





import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


_lowercase		:  int			       =get_tests_dir("""fixtures/dummy_feature_extractor_config.json""")
_lowercase		:  List[str]			       =get_tests_dir("""fixtures/vocab.json""")
_lowercase		:  str			       =get_tests_dir("""fixtures""")






class 						UpperCamelCase_  (  unittest.TestCase							):
					_a      :						List[Any]						    =					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']



					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Tuple 						=      0



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Union[str, Any] 						=      AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'						)
							self.assertIsInstance(UpperCamelCase_       ,      UpperCamelCase_						)



					def   __a     (		self     :     Optional[int]						):



							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase_	:     str 						=      WavaVecaConfig()
									lowerCamelCase_	:     str 						=      AutoProcessor.from_pretrained('facebook/wav2vec2-base-960h'						)

									# save in new folder
									model_config.save_pretrained(UpperCamelCase_						)
									processor.save_pretrained(UpperCamelCase_						)

									lowerCamelCase_	:     List[str] 						=      AutoProcessor.from_pretrained(UpperCamelCase_						)

							self.assertIsInstance(UpperCamelCase_       ,      UpperCamelCase_						)



					def   __a     (		self     :     Union[str, Any]						):



							with tempfile.TemporaryDirectory() as tmpdirname:
									# copy relevant files
									copyfile(UpperCamelCase_       ,      os.path.join(UpperCamelCase_       ,      UpperCamelCase_						)						)
									copyfile(UpperCamelCase_       ,      os.path.join(UpperCamelCase_       ,      'vocab.json'						)						)

									lowerCamelCase_	:     List[Any] 						=      AutoProcessor.from_pretrained(UpperCamelCase_						)

							self.assertIsInstance(UpperCamelCase_       ,      UpperCamelCase_						)



					def   __a     (		self     :     Dict						):



							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase_	:     List[str] 						=      WavaVecaFeatureExtractor()
									lowerCamelCase_	:     Optional[Any] 						=      AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'						)

									lowerCamelCase_	:     List[str] 						=      WavaVecaProcessor(UpperCamelCase_       ,      UpperCamelCase_						)

									# save in new folder
									processor.save_pretrained(UpperCamelCase_						)

									# drop `processor_class` in tokenizer
									with open(os.path.join(UpperCamelCase_       ,      UpperCamelCase_						)       ,      'r'						) as f:
											lowerCamelCase_	:     Union[str, Any] 						=      json.load(UpperCamelCase_						)
											config_dict.pop('processor_class'						)

									with open(os.path.join(UpperCamelCase_       ,      UpperCamelCase_						)       ,      'w'						) as f:
											f.write(json.dumps(UpperCamelCase_						)						)

									lowerCamelCase_	:     Optional[int] 						=      AutoProcessor.from_pretrained(UpperCamelCase_						)

							self.assertIsInstance(UpperCamelCase_       ,      UpperCamelCase_						)



					def   __a     (		self     :     int						):



							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase_	:     str 						=      WavaVecaFeatureExtractor()
									lowerCamelCase_	:     Union[str, Any] 						=      AutoTokenizer.from_pretrained('facebook/wav2vec2-base-960h'						)

									lowerCamelCase_	:     Any 						=      WavaVecaProcessor(UpperCamelCase_       ,      UpperCamelCase_						)

									# save in new folder
									processor.save_pretrained(UpperCamelCase_						)

									# drop `processor_class` in feature extractor
									with open(os.path.join(UpperCamelCase_       ,      UpperCamelCase_						)       ,      'r'						) as f:
											lowerCamelCase_	:     Tuple 						=      json.load(UpperCamelCase_						)
											config_dict.pop('processor_class'						)

									with open(os.path.join(UpperCamelCase_       ,      UpperCamelCase_						)       ,      'w'						) as f:
											f.write(json.dumps(UpperCamelCase_						)						)

									lowerCamelCase_	:     Optional[Any] 						=      AutoProcessor.from_pretrained(UpperCamelCase_						)

							self.assertIsInstance(UpperCamelCase_       ,      UpperCamelCase_						)



					def   __a     (		self     :     Optional[Any]						):



							with tempfile.TemporaryDirectory() as tmpdirname:
									lowerCamelCase_	:     Optional[int] 						=      WavaVecaConfig(processor_class='Wav2Vec2Processor'						)
									model_config.save_pretrained(UpperCamelCase_						)
									# copy relevant files
									copyfile(UpperCamelCase_       ,      os.path.join(UpperCamelCase_       ,      'vocab.json'						)						)
									# create emtpy sample processor
									with open(os.path.join(UpperCamelCase_       ,      UpperCamelCase_						)       ,      'w'						) as f:
											f.write('{}'						)

									lowerCamelCase_	:     Optional[Any] 						=      AutoProcessor.from_pretrained(UpperCamelCase_						)

							self.assertIsInstance(UpperCamelCase_       ,      UpperCamelCase_						)



					def   __a     (		self     :     Any						):



							with self.assertRaises(UpperCamelCase_						):
									lowerCamelCase_	:     Union[str, Any] 						=      AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'						)
							# If remote code is disabled, we can't load this config.
							with self.assertRaises(UpperCamelCase_						):
									lowerCamelCase_	:     List[Any] 						=      AutoProcessor.from_pretrained(
									    'hf-internal-testing/test_dynamic_processor'       ,      trust_remote_code=UpperCamelCase_						)

							lowerCamelCase_	:     Optional[Any] 						=      AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'       ,      trust_remote_code=UpperCamelCase_						)
							self.assertTrue(processor.special_attribute_present						)
							self.assertEqual(processor.__class__.__name__       ,      'NewProcessor'						)

							lowerCamelCase_	:     Any 						=      processor.feature_extractor
							self.assertTrue(feature_extractor.special_attribute_present						)
							self.assertEqual(feature_extractor.__class__.__name__       ,      'NewFeatureExtractor'						)

							lowerCamelCase_	:     Any 						=      processor.tokenizer
							self.assertTrue(tokenizer.special_attribute_present						)
							if is_tokenizers_available():
									self.assertEqual(tokenizer.__class__.__name__       ,      'NewTokenizerFast'						)

									# Test we can also load the slow version
									lowerCamelCase_	:     Dict 						=      AutoProcessor.from_pretrained(
									    'hf-internal-testing/test_dynamic_processor'       ,      trust_remote_code=UpperCamelCase_       ,      use_fast=UpperCamelCase_						)
									lowerCamelCase_	:     List[Any] 						=      new_processor.tokenizer
									self.assertTrue(new_tokenizer.special_attribute_present						)
									self.assertEqual(new_tokenizer.__class__.__name__       ,      'NewTokenizer'						)
							else:
									self.assertEqual(tokenizer.__class__.__name__       ,      'NewTokenizer'						)



					def   __a     (		self     :     List[Any]						):



							try:
									AutoConfig.register('custom'       ,      UpperCamelCase_						)
									AutoFeatureExtractor.register(UpperCamelCase_       ,      UpperCamelCase_						)
									AutoTokenizer.register(UpperCamelCase_       ,      slow_tokenizer_class=UpperCamelCase_						)
									AutoProcessor.register(UpperCamelCase_       ,      UpperCamelCase_						)
									# Trying to register something existing in the Transformers library will raise an error
									with self.assertRaises(UpperCamelCase_						):
											AutoProcessor.register(UpperCamelCase_       ,      UpperCamelCase_						)

									# Now that the config is registered, it can be used as any other config with the auto-API
									lowerCamelCase_	:     str 						=      CustomFeatureExtractor.from_pretrained(UpperCamelCase_						)

									with tempfile.TemporaryDirectory() as tmp_dir:
											lowerCamelCase_	:     List[Any] 						=      os.path.join(UpperCamelCase_       ,      'vocab.txt'						)
											with open(UpperCamelCase_       ,      'w'       ,      encoding='utf-8'						) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]						)						)
											lowerCamelCase_	:     Any 						=      CustomTokenizer(UpperCamelCase_						)

									lowerCamelCase_	:     Any 						=      CustomProcessor(UpperCamelCase_       ,      UpperCamelCase_						)

									with tempfile.TemporaryDirectory() as tmp_dir:
											processor.save_pretrained(UpperCamelCase_						)
											lowerCamelCase_	:     Tuple 						=      AutoProcessor.from_pretrained(UpperCamelCase_						)
											self.assertIsInstance(UpperCamelCase_       ,      UpperCamelCase_						)

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
											del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
											del TOKENIZER_MAPPING._extra_content[CustomConfig]
									if CustomConfig in PROCESSOR_MAPPING._extra_content:
											del PROCESSOR_MAPPING._extra_content[CustomConfig]



					def   __a     (		self     :     int						):






							class 						UpperCamelCase_  (  lowercase_							):
												_a      :						List[Any]						    =					False






							class 						UpperCamelCase_  (  lowercase_							):
												_a      :						str						    =					False






							class 						UpperCamelCase_  (  lowercase_							):
												_a      :						List[str]						    =					'AutoFeatureExtractor'
												_a      :						int						    =					'AutoTokenizer'
												_a      :						Union[str, Any]						    =					False

							try:
									AutoConfig.register('custom'       ,      UpperCamelCase_						)
									AutoFeatureExtractor.register(UpperCamelCase_       ,      UpperCamelCase_						)
									AutoTokenizer.register(UpperCamelCase_       ,      slow_tokenizer_class=UpperCamelCase_						)
									AutoProcessor.register(UpperCamelCase_       ,      UpperCamelCase_						)
									# If remote code is not set, the default is to use local classes.
									lowerCamelCase_	:     List[Any] 						=      AutoProcessor.from_pretrained('hf-internal-testing/test_dynamic_processor'						)
									self.assertEqual(processor.__class__.__name__       ,      'NewProcessor'						)
									self.assertFalse(processor.special_attribute_present						)
									self.assertFalse(processor.feature_extractor.special_attribute_present						)
									self.assertFalse(processor.tokenizer.special_attribute_present						)

