

import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_ :				Union[str, Any]						 =    get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Any													= PegasusTokenizer
					UpperCAmelCase__:     Optional[int]													= PegasusTokenizerFast
					UpperCAmelCase__:     Optional[Any]													= True
					UpperCAmelCase__:     Any													= True






					def 						__A    (				self    ):
						super().setUp()

						# We have a SentencePiece fixture for testing
						A__							:					List[str]       =	PegasusTokenizer(A__    )
						tokenizer.save_pretrained(self.tmpdirname    )






					@cached_property
					def 						__A    (				self    ):
						return PegasusTokenizer.from_pretrained("""google/pegasus-large"""    )






					def 						__A    (				self					, **A__    ):
						return PegasusTokenizer.from_pretrained(self.tmpdirname					, **A__    )






					def 						__A    (				self					, A__    ):
						return ("This is a test", "This is a test")






					def 						__A    (				self    ):
						A__							:					Dict       =	"""</s>"""
						A__							:					Union[str, Any]       =	1

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






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	list(self.get_tokenizer().get_vocab().keys()    )

						self.assertEqual(vocab_keys[0]					, """<pad>"""    )
						self.assertEqual(vocab_keys[1]					, """</s>"""    )
						self.assertEqual(vocab_keys[-1]					, """v"""    )
						self.assertEqual(len(A__    )					, 1103    )






					def 						__A    (				self    ):
						self.assertEqual(self.get_tokenizer().vocab_size					, 1103    )






					def 						__A    (				self    ):
						A__							:					Union[str, Any]       =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
						A__							:					int       =	self.tokenizer_class.from_pretrained(self.tmpdirname    )
						A__							:					int       =	(
						    """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
						    """ </s> <pad> <pad> <pad>"""
						)
						A__							:					Any       =	rust_tokenizer([raw_input_str]					, return_tensors=A__					, add_special_tokens=A__    ).input_ids[0]
						A__							:					Any       =	py_tokenizer([raw_input_str]					, return_tensors=A__					, add_special_tokens=A__    ).input_ids[0]
						self.assertListEqual(A__					, A__    )






					def 						__A    (				self    ):
						A__							:					Tuple       =	self._large_tokenizer
						# <mask_1> masks whole sentence while <mask_2> masks single word
						A__							:					List[str]       =	"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
						A__							:					int       =	[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
						A__							:					Tuple       =	tokenizer([raw_input_str]					, return_tensors=A__    ).input_ids[0]
						self.assertListEqual(A__					, A__    )






					def 						__A    (				self    ):
						A__							:					Optional[Any]       =	self._large_tokenizer
						# The tracebacks for the following asserts are **better** without messages or self.assertEqual
						assert tokenizer.vocab_size == 9_6103
						assert tokenizer.pad_token_id == 0
						assert tokenizer.eos_token_id == 1
						assert tokenizer.offset == 103
						assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
						assert tokenizer.unk_token == "<unk>"
						assert tokenizer.model_max_length == 1024
						A__							:					str       =	"""To ensure a smooth flow of bank resolutions."""
						A__							:					Union[str, Any]       =	[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
						A__							:					int       =	tokenizer([raw_input_str]					, return_tensors=A__    ).input_ids[0]
						self.assertListEqual(A__					, A__    )
						assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]    ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






					@require_torch
					def 						__A    (				self    ):
						A__							:					List[str]       =	["""This is going to be way too long.""" * 150, """short example"""]
						A__							:					Optional[int]       =	["""not super long but more than 5 tokens""", """tiny"""]
						A__							:					Dict       =	self._large_tokenizer(A__					, padding=A__					, truncation=A__					, return_tensors="""pt"""    )
						A__							:					List[Any]       =	self._large_tokenizer(
						    text_target=A__					, max_length=5					, padding=A__					, truncation=A__					, return_tensors="""pt"""    )

						assert batch.input_ids.shape == (2, 1024)
						assert batch.attention_mask.shape == (2, 1024)
						assert targets["input_ids"].shape == (2, 5)
						assert len(A__    ) == 2  # input_ids, attention_mask.






