





'''simple docstring'''

import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							(UniPCMultistepScheduler,)
 __lowercase  						=							(("""num_inference_steps""", 25),)
 def 	UpperCAmelCase_				(							self :Optional[int]		,			**lowercase_ :str				)->				Optional[int]:
     A__							=     {
         "num_train_timesteps": 10_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
         "solver_order": 2,
         "solver_type": "bh2",
     }

     config.update(**lowercase_				)
     return config
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Dict=0		,			**lowercase_ :Tuple				)->				int:
     A__							=     dict(self.forward_default_kwargs				)
     A__							=     kwargs.pop("num_inference_steps"		,			lowercase_				)
     A__							=     self.dummy_sample
     A__							=     0.1 * sample
     A__							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
         A__							=     self.get_scheduler_config(**lowercase_				)
         A__							=     scheduler_class(**lowercase_				)
         scheduler.set_timesteps(lowercase_				)
         # copy over dummy past residuals
         A__							=     dummy_past_residuals[: scheduler.config.solver_order]

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

         A__,      A__							=     sample, sample
         for t in range(lowercase_		,			time_step + scheduler.config.solver_order + 1				):
             A__							=     scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			**lowercase_				).prev_sample
             A__							=     new_scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			**lowercase_				).prev_sample

             assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
 def 	UpperCAmelCase_				(							self :List[str]		,			lowercase_ :Optional[Any]=0		,			**lowercase_ :Any				)->				Union[str, Any]:
     A__							=     dict(self.forward_default_kwargs				)
     A__							=     kwargs.pop("num_inference_steps"		,			lowercase_				)
     A__							=     self.dummy_sample
     A__							=     0.1 * sample
     A__							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

     for scheduler_class in self.scheduler_classes:
         A__							=     self.get_scheduler_config()
         A__							=     scheduler_class(**lowercase_				)
         scheduler.set_timesteps(lowercase_				)

         # copy over dummy past residuals (must be after setting timesteps)
         A__							=     dummy_past_residuals[: scheduler.config.solver_order]

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

             # copy over dummy past residual (must be after setting timesteps)
             A__							=     dummy_past_residuals[: new_scheduler.config.solver_order]

         A__							=     scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			**lowercase_				).prev_sample
         A__							=     new_scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			**lowercase_				).prev_sample

         assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"
 def 	UpperCAmelCase_				(							self :int		,			lowercase_ :Any=None		,			**lowercase_ :int				)->				Optional[Any]:
     if scheduler is None:
         A__							=     self.scheduler_classes[0]
         A__							=     self.get_scheduler_config(**lowercase_				)
         A__							=     scheduler_class(**lowercase_				)

     A__							=     self.scheduler_classes[0]
     A__							=     self.get_scheduler_config(**lowercase_				)
     A__							=     scheduler_class(**lowercase_				)

     A__							=     10
     A__							=     self.dummy_model()
     A__							=     self.dummy_sample_deter
     scheduler.set_timesteps(lowercase_				)

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

     return sample
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Any:
     A__							=     dict(self.forward_default_kwargs				)

     A__							=     kwargs.pop("num_inference_steps"		,			lowercase_				)

     for scheduler_class in self.scheduler_classes:
         A__							=     self.get_scheduler_config()
         A__							=     scheduler_class(**lowercase_				)

         A__							=     self.dummy_sample
         A__							=     0.1 * sample

         if num_inference_steps is not None and hasattr(lowercase_		,			"set_timesteps"				):
             scheduler.set_timesteps(lowercase_				)
         elif num_inference_steps is not None and not hasattr(lowercase_		,			"set_timesteps"				):
             A__							=     num_inference_steps

         # copy over dummy past residuals (must be done after set_timesteps)
         A__							=     [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
         A__							=     dummy_past_residuals[: scheduler.config.solver_order]

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

         A__							=     scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			**lowercase_				).prev_sample
         A__							=     scheduler.step(lowercase_		,			lowercase_		,			lowercase_		,			**lowercase_				).prev_sample

         self.assertEqual(output_a.shape		,			sample.shape				)
         self.assertEqual(output_a.shape		,			output_a.shape				)
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				List[str]:
     # make sure that iterating over schedulers with same config names gives same results
     # for defaults
     A__							=     UniPCMultistepScheduler(**self.get_scheduler_config()				)
     A__							=     self.full_loop(scheduler=lowercase_				)
     A__							=     torch.mean(torch.abs(lowercase_				)				)

     assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3

     A__							=     DPMSolverSinglestepScheduler.from_config(scheduler.config				)
     A__							=     DEISMultistepScheduler.from_config(scheduler.config				)
     A__							=     DPMSolverMultistepScheduler.from_config(scheduler.config				)
     A__							=     UniPCMultistepScheduler.from_config(scheduler.config				)

