



import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__snake_case     :List[str]			       ='src/diffusers'

# Matches is_xxx_available()
__snake_case     :Union[str, Any]			       =re.compile(r'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
__snake_case     :Optional[int]			       =re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


__snake_case     :Union[str, Any]			       ='\n{0} = None\n'

__snake_case     :List[str]			       ='\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


__snake_case     :str			       ='\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]				)       ->							List[str]:

							'''simple docstring'''






							A      						=    _re_backend.findall(lowerCAmelCase__				)
							if len(lowerCAmelCase__				) == 0:
														return None

							return "_and_".join(lowerCAmelCase__				)






def  lowerCamelCase_	(      )       ->							Tuple:

							'''simple docstring'''






							with open(os.path.join(lowerCAmelCase__			,	'__init__.py'				)			,	'r'			,	encoding='utf-8'			,	newline='\n'				) as f:
														A      						=    f.readlines()

							# Get to the point we do the actual imports for type checking
							A      						=    0
							A      						=    {}
							# Go through the end of the file
							while line_index < len(lowerCAmelCase__				):
														# If the line contains is_backend_available, we grab all objects associated with the `else` block
														A      						=    find_backend(lines[line_index]				)
														if backend is not None:
																					while not lines[line_index].startswith('else:'				):
																												line_index += 1
																					line_index += 1
																					A      						=    []
																					# Until we unindent, add backend objects to the list
																					while line_index < len(lowerCAmelCase__				) and len(lines[line_index]				) > 1:
																												A      						=    lines[line_index]
																												A      						=    _re_single_line_import.search(lowerCAmelCase__				)
																												if single_line_import_search is not None:
																																			objects.extend(single_line_import_search.groups()[0].split(', '				)				)
																												elif line.startswith(' ' * 8				):
																																			objects.append(line[8:-2]				)
																												line_index += 1

																					if len(lowerCAmelCase__				) > 0:
																												A      						=    objects
														else:
																					line_index += 1

							return backend_specific_objects






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Tuple				)       ->							Dict:

							'''simple docstring'''






							if name.isupper():
														return DUMMY_CONSTANT.format(lowerCAmelCase__				)
							elif name.islower():
														return DUMMY_FUNCTION.format(lowerCAmelCase__			,	lowerCAmelCase__				)
							else:
														return DUMMY_CLASS.format(lowerCAmelCase__			,	lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any=None				)       ->							int:

							'''simple docstring'''






							if backend_specific_objects is None:
														A      						=    read_init()
							# For special correspondence backend to module name as used in the function requires_modulename
							A      						=    {}

							for backend, objects in backend_specific_objects.items():
														A      						=    '[' + ', '.join(F'''"{b}"''' for b in backend.split('_and_'				)				) + ']'
														A      						=    '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
														dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
														dummy_file += "\n".join([create_dummy_object(lowerCAmelCase__			,	lowerCAmelCase__				) for o in objects]				)
														A      						=    dummy_file

							return dummy_files






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict=False				)       ->							Any:

							'''simple docstring'''






							A      						=    create_dummy_files()
							# For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
							A      						=    {'torch': 'pt'}

							# Locate actual dummy modules and read their content.
							A      						=    os.path.join(lowerCAmelCase__			,	'utils'				)
							A      						=    {
							    backend: os.path.join(lowerCAmelCase__			,	F'''dummy_{short_names.get(lowerCAmelCase__			,	lowerCAmelCase__				)}_objects.py'''				)
							    for backend in dummy_files.keys()
							}

							A      						=    {}
							for backend, file_path in dummy_file_paths.items():
														if os.path.isfile(lowerCAmelCase__				):
																					with open(lowerCAmelCase__			,	'r'			,	encoding='utf-8'			,	newline='\n'				) as f:
																												A      						=    f.read()
														else:
																					A      						=    ''

							for backend in dummy_files.keys():
														if dummy_files[backend] != actual_dummies[backend]:
																					if overwrite:
																												print(
																												    F'''Updating diffusers.utils.dummy_{short_names.get(lowerCAmelCase__			,	lowerCAmelCase__				)}_objects.py as the main '''
																												    '__init__ has new objects.'				)
																												with open(dummy_file_paths[backend]			,	'w'			,	encoding='utf-8'			,	newline='\n'				) as f:
																																			f.write(dummy_files[backend]				)
																					else:
																												raise ValueError(
																												    'The main __init__ has objects that are not present in '
																												    F'''diffusers.utils.dummy_{short_names.get(lowerCAmelCase__			,	lowerCAmelCase__				)}_objects.py. Run `make fix-copies` '''
																												    'to fix this.'				)


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

				check_dummies(args.fix_and_overwrite)




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>']       )