




'''simple docstring'''


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
_snake_case   :   Optional[int]     							=	'src/diffusers'
_snake_case   :   List[Any]     							=	'.'


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





							'''simple docstring'''


							return line.startswith(__A   ) or len(__A   ) <= 1 or re.search(R'''^\s*\)(\s*->.*:|:)\s*$'''				,		__A   ) is not None
def 	snake_case_					(UpperCamelCase :						str   ):





							'''simple docstring'''


							_a			  =       object_name.split('''.'''   )
							_a			  =       0

							# First let's find the module where our object lives.
							_a			  =       parts[i]
							while i < len(__A   ) and not os.path.isfile(os.path.join(__A				,		f'{module}.py'   )   ):
														i += 1
														if i < len(__A   ):
																					_a			  =       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:
														_a			  =       f.readlines()

							# Now let's find the class / func in the code!
							_a			  =       ''''''
							_a			  =       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).
							_a			  =       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

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


_snake_case   :   Optional[Any]     							=	re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
_snake_case   :   List[str]     							=	re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
_snake_case   :   Optional[int]     							=	re.compile(R'<FILL\s+[^>]*>')
def 	snake_case_					(UpperCamelCase :						Optional[int]   ):





							'''simple docstring'''


							_a			  =       code.split('''\n'''   )
							_a			  =       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 	snake_case_					(UpperCamelCase :						Dict   ):





							'''simple docstring'''


							_a			  =       len(get_indent(__A   )   ) > 0
							if has_indent:
														_a			  =       f'class Bla:\n{code}'
							_a			  =       black.Mode(target_versions={black.TargetVersion.PYaa}				,		line_length=119				,		preview=__A   )
							_a			  =       black.format_str(__A				,		mode=__A   )
							_a			  =       style_docstrings_in_code(__A   )
							return result[len('''class Bla:\n'''   ) :] if has_indent else result
def 	snake_case_					(UpperCamelCase :						Tuple				,		UpperCamelCase :						Dict=False   ):





							'''simple docstring'''


							with open(__A				,		'''r'''				,		encoding='''utf-8'''				,		newline='''\n'''   ) as f:
														_a			  =       f.readlines()
							_a			  =       []
							_a			  =       0
							# Not a for loop cause `lines` is going to change (if `overwrite=True`).
							while line_index < len(__A   ):
														_a			  =       _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.
														_a			  =       search.groups()
														_a			  =       find_code_in_diffusers(__A   )
														_a			  =       get_indent(__A   )

														_a			  =       line_index + 1 if indent == theoretical_indent else line_index + 2
														_a			  =       theoretical_indent
														_a			  =       start_index

														# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
														_a			  =       True
														while line_index < len(__A   ) and should_continue:
																					line_index += 1
																					if line_index >= len(__A   ):
																												break
																					_a			  =       lines[line_index]
																					_a			  =       _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

														_a			  =       lines[start_index:line_index]
														_a			  =       ''''''.join(__A   )

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

														# Before comparing, use the `replace_pattern` on the original code.
														if len(__A   ) > 0:
																					_a			  =       replace_pattern.replace('''with'''				,		''''''   ).split(''','''   )
																					_a			  =       [_re_replace_pattern.search(__A   ) for p in patterns]
																					for pattern in patterns:
																												if pattern is None:
																																			continue
																												_a			  =       pattern.groups()
																												_a			  =       re.sub(__A				,		__A				,		__A   )
																												if option.strip() == "all-casing":
																																			_a			  =       re.sub(obja.lower()				,		obja.lower()				,		__A   )
																																			_a			  =       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
																					_a			  =       blackify(lines[start_index - 1] + theoretical_code   )
																					_a			  =       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:
																												_a			  =       lines[:start_index] + [theoretical_code] + lines[line_index:]
																												_a			  =       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 	snake_case_					(UpperCamelCase :						bool = False   ):





							'''simple docstring'''


							_a			  =       glob.glob(os.path.join(__A				,		'''**/*.py'''   )				,		recursive=__A   )
							_a			  =       []
							for filename in all_files:
														_a			  =       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:
														_a			  =       '''\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__":
				_snake_case   :   str     							=	argparse.ArgumentParser()
				parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
				_snake_case   :   str     							=	parser.parse_args()

				check_copies(args.fix_and_overwrite)






