
import argparse
import os
import re

import packaging.version


A	:      List[str]  					=					'examples/'
A	:      Union[str, 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	:      Dict  					=					{
    'init': 'src/diffusers/__init__.py',
    'setup': 'setup.py',
}
A	:      List[Any]  					=					'README.md'


def        __lowerCAmelCase     (     a__		,       a__		,       a__    )			->    Tuple:






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


def        __lowerCAmelCase     (     a__    )			->    Dict:






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


def        __lowerCAmelCase     (     a__		,       a__=False    )			->    Optional[Any]:






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


def        __lowerCAmelCase     (     )			->    Tuple:
				__a     				=       '''🤗 Transformers currently provides the following architectures'''
				__a     				=       '''1. Want to contribute a new model?'''
				with open(lowerCAmelCase__		,       '''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(lowerCAmelCase__		,       '''w'''		,       encoding='''utf-8'''		,       newline='''\n'''    ) as f:
								f.writelines(lowerCAmelCase__    )


def        __lowerCAmelCase     (     )			->    Tuple:






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


def        __lowerCAmelCase     (     a__=False    )			->    Tuple:
				__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(lowerCAmelCase__    ) == 0:
								__a     				=       default_version

				print(F"""Updating version to {version}."""    )
				global_version_update(lowerCAmelCase__		,       patch=lowerCAmelCase__    )


def        __lowerCAmelCase     (     )			->    Optional[Any]:
				__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(lowerCAmelCase__    ) == 0:
								__a     				=       dev_version

				print(F"""Updating version to {version}."""    )
				global_version_update(lowerCAmelCase__    )


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


if __name__ == "__main__":
					A	:      Tuple  					=					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	:      int  					=					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()




import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Dict								=     LEDTokenizer
				A_   :     Union[str, Any]								=     LEDTokenizerFast
				A_   :     Tuple								=     True


				def __UpperCamelCase						(							self      :  str       )		->     str:
											super().setUp()
											A      						=    [
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											A      						=    dict(zip(__UpperCamelCase						, range(len(__UpperCamelCase       )       )       )       )
											A      						=    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											A      						=    {'unk_token': '<unk>'}

											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['vocab_file']       )
											A      						=    os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['merges_file']       )
											with open(self.vocab_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write(json.dumps(__UpperCamelCase       ) + '\n'       )
											with open(self.merges_file						, 'w'						, encoding='utf-8'       ) as fp:
																		fp.write('\n'.join(__UpperCamelCase       )       )


				def __UpperCamelCase						(							self      :  str						, **__UpperCamelCase      :  str       )		->     Tuple:
											kwargs.update(self.special_tokens_map       )
											return self.tokenizer_class.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  List[str]						, **__UpperCamelCase      :  List[Any]       )		->     str:
											kwargs.update(self.special_tokens_map       )
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Optional[Any]						, __UpperCamelCase      :  Optional[int]       )		->     Dict:
											return "lower newer", "lower newer"


				@cached_property
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Any:
											return LEDTokenizer.from_pretrained('allenai/led-base-16384'       )


				@cached_property
				def __UpperCamelCase						(							self      :  str       )		->     str:
											return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'       )


				@require_torch
				def __UpperCamelCase						(							self      :  str       )		->     Optional[Any]:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											A      						=    [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, max_length=len(__UpperCamelCase       )						, padding=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )

																		self.assertEqual((2, 9)						, batch.input_ids.shape       )
																		self.assertEqual((2, 9)						, batch.attention_mask.shape       )
																		A      						=    batch.input_ids.tolist()[0]
																		self.assertListEqual(__UpperCamelCase						, __UpperCamelCase       )


				@require_torch
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     str:
											A      						=    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIn('input_ids'						, __UpperCamelCase       )
																		self.assertIn('attention_mask'						, __UpperCamelCase       )
																		self.assertNotIn('labels'						, __UpperCamelCase       )
																		self.assertNotIn('decoder_attention_mask'						, __UpperCamelCase       )


				@require_torch
				def __UpperCamelCase						(							self      :  List[Any]       )		->     Union[str, Any]:
											A      						=    [
											    'Summary of the text.',
											    'Another summary.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(text_target=__UpperCamelCase						, max_length=32						, padding='max_length'						, return_tensors='pt'       )
																		self.assertEqual(32						, targets['input_ids'].shape[1]       )


				@require_torch
				def __UpperCamelCase						(							self      :  List[str]       )		->     Any:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(
																		    ['I am a small frog' * 1_024, 'I am a small frog']						, padding=__UpperCamelCase						, truncation=__UpperCamelCase						, return_tensors='pt'       )
																		self.assertIsInstance(__UpperCamelCase						, __UpperCamelCase       )
																		self.assertEqual(batch.input_ids.shape						, (2, 5_122)       )


				@require_torch
				def __UpperCamelCase						(							self      :  int       )		->     Optional[Any]:
											A      						=    ['A long paragraph for summarization.']
											A      						=    [
											    'Summary of the text.',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    tokenizer(__UpperCamelCase						, return_tensors='pt'       )
																		A      						=    tokenizer(text_target=__UpperCamelCase						, return_tensors='pt'       )
																		A      						=    inputs['input_ids']
																		A      						=    targets['input_ids']
																		self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
																		self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
																		self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
																		self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )


				@require_torch
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		A      						=    ['Summary of the text.', 'Another summary.']
																		A      						=    [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																		A      						=    tokenizer(__UpperCamelCase						, padding=__UpperCamelCase       )
																		A      						=    [[0] * len(__UpperCamelCase       ) for x in encoded_output['input_ids']]
																		A      						=    tokenizer.pad(__UpperCamelCase       )
																		self.assertSequenceEqual(outputs['global_attention_mask']						, __UpperCamelCase       )


				def __UpperCamelCase						(							self      :  str       )		->     int:
											pass


				def __UpperCamelCase						(							self      :  Dict       )		->     Any:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
																									A      						=    self.rust_tokenizer_class.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																									A      						=    self.tokenizer_class.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																									A      						=    'A, <mask> AllenNLP sentence.'
																									A      						=    tokenizer_r.encode_plus(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase       )
																									A      						=    tokenizer_p.encode_plus(__UpperCamelCase						, add_special_tokens=__UpperCamelCase						, return_token_type_ids=__UpperCamelCase       )
																									self.assertEqual(sum(tokens_r['token_type_ids']       )						, sum(tokens_p['token_type_ids']       )       )
																									self.assertEqual(
																									    sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )						, sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )						, )

																									A      						=    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
																									A      						=    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )
																									self.assertSequenceEqual(tokens_p['input_ids']						, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )
																									self.assertSequenceEqual(tokens_r['input_ids']						, [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]       )

																									self.assertSequenceEqual(
																									    __UpperCamelCase						, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
																									self.assertSequenceEqual(
																									    __UpperCamelCase						, ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )