





import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCAmelCase_					:   str 							=						logging.get_logger(__name__)





@dataclass
class      lowercase__    :
  __UpperCamelCase	     =  field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()  )}  )
  __UpperCamelCase	     =  field(
      metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}  )
  __UpperCamelCase	     =  field(
      default=128     ,					metadata={
          """help""": (
              """The maximum total input sequence length after tokenization. Sequences longer """
              """than this will be truncated, sequences shorter will be padded."""
          )
      }     ,					)
  __UpperCamelCase	     =  field(
      default=__A     ,					metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )







  def 	UpperCAmelCase__		(	self						):
       lowerCAmelCase_					:       List[str]    =		self.task_name.lower()





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """train"""
  __UpperCamelCase	     =  """dev"""
  __UpperCamelCase	     =  """test"""





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42
  __UpperCamelCase	     =  42







  def __init__(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = Split.train    ,						_lowercase = None    ,						):
       warnings.warn(
           """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
           """library. You can have a look at this example script for pointers: """
           """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""    ,						_lowercase    ,						)
       lowerCAmelCase_					:       Any    =		args
       lowerCAmelCase_					:       List[str]    =		glue_processors[args.task_name]()
       lowerCAmelCase_					:       Tuple    =		glue_output_modes[args.task_name]
       if isinstance(_lowercase    ,						_lowercase						):
            try:
                 lowerCAmelCase_					:       Dict    =		Split[mode]
            except KeyError:
                 raise KeyError("""mode is not a valid split name"""						)
        # Load data features from cache or dataset file
       lowerCAmelCase_					:       Optional[int]    =		os.path.join(
           cache_dir if cache_dir is not None else args.data_dir    ,						F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'    ,						)
       lowerCAmelCase_					:       List[str]    =		self.processor.get_labels()
       if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
           "RobertaTokenizer",
           "RobertaTokenizerFast",
           "XLMRobertaTokenizer",
           "BartTokenizer",
           "BartTokenizerFast",
       ):
            # HACK(label indices are swapped in RoBERTa pretrained model)
            lowerCAmelCase_   ,  lowerCAmelCase_					:       List[str]    =		label_list[2], label_list[1]
       lowerCAmelCase_					:       int    =		label_list

       # Make sure only the first process in distributed training processes the dataset,
       # and the others will use the cache.
       lowerCAmelCase_					:       Optional[int]    =		cached_features_file + """.lock"""
       with FileLock(_lowercase						):
            if os.path.exists(_lowercase						) and not args.overwrite_cache:
                 lowerCAmelCase_					:       Dict    =		time.time()
                 lowerCAmelCase_					:       str    =		torch.load(_lowercase						)
                 logger.info(
                     F'Loading features from cached file {cached_features_file} [took %.3f s]'    ,						time.time() - start						)
            else:
                 logger.info(F'Creating features from dataset file at {args.data_dir}'						)

                 if mode == Split.dev:
                      lowerCAmelCase_					:       List[Any]    =		self.processor.get_dev_examples(args.data_dir						)
                 elif mode == Split.test:
                      lowerCAmelCase_					:       Dict    =		self.processor.get_test_examples(args.data_dir						)
                 else:
                      lowerCAmelCase_					:       List[str]    =		self.processor.get_train_examples(args.data_dir						)
                 if limit_length is not None:
                      lowerCAmelCase_					:       Optional[int]    =		examples[:limit_length]
                 lowerCAmelCase_					:       Any    =		glue_convert_examples_to_features(
                     _lowercase    ,						_lowercase    ,						max_length=args.max_seq_length    ,						label_list=_lowercase    ,						output_mode=self.output_mode    ,						)
                 lowerCAmelCase_					:       str    =		time.time()
                 torch.save(self.features    ,						_lowercase						)
                 # ^ This seems to take a lot of time so I want to investigate why and how we can improve.
                 logger.info(
                     F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'						)







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







  def __getitem__(	self    ,						_lowercase						):
       return self.features[i]







  def 	UpperCAmelCase__		(	self						):
       return self.label_list







import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase_					:   Tuple 							=						"""src/diffusers"""
UpperCAmelCase_					:   str 							=						"""."""