     A__							=     self.full_loop(scheduler=lowercase_				)
     A__							=     torch.mean(torch.abs(lowercase_				)				)

     assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3
 def 	UpperCAmelCase_				(							self :Any				)->				Optional[int]:
     for timesteps in [25, 50, 1_00, 9_99, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Any:
     self.check_over_configs(thresholding=lowercase_				)
     for order in [1, 2, 3]:
         for solver_type in ["bh1", "bh2"]:
             for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=lowercase_		,			prediction_type=lowercase_		,			sample_max_value=lowercase_		,			solver_order=lowercase_		,			solver_type=lowercase_		,			)
 def 	UpperCAmelCase_				(							self :str				)->				List[Any]:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_				)
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Tuple:
     for solver_type in ["bh1", "bh2"]:
         for order in [1, 2, 3]:
             for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=lowercase_		,			solver_type=lowercase_		,			prediction_type=lowercase_		,			)
                 A__							=     self.full_loop(
                     solver_order=lowercase_		,			solver_type=lowercase_		,			prediction_type=lowercase_		,			)
                 assert not torch.isnan(lowercase_				).any(), "Samples have nan numbers"
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				Optional[Any]:
     self.check_over_configs(lower_order_final=lowercase_				)
     self.check_over_configs(lower_order_final=lowercase_				)
 def 	UpperCAmelCase_				(							self :List[str]				)->				Union[str, Any]:
     for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
         self.check_over_forward(num_inference_steps=lowercase_		,			time_step=0				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				List[str]:
     A__							=     self.full_loop()
     A__							=     torch.mean(torch.abs(lowercase_				)				)

     assert abs(result_mean.item() - 0.2_4_6_4				) < 1E-3
 def 	UpperCAmelCase_				(							self :Any				)->				Optional[int]:
     A__							=     self.full_loop(prediction_type="v_prediction"				)
     A__							=     torch.mean(torch.abs(lowercase_				)				)

     assert abs(result_mean.item() - 0.1_0_1_4				) < 1E-3
 def 	UpperCAmelCase_				(							self :List[str]				)->				Optional[int]:
     A__							=     self.scheduler_classes[0]
     A__							=     self.get_scheduler_config(thresholding=lowercase_		,			dynamic_thresholding_ratio=0				)
     A__							=     scheduler_class(**lowercase_				)

     A__							=     10
     A__							=     self.dummy_model()
     A__							=     self.dummy_sample_deter.half()
     scheduler.set_timesteps(lowercase_				)

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

     assert sample.dtype == torch.floataa



 def 	UpperCAmelCase_				(							self :Union[str, Any]		,			**lowercase_ :Any				)->				Tuple:
     for scheduler_class in self.scheduler_classes:
         A__							=     self.get_scheduler_config(**lowercase_				)
         A__							=     scheduler_class(**lowercase_				)

         scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
         assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps










'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowerCAmelCase				:      Optional[Any]						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      ={
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""distilbert"""
 __lowercase  						=							{
     """hidden_size""": """dim""",
     """num_attention_heads""": """n_heads""",
     """num_hidden_layers""": """n_layers""",
 }
 def __init__(							self :Tuple		,			lowercase_ :List[str]=3_05_22		,			lowercase_ :Tuple=5_12		,			lowercase_ :List[str]=False		,			lowercase_ :int=6		,			lowercase_ :Optional[int]=12		,			lowercase_ :Optional[int]=7_68		,			lowercase_ :str=4 * 7_68		,			lowercase_ :List[Any]=0.1		,			lowercase_ :str=0.1		,			lowercase_ :Optional[int]="gelu"		,			lowercase_ :Optional[Any]=0.0_2		,			lowercase_ :Optional[Any]=0.1		,			lowercase_ :List[Any]=0.2		,			lowercase_ :str=0		,			**lowercase_ :Union[str, Any]		,			)->				str:
     A__							=     vocab_size
     A__							=     max_position_embeddings
     A__							=     sinusoidal_pos_embds
     A__							=     n_layers
     A__							=     n_heads
     A__							=     dim
     A__							=     hidden_dim
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation
     A__							=     initializer_range
     A__							=     qa_dropout
     A__							=     seq_classif_dropout
     super().__init__(**lowercase_		,			pad_token_id=lowercase_				)






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