'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case   :   str     							=	get_tests_dir('fixtures/test_sentencepiece.model')


if is_torch_available():
				from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

_snake_case   :   Union[str, Any]     							=	256047
_snake_case   :   Tuple     							=	256145

@require_sentencepiece
@require_tokenizers
class     A					(	_a  ,unittest.TestCase					):
							lowercase_           =       NllbTokenizer
							lowercase_           =       NllbTokenizerFast
							lowercase_           =       True
							lowercase_           =       True
							lowercase_           =       {}







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							int:


														"""simple docstring"""


														super().setUp()

														# We have a SentencePiece fixture for testing
														_a			  =       NllbTokenizer(lowerCAmelCase_							,    keep_accents=lowerCAmelCase_      )
														tokenizer.save_pretrained(self.tmpdirname      )







							def      __lowerCAmelCase    (						self :						Tuple      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       NllbTokenizer(lowerCAmelCase_							,    keep_accents=lowerCAmelCase_      )

														_a			  =       tokenizer.tokenize('''This is a test'''      )
														self.assertListEqual(lowerCAmelCase_							,    ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

														self.assertListEqual(
														    tokenizer.convert_tokens_to_ids(lowerCAmelCase_      )							,    [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]							,    )

														_a			  =       tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
														self.assertListEqual(
														    lowerCAmelCase_							,    [
														        SPIECE_UNDERLINE + '''I''',
														        SPIECE_UNDERLINE + '''was''',
														        SPIECE_UNDERLINE + '''b''',
														        '''or''',
														        '''n''',
														        SPIECE_UNDERLINE + '''in''',
														        SPIECE_UNDERLINE + '''''',
														        '''9''',
														        '''2''',
														        '''0''',
														        '''0''',
														        '''0''',
														        ''',''',
														        SPIECE_UNDERLINE + '''and''',
														        SPIECE_UNDERLINE + '''this''',
														        SPIECE_UNDERLINE + '''is''',
														        SPIECE_UNDERLINE + '''f''',
														        '''al''',
														        '''s''',
														        '''é''',
														        '''.''',
														    ]							,    )
														_a			  =       tokenizer.convert_tokens_to_ids(lowerCAmelCase_      )
														self.assertListEqual(
														    lowerCAmelCase_							,    [
														        value + tokenizer.fairseq_offset
														        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
														    ]							,    )

														_a			  =       tokenizer.convert_ids_to_tokens(lowerCAmelCase_      )
														self.assertListEqual(
														    lowerCAmelCase_							,    [
														        SPIECE_UNDERLINE + '''I''',
														        SPIECE_UNDERLINE + '''was''',
														        SPIECE_UNDERLINE + '''b''',
														        '''or''',
														        '''n''',
														        SPIECE_UNDERLINE + '''in''',
														        SPIECE_UNDERLINE + '''''',
														        '''<unk>''',
														        '''2''',
														        '''0''',
														        '''0''',
														        '''0''',
														        ''',''',
														        SPIECE_UNDERLINE + '''and''',
														        SPIECE_UNDERLINE + '''this''',
														        SPIECE_UNDERLINE + '''is''',
														        SPIECE_UNDERLINE + '''f''',
														        '''al''',
														        '''s''',
														        '''<unk>''',
														        '''.''',
														    ]							,    )







							def      __lowerCAmelCase    (						self :						Optional[int]      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-nllb''', {})
														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(lowerCAmelCase_							,    **lowerCAmelCase_      )
																												_a			  =       self.tokenizer_class.from_pretrained(lowerCAmelCase_							,    **lowerCAmelCase_      )

																												_a			  =       tempfile.mkdtemp()

																												_a			  =       tokenizer_r.save_pretrained(lowerCAmelCase_      )
																												_a			  =       tokenizer_p.save_pretrained(lowerCAmelCase_      )

																												# Checks it save with the same files + the tokenizer.json file for the fast one
																												self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files      )      )
																												_a			  =       tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f      )
																												self.assertSequenceEqual(lowerCAmelCase_							,    lowerCAmelCase_      )