									# If remote code is disabled, we load the local ones.
									lowerCamelCase_	:     Union[str, Any] 						=      AutoProcessor.from_pretrained(
									    'hf-internal-testing/test_dynamic_processor'       ,      trust_remote_code=UpperCamelCase_						)
									self.assertEqual(processor.__class__.__name__       ,      'NewProcessor'						)
									self.assertFalse(processor.special_attribute_present						)
									self.assertFalse(processor.feature_extractor.special_attribute_present						)
									self.assertFalse(processor.tokenizer.special_attribute_present						)

									# If remote is enabled, we load from the Hub.
									lowerCamelCase_	:     str 						=      AutoProcessor.from_pretrained(
									    'hf-internal-testing/test_dynamic_processor'       ,      trust_remote_code=UpperCamelCase_						)
									self.assertEqual(processor.__class__.__name__       ,      'NewProcessor'						)
									self.assertTrue(processor.special_attribute_present						)
									self.assertTrue(processor.feature_extractor.special_attribute_present						)
									self.assertTrue(processor.tokenizer.special_attribute_present						)

							finally:
									if "custom" in CONFIG_MAPPING._extra_content:
											del CONFIG_MAPPING._extra_content["custom"]
									if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
											del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
									if CustomConfig in TOKENIZER_MAPPING._extra_content:
											del TOKENIZER_MAPPING._extra_content[CustomConfig]
									if CustomConfig in PROCESSOR_MAPPING._extra_content:
											del PROCESSOR_MAPPING._extra_content[CustomConfig]



					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Optional[int] 						=      AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-bert'						)
							self.assertEqual(processor.__class__.__name__       ,      'BertTokenizerFast'						)





					def   __a     (		self     :     Any						):
							lowerCamelCase_	:     Union[str, Any] 						=      AutoProcessor.from_pretrained('hf-internal-testing/tiny-random-convnext'						)
							self.assertEqual(processor.__class__.__name__       ,      'ConvNextImageProcessor'						)






@is_staging_test
class 						UpperCamelCase_  (  unittest.TestCase							):
					_a      :						Optional[Any]						    =					['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']



					@classmethod
					def   __a     (		cls     :     Dict						):
							lowerCamelCase_	:     Any 						=      TOKEN
							HfFolder.save_token(UpperCamelCase_						)



					@classmethod
					def   __a     (		cls     :     Tuple						):



							try:
									delete_repo(token=cls._token       ,      repo_id='test-processor'						)
							except HTTPError:
									pass

							try:
									delete_repo(token=cls._token       ,      repo_id='valid_org/test-processor-org'						)
							except HTTPError:
									pass

							try:
									delete_repo(token=cls._token       ,      repo_id='test-dynamic-processor'						)
							except HTTPError:
									pass



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     List[Any] 						=      WavaVecaProcessor.from_pretrained(UpperCamelCase_						)
							with tempfile.TemporaryDirectory() as tmp_dir:
									processor.save_pretrained(
									    os.path.join(UpperCamelCase_       ,      'test-processor'						)       ,      push_to_hub=UpperCamelCase_       ,      use_auth_token=self._token						)

									lowerCamelCase_	:     List[str] 						=      WavaVecaProcessor.from_pretrained(F"{USER}/test-processor"						)
									for k, v in processor.feature_extractor.__dict__.items():
											self.assertEqual(UpperCamelCase_       ,      getattr(new_processor.feature_extractor       ,      UpperCamelCase_						)						)
									self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,      processor.tokenizer.get_vocab()						)



					def   __a     (		self     :     int						):
							lowerCamelCase_	:     Optional[Any] 						=      WavaVecaProcessor.from_pretrained(UpperCamelCase_						)

							with tempfile.TemporaryDirectory() as tmp_dir:
									processor.save_pretrained(
									    os.path.join(UpperCamelCase_       ,      'test-processor-org'						)       ,      push_to_hub=UpperCamelCase_       ,      use_auth_token=self._token       ,      organization='valid_org'       ,      )

									lowerCamelCase_	:     List[Any] 						=      WavaVecaProcessor.from_pretrained('valid_org/test-processor-org'						)
									for k, v in processor.feature_extractor.__dict__.items():
											self.assertEqual(UpperCamelCase_       ,      getattr(new_processor.feature_extractor       ,      UpperCamelCase_						)						)
									self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,      processor.tokenizer.get_vocab()						)





