code: 
  
  
  '''simple docstring'''
  
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  __lowercase			:		Optional[int] =			logging.get_logger(__name__)
  
  
  
  
  
  @dataclass
  class 							__UpperCamelCase       :
  	A_					    =					42
  	A_					    =					42
  	A_					    =					None
  	A_					    =					None
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return json.dumps(dataclasses.asdict(self							)		,       indent=2							) + "\n"
  
  
  
  
  
  @dataclass(frozen=lowerCAmelCase_      )
  class 							__UpperCamelCase       :
  	A_					    =					42
  	A_					    =					None
  	A_					    =					None
  	A_					    =					None
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return json.dumps(dataclasses.asdict(self							)							) + "\n"
  
  
  
  
  
  class 							__UpperCamelCase       :
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						raise NotImplementedError()
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						raise NotImplementedError()
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						raise NotImplementedError()
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						raise NotImplementedError()
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						raise NotImplementedError()
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if len(self.get_labels()							) > 1:
  											__a    : Optional[int]      					=						self.get_labels()[int(example.label							)]
  						return example
  
  
  
  
  
  	@classmethod
  	def    __UpperCAmelCase							(     cls		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						with open(__a		,       'r'		,       encoding='utf-8-sig'							) as f:
  											return list(csv.reader(__a		,       delimiter='\t'		,       quotechar=__a							)							)
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def __init__(     self		,       __a=None		,       __a=None		,       __a="classification"		,       __a=False							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						[] if labels is None else labels
  						__a    : Union[str, Any]      					=						[] if examples is None else examples
  						__a    : Union[str, Any]      					=						mode
  						__a    : int      					=						verbose
  
  
  
  
  	def __len__(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						return len(self.examples							)
  
  
  
  
  	def __getitem__(     self		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if isinstance(__a		,       __a							):
  											return SingleSentenceClassificationProcessor(labels=self.labels		,       examples=self.examples[idx]							)
  						return self.examples[idx]
  
  
  
  
  	@classmethod
  	def    __UpperCAmelCase							(     cls		,       __a		,       __a=""		,       __a=0		,       __a=1		,       __a=None		,       __a=False		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : int      					=						cls(**__a							)
  						processor.add_examples_from_csv(
  						    __a		,       split_name=__a		,       column_label=__a		,       column_text=__a		,       column_id=__a		,       skip_first_row=__a		,       overwrite_labels=__a		,       overwrite_examples=__a		,       )
  						return processor
  
  
  
  
  	@classmethod
  	def    __UpperCAmelCase							(     cls		,       __a		,       __a=None		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Any      					=						cls(**__a							)
  						processor.add_examples(__a		,       labels=__a							)
  						return processor
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a=""		,       __a=0		,       __a=1		,       __a=None		,       __a=False		,       __a=False		,       __a=False		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						self._read_tsv(__a							)
  						if skip_first_row:
  											__a    : Dict      					=						lines[1:]
  						__a    : Tuple      					=						[]
  						__a    : str      					=						[]
  						__a    : List[Any]      					=						[]
  						for i, line in enumerate(__a							):
  											texts.append(line[column_text]							)
  											labels.append(line[column_label]							)
  											if column_id is not None:
  																ids.append(line[column_id]							)
  											else:
  																__a    : Tuple      					=						f"""{split_name}-{i}""" if split_name else str(__a							)
  																ids.append(__a							)
  
  						return self.add_examples(
  						    __a		,       __a		,       __a		,       overwrite_labels=__a		,       overwrite_examples=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a=None		,       __a=None		,       __a=False		,       __a=False							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if labels is not None and len(__a							) != len(__a							):
  											raise ValueError(
  											    f"""Text and labels have mismatched lengths {len(__a							)} and {len(__a							)}"""							)
  						if ids is not None and len(__a							) != len(__a							):
  											raise ValueError(f"""Text and ids have mismatched lengths {len(__a							)} and {len(__a							)}"""							)
  						if ids is None:
  											__a    : Optional[int]      					=						[None] * len(__a							)
  						if labels is None:
  											__a    : Dict      					=						[None] * len(__a							)
  						__a    : Union[str, Any]      					=						[]
  						__a    : int      					=						set()
  						for text_or_text_and_label, label, guid in zip(__a		,       __a		,       __a							):
  											if isinstance(__a		,       (tuple, list)							) and label is None:
  																__a					,       __a    : List[str]      					=						text_or_text_and_label
  											else:
  																__a    : int      					=						text_or_text_and_label
  											added_labels.add(__a							)
  											examples.append(InputExample(guid=__a		,       text_a=__a		,       text_b=__a		,       label=__a							)							)
  
