




'''simple docstring'''





import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
		from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase	:       int     =	get_tests_dir('fixtures/test_sentencepiece.model')

__lowerCAmelCase	:       List[Any]     =	{"target_lang": "fi", "source_lang": "en"}
__lowerCAmelCase	:       int     =	">>zh<<"
__lowerCAmelCase	:       str     =	"Helsinki-NLP/"

if is_torch_available():
		__lowerCAmelCase	:       int     =	"pt"
elif is_tf_available():
		__lowerCAmelCase	:       Optional[Any]     =	"tf"
else:
		__lowerCAmelCase	:       int     =	"jax"




@require_sentencepiece
class 					snake_case__							(_UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Any											=      MarianTokenizer
				SCREAMING_SNAKE_CASE_ :							List[Any]											=      False
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      True





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Union[str, Any]:
							super().setUp()
							a					=    ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
							a					=    dict(zip(__UpperCamelCase      ,   range(len(__UpperCamelCase							)							)							)							)
							a					=    Path(self.tmpdirname							)
							save_json(__UpperCamelCase      ,   save_dir / VOCAB_FILES_NAMES["vocab"]							)
							save_json(__UpperCamelCase      ,   save_dir / VOCAB_FILES_NAMES["tokenizer_config_file"]							)
							if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
										copyfile(__UpperCamelCase      ,   save_dir / VOCAB_FILES_NAMES["source_spm"]							)
										copyfile(__UpperCamelCase      ,   save_dir / VOCAB_FILES_NAMES["target_spm"]							)

							a					=    MarianTokenizer.from_pretrained(self.tmpdirname							)
							tokenizer.save_pretrained(self.tmpdirname							)





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   **__lowerCamelCase       :  List[Any]							)      ->  MarianTokenizer:
							return MarianTokenizer.from_pretrained(self.tmpdirname      ,   **__UpperCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  List[Any]							)      ->  List[str]:
							return (
							    "This is a test",
							    "This is a test",
							)





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Any:
							a					=    "</s>"
							a					=    0

							self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCamelCase							)      ,   __UpperCamelCase							)
							self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCamelCase							)      ,   __UpperCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  int:
							a					=    list(self.get_tokenizer().get_vocab().keys()							)

							self.assertEqual(vocab_keys[0]      ,   "</s>"							)
							self.assertEqual(vocab_keys[1]      ,   "<unk>"							)
							self.assertEqual(vocab_keys[-1]      ,   "<pad>"							)
							self.assertEqual(len(__UpperCamelCase							)      ,   9							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Tuple:
							self.assertEqual(self.get_tokenizer().vocab_size      ,   9							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Union[str, Any]:
							a					=    MarianTokenizer.from_pretrained(f"""{ORG_NAME}opus-mt-en-de"""							)
							a					=    en_de_tokenizer(["I am a small frog"]      ,   return_tensors=__UpperCamelCase							)
							self.assertIsInstance(__UpperCamelCase      ,   __UpperCamelCase							)
							a					=    [38, 1_21, 14, 6_97, 3_88_48, 0]
							self.assertListEqual(__UpperCamelCase      ,   batch.input_ids[0]							)

							a					=    tempfile.mkdtemp()
							en_de_tokenizer.save_pretrained(__UpperCamelCase							)
							a					=    [x.name for x in Path(__UpperCamelCase							).glob("*"							)]
							self.assertIn("source.spm"      ,   __UpperCamelCase							)
							MarianTokenizer.from_pretrained(__UpperCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Optional[Any]:
							a					=    self.get_tokenizer()

							a					=    tok(
							    ["I am a small frog" * 10_00, "I am a small frog"]      ,   padding=__UpperCamelCase      ,   truncation=__UpperCamelCase      ,   return_tensors=__UpperCamelCase							)
							self.assertIsInstance(__UpperCamelCase      ,   __UpperCamelCase							)
							self.assertEqual(batch.input_ids.shape      ,   (2, 5_12)							)





				def 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[int]:
							a					=    self.get_tokenizer()
							a					=    tok(["I am a tiny frog", "I am a small frog"]      ,   padding=__UpperCamelCase      ,   return_tensors=__UpperCamelCase							)
							self.assertIsInstance(__UpperCamelCase      ,   __UpperCamelCase							)
							self.assertEqual(batch_smaller.input_ids.shape      ,   (2, 10)							)





