



"""simple docstring"""







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       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       int,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       List[Any]     )	-> List[Any]:






	"""simple docstring"""





	if isinstance(UpperCAmelCase_,	UpperCAmelCase_     ):
		A__											=					np.full((len(UpperCAmelCase_     ), sequence_length, 2),	UpperCAmelCase_     )
	else:
		A__											=					np.full((len(UpperCAmelCase_     ), sequence_length),	UpperCAmelCase_     )

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

	return out_tensor.tolist()

def       _lowerCamelCase				(							UpperCAmelCase_ :       Tuple     )	-> List[Any]:






	"""simple docstring"""





	A__											=					ord(UpperCAmelCase_     )
	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
	A__											=					unicodedata.category(UpperCAmelCase_     )
	if cat.startswith("P"     ):
		return True
	return False






@dataclass
class       UpperCamelCase__			(   SCREAMING_SNAKE_CASE__      ):

			"""simple docstring"""


			A__	:					Any							  =     4_2
			A__	:					Tuple							  =     True
			A__	:					Tuple							  =     None
			A__	:					Union[str, Any]							  =     None
			A__	:					Any							  =     -1_0_0
			A__	:					Optional[int]							  =     "pt"


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	List[str]:
				import torch

				A__											=					"label" if "label" in features[0].keys() else "labels"
				A__											=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
				A__											=					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

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

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

				return batch










import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class    lowercase_		(SCREAMING_SNAKE_CASE__ ):
						lowerCAmelCase__			      =(KDPMaDiscreteScheduler,)
						lowerCAmelCase__			      =10



						def 	__a					(			self	:   Dict					,							**snake_case__	:   str  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							{
								    'num_train_timesteps': 11_00,
								    'beta_start': 0.00_01,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								}

								config.update(**snake_case__  )
								return config



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								for timesteps in [10, 50, 1_00, 10_00]:
										self.check_over_configs(num_train_timesteps=snake_case__  )



						def 	__a					(			self	:   List[Any]  ):

								"""simple docstring"""

								for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,							[0.00_02, 0.0_02, 0.02]  ):
										self.check_over_configs(beta_start=snake_case__					,							beta_end=snake_case__  )



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

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



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE_					      =							self.get_scheduler_config(prediction_type='v_prediction'  )
								SCREAMING_SNAKE_CASE_					      =							scheduler_class(**snake_case__  )

								scheduler.set_timesteps(self.num_inference_steps  )

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter * scheduler.init_noise_sigma
								SCREAMING_SNAKE_CASE_					      =							sample.to(snake_case__  )

								for i, t in enumerate(scheduler.timesteps  ):
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 4.69_34e-07  ) < 1e-2
										assert abs(result_mean.item() - 6.11_12e-10  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07  ) < 1e-2
										assert abs(result_mean.item() - 0.00_02  ) < 1e-3



						def 	__a					(			self	:   Union[str, Any]  ):

								"""simple docstring"""

								if torch_device == "mps":
										return
								SCREAMING_SNAKE_CASE_					      =							self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE_					      =							self.get_scheduler_config()
								SCREAMING_SNAKE_CASE_					      =							scheduler_class(**snake_case__  )

								scheduler.set_timesteps(self.num_inference_steps  )

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter * scheduler.init_noise_sigma
								SCREAMING_SNAKE_CASE_					      =							sample.to(snake_case__  )

								for i, t in enumerate(scheduler.timesteps  ):
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if torch_device in ["cpu", "mps"]:
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3






						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								if torch_device == "mps":
										return
								SCREAMING_SNAKE_CASE_					      =							self.scheduler_classes[0]
								SCREAMING_SNAKE_CASE_					      =							self.get_scheduler_config()
								SCREAMING_SNAKE_CASE_					      =							scheduler_class(**snake_case__  )

								scheduler.set_timesteps(self.num_inference_steps					,							device=snake_case__  )

								SCREAMING_SNAKE_CASE_					      =							self.dummy_model()
								SCREAMING_SNAKE_CASE_					      =							self.dummy_sample_deter.to(snake_case__  ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
										SCREAMING_SNAKE_CASE_					      =							scheduler.scale_model_input(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							model(snake_case__					,							snake_case__  )

										SCREAMING_SNAKE_CASE_					      =							scheduler.step(snake_case__					,							snake_case__					,							snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							output.prev_sample

								SCREAMING_SNAKE_CASE_					      =							torch.sum(torch.abs(snake_case__  )  )
								SCREAMING_SNAKE_CASE_					      =							torch.mean(torch.abs(snake_case__  )  )

								if str(snake_case__  ).startswith('cpu'  ):
										# The following sum varies between 148 and 156 on mps. Why?
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3
								else:
										# CUDA
										assert abs(result_sum.item() - 20.41_25  ) < 1e-2
										assert abs(result_mean.item() - 0.02_66  ) < 1e-3