
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 			UpperCamelCase					(    __lowerCamelCase     : Dict				,					__lowerCamelCase     : int				,					__lowerCamelCase     : Dict				,					__lowerCamelCase     : Any    ):
       if isinstance(__lowerCamelCase				,					__lowerCamelCase    ):
              snake_case       :   int							=      np.full((len(__lowerCamelCase    ), sequence_length, 2)				,					__lowerCamelCase    )
       else:
              snake_case       :   List[Any]							=      np.full((len(__lowerCamelCase    ), sequence_length)				,					__lowerCamelCase    )

       for i, tensor in enumerate(__lowerCamelCase    ):
              if padding_side == "right":
                     if isinstance(__lowerCamelCase				,					__lowerCamelCase    ):
                            snake_case       :   Dict							=      tensor[:sequence_length]
                     else:
                            snake_case       :   Tuple							=      tensor[:sequence_length]
              else:
                     if isinstance(__lowerCamelCase				,					__lowerCamelCase    ):
                            snake_case       :   str							=      tensor[:sequence_length]
                     else:
                            snake_case       :   Optional[int]							=      tensor[:sequence_length]

       return out_tensor.tolist()


def 			UpperCamelCase					(    __lowerCamelCase     : List[Any]    ):
       snake_case       :   Any							=      ord(__lowerCamelCase    )
       if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126):
              return True
       snake_case       :   List[str]							=      unicodedata.category(__lowerCamelCase    )
       if cat.startswith("P"    ):
              return True
       return False






@dataclass
class   UpperCAmelCase		(						A_ ):
   A__					:		PreTrainedTokenizerBase
   A__					:		Union[bool, str, PaddingStrategy]						     =     True
   A__					:		Optional[int]						     =     None
   A__					:		Optional[int]						     =     None
   A__					:		int						     =     -1_00
   A__					:		str						     =     "pt"



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Tuple )		->	int:


          '''simple docstring'''


          import torch

          snake_case       :   Optional[Any]							=      "label" if "label" in features[0].keys() else "labels"
          snake_case       :   str							=      [feature[label_name] for feature in features] if label_name in features[0].keys() else None
          snake_case       :   List[Any]							=      self.tokenizer.pad(
              snake_case__    ,						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

          snake_case       :   int							=      torch.tensor(batch["entity_ids"] ).shape[1]
          snake_case       :   List[Any]							=      self.tokenizer.padding_side
          if padding_side == "right":
                 snake_case       :   Tuple							=      [
                     list(snake_case__ ) + [self.label_pad_token_id] * (sequence_length - len(snake_case__ )) for label in labels
                 ]
          else:
                 snake_case       :   str							=      [
                     [self.label_pad_token_id] * (sequence_length - len(snake_case__ )) + list(snake_case__ ) for label in labels
                 ]

          snake_case       :   int							=      [feature["ner_tags"] for feature in features]
          snake_case       :   int							=      padding_tensor(snake_case__    ,						-1    ,						snake_case__    ,						snake_case__ )
          snake_case       :   str							=      [feature["original_entity_spans"] for feature in features]
          snake_case       :   Dict							=      padding_tensor(snake_case__    ,						(-1, -1)    ,						snake_case__    ,						snake_case__ )
          snake_case       :   Dict							=      {k: torch.tensor(snake_case__    ,						dtype=torch.intaa ) for k, v in batch.items()}

          return batch







import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class   UpperCAmelCase		(						A_ ):
   A__					:		Dict						     =     (DDIMParallelScheduler,)
   A__					:		Tuple						     =     (("eta", 0.0), ("num_inference_steps", 50))



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						**snake_case__		:					Optional[int] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Any							=      {
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**snake_case__ )
          return config



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict    ,						**snake_case__		:					Optional[int] )		->	Any:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.scheduler_classes[0]
          snake_case       :   Any							=      self.get_scheduler_config(**snake_case__ )
          snake_case       :   Any							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Union[str, Any]							=      10, 0.0

          snake_case       :   List[Any]							=      self.dummy_model()
          snake_case       :   Any							=      self.dummy_sample_deter

          scheduler.set_timesteps(snake_case__ )

          for t in scheduler.timesteps:
                 snake_case       :   Optional[int]							=      model(snake_case__    ,						snake_case__ )
                 snake_case       :   List[str]							=      scheduler.step(snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__ ).prev_sample

          return sample



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


          '''simple docstring'''


          for timesteps in [1_00, 5_00, 10_00]:
                 self.check_over_configs(num_train_timesteps=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	int:


          '''simple docstring'''


          for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=snake_case__ )

          snake_case       :   Optional[int]							=      self.scheduler_classes[0]
          snake_case       :   Optional[int]							=      self.get_scheduler_config(steps_offset=1 )
          snake_case       :   Union[str, Any]							=      scheduler_class(**snake_case__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,						torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Tuple:


          '''simple docstring'''


          for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]    ,						[0.002, 0.02, 0.2, 2] ):
                 self.check_over_configs(beta_start=snake_case__    ,						beta_end=snake_case__ )



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


          '''simple docstring'''


          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=snake_case__ )



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


          '''simple docstring'''


          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any] )		->	List[Any]:


          '''simple docstring'''


          for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=snake_case__ )



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


          '''simple docstring'''


          for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          self.check_over_configs(thresholding=snake_case__ )
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=snake_case__    ,						prediction_type=snake_case__    ,						sample_max_value=snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Any:


          '''simple docstring'''


          for t in [1, 10, 49]:
                 self.check_over_forward(time_step=snake_case__ )



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


          '''simple docstring'''


          for t, num_inference_steps in zip([1, 10, 50]    ,						[10, 50, 5_00] ):
                 self.check_over_forward(time_step=snake_case__    ,						num_inference_steps=snake_case__ )



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


          '''simple docstring'''


          for t, eta in zip([1, 10, 49]    ,						[0.0, 0.5, 1.0] ):
                 self.check_over_forward(time_step=snake_case__    ,						eta=snake_case__ )



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


          '''simple docstring'''


          snake_case       :   Dict							=      self.scheduler_classes[0]
          snake_case       :   Tuple							=      self.get_scheduler_config()
          snake_case       :   Dict							=      scheduler_class(**snake_case__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,						4_00 ) - 0.14771 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,						9_60 ) - 0.32460 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,						0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,						4_86 ) - 0.00979 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,						9_98 ) - 0.02 ) ) < 1e-5



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Dict:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      self.scheduler_classes[0]
          snake_case       :   List[Any]							=      self.get_scheduler_config()
          snake_case       :   int							=      scheduler_class(**snake_case__ )

          snake_case				,					snake_case       :   Any							=      10, 0.0
          scheduler.set_timesteps(snake_case__ )

          snake_case       :   Optional[Any]							=      self.dummy_model()
          snake_case       :   str							=      self.dummy_sample_deter
          snake_case       :   Dict							=      self.dummy_sample_deter + 0.1
          snake_case       :   Dict							=      self.dummy_sample_deter - 0.1

          snake_case       :   Optional[Any]							=      samplea.shape[0]
          snake_case       :   str							=      torch.stack([samplea, samplea, samplea]    ,						dim=0 )
          snake_case       :   Tuple							=      torch.arange(snake_case__ )[0:3, None].repeat(1    ,						snake_case__ )

          snake_case       :   Tuple							=      model(samples.flatten(0    ,						1 )    ,						timesteps.flatten(0    ,						1 ) )
          snake_case       :   List[str]							=      scheduler.batch_step_no_noise(snake_case__    ,						timesteps.flatten(0    ,						1 )    ,						samples.flatten(0    ,						1 )    ,						snake_case__ )

          snake_case       :   Dict							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 1147.7904 ) < 1e-2
          assert abs(result_mean.item() - 0.4982 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.full_loop()

          snake_case       :   Optional[Any]							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 172.0067 ) < 1e-2
          assert abs(result_mean.item() - 0.223967 ) < 1e-3



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


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(prediction_type="v_prediction" )

          snake_case       :   int							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[int]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 52.5302 ) < 1e-2
          assert abs(result_mean.item() - 0.0684 ) < 1e-3



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[Any]:


          '''simple docstring'''


          snake_case       :   Dict							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   str							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   Optional[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.8295 ) < 1e-2
          assert abs(result_mean.item() - 0.1951 ) < 1e-3



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


          '''simple docstring'''


          snake_case       :   int							=      self.full_loop(set_alpha_to_one=snake_case__    ,						beta_start=0.01 )
          snake_case       :   Tuple							=      torch.sum(torch.abs(snake_case__ ) )
          snake_case       :   List[Any]							=      torch.mean(torch.abs(snake_case__ ) )

          assert abs(result_sum.item() - 149.0784 ) < 1e-2
          assert abs(result_mean.item() - 0.1941 ) < 1e-3





