
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


__snake_case					:							List[str]           =							logging.get_logger(__name__)


@dataclass
class 	UpperCamelCase__       :

						'''simple docstring'''



						__a     :    str
						__a     :    str
						__a     :    Optional[str]												=			None
						__a     :    Optional[str]												=			None


						def    A__       (  self	)  ->Any:
									return json.dumps(dataclasses.asdict(self	)  , indent=2	) + "\n"


@dataclass(frozen=UpperCAmelCase__)
class 	UpperCamelCase__       :

						'''simple docstring'''



						__a     :    List[int]
						__a     :    Optional[List[int]]												=			None
						__a     :    Optional[List[int]]												=			None
						__a     :    Optional[Union[int, float]]												=			None


						def    A__       (  self	)  ->Dict:
									return json.dumps(dataclasses.asdict(self	)	) + "\n"


class 	UpperCamelCase__       :

						'''simple docstring'''



						def    A__       (  self  , A	)  ->Tuple:
									raise NotImplementedError()


						def    A__       (  self  , A	)  ->int:
									raise NotImplementedError()


						def    A__       (  self  , A	)  ->List[str]:
									raise NotImplementedError()


						def    A__       (  self  , A	)  ->Tuple:
									raise NotImplementedError()


						def    A__       (  self	)  ->List[str]:
									raise NotImplementedError()


						def    A__       (  self  , A	)  ->List[Any]:
									if len(self.get_labels()	) > 1:
												UpperCAmelCase__		:Union[str, Any] 		=    self.get_labels()[int(example.label	)]
									return example


						@classmethod
						def    A__       (  cls  , A  , A=None	)  ->Dict:
									with open(A  , 'r'  , encoding='utf-8-sig'	) as f:
												return list(csv.reader(A  , delimiter='\t'  , quotechar=A	)	)


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A=None  , A=None  , A="classification"  , A=False	)  ->List[Any]:
									UpperCAmelCase__		:int 		=    [] if labels is None else labels
									UpperCAmelCase__		:Tuple 		=    [] if examples is None else examples
									UpperCAmelCase__		:List[Any] 		=    mode
									UpperCAmelCase__		:Tuple 		=    verbose


						def __len__(  self	)  ->List[Any]:
									return len(self.examples	)


						def __getitem__(  self  , A	)  ->List[str]:
									if isinstance(A  , A	):
												return SingleSentenceClassificationProcessor(labels=self.labels  , examples=self.examples[idx]	)
									return self.examples[idx]


						@classmethod
						def    A__       (  cls  , A  , A=""  , A=0  , A=1  , A=None  , A=False  , **A	)  ->List[Any]:
									UpperCAmelCase__		:str 		=    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    A__       (  cls  , A  , A=None  , **A	)  ->Any:
									UpperCAmelCase__		:Optional[Any] 		=    cls(**A	)
									processor.add_examples(A  , labels=A	)
									return processor


						def    A__       (  self  , A  , A=""  , A=0  , A=1  , A=None  , A=False  , A=False  , A=False  , )  ->List[Any]:
									UpperCAmelCase__		:Tuple 		=    self._read_tsv(A	)
									if skip_first_row:
												UpperCAmelCase__		:Optional[Any] 		=    lines[1:]
									UpperCAmelCase__		:Any 		=    []
									UpperCAmelCase__		:int 		=    []
									UpperCAmelCase__		: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:
															UpperCAmelCase__		:Optional[int] 		=    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    A__       (  self  , A  , A=None  , A=None  , A=False  , A=False	)  ->Union[str, Any]:
									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:
												UpperCAmelCase__		:List[Any] 		=    [None] * len(A	)
									if labels is None:
												UpperCAmelCase__		:Optional[int] 		=    [None] * len(A	)
									UpperCAmelCase__		:Union[str, Any] 		=    []
									UpperCAmelCase__		:int 		=    set()
									for text_or_text_and_label, label, guid in zip(A  , A  , A	):
												if isinstance(A  , (tuple, list)	) and label is None:
															UpperCAmelCase__		,       UpperCAmelCase__		:Union[str, Any] 		=    text_or_text_and_label
												else:
															UpperCAmelCase__		:Union[str, Any] 		=    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:
												UpperCAmelCase__		:Optional[int] 		=    examples
									else:
												self.examples.extend(A	)