  						# Update examples
  						if overwrite_examples:
  											__a    : Tuple      					=						examples
  						else:
  											self.examples.extend(__a							)
  
  						# Update labels
  						if overwrite_labels:
  											__a    : str      					=						list(__a							)
  						else:
  											__a    : str      					=						list(set(self.labels							).union(__a							)							)
  
  						return self.examples
  
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a=None		,       __a=False		,       __a=0		,       __a=True		,       __a=None		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						if max_length is None:
  											__a    : Tuple      					=						tokenizer.max_len
  
  						__a    : Tuple      					=						{label: i for i, label in enumerate(self.labels							)}
  
  						__a    : Optional[int]      					=						[]
  						for ex_index, example in enumerate(self.examples							):
  											if ex_index % 1_0000 == 0:
  																logger.info(f"""Tokenizing example {ex_index}"""							)
  
  											__a    : Optional[Any]      					=						tokenizer.encode(
  											    example.text_a		,       add_special_tokens=__a		,       max_length=min(__a		,       tokenizer.max_len							)		,       )
  											all_input_ids.append(__a							)
  
  						__a    : Tuple      					=						max(len(__a							) for input_ids in all_input_ids							)
  
  						__a    : Optional[int]      					=						[]
  						for ex_index, (input_ids, example) in enumerate(zip(__a		,       self.examples							)							):
  											if ex_index % 1_0000 == 0:
  																logger.info(f"""Writing example {ex_index}/{len(self.examples							)}"""							)
  											# The mask has 1 for real tokens and 0 for padding tokens. Only real
  											# tokens are attended to.
  											__a    : int      					=						[1 if mask_padding_with_zero else 0] * len(__a							)
  
  											# Zero-pad up to the sequence length.
  											__a    : str      					=						batch_length - len(__a							)
  											if pad_on_left:
  																__a    : Union[str, Any]      					=						([pad_token] * padding_length) + input_ids
  																__a    : str      					=						([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
  											else:
  																__a    : Dict      					=						input_ids + ([pad_token] * padding_length)
  																__a    : str      					=						attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
  											if len(__a							) != batch_length:
  																raise ValueError(f"""Error with input length {len(__a							)} vs {batch_length}"""							)
  											if len(__a							) != batch_length:
  																raise ValueError(f"""Error with input length {len(__a							)} vs {batch_length}"""							)
  
  											if self.mode == "classification":
  																__a    : Tuple      					=						label_map[example.label]
  											elif self.mode == "regression":
  																__a    : Any      					=						float(example.label							)
  											else:
  																raise ValueError(self.mode							)
  
  											if ex_index < 5 and self.verbose:
  																logger.info('*** Example ***'							)
  																logger.info(f"""guid: {example.guid}"""							)
  																logger.info(f"""input_ids: {" ".join([str(__a							) for x in input_ids]							)}"""							)
  																logger.info(f"""attention_mask: {" ".join([str(__a							) for x in attention_mask]							)}"""							)
  																logger.info(f"""label: {example.label} (id = {label})"""							)
  
  											features.append(InputFeatures(input_ids=__a		,       attention_mask=__a		,       label=__a							)							)
  
  						if return_tensors is None:
  											return features
  						elif return_tensors == "tf":
  											if not is_tf_available():
  																raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'							)
  											import tensorflow as tf
  
  											def gen():
  																for ex in features:
  																					yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
  											__a    : Optional[Any]      					=						tf.data.Dataset.from_generator(
  											    __a		,       ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)		,       ({'input_ids': tf.TensorShape([None]							), 'attention_mask': tf.TensorShape([None]							)}, tf.TensorShape([]							))		,       )
  											return dataset
  						elif return_tensors == "pt":
  											if not is_torch_available():
  																raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'							)
  											import torch
  											from torch.utils.data import TensorDataset
  