																												# Checks everything loads correctly in the same way
																												_a			  =       tokenizer_r.from_pretrained(lowerCAmelCase_      )
																												_a			  =       tokenizer_p.from_pretrained(lowerCAmelCase_      )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCAmelCase_							,    lowerCAmelCase_      )      )

																												shutil.rmtree(lowerCAmelCase_      )

																												# Save tokenizer rust, legacy_format=True
																												_a			  =       tempfile.mkdtemp()

																												_a			  =       tokenizer_r.save_pretrained(lowerCAmelCase_							,    legacy_format=lowerCAmelCase_      )
																												_a			  =       tokenizer_p.save_pretrained(lowerCAmelCase_      )

																												# Checks it save with the same files
																												self.assertSequenceEqual(lowerCAmelCase_							,    lowerCAmelCase_      )

																												# Checks everything loads correctly in the same way
																												_a			  =       tokenizer_r.from_pretrained(lowerCAmelCase_      )
																												_a			  =       tokenizer_p.from_pretrained(lowerCAmelCase_      )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCAmelCase_							,    lowerCAmelCase_      )      )

																												shutil.rmtree(lowerCAmelCase_      )

																												# Save tokenizer rust, legacy_format=False
																												_a			  =       tempfile.mkdtemp()

																												_a			  =       tokenizer_r.save_pretrained(lowerCAmelCase_							,    legacy_format=lowerCAmelCase_      )
																												_a			  =       tokenizer_p.save_pretrained(lowerCAmelCase_      )

																												# Checks it saved the tokenizer.json file
																												self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files      )      )

																												# Checks everything loads correctly in the same way
																												_a			  =       tokenizer_r.from_pretrained(lowerCAmelCase_      )
																												_a			  =       tokenizer_p.from_pretrained(lowerCAmelCase_      )

																												# Check special tokens are set accordingly on Rust and Python
																												for key in tokenizer_pp.special_tokens_map:
																																			self.assertTrue(hasattr(lowerCAmelCase_							,    lowerCAmelCase_      )      )

																												shutil.rmtree(lowerCAmelCase_      )







							@require_torch
							def      __lowerCAmelCase    (						self :						Union[str, Any]      )    ->							Dict:


														"""simple docstring"""


														if not self.test_seqaseq:
																					return

														_a			  =       self.get_tokenizers()
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'      ):
																												# Longer text that will definitely require truncation.
																												_a			  =       [
																												    ''' UN Chief Says There Is No Military Solution in Syria''',
																												    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'''
																												    ''' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'''
																												    ''' will only worsen the violence and misery for millions of people.''',
																												]
																												_a			  =       [
																												    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
																												    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'''
																												    ''' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'''
																												    ''' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
																												]
																												try:
																																			_a			  =       tokenizer.prepare_seqaseq_batch(
																																			    src_texts=lowerCAmelCase_							,    tgt_texts=lowerCAmelCase_							,    max_length=3							,    max_target_length=10							,    return_tensors='''pt'''							,    src_lang='''eng_Latn'''							,    tgt_lang='''ron_Latn'''							,    )
																												except NotImplementedError:
																																			return
																												self.assertEqual(batch.input_ids.shape[1]							,    3      )
																												self.assertEqual(batch.labels.shape[1]							,    10      )
																												# max_target_length will default to max_length if not specified
																												_a			  =       tokenizer.prepare_seqaseq_batch(
																												    lowerCAmelCase_							,    tgt_texts=lowerCAmelCase_							,    max_length=3							,    return_tensors='''pt'''      )
																												self.assertEqual(batch.input_ids.shape[1]							,    3      )
																												self.assertEqual(batch.labels.shape[1]							,    3      )

																												_a			  =       tokenizer.prepare_seqaseq_batch(
																												    src_texts=lowerCAmelCase_							,    max_length=3							,    max_target_length=10							,    return_tensors='''pt'''      )
																												self.assertEqual(batch_encoder_only.input_ids.shape[1]							,    3      )
																												self.assertEqual(batch_encoder_only.attention_mask.shape[1]							,    3      )
																												self.assertNotIn('''decoder_input_ids'''							,    lowerCAmelCase_      )







							@unittest.skip('''Unfortunately way too slow to build a BPE with SentencePiece.'''      )
							def      __lowerCAmelCase    (						self :						Dict      )    ->							Tuple:


														"""simple docstring"""


														pass







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[Any]:


														"""simple docstring"""


														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
																												_a			  =       [AddedToken('''<special>'''							,    lstrip=lowerCAmelCase_      )]

																												_a			  =       self.rust_tokenizer_class.from_pretrained(
																												    lowerCAmelCase_							,    additional_special_tokens=lowerCAmelCase_							,    **lowerCAmelCase_      )
																												_a			  =       tokenizer_r.encode('''Hey this is a <special> token'''      )

																												_a			  =       tokenizer_r.encode('''<special>'''							,    add_special_tokens=lowerCAmelCase_      )[0]

																												self.assertTrue(special_token_id in r_output      )

																												if self.test_slow_tokenizer:
																																			_a			  =       self.rust_tokenizer_class.from_pretrained(
																																			    lowerCAmelCase_							,    additional_special_tokens=lowerCAmelCase_							,    **lowerCAmelCase_							,    )
																																			_a			  =       self.tokenizer_class.from_pretrained(
																																			    lowerCAmelCase_							,    additional_special_tokens=lowerCAmelCase_							,    **lowerCAmelCase_      )

																																			_a			  =       tokenizer_p.encode('''Hey this is a <special> token'''      )

																																			_a			  =       tokenizer_cr.encode('''Hey this is a <special> token'''      )

																																			self.assertEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
																																			self.assertEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
																																			self.assertTrue(special_token_id in p_output      )
																																			self.assertTrue(special_token_id in cr_output      )



@require_torch
@require_sentencepiece
@require_tokenizers
class     A					(	unittest.TestCase					):
							lowercase_           =       'facebook/nllb-200-distilled-600M'
							lowercase_           =       [
							    ' UN Chief Says There Is No Military Solution in Syria',
							    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
							]
							lowercase_           =       [
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
							    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
							    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
							]
							lowercase_           =       [
							    25_6047,
							    1_6297,
							    13_4408,
							    8165,
							    24_8066,
							    1_4734,
							    950,
							    1135,
							    10_5721,
							    3573,
							    83,
							    2_7352,
							    108,
							    4_9486,
							    2,
							]







							@classmethod
							def      __lowerCAmelCase    (						cls :						Tuple      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       NllbTokenizer.from_pretrained(
														    cls.checkpoint_name							,    src_lang='''eng_Latn'''							,    tgt_lang='''ron_Latn'''      )
														_a			  =       1
														return cls







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


														"""simple docstring"""


														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Arab''']							,    25_60_01      )
														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ace_Latn''']							,    25_60_02      )
														self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''fra_Latn''']							,    25_60_57      )







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							int:


														"""simple docstring"""


														_a			  =       self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
														self.assertListEqual(self.expected_src_tokens							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						int      )    ->							List[Any]:


														"""simple docstring"""


														self.assertIn(lowerCAmelCase_							,    self.tokenizer.all_special_ids      )
														# fmt: off
														_a			  =       [RO_CODE, 42_54, 9_80_68, 11_29_23, 3_90_72, 39_09, 7_13, 10_27_67, 26, 1_73_14, 3_56_42, 1_46_83, 3_31_18, 20_22, 6_69_87, 2, 25_60_47]
														# fmt: on

														_a			  =       self.tokenizer.decode(lowerCAmelCase_							,    skip_special_tokens=lowerCAmelCase_      )
														_a			  =       self.tokenizer.decode(generated_ids[1:]							,    skip_special_tokens=lowerCAmelCase_      )
														self.assertEqual(lowerCAmelCase_							,    lowerCAmelCase_      )
														self.assertNotIn(self.tokenizer.eos_token							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						Dict      )    ->							Union[str, Any]:


														"""simple docstring"""


														_a			  =       ['''this is gunna be a long sentence ''' * 20]
														assert isinstance(src_text[0]							,    lowerCAmelCase_      )
														_a			  =       10
														_a			  =       self.tokenizer(lowerCAmelCase_							,    max_length=lowerCAmelCase_							,    truncation=lowerCAmelCase_      ).input_ids[0]
														self.assertEqual(ids[-1]							,    2      )
														self.assertEqual(ids[0]							,    lowerCAmelCase_      )
														self.assertEqual(len(lowerCAmelCase_      )							,    lowerCAmelCase_      )







