




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													=			get_tests_dir("fixtures/test_sentencepiece_no_bos.model")




@require_sentencepiece
@require_tokenizers
class       _A				(		__lowercase       ,     unittest.TestCase     ):
		__a					    =       PegasusTokenizer
		__a					    =       PegasusTokenizerFast
		__a					    =       True
		__a					    =       True






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

								# We have a SentencePiece fixture for testing
								_UpperCAmelCase      =			PegasusTokenizer(_SCREAMING_SNAKE_CASE			)
								tokenizer.save_pretrained(self.tmpdirname			)






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






		def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
								return PegasusTokenizer.from_pretrained(self.tmpdirname						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								return ("This is a test", "This is a test")






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			"""</s>"""
								_UpperCAmelCase      =			1

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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			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(_SCREAMING_SNAKE_CASE			)						,		1103			)






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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			self.rust_tokenizer_class.from_pretrained(self.tmpdirname			)
								_UpperCAmelCase      =			self.tokenizer_class.from_pretrained(self.tmpdirname			)
								_UpperCAmelCase      =			(
								    """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>"""
								)
								_UpperCAmelCase      =			rust_tokenizer([raw_input_str]						,		return_tensors=_SCREAMING_SNAKE_CASE						,		add_special_tokens=_SCREAMING_SNAKE_CASE			).input_ids[0]
								_UpperCAmelCase      =			py_tokenizer([raw_input_str]						,		return_tensors=_SCREAMING_SNAKE_CASE						,		add_special_tokens=_SCREAMING_SNAKE_CASE			).input_ids[0]
								self.assertListEqual(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






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






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			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
								_UpperCAmelCase      =			"""To ensure a smooth flow of bank resolutions."""
								_UpperCAmelCase      =			[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
								_UpperCAmelCase      =			tokenizer([raw_input_str]						,		return_tensors=_SCREAMING_SNAKE_CASE			).input_ids[0]
								self.assertListEqual(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)
								assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]			) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]






		@require_torch
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			["""This is going to be way too long.""" * 150, """short example"""]
								_UpperCAmelCase      =			["""not super long but more than 5 tokens""", """tiny"""]
								_UpperCAmelCase      =			self._large_tokenizer(_SCREAMING_SNAKE_CASE						,		padding=_SCREAMING_SNAKE_CASE						,		truncation=_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)
								_UpperCAmelCase      =			self._large_tokenizer(
								    text_target=_SCREAMING_SNAKE_CASE						,		max_length=5						,		padding=_SCREAMING_SNAKE_CASE						,		truncation=_SCREAMING_SNAKE_CASE						,		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(_SCREAMING_SNAKE_CASE			) == 2  # input_ids, attention_mask.






		@slow
		def 				UpperCAmelCase (    self			):
								# fmt: off
								_UpperCAmelCase      =			{"""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=_SCREAMING_SNAKE_CASE						,		model_name="""google/bigbird-pegasus-large-arxiv"""						,		revision="""ba85d0851d708441f91440d509690f1ab6353415"""						,		)




@require_sentencepiece
@require_tokenizers
class       _A				(		__lowercase       ,     unittest.TestCase     ):
		__a					    =       PegasusTokenizer
		__a					    =       PegasusTokenizerFast
		__a					    =       True
		__a					    =       True






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

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






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






		def 				UpperCAmelCase (    self						,		**_SCREAMING_SNAKE_CASE			):
								return PegasusTokenizer.from_pretrained(self.tmpdirname						,		**_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self						,		_SCREAMING_SNAKE_CASE			):
								return ("This is a test", "This is a test")






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






		@require_torch
		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			["""This is going to be way too long.""" * 1000, """short example"""]
								_UpperCAmelCase      =			["""not super long but more than 5 tokens""", """tiny"""]
								_UpperCAmelCase      =			self._large_tokenizer(_SCREAMING_SNAKE_CASE						,		padding=_SCREAMING_SNAKE_CASE						,		truncation=_SCREAMING_SNAKE_CASE						,		return_tensors="""pt"""			)
								_UpperCAmelCase      =			self._large_tokenizer(
								    text_target=_SCREAMING_SNAKE_CASE						,		max_length=5						,		padding=_SCREAMING_SNAKE_CASE						,		truncation=_SCREAMING_SNAKE_CASE						,		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(_SCREAMING_SNAKE_CASE			) == 2  # input_ids, attention_mask.