									# Update labels
									if overwrite_labels:
												UpperCAmelCase__		:str 		=    list(A	)
									else:
												UpperCAmelCase__		:List[str] 		=    list(set(self.labels	).union(A	)	)

									return self.examples


						def    A__       (  self  , A  , A=None  , A=False  , A=0  , A=True  , A=None  , )  ->Optional[int]:
									if max_length is None:
												UpperCAmelCase__		:str 		=    tokenizer.max_len

									UpperCAmelCase__		:int 		=    {label: i for i, label in enumerate(self.labels	)}

									UpperCAmelCase__		:str 		=    []
									for ex_index, example in enumerate(self.examples	):
												if ex_index % 1_00_00 == 0:
															logger.info(F"""Tokenizing example {ex_index}"""	)

												UpperCAmelCase__		:Dict 		=    tokenizer.encode(
												    example.text_a  , add_special_tokens=A  , max_length=min(A  , tokenizer.max_len	)  , )
												all_input_ids.append(A	)

									UpperCAmelCase__		:Any 		=    max(len(A	) for input_ids in all_input_ids	)

									UpperCAmelCase__		:Union[str, Any] 		=    []
									for ex_index, (input_ids, example) in enumerate(zip(A  , self.examples	)	):
												if ex_index % 1_00_00 == 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.
												UpperCAmelCase__		:Tuple 		=    [1 if mask_padding_with_zero else 0] * len(A	)

												# Zero-pad up to the sequence length.
												UpperCAmelCase__		:int 		=    batch_length - len(A	)
												if pad_on_left:
															UpperCAmelCase__		:List[str] 		=    ([pad_token] * padding_length) + input_ids
															UpperCAmelCase__		:List[str] 		=    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
												else:
															UpperCAmelCase__		:Union[str, Any] 		=    input_ids + ([pad_token] * padding_length)
															UpperCAmelCase__		:int 		=    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":
															UpperCAmelCase__		:Tuple 		=    label_map[example.label]
												elif self.mode == "regression":
															UpperCAmelCase__		:List[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)

												UpperCAmelCase__		:Dict 		=    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

												UpperCAmelCase__		:int 		=    torch.tensor([f.input_ids for f in features]  , dtype=torch.long	)
												UpperCAmelCase__		:int 		=    torch.tensor([f.attention_mask for f in features]  , dtype=torch.long	)
												if self.mode == "classification":
															UpperCAmelCase__		:Any 		=    torch.tensor([f.label for f in features]  , dtype=torch.long	)
												elif self.mode == "regression":
															UpperCAmelCase__		:int 		=    torch.tensor([f.label for f in features]  , dtype=torch.float	)

												UpperCAmelCase__		:Optional[int] 		=    TensorDataset(A  , A  , A	)
												return dataset
									else:
												raise ValueError('return_tensors should be one of \'tf\' or \'pt\''	)







import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						__a     :    List[Any]												=			(DPMSolverMultistepScheduler,)
						__a     :    Dict												=			(("""num_inference_steps""", 2_5),)


						def    A__       (  self  , **A	)  ->List[Any]:
									UpperCAmelCase__		:int 		=    {
									    'num_train_timesteps': 10_00,
									    'beta_start': 0.0001,
									    'beta_end': 0.02,
									    'beta_schedule': 'linear',
									    'solver_order': 2,
									    'prediction_type': 'epsilon',
									    'thresholding': False,
									    'sample_max_value': 1.0,
									    'algorithm_type': 'dpmsolver++',
									    'solver_type': 'midpoint',
									    'lower_order_final': False,
									    'lambda_min_clipped': -float('inf'	),
									    'variance_type': None,
									}

