code: 
  
  
  import logging
  import os
  import sys
  import threading
  from logging import (
      CRITICAL,  # NOQA
      DEBUG,  # NOQA
      ERROR,  # NOQA
      FATAL,  # NOQA
      INFO,  # NOQA
      NOTSET,  # NOQA
      WARN,  # NOQA
      WARNING,  # NOQA
  )
  from typing import Optional
  
  from tqdm import auto as tqdm_lib
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					threading.Lock()
  _SCREAMING_SNAKE_CASE    :				Optional[logging.Handler]       =					None
  
  _SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
      "debug": logging.DEBUG,
      "info": logging.INFO,
      "warning": logging.WARNING,
      "error": logging.ERROR,
      "critical": logging.CRITICAL,
  }
  
  _SCREAMING_SNAKE_CASE    :				str       =					logging.WARNING
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					True
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       os.getenv('''DIFFUSERS_VERBOSITY'''							,UpperCamelCase_       )
       if env_level_str:
            if env_level_str in log_levels:
                 return log_levels[env_level_str]
            else:
                 logging.getLogger().warning(
                     F'''Unknown option DIFFUSERS_VERBOSITY={env_level_str}, '''
                     F'''has to be one of: { ", ".join(log_levels.keys()       ) }'''       )
       return _default_log_level
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return __name__.split('''.'''       )[0]
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return logging.getLogger(_get_library_name()       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       global _default_handler
  
       with _lock:
            if _default_handler:
                 # This library has already configured the library root logger.
                 return
            snake_case					=       logging.StreamHandler()  # Set sys.stderr as stream.
            snake_case					=       sys.stderr.flush
  
            # Apply our default configuration to the library root logger.
            snake_case					=       _get_library_root_logger()
            library_root_logger.addHandler(_default_handler       )
            library_root_logger.setLevel(_get_default_logging_level()       )
            snake_case					=       False
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       global _default_handler
  
       with _lock:
            if not _default_handler:
                 return
  
            snake_case					=       _get_library_root_logger()
            library_root_logger.removeHandler(_default_handler       )
            library_root_logger.setLevel(logging.NOTSET       )
            snake_case					=       None
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return log_levels
  def     UpperCAmelCase__		(UpperCamelCase_ = None       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if name is None:
            snake_case					=       _get_library_name()
  
       _configure_library_root_logger()
       return logging.getLogger(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       _configure_library_root_logger()
       return _get_library_root_logger().getEffectiveLevel()
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       _configure_library_root_logger()
       _get_library_root_logger().setLevel(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return set_verbosity(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return set_verbosity(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return set_verbosity(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return set_verbosity(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       _configure_library_root_logger()
  
       assert _default_handler is not None
       _get_library_root_logger().removeHandler(_default_handler       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       _configure_library_root_logger()
  
       assert _default_handler is not None
       _get_library_root_logger().addHandler(_default_handler       )
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       _configure_library_root_logger()
  
       assert handler is not None
       _get_library_root_logger().addHandler(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       _configure_library_root_logger()
  
       assert handler is not None and handler not in _get_library_root_logger().handlers
       _get_library_root_logger().removeHandler(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       _configure_library_root_logger()
       snake_case					=       False
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       _configure_library_root_logger()
       snake_case					=       True
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       _get_library_root_logger().handlers
  
       for handler in handlers:
            snake_case					=       logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''       )
            handler.setFormatter(UpperCamelCase_       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       _get_library_root_logger().handlers
  
       for handler in handlers:
            handler.setFormatter(UpperCamelCase_       )
  def     UpperCAmelCase__		(self							,*UpperCamelCase_							,**UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       os.getenv('''DIFFUSERS_NO_ADVISORY_WARNINGS'''							,UpperCamelCase_       )
       if no_advisory_warnings:
            return
       self.warning(*UpperCamelCase_							,**UpperCamelCase_       )
  
  
  _SCREAMING_SNAKE_CASE    :				int       =					warning_advice
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      *__snake_case    ,      **__snake_case					):  # pylint: disable=unused-argument
           snake_case					=       args[0] if args else None
      def __iter__( self					):
           return iter(self._iterator					)
      def __getattr__( self    ,      __snake_case					):
  
           def empty_fn(*__snake_case    ,      **__snake_case					):  # pylint: disable=unused-argument
                return
  
           return empty_fn
      def __enter__( self					):
           return self
  
  
  
  
      def __exit__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
           return
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __call__( self    ,      *__snake_case    ,      **__snake_case					):
           if _tqdm_active:
                return tqdm_lib.tqdm(*__snake_case    ,      **__snake_case					)
           else:
                return EmptyTqdm(*__snake_case    ,      **__snake_case					)
      def 	a_    ( self    ,      *__snake_case    ,      **__snake_case					):
           snake_case					=       None
           if _tqdm_active:
                return tqdm_lib.tqdm.set_lock(*__snake_case    ,      **__snake_case					)
  