							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[int]:


														"""simple docstring"""


														self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']      )							,    [25_62_03, 3]      )







							def      __lowerCAmelCase    (						self :						List[str]      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       tempfile.mkdtemp()
														_a			  =       self.tokenizer.fairseq_tokens_to_ids
														self.tokenizer.save_pretrained(lowerCAmelCase_      )
														_a			  =       NllbTokenizer.from_pretrained(lowerCAmelCase_      )
														self.assertDictEqual(new_tok.fairseq_tokens_to_ids							,    lowerCAmelCase_      )







							@require_torch
							def      __lowerCAmelCase    (						self :						str      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.tokenizer(
														    self.src_text							,    text_target=self.tgt_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=len(self.expected_src_tokens      )							,    return_tensors='''pt'''							,    )
														_a			  =       shift_tokens_right(
														    batch['''labels''']							,    self.tokenizer.pad_token_id							,    self.tokenizer.lang_code_to_id['''ron_Latn''']      )

														self.assertIsInstance(lowerCAmelCase_							,    lowerCAmelCase_      )

														self.assertEqual((2, 15)							,    batch.input_ids.shape      )
														self.assertEqual((2, 15)							,    batch.attention_mask.shape      )
														_a			  =       batch.input_ids.tolist()[0]
														self.assertListEqual(self.expected_src_tokens							,    lowerCAmelCase_      )
														self.assertEqual(lowerCAmelCase_							,    batch.decoder_input_ids[0, 0]      )  # EOS
														# Test that special tokens are reset
														self.assertEqual(self.tokenizer.prefix_tokens							,    [EN_CODE]      )
														self.assertEqual(self.tokenizer.suffix_tokens							,    [self.tokenizer.eos_token_id]      )







							def      __lowerCAmelCase    (						self :						Optional[Any]      )    ->							Any:


														"""simple docstring"""


														_a			  =       self.tokenizer(self.src_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=3							,    return_tensors='''pt'''      )
														_a			  =       self.tokenizer(
														    text_target=self.tgt_text							,    padding=lowerCAmelCase_							,    truncation=lowerCAmelCase_							,    max_length=10							,    return_tensors='''pt'''      )
														_a			  =       targets['''input_ids''']
														_a			  =       shift_tokens_right(
														    lowerCAmelCase_							,    self.tokenizer.pad_token_id							,    decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]							,    )

														self.assertEqual(batch.input_ids.shape[1]							,    3      )
														self.assertEqual(batch.decoder_input_ids.shape[1]							,    10      )







							@require_torch
							def      __lowerCAmelCase    (						self :						List[Any]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       self.tokenizer._build_translation_inputs(
														    '''A test'''							,    return_tensors='''pt'''							,    src_lang='''eng_Latn'''							,    tgt_lang='''fra_Latn'''      )

														self.assertEqual(
														    nested_simplify(lowerCAmelCase_      )							,    {
														        # A, test, EOS, en_XX
														        '''input_ids''': [[25_60_47, 70, 73_56, 2]],
														        '''attention_mask''': [[1, 1, 1, 1]],
														        # ar_AR
														        '''forced_bos_token_id''': 25_60_57,
														    }							,    )







							@require_torch
							def      __lowerCAmelCase    (						self :						Any      )    ->							Tuple:


														"""simple docstring"""


														_a			  =       True
														_a			  =       self.tokenizer(
														    '''UN Chief says there is no military solution in Syria'''							,    src_lang='''eng_Latn'''							,    tgt_lang='''fra_Latn'''      )
														self.assertEqual(
														    inputs.input_ids							,    [1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2, 25_60_47]      )

														_a			  =       False
														_a			  =       self.tokenizer(
														    '''UN Chief says there is no military solution in Syria'''							,    src_lang='''eng_Latn'''							,    tgt_lang='''fra_Latn'''      )
														self.assertEqual(
														    inputs.input_ids							,    [25_60_47, 1_62_97, 13_44_08, 2_56_53, 63_70, 2_48, 2_54, 10_39_29, 9_49_95, 1_08, 4_94_86, 2]      )