  											__a    : Tuple      					=						torch.tensor([f.input_ids for f in features]		,       dtype=torch.long							)
  											__a    : Tuple      					=						torch.tensor([f.attention_mask for f in features]		,       dtype=torch.long							)
  											if self.mode == "classification":
  																__a    : List[Any]      					=						torch.tensor([f.label for f in features]		,       dtype=torch.long							)
  											elif self.mode == "regression":
  																__a    : Optional[int]      					=						torch.tensor([f.label for f in features]		,       dtype=torch.float							)
  
  											__a    : Dict      					=						TensorDataset(__a		,       __a		,       __a							)
  											return dataset
  						else:
  											raise ValueError('return_tensors should be one of \'tf\' or \'pt\''							)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  import torch
  
  from diffusers import KDPMaAncestralDiscreteScheduler
  from diffusers.utils import torch_device
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  	A_			    =					(KDPMaAncestralDiscreteScheduler,)
  	A_			    =					10
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       **__a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						{
  						    'num_train_timesteps': 1100,
  						    'beta_start': 0.0001,
  						    'beta_end': 0.02,
  						    'beta_schedule': 'linear',
  						}
  
  						config.update(**__a							)
  						return config
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for timesteps in [10, 50, 100, 1000]:
  											self.check_over_configs(num_train_timesteps=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]		,       [0.0002, 0.002, 0.02]							):
  											self.check_over_configs(beta_start=__a		,       beta_end=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for schedule in ["linear", "scaled_linear"]:
  											self.check_over_configs(beta_schedule=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Tuple      					=						self.get_scheduler_config()
  						__a    : Optional[int]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : int      					=						torch.manual_seed(0							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[Any]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : int      					=						sample.to(__a							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : Any      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : int      					=						model(__a		,       __a							)
  
  											__a    : Dict      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : int      					=						output.prev_sample
  
  						__a    : Tuple      					=						torch.sum(torch.abs(__a							)							)
  						__a    : List[str]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3877							) < 1E-2
  						assert abs(result_mean.item() - 18.0331							) < 5E-3
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						for prediction_type in ["epsilon", "v_prediction"]:
  											self.check_over_configs(prediction_type=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : Dict      					=						self.scheduler_classes[0]
  						__a    : Dict      					=						self.get_scheduler_config(prediction_type='v_prediction'							)
  						__a    : List[str]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps							)
  
  						__a    : Dict      					=						self.dummy_model()
  						__a    : List[str]      					=						self.dummy_sample_deter * scheduler.init_noise_sigma
  						__a    : List[str]      					=						sample.to(__a							)
  
  						__a    : Tuple      					=						torch.manual_seed(0							)
  
  						for i, t in enumerate(scheduler.timesteps							):
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : Optional[int]      					=						model(__a		,       __a							)
  
  											__a    : Union[str, Any]      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Union[str, Any]      					=						output.prev_sample
  
  						__a    : Any      					=						torch.sum(torch.abs(__a							)							)
  						__a    : Optional[int]      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 328.9970							) < 1E-2
  						assert abs(result_mean.item() - 0.4284							) < 1E-3
  
  
  
  
  
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if torch_device == "mps":
  											return
  						__a    : List[str]      					=						self.scheduler_classes[0]
  						__a    : Union[str, Any]      					=						self.get_scheduler_config()
  						__a    : List[Any]      					=						scheduler_class(**__a							)
  
  						scheduler.set_timesteps(self.num_inference_steps		,       device=__a							)
  						__a    : Union[str, Any]      					=						torch.manual_seed(0							)
  
  						__a    : List[str]      					=						self.dummy_model()
  						__a    : Any      					=						self.dummy_sample_deter.to(__a							) * scheduler.init_noise_sigma
  
  						for t in scheduler.timesteps:
  											__a    : List[Any]      					=						scheduler.scale_model_input(__a		,       __a							)
  
  											__a    : str      					=						model(__a		,       __a							)
  
  											__a    : str      					=						scheduler.step(__a		,       __a		,       __a		,       generator=__a							)
  											__a    : Any      					=						output.prev_sample
  
  						__a    : Union[str, Any]      					=						torch.sum(torch.abs(__a							)							)
  						__a    : str      					=						torch.mean(torch.abs(__a							)							)
  
  						assert abs(result_sum.item() - 13849.3818							) < 1E-1
  						assert abs(result_mean.item() - 18.0331							) < 1E-3
  
  
  
  