



'''simple docstring'''





import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  (DEISMultistepScheduler,)
 lowerCAmelCase__       					=  (("""num_inference_steps""", 2_5),)




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    {
        '''num_train_timesteps''': 1000,
        '''beta_start''': 0.0_001,
        '''beta_end''': 0.02,
        '''beta_schedule''': '''linear''',
        '''solver_order''': 2,
    }

    config.update(**__UpperCAmelCase				)
    return config




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=0	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    dict(self.forward_default_kwargs				)
    __lowerCamelCase													=    kwargs.pop('''num_inference_steps'''	,				__UpperCAmelCase				)
    __lowerCamelCase													=    self.dummy_sample
    __lowerCamelCase													=    0.1 * sample
    __lowerCamelCase													=    [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=0	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    dict(self.forward_default_kwargs				)
    __lowerCamelCase													=    kwargs.pop('''num_inference_steps'''	,				__UpperCAmelCase				)
    __lowerCamelCase													=    self.dummy_sample
    __lowerCamelCase													=    0.1 * sample
    __lowerCamelCase													=    [residual + 0.2, residual + 0.15, residual + 0.10]

    for scheduler_class in self.scheduler_classes:
       __lowerCamelCase													=    self.get_scheduler_config()
       __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)
       scheduler.set_timesteps(__UpperCAmelCase				)

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

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

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

       __lowerCamelCase													=    scheduler.step(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				**__UpperCAmelCase				).prev_sample
       __lowerCamelCase													=    new_scheduler.step(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				**__UpperCAmelCase				).prev_sample

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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=None	,				**__UpperCAmelCase				):



    '''simple docstring'''
    if scheduler is None:
       __lowerCamelCase													=    self.scheduler_classes[0]
       __lowerCamelCase													=    self.get_scheduler_config(**__UpperCAmelCase				)
       __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config(**__UpperCAmelCase				)
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    10
    __lowerCamelCase													=    self.dummy_model()
    __lowerCamelCase													=    self.dummy_sample_deter
    scheduler.set_timesteps(__UpperCAmelCase				)

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

    return sample




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    dict(self.forward_default_kwargs				)

    __lowerCamelCase													=    kwargs.pop('''num_inference_steps'''	,				__UpperCAmelCase				)

    for scheduler_class in self.scheduler_classes:
       __lowerCamelCase													=    self.get_scheduler_config()
       __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

       __lowerCamelCase													=    self.dummy_sample
       __lowerCamelCase													=    0.1 * sample

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

       # copy over dummy past residuals (must be done after set_timesteps)
       __lowerCamelCase													=    [residual + 0.2, residual + 0.15, residual + 0.10]
       __lowerCamelCase													=    dummy_past_residuals[: scheduler.config.solver_order]

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

       __lowerCamelCase													=    scheduler.step(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				**__UpperCAmelCase				).prev_sample
       __lowerCamelCase													=    scheduler.step(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				**__UpperCAmelCase				).prev_sample

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    DEISMultistepScheduler(**self.get_scheduler_config()				)
    __lowerCamelCase													=    self.full_loop(scheduler=__UpperCAmelCase				)
    __lowerCamelCase													=    torch.mean(torch.abs(__UpperCAmelCase				)				)

    assert abs(result_mean.item() - 0.23_916				) < 1E-3

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

    __lowerCamelCase													=    self.full_loop(scheduler=__UpperCAmelCase				)
    __lowerCamelCase													=    torch.mean(torch.abs(__UpperCAmelCase				)				)