									config.update(**A	)
									return config


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:int 		=    self.dummy_sample
									UpperCAmelCase__		:Optional[int] 		=    0.1 * sample
									UpperCAmelCase__		:Any 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:List[Any] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)
												# copy over dummy past residuals
												UpperCAmelCase__		:Optional[Any] 		=    dummy_past_residuals[: scheduler.config.solver_order]

												with tempfile.TemporaryDirectory() as tmpdirname:
															scheduler.save_config(A	)
															UpperCAmelCase__		:List[Any] 		=    scheduler_class.from_pretrained(A	)
															new_scheduler.set_timesteps(A	)
															# copy over dummy past residuals
															UpperCAmelCase__		:Union[str, Any] 		=    dummy_past_residuals[: new_scheduler.config.solver_order]

												UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    sample, sample
												for t in range(A  , time_step + scheduler.config.solver_order + 1	):
															UpperCAmelCase__		:Dict 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
															UpperCAmelCase__		:Optional[Any] 		=    new_scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->List[Any]:
									pass


						def    A__       (  self  , A=0  , **A	)  ->Tuple:
									UpperCAmelCase__		:List[str] 		=    dict(self.forward_default_kwargs	)
									UpperCAmelCase__		:Dict 		=    kwargs.pop('num_inference_steps'  , A	)
									UpperCAmelCase__		:str 		=    self.dummy_sample
									UpperCAmelCase__		:Union[str, Any] 		=    0.1 * sample
									UpperCAmelCase__		:Union[str, Any] 		=    [residual + 0.2, residual + 0.15, residual + 0.10]

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:List[Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:List[str] 		=    scheduler_class(**A	)
												scheduler.set_timesteps(A	)

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

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

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

												UpperCAmelCase__		:Any 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:Dict 		=    new_scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self  , A=None  , **A	)  ->List[str]:
									if scheduler is None:
												UpperCAmelCase__		:Optional[Any] 		=    self.scheduler_classes[0]
												UpperCAmelCase__		:Optional[Any] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler_class(**A	)

									UpperCAmelCase__		:List[Any] 		=    10
									UpperCAmelCase__		:int 		=    self.dummy_model()
									UpperCAmelCase__		:int 		=    self.dummy_sample_deter
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:Union[str, Any] 		=    model(A  , A	)
												UpperCAmelCase__		:str 		=    scheduler.step(A  , A  , A	).prev_sample

									return sample


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Tuple 		=    dict(self.forward_default_kwargs	)

									UpperCAmelCase__		:Optional[Any] 		=    kwargs.pop('num_inference_steps'  , A	)

									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Union[str, Any] 		=    self.get_scheduler_config()
												UpperCAmelCase__		:Optional[Any] 		=    scheduler_class(**A	)

												UpperCAmelCase__		:Dict 		=    self.dummy_sample
												UpperCAmelCase__		:Dict 		=    0.1 * sample

												if num_inference_steps is not None and hasattr(A  , 'set_timesteps'	):
															scheduler.set_timesteps(A	)
												elif num_inference_steps is not None and not hasattr(A  , 'set_timesteps'	):
															UpperCAmelCase__		:List[Any] 		=    num_inference_steps

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

												UpperCAmelCase__		:str 		=    scheduler.timesteps[5]
												UpperCAmelCase__		:str 		=    scheduler.timesteps[6]

												UpperCAmelCase__		:List[str] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample
												UpperCAmelCase__		:List[Any] 		=    scheduler.step(A  , A  , A  , **A	).prev_sample