					def   __a     (		self     :     str						):
							CustomFeatureExtractor.register_for_auto_class()
							CustomTokenizer.register_for_auto_class()
							CustomProcessor.register_for_auto_class()

							lowerCamelCase_	:     Any 						=      CustomFeatureExtractor.from_pretrained(UpperCamelCase_						)

							with tempfile.TemporaryDirectory() as tmp_dir:
									lowerCamelCase_	:     Dict 						=      os.path.join(UpperCamelCase_       ,      'vocab.txt'						)
									with open(UpperCamelCase_       ,      'w'       ,      encoding='utf-8'						) as vocab_writer:
											vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]						)						)
									lowerCamelCase_	:     Union[str, Any] 						=      CustomTokenizer(UpperCamelCase_						)

							lowerCamelCase_	:     Optional[Any] 						=      CustomProcessor(UpperCamelCase_       ,      UpperCamelCase_						)

							with tempfile.TemporaryDirectory() as tmp_dir:
									create_repo(F"{USER}/test-dynamic-processor"       ,      token=self._token						)
									lowerCamelCase_	:     List[Any] 						=      Repository(UpperCamelCase_       ,      clone_from=F"{USER}/test-dynamic-processor"       ,      token=self._token						)
									processor.save_pretrained(UpperCamelCase_						)

									# This has added the proper auto_map field to the feature extractor config
									self.assertDictEqual(
									    processor.feature_extractor.auto_map       ,      {
									        'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor',
									        'AutoProcessor': 'custom_processing.CustomProcessor',
									    }       ,      )

									# This has added the proper auto_map field to the tokenizer config
									with open(os.path.join(UpperCamelCase_       ,      'tokenizer_config.json'						)						) as f:
											lowerCamelCase_	:     Optional[int] 						=      json.load(UpperCamelCase_						)
									self.assertDictEqual(
									    tokenizer_config['auto_map']       ,      {
									        'AutoTokenizer': ['custom_tokenization.CustomTokenizer', None],
									        'AutoProcessor': 'custom_processing.CustomProcessor',
									    }       ,      )

									# The code has been copied from fixtures
									self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase_       ,      'custom_feature_extraction.py'						)						)						)
									self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase_       ,      'custom_tokenization.py'						)						)						)
									self.assertTrue(os.path.isfile(os.path.join(UpperCamelCase_       ,      'custom_processing.py'						)						)						)

									repo.push_to_hub()

							lowerCamelCase_	:     str 						=      AutoProcessor.from_pretrained(F"{USER}/test-dynamic-processor"       ,      trust_remote_code=UpperCamelCase_						)
							# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
							self.assertEqual(new_processor.__class__.__name__       ,      'CustomProcessor'						)








import argparse
import os
import re

import packaging.version


__a   :							Tuple						= "examples/"
__a   :							Any						= {
    "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"),
}
__a   :							Tuple						= {
    "init": "src/transformers/__init__.py",
    "setup": "setup.py",
}
__a   :							List[str]						= "README.md"



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      List[str]      ,					__lowercase :      Optional[Any]     )    ->   List[Any]:





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[str]     )    ->   Dict:





    """simple docstring"""

    for folder, directories, fnames in os.walk(__lowercase     ):
        # 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(__lowercase      ,					__lowercase     )      ,					__lowercase      ,					pattern="""examples"""     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      Optional[int]=False     )    ->   Dict:





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			)    ->   Union[str, Any]:





    """simple docstring"""

    __A       =						"""🤗 Transformers currently provides the following architectures"""
    __A       =						"""1. Want to contribute a new model?"""
    with open(__lowercase      ,					"""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/transformers/main/model_doc"""      ,					"""https://huggingface.co/docs/transformers/model_doc"""      ,					)
        index += 1

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



def       _SCREAMING_SNAKE_CASE    (			)    ->   Dict:





    """simple docstring"""

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str=False     )    ->   Optional[Any]:





    """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(__lowercase     ) == 0:
        __A       =						default_version

    print(f"Updating version to {version}."     )
    global_version_update(__lowercase      ,					patch=__lowercase     )
    if not patch:
        print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
        clean_main_ref_in_model_list()



def       _SCREAMING_SNAKE_CASE    (			)    ->   List[str]:





    """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(__lowercase     ) == 0:
        __A       =						dev_version

    print(f"Updating version to {version}."     )
    global_version_update(__lowercase     )
    print("""Cleaning main README, don't forget to run `make fix-copies`."""     )
    clean_main_ref_in_model_list()


if __name__ == "__main__":
  __a   :							str						= 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.")
  __a   :							Union[str, Any]						= 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()