				@slow
				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Tuple:
							# fmt: off
							a					=    {"input_ids": [[4_34_95, 4_62, 20, 4_21_64, 13_69, 52, 4_64, 1_32, 17_03, 4_92, 13, 74_91, 3_89_99, 6, 8, 4_64, 1_32, 17_03, 4_92, 13, 46_69, 3_78_67, 13, 75_25, 27, 15_93, 9_88, 13, 3_39_72, 70_29, 6, 20, 82_51, 3_83, 2, 2_70, 58_66, 37_88, 2, 23_53, 82_51, 1_23_38, 2, 1_39_58, 3_87, 2, 36_29, 69_53, 1_88, 29_00, 2, 1_39_58, 80_11, 1_15_01, 23, 84_60, 40_73, 3_40_09, 20, 4_35, 1_14_39, 27, 8, 84_60, 40_73, 60_04, 20, 99_88, 3_75, 27, 33, 2_66, 19_45, 10_76, 13_50, 3_78_67, 32_88, 5, 5_77, 10_76, 43_74, 8, 50_82, 5, 2_64_53, 2_57, 5_56, 4_03, 2, 2_42, 1_32, 3_83, 3_16, 4_92, 8, 1_07_67, 6, 3_16, 3_04, 42_39, 3, 0], [1_48, 1_57_22, 19, 18_39, 12, 13_50, 13, 2_23_27, 50_82, 54_18, 4_75_67, 3_59_38, 59, 3_18, 1_95_52, 1_08, 21_83, 54, 1_49_76, 48_35, 32, 5_47, 11_14, 8, 3_15, 24_17, 5, 92, 1_90_88, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00], [36, 63_95, 1_25_70, 3_91_47, 1_15_97, 6, 2_66, 4, 4_54_05, 72_96, 3, 0, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00, 5_81_00]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=__UpperCamelCase      ,   model_name="Helsinki-NLP/opus-mt-en-de"      ,   revision="1a8c2263da11e68e50938f97e10cd57820bd504c"      ,   decode_kwargs={"use_source_tokenizer": True}      ,   )





				def 							__UpperCAmelCase							(   self       :  int							)      ->  List[Any]:
							a					=    MarianTokenizer.from_pretrained("hf-internal-testing/test-marian-two-vocabs"							)

							a					=    "Tämä on testi"
							a					=    "This is a test"

							a					=    [76, 7, 20_47, 2]
							a					=    [69, 12, 11, 9_40, 2]

							a					=    tokenizer(__UpperCamelCase							).input_ids
							self.assertListEqual(__UpperCamelCase      ,   __UpperCamelCase							)

							a					=    tokenizer(text_target=__UpperCamelCase							).input_ids
							self.assertListEqual(__UpperCamelCase      ,   __UpperCamelCase							)

							a					=    tokenizer.decode(__UpperCamelCase      ,   skip_special_tokens=__UpperCamelCase							)
							self.assertEqual(__UpperCamelCase      ,   __UpperCamelCase							)












import argparse
import os
import re


__lowerCAmelCase	:       Union[str, Any]     =	'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
__lowerCAmelCase	:       Dict     =	re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
__lowerCAmelCase	:       Any     =	re.compile(r'\s*\(\s*"(\S[^"]+)"')




def     __magic_name__       (				A	:      int,						A	:      bool = False     ):






			'''simple docstring'''







			with open(A,						"r",						encoding="utf-8"     ) as f:
						a					=    f.read()

			a					=    content.split("\n"     )
			a					=    []
			a					=    0
			while line_idx < len(A     ):
						if _re_intro_mapping.search(lines[line_idx]     ) is not None:
									a					=    len(re.search(R"^(\s*)\S",						lines[line_idx]     ).groups()[0]     ) + 8
									# Start of a new mapping!
									while not lines[line_idx].startswith(" " * indent + "("     ):
												new_lines.append(lines[line_idx]     )
												line_idx += 1

									a					=    []
									while lines[line_idx].strip() != "]":
												# Blocks either fit in one line or not
												if lines[line_idx].strip() == "(":
															a					=    line_idx
															while not lines[line_idx].startswith(" " * indent + ")"     ):
																		line_idx += 1
															blocks.append("\n".join(lines[start_idx : line_idx + 1]     )     )
												else:
															blocks.append(lines[line_idx]     )
												line_idx += 1

									# Sort blocks by their identifiers
									a					=    sorted(A,						key=lambda A     : _re_identifier.search(A     ).groups()[0]     )
									new_lines += blocks
						else:
									new_lines.append(lines[line_idx]     )
									line_idx += 1

			if overwrite:
						with open(A,						"w",						encoding="utf-8"     ) as f:
									f.write("\n".join(A     )     )
			elif "\n".join(A     ) != content:
						return True




def     __magic_name__       (				A	:      bool = False     ):






			'''simple docstring'''







			a					=    [os.path.join(A,						A     ) for f in os.listdir(A     ) if f.endswith(".py"     )]
			a					=    [sort_auto_mapping(A,						overwrite=A     ) for fname in fnames]

			if not overwrite and any(A     ):
						a					=    [f for f, d in zip(A,						A     ) if d]
						raise ValueError(
						    F"""The following files have auto mappings that need sorting: {", ".join(A     )}. Run `make style` to fix"""
						    " this."     )


if __name__ == "__main__":
		__lowerCAmelCase	:       Dict     =	argparse.ArgumentParser()
		parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
		__lowerCAmelCase	:       Optional[Any]     =	parser.parse_args()

		sort_all_auto_mappings(not args.check_only)






