self.dummy_model()
      UpperCAmelCase			:      Dict		   =					self.dummy_sample_deter.to(lowercase_			) * scheduler.init_noise_sigma
      UpperCAmelCase			:      Union[str, Any]		   =					sample.to(lowercase_			)

      for t in scheduler.timesteps:
       UpperCAmelCase			:      Optional[int]		   =					scheduler.scale_model_input(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[str]		   =					model(lowercase_	,     lowercase_			)

       UpperCAmelCase			:      List[Any]		   =					scheduler.step(lowercase_	,     lowercase_	,     lowercase_			)
       UpperCAmelCase			:      Any		   =					output.prev_sample

      UpperCAmelCase			:      List[Any]		   =					torch.sum(torch.abs(lowercase_			)			)
      UpperCAmelCase			:      Any		   =					torch.mean(torch.abs(lowercase_			)			)

      assert abs(result_sum.item() - 3812.9927			) < 2E-2
      assert abs(result_mean.item() - 4.9648			) < 1E-3