  
  
  
      def 	a_    ( self					):
           if _tqdm_active:
                return tqdm_lib.tqdm.get_lock()
  
  
  
  
  
  
  _SCREAMING_SNAKE_CASE    :				Union[str, Any]       =					_tqdm_cls()
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       global _tqdm_active
       return bool(_tqdm_active       )
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       global _tqdm_active
       snake_case					=       True
  
  def     UpperCAmelCase__		():
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       global _tqdm_active
       snake_case					=       False
  
  
  
  
  
style_context: 
  
  
  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
  
  
  _SCREAMING_SNAKE_CASE    :				Optional[Any]       =					logging.get_logger(__name__)
  
  @dataclass
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
      __magic_name__    						=	42
      __magic_name__    						=	None
      __magic_name__    						=	None
      def 	a_    ( self					):
           return json.dumps(dataclasses.asdict(self					)    ,      indent=2					) + "\n"
  
  
  
  
  
  
  @dataclass(frozen=snake_case__			)
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      __magic_name__    						=	42
      __magic_name__    						=	None
      __magic_name__    						=	None
      __magic_name__    						=	None
      def 	a_    ( self					):
           return json.dumps(dataclasses.asdict(self					)					) + "\n"
  
  
  
  
  
  
  class 						A__      :
  
  
  
  
  
  
  
      """simple docstring"""
  
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           raise NotImplementedError()
      def 	a_    ( self					):
           raise NotImplementedError()
      def 	a_    ( self    ,      __snake_case					):
           if len(self.get_labels()					) > 1:
                snake_case					=       self.get_labels()[int(example.label					)]
           return example
  
  
  
  
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None					):
           with open(__snake_case    ,      '''r'''    ,      encoding='''utf-8-sig'''					) as f:
                return list(csv.reader(__snake_case    ,      delimiter='''\t'''    ,      quotechar=__snake_case					)					)
  
  
  
  
  
  
  class 						A__      (					snake_case__			):
  
  
  
  
  
  
  
      """simple docstring"""
  
      def __init__( self    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case="classification"    ,      __snake_case=False					):
           snake_case					=       [] if labels is None else labels
           snake_case					=       [] if examples is None else examples
           snake_case					=       mode
           snake_case					=       verbose
      def __len__( self					):
           return len(self.examples					)
      def __getitem__( self    ,      __snake_case					):
           if isinstance(__snake_case    ,      __snake_case					):
                return SingleSentenceClassificationProcessor(labels=self.labels    ,      examples=self.examples[idx]					)
           return self.examples[idx]
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      **__snake_case					):
           snake_case					=       cls(**__snake_case					)
           processor.add_examples_from_csv(
               __snake_case    ,      split_name=__snake_case    ,      column_label=__snake_case    ,      column_text=__snake_case    ,      column_id=__snake_case    ,      skip_first_row=__snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case    ,      )
           return processor
      @classmethod
      def 	a_    ( cls    ,      __snake_case    ,      __snake_case=None    ,      **__snake_case					):
           snake_case					=       cls(**__snake_case					)
           processor.add_examples(__snake_case    ,      labels=__snake_case					)
           return processor
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=""    ,      __snake_case=0    ,      __snake_case=1    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False    ,      __snake_case=False    ,      ):
           snake_case					=       self._read_tsv(__snake_case					)
           if skip_first_row:
                snake_case					=       lines[1:]
           snake_case					=       []
           snake_case					=       []
           snake_case					=       []
           for i, line in enumerate(__snake_case					):
                texts.append(line[column_text]					)
                labels.append(line[column_label]					)
                if column_id is not None:
                     ids.append(line[column_id]					)
                else:
                     snake_case					=       F'''{split_name}-{i}''' if split_name else str(__snake_case					)
                     ids.append(__snake_case					)
  
