
'''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


_snake_case					: Optional[int]     			=					logging.get_logger(__name__)


@dataclass
class       lowerCAmelCase			:
		a				:       str
		a				:       str
		a				:       Optional[str]							=       None
		a				:       Optional[str]							=       None




		def      lowercase							(					self   ):
								return json.dumps(dataclasses.asdict(self   )       ,    indent=2   ) + "\n"


@dataclass(frozen=__UpperCAmelCase      )
class       lowerCAmelCase			:
		a				:       List[int]
		a				:       Optional[List[int]]							=       None
		a				:       Optional[List[int]]							=       None
		a				:       Optional[Union[int, float]]							=       None




		def      lowercase							(					self   ):
								return json.dumps(dataclasses.asdict(self   )   ) + "\n"


class       lowerCAmelCase			:




		def      lowercase							(					self       ,    UpperCamelCase   ):
								raise NotImplementedError()




		def      lowercase							(					self       ,    UpperCamelCase   ):
								raise NotImplementedError()




		def      lowercase							(					self       ,    UpperCamelCase   ):
								raise NotImplementedError()




		def      lowercase							(					self       ,    UpperCamelCase   ):
								raise NotImplementedError()




		def      lowercase							(					self   ):
								raise NotImplementedError()




		def      lowercase							(					self       ,    UpperCamelCase   ):
								if len(self.get_labels()   ) > 1:
														_SCREAMING_SNAKE_CASE					=			self.get_labels()[int(example.label   )]
								return example




		@classmethod
		def      lowercase							(					cls       ,    UpperCamelCase       ,    UpperCamelCase=None   ):
								with open(_UpperCAmelCase       ,    "r"       ,    encoding="utf-8-sig"   ) as f:
														return list(csv.reader(_UpperCAmelCase       ,    delimiter="\t"       ,    quotechar=_UpperCAmelCase   )   )


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase="classification"       ,    UpperCamelCase=False   ):
								_SCREAMING_SNAKE_CASE					=			[] if labels is None else labels
								_SCREAMING_SNAKE_CASE					=			[] if examples is None else examples
								_SCREAMING_SNAKE_CASE					=			mode
								_SCREAMING_SNAKE_CASE					=			verbose




		def __len__(					self   ):
								return len(self.examples   )




		def __getitem__(					self       ,    UpperCamelCase   ):
								if isinstance(_UpperCAmelCase       ,    _UpperCAmelCase   ):
														return SingleSentenceClassificationProcessor(labels=self.labels       ,    examples=self.examples[idx]   )
								return self.examples[idx]




		@classmethod
		def      lowercase							(					cls       ,    UpperCamelCase       ,    UpperCamelCase=""       ,    UpperCamelCase=0       ,    UpperCamelCase=1       ,    UpperCamelCase=None       ,    UpperCamelCase=False       ,    **UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			cls(**_UpperCAmelCase   )
								processor.add_examples_from_csv(
								    _UpperCAmelCase       ,    split_name=_UpperCAmelCase       ,    column_label=_UpperCAmelCase       ,    column_text=_UpperCAmelCase       ,    column_id=_UpperCAmelCase       ,    skip_first_row=_UpperCAmelCase       ,    overwrite_labels=_UpperCAmelCase       ,    overwrite_examples=_UpperCAmelCase       ,    )
								return processor




		@classmethod
		def      lowercase							(					cls       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    **UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			cls(**_UpperCAmelCase   )
								processor.add_examples(_UpperCAmelCase       ,    labels=_UpperCAmelCase   )
								return processor




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase=""       ,    UpperCamelCase=0       ,    UpperCamelCase=1       ,    UpperCamelCase=None       ,    UpperCamelCase=False       ,    UpperCamelCase=False       ,    UpperCamelCase=False       ,    ):
								_SCREAMING_SNAKE_CASE					=			self._read_tsv(_UpperCAmelCase   )
								if skip_first_row:
														_SCREAMING_SNAKE_CASE					=			lines[1:]
								_SCREAMING_SNAKE_CASE					=			[]
								_SCREAMING_SNAKE_CASE					=			[]
								_SCREAMING_SNAKE_CASE					=			[]
								for i, line in enumerate(_UpperCAmelCase   ):
														texts.append(line[column_text]   )
														labels.append(line[column_label]   )
														if column_id is not None:
																				ids.append(line[column_id]   )
														else:
																				_SCREAMING_SNAKE_CASE					=			F'{split_name}-{i}' if split_name else str(_UpperCAmelCase   )
																				ids.append(_UpperCAmelCase   )

								return self.add_examples(
								    _UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase       ,    overwrite_labels=_UpperCAmelCase       ,    overwrite_examples=_UpperCAmelCase   )