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


						def    A__       (  self	)  ->Optional[Any]:
									for timesteps in [25, 50, 1_00, 9_99, 10_00]:
												self.check_over_configs(num_train_timesteps=A	)


						def    A__       (  self	)  ->str:
									self.check_over_configs(thresholding=A	)
									for order in [1, 2, 3]:
												for solver_type in ["midpoint", "heun"]:
															for threshold in [0.5, 1.0, 2.0]:
																		for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    thresholding=A  , prediction_type=A  , sample_max_value=A  , algorithm_type='dpmsolver++'  , solver_order=A  , solver_type=A  , )


						def    A__       (  self	)  ->Optional[int]:
									for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=A	)


						def    A__       (  self	)  ->List[str]:
									for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
												for solver_type in ["midpoint", "heun"]:
															for order in [1, 2, 3]:
																		for prediction_type in ["epsilon", "sample"]:
																					if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
																								if order == 3:
																											continue
																					else:
																								self.check_over_configs(
																								    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					UpperCAmelCase__		:Optional[int] 		=    self.full_loop(
																					    solver_order=A  , solver_type=A  , prediction_type=A  , algorithm_type=A  , )
																					assert not torch.isnan(A	).any(), "Samples have nan numbers"


						def    A__       (  self	)  ->List[Any]:
									self.check_over_configs(lower_order_final=A	)
									self.check_over_configs(lower_order_final=A	)


						def    A__       (  self	)  ->Optional[Any]:
									self.check_over_configs(lambda_min_clipped=-float('inf'	)	)
									self.check_over_configs(lambda_min_clipped=-5.1	)


						def    A__       (  self	)  ->Union[str, Any]:
									self.check_over_configs(variance_type=A	)
									self.check_over_configs(variance_type='learned_range'	)


						def    A__       (  self	)  ->List[str]:
									for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
												self.check_over_forward(num_inference_steps=A  , time_step=0	)


						def    A__       (  self	)  ->int:
									UpperCAmelCase__		:int 		=    self.full_loop()
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Any:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(thresholding=A  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5	)
									UpperCAmelCase__		:Any 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 1.1364	) < 1e-3


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.full_loop(prediction_type='v_prediction'	)
									UpperCAmelCase__		:int 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2251	) < 1e-3


						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(prediction_type='v_prediction'  , use_karras_sigmas=A	)
									UpperCAmelCase__		:Optional[int] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.2096	) < 1e-3


						def    A__       (  self	)  ->List[Any]:
									# make sure that iterating over schedulers with same config names gives same results
									# for defaults
									UpperCAmelCase__		:Tuple 		=    DPMSolverMultistepScheduler(**self.get_scheduler_config()	)
									UpperCAmelCase__		:Optional[int] 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:List[Any] 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3

									UpperCAmelCase__		:Dict 		=    DPMSolverSinglestepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    UniPCMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:Union[str, Any] 		=    DEISMultistepScheduler.from_config(scheduler.config	)
									UpperCAmelCase__		:List[str] 		=    DPMSolverMultistepScheduler.from_config(scheduler.config	)

									UpperCAmelCase__		:Dict 		=    self.full_loop(scheduler=A	)
									UpperCAmelCase__		:Dict 		=    torch.mean(torch.abs(A	)	)

									assert abs(result_mean.item() - 0.3301	) < 1e-3


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:Tuple 		=    self.scheduler_classes[0]
									UpperCAmelCase__		:List[str] 		=    self.get_scheduler_config(thresholding=A  , dynamic_thresholding_ratio=0	)
									UpperCAmelCase__		:str 		=    scheduler_class(**A	)

									UpperCAmelCase__		:int 		=    10
									UpperCAmelCase__		:Optional[int] 		=    self.dummy_model()
									UpperCAmelCase__		:List[str] 		=    self.dummy_sample_deter.half()
									scheduler.set_timesteps(A	)

									for i, t in enumerate(scheduler.timesteps	):
												UpperCAmelCase__		:str 		=    model(A  , A	)
												UpperCAmelCase__		:Union[str, Any] 		=    scheduler.step(A  , A  , A	).prev_sample

									assert sample.dtype == torch.floataa


						def    A__       (  self  , **A	)  ->Any:
									for scheduler_class in self.scheduler_classes:
												UpperCAmelCase__		:Optional[int] 		=    self.get_scheduler_config(**A	)
												UpperCAmelCase__		:int 		=    scheduler_class(**A	)

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