           return self.add_examples(
               __snake_case    ,      __snake_case    ,      __snake_case    ,      overwrite_labels=__snake_case    ,      overwrite_examples=__snake_case					)
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=False					):
           if labels is not None and len(__snake_case					) != len(__snake_case					):
                raise ValueError(
                    F'''Text and labels have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
           if ids is not None and len(__snake_case					) != len(__snake_case					):
                raise ValueError(F'''Text and ids have mismatched lengths {len(__snake_case					)} and {len(__snake_case					)}'''					)
           if ids is None:
                snake_case					=       [None] * len(__snake_case					)
           if labels is None:
                snake_case					=       [None] * len(__snake_case					)
           snake_case					=       []
           snake_case					=       set()
           for text_or_text_and_label, label, guid in zip(__snake_case    ,      __snake_case    ,      __snake_case					):
                if isinstance(__snake_case    ,      (tuple, list)					) and label is None:
                     snake_case				,						snake_case					=       text_or_text_and_label
                else:
                     snake_case					=       text_or_text_and_label
                added_labels.add(__snake_case					)
                examples.append(InputExample(guid=__snake_case    ,      text_a=__snake_case    ,      text_b=__snake_case    ,      label=__snake_case					)					)
  
           # Update examples
           if overwrite_examples:
                snake_case					=       examples
           else:
                self.examples.extend(__snake_case					)
  
           # Update labels
           if overwrite_labels:
                snake_case					=       list(__snake_case					)
           else:
                snake_case					=       list(set(self.labels					).union(__snake_case					)					)
  
           return self.examples
  
  
  
  
      def 	a_    ( self    ,      __snake_case    ,      __snake_case=None    ,      __snake_case=False    ,      __snake_case=0    ,      __snake_case=True    ,      __snake_case=None    ,      ):
           if max_length is None:
                snake_case					=       tokenizer.max_len
  
           snake_case					=       {label: i for i, label in enumerate(self.labels					)}
  
           snake_case					=       []
           for ex_index, example in enumerate(self.examples					):
                if ex_index % 1_0_0_0_0 == 0:
                     logger.info(F'''Tokenizing example {ex_index}'''					)
  
                snake_case					=       tokenizer.encode(
                    example.text_a    ,      add_special_tokens=__snake_case    ,      max_length=min(__snake_case    ,      tokenizer.max_len					)    ,      )
                all_input_ids.append(__snake_case					)
  
           snake_case					=       max(len(__snake_case					) for input_ids in all_input_ids					)
  
           snake_case					=       []
           for ex_index, (input_ids, example) in enumerate(zip(__snake_case    ,      self.examples					)					):
                if ex_index % 1_0_0_0_0 == 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.
                snake_case					=       [1 if mask_padding_with_zero else 0] * len(__snake_case					)
  
                # Zero-pad up to the sequence length.
                snake_case					=       batch_length - len(__snake_case					)
                if pad_on_left:
                     snake_case					=       ([pad_token] * padding_length) + input_ids
                     snake_case					=       ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
                else:
                     snake_case					=       input_ids + ([pad_token] * padding_length)
                     snake_case					=       attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
                if len(__snake_case					) != batch_length:
                     raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
                if len(__snake_case					) != batch_length:
                     raise ValueError(F'''Error with input length {len(__snake_case					)} vs {batch_length}'''					)
  
                if self.mode == "classification":
                     snake_case					=       label_map[example.label]
                elif self.mode == "regression":
                     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(__snake_case					) for x in input_ids]					)}'''					)
                     logger.info(F'''attention_mask: {" ".join([str(__snake_case					) for x in attention_mask]					)}'''					)
                     logger.info(F'''label: {example.label} (id = {label})'''					)
  
                features.append(InputFeatures(input_ids=__snake_case    ,      attention_mask=__snake_case    ,      label=__snake_case					)					)
  
           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)
  
                snake_case					=       tf.data.Dataset.from_generator(
                    __snake_case    ,      ({'''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
  
                snake_case					=       torch.tensor([f.input_ids for f in features]    ,      dtype=torch.long					)
                snake_case					=       torch.tensor([f.attention_mask for f in features]    ,      dtype=torch.long					)
                if self.mode == "classification":
                     snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.long					)
                elif self.mode == "regression":
                     snake_case					=       torch.tensor([f.label for f in features]    ,      dtype=torch.float					)
  
                snake_case					=       TensorDataset(__snake_case    ,      __snake_case    ,      __snake_case					)
                return dataset
           else:
                raise ValueError('''return_tensors should be one of \'tf\' or \'pt\''''					)
  
  
  
  
  