



"""simple docstring"""
import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest
class 					a	(  snake_case__       ):
  UpperCamelCase	:				Tuple     						=					(IPNDMScheduler,)
  UpperCamelCase	:				str     						=					(('num_inference_steps', 5_0),)



  def 	lowerCamelCase__       (						self :					Any       ,		**lowerCAmelCase :					Optional[Any]		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			={"""num_train_timesteps""": 1000}
     config.update(**_SCREAMING_SNAKE_CASE		)
     return config



  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					Optional[int]=0       ,		**lowerCAmelCase :					Any		)     ->					Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=dict(self.forward_default_kwargs		)
     SCREAMING_SNAKE_CASE_: Tuple     			=kwargs.pop("""num_inference_steps"""       ,		_SCREAMING_SNAKE_CASE		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.dummy_sample
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=0.1 * sample
     SCREAMING_SNAKE_CASE_: Dict     			=[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
        SCREAMING_SNAKE_CASE_: Any     			=self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
        SCREAMING_SNAKE_CASE_: List[str]     			=scheduler_class(**_SCREAMING_SNAKE_CASE		)
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
        # copy over dummy past residuals
        SCREAMING_SNAKE_CASE_: List[str]     			=dummy_past_residuals[:]

        if time_step is None:
           SCREAMING_SNAKE_CASE_: Optional[int]     			=scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_SCREAMING_SNAKE_CASE		)
           SCREAMING_SNAKE_CASE_: Union[str, Any]     			=scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
           new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
           # copy over dummy past residuals
           SCREAMING_SNAKE_CASE_: List[Any]     			=dummy_past_residuals[:]

        SCREAMING_SNAKE_CASE_: Dict     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=new_scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

        SCREAMING_SNAKE_CASE_: Dict     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample
        SCREAMING_SNAKE_CASE_: List[Any]     			=new_scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					List[str]:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					str=0       ,		**lowerCAmelCase :					str		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=dict(self.forward_default_kwargs		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs.pop("""num_inference_steps"""       ,		_SCREAMING_SNAKE_CASE		)
     SCREAMING_SNAKE_CASE_: Tuple     			=self.dummy_sample
     SCREAMING_SNAKE_CASE_: List[Any]     			=0.1 * sample
     SCREAMING_SNAKE_CASE_: int     			=[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_: Tuple     			=scheduler_class(**_SCREAMING_SNAKE_CASE		)
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

        # copy over dummy past residuals (must be after setting timesteps)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=dummy_past_residuals[:]

        if time_step is None:
           SCREAMING_SNAKE_CASE_: Union[str, Any]     			=scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
           scheduler.save_config(_SCREAMING_SNAKE_CASE		)
           SCREAMING_SNAKE_CASE_: int     			=scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)
           # copy over dummy past residuals
           new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

           # copy over dummy past residual (must be after setting timesteps)
           SCREAMING_SNAKE_CASE_: int     			=dummy_past_residuals[:]

        SCREAMING_SNAKE_CASE_: str     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=new_scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"

        SCREAMING_SNAKE_CASE_: Optional[Any]     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=new_scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample

        assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		**lowerCAmelCase :					str		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=self.scheduler_classes[0]
     SCREAMING_SNAKE_CASE_: Dict     			=self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
     SCREAMING_SNAKE_CASE_: Tuple     			=scheduler_class(**_SCREAMING_SNAKE_CASE		)

     SCREAMING_SNAKE_CASE_: Any     			=10
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.dummy_model()
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.dummy_sample_deter
     scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)

     for i, t in enumerate(scheduler.timesteps		):
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=model(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE		).prev_sample

     for i, t in enumerate(scheduler.timesteps		):
        SCREAMING_SNAKE_CASE_: Tuple     			=model(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE		)
        SCREAMING_SNAKE_CASE_: List[Any]     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE		).prev_sample

     return sample



  def 	lowerCamelCase__       (						self :					str		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=dict(self.forward_default_kwargs		)

     SCREAMING_SNAKE_CASE_: List[Any]     			=kwargs.pop("""num_inference_steps"""       ,		_SCREAMING_SNAKE_CASE		)

     for scheduler_class in self.scheduler_classes:
        SCREAMING_SNAKE_CASE_: Tuple     			=self.get_scheduler_config()
        SCREAMING_SNAKE_CASE_: str     			=scheduler_class(**_SCREAMING_SNAKE_CASE		)

