



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


_lowercase		:				Dict      		=get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

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







						"""simple docstring"""




						__lowerCAmelCase					:Tuple      =					PegasusTokenizer
						__lowerCAmelCase					:Union[str, Any]      =					PegasusTokenizerFast
						__lowerCAmelCase					:Any      =					True
						__lowerCAmelCase					:List[str]      =					True

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									super().setUp()

									# We have a SentencePiece fixture for testing
									a__       :						str							=  PegasusTokenizer(__lowercase  )
									tokenizer.save_pretrained(self.tmpdirname  )

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				PegasusTokenizer:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				Any:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Dict							=  """</s>"""
									a__       :						Tuple							=  1

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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						str							=  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(__lowercase  ) ,       1_1_0_3  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									self.assertEqual(self.get_tokenizer().vocab_size ,       1_1_0_3  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""simple docstring"""




									a__       :						List[Any]							=  self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
									a__       :						int							=  self.tokenizer_class.from_pretrained(self.tmpdirname  )
									a__       :						Optional[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__       :						str							=  rust_tokenizer([raw_input_str] ,       return_tensors=__lowercase ,       add_special_tokens=__lowercase  ).input_ids[0]
									a__       :						int							=  py_tokenizer([raw_input_str] ,       return_tensors=__lowercase ,       add_special_tokens=__lowercase  ).input_ids[0]
									self.assertListEqual(__lowercase ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self._large_tokenizer
									# <mask_1> masks whole sentence while <mask_2> masks single word
									a__       :						Optional[Any]							=  """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
									a__       :						Tuple							=  [2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
									a__       :						Tuple							=  tokenizer([raw_input_str] ,       return_tensors=__lowercase  ).input_ids[0]
									self.assertListEqual(__lowercase ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




									a__       :						int							=  self._large_tokenizer
									# The tracebacks for the following asserts are **better** without messages or self.assertEqual
									assert tokenizer.vocab_size == 9_6_1_0_3
									assert tokenizer.pad_token_id == 0
									assert tokenizer.eos_token_id == 1
									assert tokenizer.offset == 1_0_3
									assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
									assert tokenizer.unk_token == "<unk>"
									assert tokenizer.model_max_length == 1_0_2_4
									a__       :						List[Any]							=  """To ensure a smooth flow of bank resolutions."""
									a__       :						str							=  [4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
									a__       :						int							=  tokenizer([raw_input_str] ,       return_tensors=__lowercase  ).input_ids[0]
									self.assertListEqual(__lowercase ,       __lowercase  )
									assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]  ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[Any]:
									"""simple docstring"""




									a__       :						Any							=  ["""This is going to be way too long.""" * 1_5_0, """short example"""]
									a__       :						Union[str, Any]							=  ["""not super long but more than 5 tokens""", """tiny"""]
									a__       :						Tuple							=  self._large_tokenizer(__lowercase ,       padding=__lowercase ,       truncation=__lowercase ,       return_tensors="""pt"""  )
									a__       :						Union[str, Any]							=  self._large_tokenizer(
									    text_target=__lowercase ,       max_length=5 ,       padding=__lowercase ,       truncation=__lowercase ,       return_tensors="""pt"""  )

									assert batch.input_ids.shape == (2, 1_0_2_4)
									assert batch.attention_mask.shape == (2, 1_0_2_4)
									assert targets["input_ids"].shape == (2, 5)
									assert len(__lowercase  ) == 2  # input_ids, attention_mask.

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[str]:
									"""simple docstring"""




									a__       :						List[Any]							=  {"""input_ids""": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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=__lowercase ,       model_name="""google/bigbird-pegasus-large-arxiv""" ,       revision="""ba85d0851d708441f91440d509690f1ab6353415""" ,       )




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







						"""simple docstring"""




						__lowerCAmelCase					:Dict      =					PegasusTokenizer
						__lowerCAmelCase					:Union[str, Any]      =					PegasusTokenizerFast
						__lowerCAmelCase					:Tuple      =					True
						__lowerCAmelCase					:Dict      =					True

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									super().setUp()

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

						@cached_property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       **__lowercase  ) ->				PegasusTokenizer:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase  ) ->				List[str]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Dict							=  self.rust_tokenizer_class.from_pretrained(self.tmpdirname  )
									a__       :						List[str]							=  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__       :						Tuple							=  rust_tokenizer([raw_input_str] ,       return_tensors=__lowercase ,       add_special_tokens=__lowercase  ).input_ids[0]
									a__       :						Optional[Any]							=  py_tokenizer([raw_input_str] ,       return_tensors=__lowercase ,       add_special_tokens=__lowercase  ).input_ids[0]
									self.assertListEqual(__lowercase ,       __lowercase  )

						@require_torch
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						str							=  ["""This is going to be way too long.""" * 1_0_0_0, """short example"""]
									a__       :						int							=  ["""not super long but more than 5 tokens""", """tiny"""]
									a__       :						Optional[Any]							=  self._large_tokenizer(__lowercase ,       padding=__lowercase ,       truncation=__lowercase ,       return_tensors="""pt"""  )
									a__       :						List[str]							=  self._large_tokenizer(
									    text_target=__lowercase ,       max_length=5 ,       padding=__lowercase ,       truncation=__lowercase ,       return_tensors="""pt"""  )

									assert batch.input_ids.shape == (2, 4_0_9_6)
									assert batch.attention_mask.shape == (2, 4_0_9_6)
									assert targets["input_ids"].shape == (2, 5)
									assert len(__lowercase  ) == 2  # input_ids, attention_mask.

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						List[Any]							=  (
									    """This is an example string that is used to test the original TF implementation against the HF"""
									    """ implementation"""
									)

									a__       :						str							=  self._large_tokenizer(__lowercase  ).input_ids

									self.assertListEqual(
									    __lowercase ,       [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1] ,       )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_lowercase		:				int      		={
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				Optional[Any]      		=[
		    "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "LongT5EncoderModel",
		    "LongT5ForConditionalGeneration",
		    "LongT5Model",
		    "LongT5PreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase		:				List[Any]      		=[
		    "FlaxLongT5ForConditionalGeneration",
		    "FlaxLongT5Model",
		    "FlaxLongT5PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_longta import (
				    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LongTaEncoderModel,
				    LongTaForConditionalGeneration,
				    LongTaModel,
				    LongTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_longta import (
				    FlaxLongTaForConditionalGeneration,
				    FlaxLongTaModel,
				    FlaxLongTaPreTrainedModel,
				)


else:
		import sys

		_lowercase		:				Any      		=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




