


'''simple docstring'''







import argparse
import os
import re


a__  :					Any        =       'src/diffusers'

# Pattern that looks at the indentation in a line.
a__  :					Any        =       re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
a__  :					List[Any]        =       re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
a__  :					Dict        =       re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
a__  :					Union[str, Any]        =       re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
a__  :					Dict        =       re.compile(R'\[([^\]]+)\]')







def _UpperCamelCase       (     __A    )							->		List[Any]:


      '''simple docstring'''



      UpperCamelCase__										=				_re_indent.search(__A    )
      return "" if search is None else search.groups()[0]







def _UpperCamelCase       (     __A      ,			__A=""      ,			__A=None      ,			__A=None    )							->		Optional[Any]:


      '''simple docstring'''



      UpperCamelCase__										=				0
      UpperCamelCase__										=				code.split("\n"    )
      if start_prompt is not None:
            while not lines[index].startswith(__A    ):
                  index += 1
            UpperCamelCase__										=				["\n".join(lines[:index]    )]
      else:
            UpperCamelCase__										=				[]

      # We split into blocks until we get to the `end_prompt` (or the end of the block).
      UpperCamelCase__										=				[lines[index]]
      index += 1
      while index < len(__A    ) and (end_prompt is None or not lines[index].startswith(__A    )):
            if len(lines[index]    ) > 0 and get_indent(lines[index]    ) == indent_level:
                  if len(__A    ) > 0 and get_indent(current_block[-1]    ).startswith(indent_level + " "    ):
                        current_block.append(lines[index]    )
                        blocks.append("\n".join(__A    )    )
                        if index < len(__A    ) - 1:
                              UpperCamelCase__										=				[lines[index + 1]]
                              index += 1
                        else:
                              UpperCamelCase__										=				[]
                  else:
                        blocks.append("\n".join(__A    )    )
                        UpperCamelCase__										=				[lines[index]]
            else:
                  current_block.append(lines[index]    )
            index += 1

      # Adds current block if it's nonempty.
      if len(__A    ) > 0:
            blocks.append("\n".join(__A    )    )

      # Add final block after end_prompt if provided.
      if end_prompt is not None and index < len(__A    ):
            blocks.append("\n".join(lines[index:]    )    )

      return blocks







def _UpperCamelCase       (     __A    )							->		Optional[Any]:


      '''simple docstring'''



      def _inner(__A    ):
            return key(__A    ).lower().replace("_"      ,			""    )

      return _inner







def _UpperCamelCase       (     __A      ,			__A=None    )							->		Optional[Any]:


      '''simple docstring'''



      def noop(__A    ):
            return x

      if key is None:
            UpperCamelCase__										=				noop
      # Constants are all uppercase, they go first.
      UpperCamelCase__										=				[obj for obj in objects if key(__A    ).isupper()]
      # Classes are not all uppercase but start with a capital, they go second.
      UpperCamelCase__										=				[obj for obj in objects if key(__A    )[0].isupper() and not key(__A    ).isupper()]
      # Functions begin with a lowercase, they go last.
      UpperCamelCase__										=				[obj for obj in objects if not key(__A    )[0].isupper()]

      UpperCamelCase__										=				ignore_underscore(__A    )
      return sorted(__A      ,			key=__A    ) + sorted(__A      ,			key=__A    ) + sorted(__A      ,			key=__A    )







def _UpperCamelCase       (     __A    )							->		Optional[int]:


      '''simple docstring'''



      def _replace(__A    ):
            UpperCamelCase__										=				match.groups()[0]
            if "," not in imports:
                  return F'''[{imports}]'''
            UpperCamelCase__										=				[part.strip().replace("\""      ,			""    ) for part in imports.split(","    )]
            # We will have a final empty element if the line finished with a comma.
            if len(keys[-1]    ) == 0:
                  UpperCamelCase__										=				keys[:-1]
            return "[" + ", ".join([F'''"{k}"''' for k in sort_objects(__A    )]    ) + "]"

      UpperCamelCase__										=				import_statement.split("\n"    )
      if len(__A    ) > 3:
            # Here we have to sort internal imports that are on several lines (one per name):
            # key: [
            #     "object1",
            #     "object2",
            #     ...
            # ]