        SCREAMING_SNAKE_CASE_: str     			=self.dummy_sample
        SCREAMING_SNAKE_CASE_: Tuple     			=0.1 * sample

        if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE       ,		"""set_timesteps"""		):
           scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
        elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE       ,		"""set_timesteps"""		):
           SCREAMING_SNAKE_CASE_: Union[str, Any]     			=num_inference_steps

        # copy over dummy past residuals (must be done after set_timesteps)
        SCREAMING_SNAKE_CASE_: Dict     			=[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
        SCREAMING_SNAKE_CASE_: Any     			=dummy_past_residuals[:]

        SCREAMING_SNAKE_CASE_: int     			=scheduler.timesteps[5]
        SCREAMING_SNAKE_CASE_: Tuple     			=scheduler.timesteps[6]

        SCREAMING_SNAKE_CASE_: int     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample
        SCREAMING_SNAKE_CASE_: Tuple     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample

        self.assertEqual(output_a.shape       ,		sample.shape		)
        self.assertEqual(output_a.shape       ,		output_a.shape		)

        SCREAMING_SNAKE_CASE_: int     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample
        SCREAMING_SNAKE_CASE_: str     			=scheduler.step(_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		_SCREAMING_SNAKE_CASE       ,		**_SCREAMING_SNAKE_CASE		).prev_sample

        self.assertEqual(output_a.shape       ,		sample.shape		)
        self.assertEqual(output_a.shape       ,		output_a.shape		)



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					List[str]:


     '''simple docstring'''




     for timesteps in [100, 1000]:
        self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE       ,		time_step=_SCREAMING_SNAKE_CASE		)



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Union[str, Any]:


     '''simple docstring'''




     for t, num_inference_steps in zip([1, 5, 10]       ,		[10, 50, 100]		):
        self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE       ,		time_step=_SCREAMING_SNAKE_CASE		)





  def 	lowerCamelCase__       (						self :					Any		)     ->					Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.full_loop()
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

     assert abs(result_mean.item() - 254_0529		) < 10








"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_UpperCAmelCase     =			{
    """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
    """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
    """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
    """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
    """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
    """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
    """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
    """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Any     						=					'albert'



  def __init__(						self :					Dict       ,		lowerCAmelCase :					List[str]=3_0000       ,		lowerCAmelCase :					List[Any]=128       ,		lowerCAmelCase :					List[str]=4096       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					str=1       ,		lowerCAmelCase :					Tuple=64       ,		lowerCAmelCase :					Dict=1_6384       ,		lowerCAmelCase :					int=1       ,		lowerCAmelCase :					str="gelu_new"       ,		lowerCAmelCase :					Dict=0       ,		lowerCAmelCase :					Optional[Any]=0       ,		lowerCAmelCase :					str=512       ,		lowerCAmelCase :					Optional[int]=2       ,		lowerCAmelCase :					List[Any]=0.0_2       ,		lowerCAmelCase :					Union[str, Any]=1E-12       ,		lowerCAmelCase :					Tuple=0.1       ,		lowerCAmelCase :					List[Any]="absolute"       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					int=2       ,		lowerCAmelCase :					Optional[int]=3       ,		**lowerCAmelCase :					int       ,		)     ->					Tuple:


     '''simple docstring'''




     super().__init__(pad_token_id=lowerCAmelCase       ,		bos_token_id=lowerCAmelCase       ,		eos_token_id=lowerCAmelCase       ,		**lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=vocab_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=embedding_size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_size
     SCREAMING_SNAKE_CASE_: Tuple     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: Any     			=num_hidden_groups
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=inner_group_num
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_act
     SCREAMING_SNAKE_CASE_: int     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Any     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=max_position_embeddings
     SCREAMING_SNAKE_CASE_: Any     			=type_vocab_size
     SCREAMING_SNAKE_CASE_: int     			=initializer_range
     SCREAMING_SNAKE_CASE_: List[Any]     			=layer_norm_eps
     SCREAMING_SNAKE_CASE_: Dict     			=classifier_dropout_prob
     SCREAMING_SNAKE_CASE_: int     			=position_embedding_type







class 					a	(  UpperCAmelCase__       ):



  @property
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




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