    assert abs(result_mean.item() - 0.23_916				) < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for timesteps in [25, 50, 100, 999, 1000]:
       self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.check_over_configs(thresholding=__UpperCAmelCase				)
    for order in [1, 2, 3]:
       for solver_type in ["logrho"]:
          for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "sample"]:
                self.check_over_configs(
                    thresholding=__UpperCAmelCase	,				prediction_type=__UpperCAmelCase	,				sample_max_value=__UpperCAmelCase	,				algorithm_type='''deis'''	,				solver_order=__UpperCAmelCase	,				solver_type=__UpperCAmelCase	,				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for algorithm_type in ["deis"]:
       for solver_type in ["logrho"]:
          for order in [1, 2, 3]:
             for prediction_type in ["epsilon", "sample"]:
                self.check_over_configs(
                    solver_order=__UpperCAmelCase	,				solver_type=__UpperCAmelCase	,				prediction_type=__UpperCAmelCase	,				algorithm_type=__UpperCAmelCase	,				)
                __lowerCamelCase													=    self.full_loop(
                    solver_order=__UpperCAmelCase	,				solver_type=__UpperCAmelCase	,				prediction_type=__UpperCAmelCase	,				algorithm_type=__UpperCAmelCase	,				)
                assert not torch.isnan(__UpperCAmelCase				).any(), "Samples have nan numbers"




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    self.check_over_configs(lower_order_final=__UpperCAmelCase				)
    self.check_over_configs(lower_order_final=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
       self.check_over_forward(num_inference_steps=__UpperCAmelCase	,				time_step=0				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.full_loop()
    __lowerCamelCase													=    torch.mean(torch.abs(__UpperCAmelCase				)				)

    assert abs(result_mean.item() - 0.23_916				) < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.full_loop(prediction_type='''v_prediction'''				)
    __lowerCamelCase													=    torch.mean(torch.abs(__UpperCAmelCase				)				)

    assert abs(result_mean.item() - 0.091				) < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config(thresholding=__UpperCAmelCase	,				dynamic_thresholding_ratio=0				)
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    10
    __lowerCamelCase													=    self.dummy_model()
    __lowerCamelCase													=    self.dummy_sample_deter.half()
    scheduler.set_timesteps(__UpperCAmelCase				)

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

    assert sample.dtype == torch.floataa











import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: Union[str, Any] ,_UpperCamelCase			: Any							):
   if isinstance(_UpperCamelCase ,_UpperCamelCase							):
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length, 2) ,_UpperCamelCase							)
   else:
      __lowerCamelCase													=    np.full((len(_UpperCamelCase							), sequence_length) ,_UpperCamelCase							)

   for i, tensor in enumerate(_UpperCamelCase							):
      if padding_side == "right":
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]
      else:
         if isinstance(_UpperCamelCase ,_UpperCamelCase							):
            __lowerCamelCase													=    tensor[:sequence_length]
         else:
            __lowerCamelCase													=    tensor[:sequence_length]

   return out_tensor.tolist()





def 		a__			(		_UpperCamelCase			: Dict							):
   __lowerCamelCase													=    ord(_UpperCamelCase							)
   if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
      return True
   __lowerCamelCase													=    unicodedata.category(_UpperCamelCase							)
   if cat.startswith('''P'''							):
      return True
   return False





@dataclass
class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  42
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  None
 lowerCAmelCase__       					=  -1_0_0
 lowerCAmelCase__       					=  "pt"




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    import torch

    __lowerCamelCase													=    '''label''' if '''label''' in features[0].keys() else '''labels'''
    __lowerCamelCase													=    [feature[label_name] for feature in features] if label_name in features[0].keys() else None
    __lowerCamelCase													=    self.tokenizer.pad(
        __UpperCAmelCase	,				padding=self.padding	,				max_length=self.max_length	,				pad_to_multiple_of=self.pad_to_multiple_of	,				return_tensors='''pt''' if labels is None else None	,				)

    if labels is None:
       return batch

    __lowerCamelCase													=    torch.tensor(batch['''entity_ids''']				).shape[1]
    __lowerCamelCase													=    self.tokenizer.padding_side
    if padding_side == "right":
       __lowerCamelCase													=    [
           list(__UpperCAmelCase				) + [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) for label in labels
       ]
    else:
       __lowerCamelCase													=    [
           [self.label_pad_token_id] * (sequence_length - len(__UpperCAmelCase				)) + list(__UpperCAmelCase				) for label in labels
       ]

    __lowerCamelCase													=    [feature['''ner_tags'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				-1	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    [feature['''original_entity_spans'''] for feature in features]
    __lowerCamelCase													=    padding_tensor(__UpperCAmelCase	,				(-1, -1)	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    {k: torch.tensor(__UpperCAmelCase	,				dtype=torch.intaa				) for k, v in batch.items()}

    return batch