            # We may have to ignore one or two lines on each side.
            UpperCamelCase__										=				2 if lines[1].strip() == "[" else 1
            UpperCamelCase__										=				[(i, _re_strip_line.search(__A    ).groups()[0]) for i, line in enumerate(lines[idx:-idx]    )]
            UpperCamelCase__										=				sort_objects(__A      ,			key=lambda __A    : x[1]    )
            UpperCamelCase__										=				[lines[x[0] + idx] for x in sorted_indices]
            return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]    )
      elif len(__A    ) == 3:
            # Here we have to sort internal imports that are on one separate line:
            # key: [
            #     "object1", "object2", ...
            # ]
            if _re_bracket_content.search(lines[1]    ) is not None:
                  UpperCamelCase__										=				_re_bracket_content.sub(_replace      ,			lines[1]    )
            else:
                  UpperCamelCase__										=				[part.strip().replace("\""      ,			""    ) for part in lines[1].split(","    )]
                  # We will have a final empty element if the line finished with a comma.
                  if len(keys[-1]    ) == 0:
                        UpperCamelCase__										=				keys[:-1]
                  UpperCamelCase__										=				get_indent(lines[1]    ) + ", ".join([F'''"{k}"''' for k in sort_objects(__A    )]    )
            return "\n".join(__A    )
      else:
            # Finally we have to deal with imports fitting on one line
            UpperCamelCase__										=				_re_bracket_content.sub(_replace      ,			__A    )
            return import_statement







def _UpperCamelCase       (     __A      ,			__A=True    )							->		Optional[int]:


      '''simple docstring'''



      with open(__A      ,			"r"    ) as f:
            UpperCamelCase__										=				f.read()

      if "_import_structure" not in code:
            return

      # Blocks of indent level 0
      UpperCamelCase__										=				split_code_in_indented_blocks(
          __A      ,			start_prompt="_import_structure = {"      ,			end_prompt="if TYPE_CHECKING:"    )

      # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
      for block_idx in range(1      ,			len(__A    ) - 1    ):
            # Check if the block contains some `_import_structure`s thingy to sort.
            UpperCamelCase__										=				main_blocks[block_idx]
            UpperCamelCase__										=				block.split("\n"    )

            # Get to the start of the imports.
            UpperCamelCase__										=				0
            while line_idx < len(__A    ) and "_import_structure" not in block_lines[line_idx]:
                  # Skip dummy import blocks
                  if "import dummy" in block_lines[line_idx]:
                        UpperCamelCase__										=				len(__A    )
                  else:
                        line_idx += 1
            if line_idx >= len(__A    ):
                  continue

            # Ignore beginning and last line: they don't contain anything.
            UpperCamelCase__										=				"\n".join(block_lines[line_idx:-1]    )
            UpperCamelCase__										=				get_indent(block_lines[1]    )
            # Slit the internal block into blocks of indent level 1.
            UpperCamelCase__										=				split_code_in_indented_blocks(__A      ,			indent_level=__A    )
            # We have two categories of import key: list or _import_structure[key].append/extend
            UpperCamelCase__										=				_re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
            # Grab the keys, but there is a trap: some lines are empty or just comments.
            UpperCamelCase__										=				[(pattern.search(__A    ).groups()[0] if pattern.search(__A    ) is not None else None) for b in internal_blocks]
            # We only sort the lines with a key.
            UpperCamelCase__										=				[(i, key) for i, key in enumerate(__A    ) if key is not None]
            UpperCamelCase__										=				[x[0] for x in sorted(__A      ,			key=lambda __A    : x[1]    )]

            # We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
            UpperCamelCase__										=				0
            UpperCamelCase__										=				[]
            for i in range(len(__A    )    ):
                  if keys[i] is None:
                        reordered_blocks.append(internal_blocks[i]    )
                  else:
                        UpperCamelCase__										=				sort_objects_in_import(internal_blocks[sorted_indices[count]]    )
                        reordered_blocks.append(__A    )
                        count += 1

        # And we put our main block back together with its first and last line.
            UpperCamelCase__										=				"\n".join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]    )

      if code != "\n".join(__A    ):
            if check_only:
                  return True
            else:
                  print(F'''Overwriting {file}.'''    )
                  with open(__A      ,			"w"    ) as f:
                        f.write("\n".join(__A    )    )







def _UpperCamelCase       (     __A=True    )							->		Optional[Any]:


      '''simple docstring'''