					@slow
					def 						__A    (				self    ):
						# fmt: off
						A__							:					Any       =	{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 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]], """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, 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, 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=A__					, model_name="""google/bigbird-pegasus-large-arxiv"""					, revision="""ba85d0851d708441f91440d509690f1ab6353415"""					, )



@require_sentencepiece
@require_tokenizers
class 					_a		(__magic_name__	,     unittest.TestCase       ):


					'''simple docstring'''






					UpperCAmelCase__:     Union[str, Any]													= PegasusTokenizer
					UpperCAmelCase__:     List[Any]													= PegasusTokenizerFast
					UpperCAmelCase__:     int													= True
					UpperCAmelCase__:     Optional[Any]													= True






					def 						__A    (				self    ):
						super().setUp()

						# We have a SentencePiece fixture for testing
						A__							:					Optional[int]       =	PegasusTokenizer(A__					, offset=0					, mask_token_sent=A__					, mask_token="""[MASK]"""    )
						tokenizer.save_pretrained(self.tmpdirname    )






					@cached_property
					def 						__A    (				self    ):
						return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""    )






					def 						__A    (				self					, **A__    ):
						return PegasusTokenizer.from_pretrained(self.tmpdirname					, **A__    )






					def 						__A    (				self					, A__    ):
						return ("This is a test", "This is a test")






					def 						__A    (				self    ):
						A__							:					int       =	self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
						A__							:					Optional[int]       =	self.tokenizer_class.from_pretrained(self.tmpdirname    )
						A__							:					Optional[int]       =	(
						    """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
						    """ <pad> <pad> <pad>"""
						)
						A__							:					List[Any]       =	rust_tokenizer([raw_input_str]					, return_tensors=A__					, add_special_tokens=A__    ).input_ids[0]
						A__							:					List[str]       =	py_tokenizer([raw_input_str]					, return_tensors=A__					, add_special_tokens=A__    ).input_ids[0]
						self.assertListEqual(A__					, A__    )






					@require_torch
					def 						__A    (				self    ):
						A__							:					int       =	["""This is going to be way too long.""" * 1000, """short example"""]
						A__							:					Any       =	["""not super long but more than 5 tokens""", """tiny"""]
						A__							:					Dict       =	self._large_tokenizer(A__					, padding=A__					, truncation=A__					, return_tensors="""pt"""    )
						A__							:					Any       =	self._large_tokenizer(
						    text_target=A__					, max_length=5					, padding=A__					, truncation=A__					, return_tensors="""pt"""    )

						assert batch.input_ids.shape == (2, 4096)
						assert batch.attention_mask.shape == (2, 4096)
						assert targets["input_ids"].shape == (2, 5)
						assert len(A__    ) == 2  # input_ids, attention_mask.






					def 						__A    (				self    ):
						A__							:					Dict       =	(
						    """This is an example string that is used to test the original TF implementation against the HF"""
						    """ implementation"""
						)

						A__							:					List[Any]       =	self._large_tokenizer(A__    ).input_ids

						self.assertListEqual(
						    A__					, [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]					, )






import os

try:
	from .build_directory_md import good_file_paths
except ImportError:
	from build_directory_md import good_file_paths  # type: ignore

A_ :				Dict						 =    list(good_file_paths())
assert filepaths, "good_file_paths() failed!"

A_ :				Optional[Any]						 =    [file for file in filepaths if file != file.lower()]
if upper_files:
	print(f'''{len(upper_files)} files contain uppercase characters:''')
	print('\n'.join(upper_files) + '\n')

A_ :				Tuple						 =    [file for file in filepaths if ' ' in file]
if space_files:
	print(f'''{len(space_files)} files contain space characters:''')
	print('\n'.join(space_files) + '\n')

A_ :				Any						 =    [file for file in filepaths if '-' in file]
if hyphen_files:
	print(f'''{len(hyphen_files)} files contain hyphen characters:''')
	print('\n'.join(hyphen_files) + '\n')

A_ :				List[str]						 =    [file for file in filepaths if os.sep not in file]
if nodir_files:
	print(f'''{len(nodir_files)} files are not in a directory:''')
	print('\n'.join(nodir_files) + '\n')

A_ :				Any						 =    len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
	import sys

	sys.exit(bad_files)



