



"""simple docstring"""






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


__snake_case				:  List[str] =       get_tests_dir('fixtures/test_sentencepiece_no_bos.model')



@require_sentencepiece
@require_tokenizers
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      PegasusTokenizer
	SCREAMING_SNAKE_CASE        =      PegasusTokenizerFast
	SCREAMING_SNAKE_CASE        =      True
	SCREAMING_SNAKE_CASE        =      True

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			List[Any]:

				"""simple docstring"""

				super().setUp()

				# We have a SentencePiece fixture for testing
				__lowerCAmelCase :    Optional[Any]				=			PegasusTokenizer(_SCREAMING_SNAKE_CASE)
				tokenizer.save_pretrained(self.tmpdirname)

	@cached_property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Any:

				"""simple docstring"""

				return PegasusTokenizer.from_pretrained("google/pegasus-large")

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     **_SCREAMING_SNAKE_CASE:						Union[str, Any])							->			PegasusTokenizer:

				"""simple docstring"""

				return PegasusTokenizer.from_pretrained(self.tmpdirname			,     **_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						Tuple)							->			Any:

				"""simple docstring"""

				return ("This is a test", "This is a test")

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			"</s>"
				__lowerCAmelCase :    Optional[int]				=			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 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    List[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(_SCREAMING_SNAKE_CASE)			,     1103)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			str:

				"""simple docstring"""

				self.assertEqual(self.get_tokenizer().vocab_size			,     1103)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			self.rust_tokenizer_class.from_pretrained(self.tmpdirname)
				__lowerCAmelCase :    int				=			self.tokenizer_class.from_pretrained(self.tmpdirname)
				__lowerCAmelCase :    List[str]				=			(
				    "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>"
				)
				__lowerCAmelCase :    Any				=			rust_tokenizer([raw_input_str]			,     return_tensors=_SCREAMING_SNAKE_CASE			,     add_special_tokens=_SCREAMING_SNAKE_CASE).input_ids[0]
				__lowerCAmelCase :    str				=			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 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Union[str, Any]:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[int])							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    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
				__lowerCAmelCase :    int				=			"To ensure a smooth flow of bank resolutions."
				__lowerCAmelCase :    Dict				=			[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
				__lowerCAmelCase :    Optional[int]				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			["This is going to be way too long." * 150, "short example"]
				__lowerCAmelCase :    Union[str, Any]				=			["not super long but more than 5 tokens", "tiny"]
				__lowerCAmelCase :    Dict				=			self._large_tokenizer(_SCREAMING_SNAKE_CASE			,     padding=_SCREAMING_SNAKE_CASE			,     truncation=_SCREAMING_SNAKE_CASE			,     return_tensors="pt")
				__lowerCAmelCase :    List[Any]				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			{"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__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      PegasusTokenizer
	SCREAMING_SNAKE_CASE        =      PegasusTokenizerFast
	SCREAMING_SNAKE_CASE        =      True
	SCREAMING_SNAKE_CASE        =      True

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

				"""simple docstring"""

				super().setUp()

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

	@cached_property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Any:

				"""simple docstring"""

				return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv")

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict			,     **_SCREAMING_SNAKE_CASE:						Optional[Any])							->			PegasusTokenizer:

				"""simple docstring"""

				return PegasusTokenizer.from_pretrained(self.tmpdirname			,     **_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

				return ("This is a test", "This is a test")

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Any:

				"""simple docstring"""

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

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			["This is going to be way too long." * 1000, "short example"]
				__lowerCAmelCase :    str				=			["not super long but more than 5 tokens", "tiny"]
				__lowerCAmelCase :    Dict				=			self._large_tokenizer(_SCREAMING_SNAKE_CASE			,     padding=_SCREAMING_SNAKE_CASE			,     truncation=_SCREAMING_SNAKE_CASE			,     return_tensors="pt")
				__lowerCAmelCase :    Optional[Any]				=			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 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			(
				    "This is an example string that is used to test the original TF implementation against the HF"
				    " implementation"
				)

				__lowerCAmelCase :    Any				=			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]			,     )




"""simple docstring"""






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case				:  Tuple =       {
    'configuration_autoformer': [
        'AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'AutoformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       [
	    'AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'AutoformerForPrediction',
	    'AutoformerModel',
	    'AutoformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	__snake_case				:  Optional[int] =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)