      UpperCamelCase__										=				[]
      for root, _, files in os.walk(__A    ):
            if "__init__.py" in files:
                  UpperCamelCase__										=				sort_imports(os.path.join(__A      ,			"__init__.py"    )      ,			check_only=__A    )
                  if result:
                        UpperCamelCase__										=				[os.path.join(__A      ,			"__init__.py"    )]
      if len(__A    ) > 0:
            raise ValueError(F'''Would overwrite {len(__A    )} files, run `make style`.'''    )


if __name__ == "__main__":
  a__  :					Optional[Any]        =       argparse.ArgumentParser()
  parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
  a__  :					Union[str, Any]        =       parser.parse_args()

  sort_imports_in_all_inits(check_only=args.check_only)












import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


_UpperCAmelCase				:       Optional[Any]	   =				logging.getLogger(__name__)

@dataclass(frozen=__UpperCamelCase			)
class    lowerCAmelCase							:
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None


@dataclass(frozen=__UpperCamelCase			)
class    lowerCAmelCase							:
 UpperCAmelCase__													=		42
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None
 UpperCAmelCase__													=		None


if is_torch_available():
    import torch
    from torch.utils.data import Dataset

    class    lowerCAmelCase							(					__UpperCamelCase			):
     UpperCAmelCase__													=		42
     def __init__(      self  :      int			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[int] = None			,     UpperCAmelCase  :      List[str]=False			,     UpperCAmelCase  :      bool = False			,     )			->	List[str]:
         lowerCamelCase__    :      int					 =		hans_processors[task]()

         lowerCamelCase__    :      Optional[Any]					 =		os.path.join(
             UpperCAmelCase			,     'cached_{}_{}_{}_{}'.format(
                 'dev' if evaluate else 'train'			,     tokenizer.__class__.__name__			,     str(UpperCAmelCase	)			,     UpperCAmelCase			,     )			,     )
         lowerCamelCase__    :      int					 =		processor.get_labels()
         if tokenizer.__class__ in (
             RobertaTokenizer,
             RobertaTokenizerFast,
             XLMRobertaTokenizer,
             BartTokenizer,
             BartTokenizerFast,
         ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             lowerCamelCase__ ,							lowerCamelCase__    :      Union[str, Any]					 =		label_list[2], label_list[1]
         lowerCamelCase__    :      List[str]					 =		label_list

         # Make sure only the first process in distributed training processes the dataset,
         # and the others will use the cache.
         lowerCamelCase__    :      str					 =		cached_features_file + '.lock'
         with FileLock(UpperCAmelCase	):
             if os.path.exists(UpperCAmelCase	) and not overwrite_cache:
                 logger.info(F"""Loading features from cached file {cached_features_file}"""	)
                 lowerCamelCase__    :      int					 =		torch.load(UpperCAmelCase	)
             else:
                 logger.info(F"""Creating features from dataset file at {data_dir}"""	)

                 lowerCamelCase__    :      str					 =		(
                     processor.get_dev_examples(UpperCAmelCase	) if evaluate else processor.get_train_examples(UpperCAmelCase	)
                 )

                 logger.info('Training examples: %s'			,     len(UpperCAmelCase	)	)
                 lowerCamelCase__    :      Dict					 =		hans_convert_examples_to_features(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)
                 logger.info('Saving features into cached file %s'			,     UpperCAmelCase	)
                 torch.save(self.features			,     UpperCAmelCase	)
     def __len__(      self  :      Optional[int]	)			->	Optional[Any]:
         return len(self.features	)
     def __getitem__(      self  :      Tuple			,     UpperCAmelCase  :      Dict	)			->	InputFeatures:
         return self.features[i]







     def      A_	(      self  :      int	)			->	int:
         return self.label_list


if is_tf_available():
    import tensorflow as tf

    class    lowerCAmelCase							:
     UpperCAmelCase__													=		42
     def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      PreTrainedTokenizer			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Optional[int] = 128			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      bool = False			,     )			->	Union[str, Any]:
         lowerCamelCase__    :      Any					 =		hans_processors[task]()
         lowerCamelCase__    :      Optional[Any]					 =		processor.get_labels()
         if tokenizer.__class__ in (
             RobertaTokenizer,
             RobertaTokenizerFast,
             XLMRobertaTokenizer,
             BartTokenizer,
             BartTokenizerFast,
         ):
             # HACK(label indices are swapped in RoBERTa pretrained model)
             lowerCamelCase__ ,							lowerCamelCase__    :      str					 =		label_list[2], label_list[1]
         lowerCamelCase__    :      Optional[int]					 =		label_list

         lowerCamelCase__    :      int					 =		processor.get_dev_examples(UpperCAmelCase	) if evaluate else processor.get_train_examples(UpperCAmelCase	)
         lowerCamelCase__    :      Union[str, Any]					 =		hans_convert_examples_to_features(UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase			,     UpperCAmelCase	)

