"""simple docstring"""




from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase    :							int													=						logging.get_logger(__name__)

__lowerCAmelCase    :							Dict													=						{
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	Optional[Any]			=    "xlm-prophetnet"
					UpperCamelCase_ :	Optional[int]			=    ["past_key_values"]
					UpperCamelCase_ :	Tuple			=    {
					    "num_attention_heads": "num_encoder_attention_heads",
					}




					def __init__(	self						:     List[Any]  ,						snake_case__						:     Optional[float] = 0.1  ,						snake_case__						:     Optional[Union[str, Callable]] = "gelu"  ,						snake_case__						:     Optional[int] = 30522  ,						snake_case__						:     Optional[int] = 1024  ,						snake_case__						:     Optional[int] = 4096  ,						snake_case__						:     Optional[int] = 12  ,						snake_case__						:     Optional[int] = 16  ,						snake_case__						:     Optional[int] = 4096  ,						snake_case__						:     Optional[int] = 12  ,						snake_case__						:     Optional[int] = 16  ,						snake_case__						:     Optional[float] = 0.1  ,						snake_case__						:     Optional[float] = 0.1  ,						snake_case__						:     Optional[int] = 512  ,						snake_case__						:     Optional[float] = 0.02  ,						snake_case__						:     Optional[bool] = True  ,						snake_case__						:     Optional[bool] = True  ,						snake_case__						:     Optional[int] = 0  ,						snake_case__						:     Optional[int] = 2  ,						snake_case__						:     Optional[int] = 32  ,						snake_case__						:     Optional[int] = 128  ,						snake_case__						:     Optional[bool] = False  ,						snake_case__						:     Optional[float] = 0.0  ,						snake_case__						:     Optional[bool] = True  ,						snake_case__						:     Optional[int] = 0  ,						snake_case__						:     Optional[int] = 1  ,						snake_case__						:     Optional[int] = 2  ,						**snake_case__						:     Tuple  ,						):
							lowerCAmelCase__        =   vocab_size
							lowerCAmelCase__        =   hidden_size
							lowerCAmelCase__        =   encoder_ffn_dim
							lowerCAmelCase__        =   num_encoder_layers
							lowerCAmelCase__        =   num_encoder_attention_heads
							lowerCAmelCase__        =   decoder_ffn_dim
							lowerCAmelCase__        =   num_decoder_layers
							lowerCAmelCase__        =   num_decoder_attention_heads
							lowerCAmelCase__        =   max_position_embeddings
							lowerCAmelCase__        =   init_std  # Normal(0, this parameter)
							lowerCAmelCase__        =   activation_function

							# parameters for xlmprophetnet
							lowerCAmelCase__        =   ngram
							lowerCAmelCase__        =   num_buckets
							lowerCAmelCase__        =   relative_max_distance
							lowerCAmelCase__        =   disable_ngram_loss
							lowerCAmelCase__        =   eps

							# 3 Types of Dropout
							lowerCAmelCase__        =   attention_dropout
							lowerCAmelCase__        =   activation_dropout
							lowerCAmelCase__        =   dropout

							lowerCAmelCase__        =   use_cache

							super().__init__(
							    pad_token_id=snake_case__  ,						bos_token_id=snake_case__  ,						eos_token_id=snake_case__  ,						is_encoder_decoder=snake_case__  ,						add_cross_attention=snake_case__  ,						decoder_start_token_id=snake_case__  ,						**snake_case__  ,						)




					@property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]     ):
							return self.num_encoder_layers + self.num_decoder_layers




					@num_hidden_layers.setter
					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						snake_case__						:     Optional[int]     ):
							raise NotImplementedError(
							    """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
							    """ `num_decoder_layers`."""     )





"""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


__lowerCAmelCase    :							Optional[Any]													=						get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	Tuple			=    PegasusTokenizer
					UpperCamelCase_ :	Any			=    PegasusTokenizerFast
					UpperCamelCase_ :	int			=    True
					UpperCamelCase_ :	Any			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple     ):
							super().setUp()

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




					@cached_property
					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							return PegasusTokenizer.from_pretrained("""google/pegasus-large"""     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						**snake_case__						:     Optional[Any]     ):
							return PegasusTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]     ):
							return ("This is a test", "This is a test")




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							lowerCAmelCase__        =   """</s>"""
							lowerCAmelCase__        =   1

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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]     ):
							lowerCAmelCase__        =   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(snake_case__     )  ,						1103     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							self.assertEqual(self.get_tokenizer().vocab_size  ,						1103     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self.rust_tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   self.tokenizer_class.from_pretrained(self.tmpdirname     )
							lowerCAmelCase__        =   (
							    """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__        =   rust_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							lowerCAmelCase__        =   py_tokenizer([raw_input_str]  ,						return_tensors=snake_case__  ,						add_special_tokens=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   self._large_tokenizer
							# <mask_1> masks whole sentence while <mask_2> masks single word
							lowerCAmelCase__        =   """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
							lowerCAmelCase__        =   [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
							lowerCAmelCase__        =   tokenizer([raw_input_str]  ,						return_tensors=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   self._large_tokenizer
							# The tracebacks for the following asserts are **better** without messages or self.assertEqual
							assert tokenizer.vocab_size == 96103
							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__        =   """To ensure a smooth flow of bank resolutions."""
							lowerCAmelCase__        =   [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
							lowerCAmelCase__        =   tokenizer([raw_input_str]  ,						return_tensors=snake_case__     ).input_ids[0]
							self.assertListEqual(snake_case__  ,						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						:     List[str]     ):
							lowerCAmelCase__        =   ["""This is going to be way too long.""" * 150, """short example"""]
							lowerCAmelCase__        =   ["""not super long but more than 5 tokens""", """tiny"""]
							lowerCAmelCase__        =   self._large_tokenizer(snake_case__  ,						padding=snake_case__  ,						truncation=snake_case__  ,						return_tensors="""pt"""     )
							lowerCAmelCase__        =   self._large_tokenizer(
							    text_target=snake_case__  ,						max_length=5  ,						padding=snake_case__  ,						truncation=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(snake_case__     ) == 2  # input_ids, attention_mask.




					@slow
					def 						_SCREAMING_SNAKE_CASE						(	self						:     str     ):
							# fmt: off
							lowerCAmelCase__        =   {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 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, 18289, 17780, 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=snake_case__  ,						model_name="""google/bigbird-pegasus-large-arxiv"""  ,						revision="""ba85d0851d708441f91440d509690f1ab6353415"""  ,						)

@require_sentencepiece
@require_tokenizers
class       a_      (     __UpperCamelCase ,		unittest.TestCase			):
					UpperCamelCase_ :	str			=    PegasusTokenizer
					UpperCamelCase_ :	Optional[int]			=    PegasusTokenizerFast
					UpperCamelCase_ :	Union[str, Any]			=    True
					UpperCamelCase_ :	Optional[int]			=    True




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[str]     ):
							super().setUp()

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




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     List[Any]  ,						**snake_case__						:     List[Any]     ):
							return PegasusTokenizer.from_pretrained(self.tmpdirname  ,						**snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     Dict     ):
							return ("This is a test", "This is a test")




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




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




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

							lowerCAmelCase__        =   self._large_tokenizer(snake_case__     ).input_ids

							self.assertListEqual(
							    snake_case__  ,						[182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]  ,						)