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

								_UpperCAmelCase      =			self._large_tokenizer(_SCREAMING_SNAKE_CASE			).input_ids

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





import os
import sys
import unittest


a													=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
a													=			os.path.join(git_repo_path, "src", "transformers")

a													=			"\n{0} = None\n"

a													=			"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


a													=			"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"




class       _A				(		unittest.TestCase     ):






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			find_backend("""    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"""			)
								self.assertIsNone(_SCREAMING_SNAKE_CASE			)

								_UpperCAmelCase      =			find_backend("""    if not is_tokenizers_available():"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""tokenizers"""			)

								_UpperCAmelCase      =			find_backend("""    if not is_tensorflow_text_available():"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""tensorflow_text"""			)

								_UpperCAmelCase      =			find_backend("""    if not (is_sentencepiece_available() and is_tokenizers_available()):"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""sentencepiece_and_tokenizers"""			)

								_UpperCAmelCase      =			find_backend(
								    """    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""sentencepiece_and_tensorflow_text"""			)

								_UpperCAmelCase      =			find_backend(
								    """    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""sentencepiece_and_tokenizers_and_vision"""			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			read_init()
								# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
								self.assertIn("""torch"""						,		_SCREAMING_SNAKE_CASE			)
								self.assertIn("""tensorflow_text"""						,		_SCREAMING_SNAKE_CASE			)
								self.assertIn("""sentencepiece_and_tokenizers"""						,		_SCREAMING_SNAKE_CASE			)

								# Likewise, we can't assert on the exact content of a key
								self.assertIn("""BertModel"""						,		objects["""torch"""]			)
								self.assertIn("""TFBertModel"""						,		objects["""tf"""]			)
								self.assertIn("""FlaxBertModel"""						,		objects["""flax"""]			)
								self.assertIn("""BertModel"""						,		objects["""torch"""]			)
								self.assertIn("""TFBertTokenizer"""						,		objects["""tensorflow_text"""]			)
								self.assertIn("""convert_slow_tokenizer"""						,		objects["""sentencepiece_and_tokenizers"""]			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			create_dummy_object("""CONSTANT"""						,		"""'torch'"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		"""\nCONSTANT = None\n"""			)

								_UpperCAmelCase      =			create_dummy_object("""function"""						,		"""'torch'"""			)
								self.assertEqual(
								    _SCREAMING_SNAKE_CASE						,		"""\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"""			)

								_UpperCAmelCase      =			"""
class FakeClass(metaclass=DummyObject):
    _backends = 'torch'

    def __init__(self, *args, **kwargs):
        requires_backends(self, 'torch')
"""
								_UpperCAmelCase      =			create_dummy_object("""FakeClass"""						,		"""'torch'"""			)
								self.assertEqual(_SCREAMING_SNAKE_CASE						,		_SCREAMING_SNAKE_CASE			)






		def 				UpperCAmelCase (    self			):
								_UpperCAmelCase      =			"""# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends


CONSTANT = None


def function(*args, **kwargs):
    requires_backends(function, [\"torch\"])


class FakeClass(metaclass=DummyObject):
    _backends = [\"torch\"]

    def __init__(self, *args, **kwargs):
        requires_backends(self, [\"torch\"])
"""
								_UpperCAmelCase      =			create_dummy_files({"""torch""": ["""CONSTANT""", """function""", """FakeClass"""]}			)
								self.assertEqual(dummy_files["""torch"""]						,		_SCREAMING_SNAKE_CASE			)