


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						(IPNDMScheduler,)
						UpperCAmelCase             =						(("num_inference_steps", 50),)




						def 							__UpperCamelCase				(       self					:      int				,    **_a					:      int  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ={'''num_train_timesteps''': 1000}
										config.update(**_a  )
										return config




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    _a					:      str=0				,    **_a					:      Any  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =dict(self.forward_default_kwargs  )
										_SCREAMING_SNAKE_CASE			  =kwargs.pop('''num_inference_steps'''				,    _a  )
										_SCREAMING_SNAKE_CASE			  =self.dummy_sample
										_SCREAMING_SNAKE_CASE			  =0.1 * sample
										_SCREAMING_SNAKE_CASE			  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

														with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(_a  )
																		_SCREAMING_SNAKE_CASE			  =scheduler_class.from_pretrained(_a  )
																		new_scheduler.set_timesteps(_a  )
																		# copy over dummy past residuals
																		_SCREAMING_SNAKE_CASE			  =dummy_past_residuals[:]

														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample
														_SCREAMING_SNAKE_CASE			  =new_scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample

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

														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample
														_SCREAMING_SNAKE_CASE			  =new_scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample

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




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


										"""simple docstring"""





										pass




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      Union[str, Any]=0				,    **_a					:      Dict  )							->     str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =dict(self.forward_default_kwargs  )
										_SCREAMING_SNAKE_CASE			  =kwargs.pop('''num_inference_steps'''				,    _a  )
										_SCREAMING_SNAKE_CASE			  =self.dummy_sample
										_SCREAMING_SNAKE_CASE			  =0.1 * sample
										_SCREAMING_SNAKE_CASE			  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

										for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
														_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )
														scheduler.set_timesteps(_a  )

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

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

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

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

														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample
														_SCREAMING_SNAKE_CASE			  =new_scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample

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

														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample
														_SCREAMING_SNAKE_CASE			  =new_scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample

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




						def 							__UpperCamelCase				(       self					:      List[Any]				,    **_a					:      List[str]  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.scheduler_classes[0]
										_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config(**_a  )
										_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

										_SCREAMING_SNAKE_CASE			  =10
										_SCREAMING_SNAKE_CASE			  =self.dummy_model()
										_SCREAMING_SNAKE_CASE			  =self.dummy_sample_deter
										scheduler.set_timesteps(_a  )

										for i, t in enumerate(scheduler.timesteps  ):
														_SCREAMING_SNAKE_CASE			  =model(_a				,    _a  )
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a  ).prev_sample

										for i, t in enumerate(scheduler.timesteps  ):
														_SCREAMING_SNAKE_CASE			  =model(_a				,    _a  )
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a  ).prev_sample

										return sample




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =dict(self.forward_default_kwargs  )

										_SCREAMING_SNAKE_CASE			  =kwargs.pop('''num_inference_steps'''				,    _a  )

										for scheduler_class in self.scheduler_classes:
														_SCREAMING_SNAKE_CASE			  =self.get_scheduler_config()
														_SCREAMING_SNAKE_CASE			  =scheduler_class(**_a  )

														_SCREAMING_SNAKE_CASE			  =self.dummy_sample
														_SCREAMING_SNAKE_CASE			  =0.1 * sample

														if num_inference_steps is not None and hasattr(_a				,    '''set_timesteps'''  ):
																		scheduler.set_timesteps(_a  )
														elif num_inference_steps is not None and not hasattr(_a				,    '''set_timesteps'''  ):
																		_SCREAMING_SNAKE_CASE			  =num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														_SCREAMING_SNAKE_CASE			  =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
														_SCREAMING_SNAKE_CASE			  =dummy_past_residuals[:]

														_SCREAMING_SNAKE_CASE			  =scheduler.timesteps[5]
														_SCREAMING_SNAKE_CASE			  =scheduler.timesteps[6]

														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample

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

														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample
														_SCREAMING_SNAKE_CASE			  =scheduler.step(_a				,    _a				,    _a				,    **_a  ).prev_sample

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     int:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Optional[int]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      str  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.full_loop()
										_SCREAMING_SNAKE_CASE			  =torch.mean(torch.abs(_a  )  )

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



from collections import OrderedDict
from typing import Mapping

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


snake_case_						:			str													=							logging.get_logger(__name__)

snake_case_						:			str													=							{
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"data2vec-text"




						def __init__(       self					:      Optional[int]				,    _a					:      Union[str, Any]=3_0522				,    _a					:      Any=768				,    _a					:      List[Any]=12				,    _a					:      Any=12				,    _a					:      List[str]=3072				,    _a					:      Optional[Any]="gelu"				,    _a					:      List[str]=0.1				,    _a					:      Optional[Any]=0.1				,    _a					:      Optional[Any]=512				,    _a					:      Any=2				,    _a					:      Optional[Any]=0.02				,    _a					:      Dict=1E-12				,    _a					:      int=1				,    _a					:      Any=0				,    _a					:      List[Any]=2				,    _a					:      Dict="absolute"				,    _a					:      Optional[Any]=True				,    _a					:      Dict=None				,    **_a					:      Optional[Any]				,    )							->     Union[str, Any]:


										"""simple docstring"""





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

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =use_cache
										_SCREAMING_SNAKE_CASE			  =classifier_dropout

class 						A__							(		UpperCamelCase__  ):




						@property
						def 							__UpperCamelCase				(       self					:      List[str]  )							->     Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





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