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=False       ,    UpperCamelCase=False   ):
								if labels is not None and len(_UpperCAmelCase   ) != len(_UpperCAmelCase   ):
														raise ValueError(
														    F'Text and labels have mismatched lengths {len(_UpperCAmelCase   )} and {len(_UpperCAmelCase   )}'   )
								if ids is not None and len(_UpperCAmelCase   ) != len(_UpperCAmelCase   ):
														raise ValueError(F'Text and ids have mismatched lengths {len(_UpperCAmelCase   )} and {len(_UpperCAmelCase   )}'   )
								if ids is None:
														_SCREAMING_SNAKE_CASE					=			[None] * len(_UpperCAmelCase   )
								if labels is None:
														_SCREAMING_SNAKE_CASE					=			[None] * len(_UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			[]
								_SCREAMING_SNAKE_CASE					=			set()
								for text_or_text_and_label, label, guid in zip(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   ):
														if isinstance(_UpperCAmelCase       ,    (tuple, list)   ) and label is None:
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			text_or_text_and_label
														else:
																				_SCREAMING_SNAKE_CASE					=			text_or_text_and_label
														added_labels.add(_UpperCAmelCase   )
														examples.append(InputExample(guid=_UpperCAmelCase       ,    text_a=_UpperCAmelCase       ,    text_b=_UpperCAmelCase       ,    label=_UpperCAmelCase   )   )

								# Update examples
								if overwrite_examples:
														_SCREAMING_SNAKE_CASE					=			examples
								else:
														self.examples.extend(_UpperCAmelCase   )

								# Update labels
								if overwrite_labels:
														_SCREAMING_SNAKE_CASE					=			list(_UpperCAmelCase   )
								else:
														_SCREAMING_SNAKE_CASE					=			list(set(self.labels   ).union(_UpperCAmelCase   )   )

								return self.examples




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=False       ,    UpperCamelCase=0       ,    UpperCamelCase=True       ,    UpperCamelCase=None       ,    ):
								if max_length is None:
														_SCREAMING_SNAKE_CASE					=			tokenizer.max_len

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

								_SCREAMING_SNAKE_CASE					=			[]
								for ex_index, example in enumerate(self.examples   ):
														if ex_index % 10_000 == 0:
																				logger.info(F'Tokenizing example {ex_index}'   )

														_SCREAMING_SNAKE_CASE					=			tokenizer.encode(
														    example.text_a       ,    add_special_tokens=_UpperCAmelCase       ,    max_length=min(_UpperCAmelCase       ,    tokenizer.max_len   )       ,    )
														all_input_ids.append(_UpperCAmelCase   )

								_SCREAMING_SNAKE_CASE					=			max(len(_UpperCAmelCase   ) for input_ids in all_input_ids   )

								_SCREAMING_SNAKE_CASE					=			[]
								for ex_index, (input_ids, example) in enumerate(zip(_UpperCAmelCase       ,    self.examples   )   ):
														if ex_index % 10_000 == 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.
														_SCREAMING_SNAKE_CASE					=			[1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase   )

														# Zero-pad up to the sequence length.
														_SCREAMING_SNAKE_CASE					=			batch_length - len(_UpperCAmelCase   )
														if pad_on_left:
																				_SCREAMING_SNAKE_CASE					=			([pad_token] * padding_length) + input_ids
																				_SCREAMING_SNAKE_CASE					=			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
														else:
																				_SCREAMING_SNAKE_CASE					=			input_ids + ([pad_token] * padding_length)
																				_SCREAMING_SNAKE_CASE					=			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

														if len(_UpperCAmelCase   ) != batch_length:
																				raise ValueError(F'Error with input length {len(_UpperCAmelCase   )} vs {batch_length}'   )
														if len(_UpperCAmelCase   ) != batch_length:
																				raise ValueError(F'Error with input length {len(_UpperCAmelCase   )} vs {batch_length}'   )

														if self.mode == "classification":
																				_SCREAMING_SNAKE_CASE					=			label_map[example.label]
														elif self.mode == "regression":
																				_SCREAMING_SNAKE_CASE					=			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(_UpperCAmelCase   ) for x in input_ids]   )}'   )
																				logger.info(F'attention_mask: {" ".join([str(_UpperCAmelCase   ) for x in attention_mask]   )}'   )
																				logger.info(F'label: {example.label} (id = {label})'   )

														features.append(InputFeatures(input_ids=_UpperCAmelCase       ,    attention_mask=_UpperCAmelCase       ,    label=_UpperCAmelCase   )   )

								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)

														_SCREAMING_SNAKE_CASE					=			tf.data.Dataset.from_generator(
														    _UpperCAmelCase       ,    ({"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

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

														_SCREAMING_SNAKE_CASE					=			TensorDataset(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )
														return dataset
								else:
														raise ValueError("return_tensors should be one of \'tf\' or \'pt\'"   )

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Any						 =      (LMSDiscreteScheduler,)
      a		:Dict						 =      10







      def lowercase__		( self					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_UpperCAmelCase)
            return config







      def lowercase__		( self):
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=_UpperCAmelCase)







      def lowercase__		( self):
            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=_UpperCAmelCase					,					beta_end=_UpperCAmelCase)







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







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







      def lowercase__		( self):
            for t in [0, 500, 800]:
                  self.check_over_forward(time_step=_UpperCAmelCase)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config(prediction_type='''v_prediction''')
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 0.0017) < 1E-2
            assert abs(result_mean.item() - 2.2676E-06) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

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

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase					,					use_karras_sigmas=_UpperCAmelCase)

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

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter.to(_UpperCAmelCase) * scheduler.init_noise_sigma
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for t in scheduler.timesteps:
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 3812.9927) < 2E-2
            assert abs(result_mean.item() - 4.9648) < 1E-3