# This is to make sure the diffusers module imported is the one in the repo.
UpperCAmelCase_					:   Any 							=						importlib.util.spec_from_file_location(
    """diffusers""",
    os.path.join(DIFFUSERS_PATH, """__init__.py"""),
    submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCAmelCase_					:   Union[str, Any] 							=						spec.loader.load_module()






def 							_lowerCAmelCase (  _a   :					Optional[int]					,   _a   :					Optional[int]			)	->			Tuple:
     return line.startswith(_a			) or len(_a			) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$"""					,   _a			) is not None






def 							_lowerCAmelCase (  _a   :					List[Any]			)	->			int:
     lowerCAmelCase_					:       List[str]    =		object_name.split("""."""			)
     lowerCAmelCase_					:       List[Any]    =		0

     # First let's find the module where our object lives.
     lowerCAmelCase_					:       Optional[int]    =		parts[i]
     while i < len(_a			) and not os.path.isfile(os.path.join(_a					,   F'{module}.py'			)			):
          i += 1
          if i < len(_a			):
               lowerCAmelCase_					:       Dict    =		os.path.join(_a					,   parts[i]			)
     if i >= len(_a			):
          raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'			)

     with open(os.path.join(_a					,   F'{module}.py'			)					,   """r"""					,   encoding="""utf-8"""					,   newline="""\n"""			) as f:
          lowerCAmelCase_					:       Union[str, Any]    =		f.readlines()

     # Now let's find the class / func in the code!
     lowerCAmelCase_					:       List[Any]    =		""""""
     lowerCAmelCase_					:       Optional[int]    =		0
     for name in parts[i + 1 :]:
          while (
              line_index < len(_a			) and re.search(RF'^{indent}(class|def)\s+{name}(\(|\:)'					,   lines[line_index]			) is None
          ):
               line_index += 1
          indent += "    "
          line_index += 1

     if line_index >= len(_a			):
          raise ValueError(F' {object_name} does not match any function or class in {module}.'			)

     # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
     lowerCAmelCase_					:       str    =		line_index
     while line_index < len(_a			) and _should_continue(lines[line_index]					,   _a			):
          line_index += 1
     # Clean up empty lines at the end (if any).
     while len(lines[line_index - 1]			) <= 1:
          line_index -= 1

     lowerCAmelCase_					:       int    =		lines[start_index:line_index]
     return "".join(_a			)


UpperCAmelCase_					:   str 							=						re.compile(r"""^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)""")
UpperCAmelCase_					:   int 							=						re.compile(r"""^\s*(\S+)->(\S+)(\s+.*|$)""")
UpperCAmelCase_					:   List[str] 							=						re.compile(r"""<FILL\s+[^>]*>""")






def 							_lowerCAmelCase (  _a   :					Dict			)	->			List[Any]:
     lowerCAmelCase_					:       int    =		code.split("""\n"""			)
     lowerCAmelCase_					:       Any    =		0
     while idx < len(_a			) and len(lines[idx]			) == 0:
          idx += 1
     if idx < len(_a			):
          return re.search(R"""^(\s*)\S"""					,   lines[idx]			).groups()[0]
     return ""






def 							_lowerCAmelCase (  _a   :					Optional[int]			)	->			Union[str, Any]:
     lowerCAmelCase_					:       Tuple    =		len(get_indent(_a			)			) > 0
     if has_indent:
          lowerCAmelCase_					:       Union[str, Any]    =		F'class Bla:\n{code}'
     lowerCAmelCase_					:       Tuple    =		black.Mode(target_versions={black.TargetVersion.PYaa}					,   line_length=1_19					,   preview=_a			)
     lowerCAmelCase_					:       str    =		black.format_str(_a					,   mode=_a			)
     lowerCAmelCase_   ,  lowerCAmelCase_					:       List[str]    =		style_docstrings_in_code(_a			)
     return result[len("""class Bla:\n"""			) :] if has_indent else result






def 							_lowerCAmelCase (  _a   :					Tuple					,   _a   :					str=False			)	->			Tuple:
     with open(_a					,   """r"""					,   encoding="""utf-8"""					,   newline="""\n"""			) as f:
          lowerCAmelCase_					:       List[str]    =		f.readlines()
     lowerCAmelCase_					:       Union[str, Any]    =		[]
     lowerCAmelCase_					:       Dict    =		0
     # Not a for loop cause `lines` is going to change (if `overwrite=True`).
     while line_index < len(_a			):
          lowerCAmelCase_					:       str    =		_re_copy_warning.search(lines[line_index]			)
          if search is None:
               line_index += 1
               continue