         def gen():
             for ex_index, ex in tqdm.tqdm(enumerate(self.features	)			,     desc='convert examples to features'	):
                 if ex_index % 10000 == 0:
                     logger.info('Writing example %d of %d' % (ex_index, len(UpperCAmelCase	))	)

                 yield (
                     {
                         "example_id": 0,
                         "input_ids": ex.input_ids,
                         "attention_mask": ex.attention_mask,
                         "token_type_ids": ex.token_type_ids,
                     },
                     ex.label,
                 )

         lowerCamelCase__    :      Optional[int]					 =		tf.data.Dataset.from_generator(
             UpperCAmelCase			,     (
                 {
                     'example_id': tf.intaa,
                     'input_ids': tf.intaa,
                     'attention_mask': tf.intaa,
                     'token_type_ids': tf.intaa,
                 },
                 tf.intaa,
             )			,     (
                 {
                     'example_id': tf.TensorShape([]	),
                     'input_ids': tf.TensorShape([None, None]	),
                     'attention_mask': tf.TensorShape([None, None]	),
                     'token_type_ids': tf.TensorShape([None, None]	),
                 },
                 tf.TensorShape([]	),
             )			,     )
     def      A_	(      self  :      Any	)			->	Any:
         return self.dataset
     def __len__(      self  :      Tuple	)			->	int:
         return len(self.features	)
     def __getitem__(      self  :      List[str]			,     UpperCAmelCase  :      Any	)			->	InputFeatures:
         return self.features[i]







     def      A_	(      self  :      Dict	)			->	str:
         return self.label_list

class    lowerCAmelCase							(					__UpperCamelCase			):
 def      A_	(      self  :      int			,     UpperCAmelCase  :      List[Any]	)			->	int:
     return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase			,     'heuristics_train_set.txt'	)	)			,     'train'	)
 def      A_	(      self  :      Any			,     UpperCAmelCase  :      int	)			->	List[Any]:
     return self._create_examples(self._read_tsv(os.path.join(UpperCAmelCase			,     'heuristics_evaluation_set.txt'	)	)			,     'dev'	)
 def      A_	(      self  :      Any	)			->	List[Any]:
     return ["contradiction", "entailment", "neutral"]







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      List[str]	)			->	List[str]:
     lowerCamelCase__    :      List[str]					 =		[]
     for i, line in enumerate(UpperCAmelCase	):
         if i == 0:
             continue
         lowerCamelCase__    :      Tuple					 =		'%s-%s' % (set_type, line[0])
         lowerCamelCase__    :      str					 =		line[5]
         lowerCamelCase__    :      Dict					 =		line[6]
         lowerCamelCase__    :      int					 =		line[7][2:] if line[7].startswith('ex'	) else line[7]
         lowerCamelCase__    :      Dict					 =		line[0]
         examples.append(InputExample(guid=UpperCAmelCase			,     text_a=UpperCAmelCase			,     text_b=UpperCAmelCase			,     label=UpperCAmelCase			,     pairID=UpperCAmelCase	)	)
     return examples


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase						,	)			->     Optional[int]:
    lowerCamelCase__    :      int					 =		{label: i for i, label in enumerate(_UpperCAmelCase )}

    lowerCamelCase__    :      List[Any]					 =		[]
    for ex_index, example in tqdm.tqdm(enumerate(_UpperCAmelCase )						,	desc='convert examples to features' ):
        if ex_index % 1_0000 == 0:
            logger.info('Writing example %d' % (ex_index) )

        lowerCamelCase__    :      List[Any]					 =		tokenizer(
            example.text_a						,	example.text_b						,	add_special_tokens=_UpperCAmelCase						,	max_length=_UpperCAmelCase						,	padding='max_length'						,	truncation=_UpperCAmelCase						,	return_overflowing_tokens=_UpperCAmelCase						,	)

        lowerCamelCase__    :      List[str]					 =		label_map[example.label] if example.label in label_map else 0

        lowerCamelCase__    :      Optional[int]					 =		int(example.pairID )

        features.append(InputFeatures(**_UpperCAmelCase						,	label=_UpperCAmelCase						,	pairID=_UpperCAmelCase ) )

    for i, example in enumerate(examples[:5] ):
        logger.info('*** Example ***' )
        logger.info(F"""guid: {example}""" )
        logger.info(F"""features: {features[i]}""" )

    return features


_UpperCAmelCase				:       str	   =				{
    """hans""": 3,
}


_UpperCAmelCase				:       List[Any]	   =				{
    """hans""": HansProcessor,
}





