
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


SCREAMING_SNAKE_CASE							:List[Any]										=    logging.get_logger(__name__)





@dataclass
class 							UpperCAmelCase				:




	'''simple docstring'''





	snake_case_    =					field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()  )}  )
	snake_case_    =					field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}  )
	snake_case_    =					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."
	        )
	    }      ,      )
	snake_case_    =					field(
	    default=__SCREAMING_SNAKE_CASE      ,      metadata={"help": "Overwrite the cached training and evaluation sets"}  )






	def     UpperCamelCase_  (	self :			int			):
					__A             =			self.task_name.lower()





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"train"
	snake_case_    =					"dev"
	snake_case_    =					"test"





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					42
	snake_case_    =					42
	snake_case_    =					42






	def __init__(	self :			List[Any]							,A :			GlueDataTrainingArguments							,A :			PreTrainedTokenizerBase							,A :			Optional[int] = None							,A :			Union[str, Split] = Split.train							,A :			Optional[str] = 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"							,A							,)
					__A             =			args
					__A             =			glue_processors[args.task_name]()
					__A             =			glue_output_modes[args.task_name]
					if isinstance(A							,A			):
									try:
													__A             =			Split[mode]
									except KeyError:
													raise KeyError("mode is not a valid split name"			)
        # Load data features from cache or dataset file
					__A             =			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}'''							,)
					__A             =			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)
									__A      ,							__A             =			label_list[2], label_list[1]
					__A             =			label_list

					# Make sure only the first process in distributed training processes the dataset,
					# and the others will use the cache.
					__A             =			cached_features_file + ".lock"
					with FileLock(A			):
									if os.path.exists(A			) and not args.overwrite_cache:
													__A             =			time.time()
													__A             =			torch.load(A			)
													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:
																	__A             =			self.processor.get_dev_examples(args.data_dir			)
													elif mode == Split.test:
																	__A             =			self.processor.get_test_examples(args.data_dir			)
													else:
																	__A             =			self.processor.get_train_examples(args.data_dir			)
													if limit_length is not None:
																	__A             =			examples[:limit_length]
													__A             =			glue_convert_examples_to_features(
													    A							,A							,max_length=args.max_seq_length							,label_list=A							,output_mode=self.output_mode							,)
													__A             =			time.time()
													torch.save(self.features							,A			)
													# ^ 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 :			str			):
					return len(self.features			)






	def __getitem__(	self :			Dict							,A :			List[str]			):
					return self.features[i]







	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					return self.label_list


import argparse
import os
import re

import packaging.version


SCREAMING_SNAKE_CASE							:int										=    'examples/'
SCREAMING_SNAKE_CASE							:str										=    {
    'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
    'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__version__ = "VERSION"\n'),
    'setup': (re.compile(R'^(\s*)version\s*=\s*"[^"]+",', re.MULTILINE), R'\1version="VERSION",'),
    'doc': (re.compile(R'^(\s*)release\s*=\s*"[^"]+"$', re.MULTILINE), 'release = "VERSION"\n'),
}
SCREAMING_SNAKE_CASE							:int										=    {
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
SCREAMING_SNAKE_CASE							:List[str]										=    'README.md'


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     Tuple:







				"""simple docstring"""



				with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.read()
				__A      ,							__A             =			REPLACE_PATTERNS[pattern]
				__A             =			replace.replace("VERSION"		,       a_						)
				__A             =			re_pattern.sub(a_		,       a_						)
				with open(a_		,       "w"		,       encoding="utf-8"		,       newline="\n"						) as f:
								f.write(a_						)


def 						UpperCAmelCase      (     a_						)		->     Optional[Any]:







				"""simple docstring"""



				for folder, directories, fnames in os.walk(a_						):
								# Removing some of the folders with non-actively maintained examples from the walk
								if "research_projects" in directories:
												directories.remove("research_projects"						)
								if "legacy" in directories:
												directories.remove("legacy"						)
								for fname in fnames:
												if fname.endswith(".py"						):
																update_version_in_file(os.path.join(a_		,       a_						)		,       a_		,       pattern="examples"						)


def 						UpperCAmelCase      (     a_		,       a_=False						)		->     str:







				"""simple docstring"""



				for pattern, fname in REPLACE_FILES.items():
								update_version_in_file(a_		,       a_		,       a_						)
				if not patch:
								update_version_in_examples(a_						)


def 						UpperCAmelCase      (     )		->     Optional[int]:







				"""simple docstring"""



				__A             =			"🤗 Transformers currently provides the following architectures"
				__A             =			"1. Want to contribute a new model?"
				with open(a_		,       "r"		,       encoding="utf-8"		,       newline="\n"						) as f:
								__A             =			f.readlines()

				# Find the start of the list.
				__A             =			0
				while not lines[start_index].startswith(_start_prompt						):
								start_index += 1
				start_index += 1

				__A             =			start_index
				# Update the lines in the model list.
				while not lines[index].startswith(_end_prompt						):
								if lines[index].startswith("1."						):
												__A             =			lines[index].replace(
												    "https://huggingface.co/docs/diffusers/main/model_doc"		,       "https://huggingface.co/docs/diffusers/model_doc"		,       )
								index += 1

				with open(a_		,       "w"		,       encoding="utf-8"		,       newline="\n"						) as f:
								f.writelines(a_						)


def 						UpperCAmelCase      (     )		->     Dict:







				"""simple docstring"""



				with open(REPLACE_FILES["init"]		,       "r"						) as f:
								__A             =			f.read()
				__A             =			REPLACE_PATTERNS["init"][0].search(a_						).groups()[0]
				return packaging.version.parse(a_						)


def 						UpperCAmelCase      (     a_=False						)		->     str:







				"""simple docstring"""



				__A             =			get_version()
				if patch and default_version.is_devrelease:
								raise ValueError("Can't create a patch version from the dev branch, checkout a released version!"						)
				if default_version.is_devrelease:
								__A             =			default_version.base_version
				elif patch:
								__A             =			F'''{default_version.major}.{default_version.minor}.{default_version.micro + 1}'''
				else:
								__A             =			F'''{default_version.major}.{default_version.minor + 1}.0'''

				# Now let's ask nicely if that's the right one.
				__A             =			input(F'''Which version are you releasing? [{default_version}]'''						)
				if len(a_						) == 0:
								__A             =			default_version

				print(F'''Updating version to {version}.'''						)
				global_version_update(a_		,       patch=a_						)


def 						UpperCAmelCase      (     )		->     Optional[int]:







				"""simple docstring"""



				__A             =			get_version()
				__A             =			F'''{current_version.major}.{current_version.minor + 1}.0.dev0'''
				__A             =			current_version.base_version

				# Check with the user we got that right.
				__A             =			input(F'''Which version are we developing now? [{dev_version}]'''						)
				if len(a_						) == 0:
								__A             =			dev_version

				print(F'''Updating version to {version}.'''						)
				global_version_update(a_						)


#    print("Cleaning main README, don't forget to run `make fix-copies`.")
#    clean_main_ref_in_model_list()


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    argparse.ArgumentParser()
					parser.add_argument('--post_release', action='store_true', help='Whether this is pre or post release.')
					parser.add_argument('--patch', action='store_true', help='Whether or not this is a patch release.')
					SCREAMING_SNAKE_CASE							:List[str]										=    parser.parse_args()
					if not args.post_release:
										pre_release_work(patch=args.patch)
					elif args.patch:
										print('Nothing to do after a patch :-)')
					else:
										post_release_work()