          # There is some copied code here, let's retrieve the original.
          lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       str    =		search.groups()
          lowerCAmelCase_					:       List[Any]    =		find_code_in_diffusers(_a			)
          lowerCAmelCase_					:       Tuple    =		get_indent(_a			)

          lowerCAmelCase_					:       Union[str, Any]    =		line_index + 1 if indent == theoretical_indent else line_index + 2
          lowerCAmelCase_					:       str    =		theoretical_indent
          lowerCAmelCase_					:       Tuple    =		start_index

          # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
          lowerCAmelCase_					:       List[Any]    =		True
          while line_index < len(_a			) and should_continue:
               line_index += 1
               if line_index >= len(_a			):
                    break
               lowerCAmelCase_					:       List[Any]    =		lines[line_index]
               lowerCAmelCase_					:       Tuple    =		_should_continue(_a					,   _a			) and re.search(F'^{indent}# End copy'					,   _a			) is None
          # Clean up empty lines at the end (if any).
          while len(lines[line_index - 1]			) <= 1:
               line_index -= 1

          lowerCAmelCase_					:       str    =		lines[start_index:line_index]
          lowerCAmelCase_					:       Optional[Any]    =		"""""".join(_a			)

          # Remove any nested `Copied from` comments to avoid circular copies
          lowerCAmelCase_					:       Optional[int]    =		[line for line in theoretical_code.split("""\n"""			) if _re_copy_warning.search(_a			) is None]
          lowerCAmelCase_					:       str    =		"""\n""".join(_a			)

          # Before comparing, use the `replace_pattern` on the original code.
          if len(_a			) > 0:
               lowerCAmelCase_					:       List[str]    =		replace_pattern.replace("""with"""					,   """"""			).split(""","""			)
               lowerCAmelCase_					:       Any    =		[_re_replace_pattern.search(_a			) for p in patterns]
               for pattern in patterns:
                    if pattern is None:
                         continue
                    lowerCAmelCase_   ,  lowerCAmelCase_   ,  lowerCAmelCase_					:       List[Any]    =		pattern.groups()
                    lowerCAmelCase_					:       Union[str, Any]    =		re.sub(_a					,   _a					,   _a			)
                    if option.strip() == "all-casing":
                         lowerCAmelCase_					:       Dict    =		re.sub(obja.lower()					,   obja.lower()					,   _a			)
                         lowerCAmelCase_					:       Dict    =		re.sub(obja.upper()					,   obja.upper()					,   _a			)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
               lowerCAmelCase_					:       str    =		blackify(lines[start_index - 1] + theoretical_code			)
               lowerCAmelCase_					:       Optional[Any]    =		theoretical_code[len(lines[start_index - 1]			) :]

          # Test for a diff and act accordingly.
          if observed_code != theoretical_code:
               diffs.append([object_name, start_index]			)
               if overwrite:
                    lowerCAmelCase_					:       Any    =		lines[:start_index] + [theoretical_code] + lines[line_index:]
                    lowerCAmelCase_					:       Dict    =		start_index + 1

     if overwrite and len(_a			) > 0:
          # Warn the user a file has been modified.
          print(F'Detected changes, rewriting {filename}.'			)
          with open(_a					,   """w"""					,   encoding="""utf-8"""					,   newline="""\n"""			) as f:
               f.writelines(_a			)
     return diffs






def 							_lowerCAmelCase (  _a   :					bool = False			)	->			List[Any]:
     lowerCAmelCase_					:       Optional[Any]    =		glob.glob(os.path.join(_a					,   """**/*.py"""			)					,   recursive=_a			)
     lowerCAmelCase_					:       Union[str, Any]    =		[]
     for filename in all_files:
          lowerCAmelCase_					:       Union[str, Any]    =		is_copy_consistent(_a					,   _a			)
          diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
     if not overwrite and len(_a			) > 0:
          lowerCAmelCase_					:       List[Any]    =		"""\n""".join(_a			)
          raise Exception(
              """Found the following copy inconsistencies:\n"""
              + diff
              + """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."""			)


if __name__ == "__main__":
      UpperCAmelCase_					:   Optional[int] 							=						argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      UpperCAmelCase_					:   List[str] 							=						parser.parse_args()

      check_copies(args.fix_and_overwrite)
