




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







@require_sentencepiece
@require_tokenizers
class  UpperCAmelCase(	lowercase_       ,      unittest.TestCase	):



	"""simple docstring"""






	a      :   List[str]			    =	PegasusTokenizer
	a      :   Optional[int]			    =	PegasusTokenizerFast
	a      :   int			    =	True
	a      :   List[Any]			    =	True

	def        __a      (	self							)   ->     Tuple:





								"""simple docstring"""


								super().setUp()

								# We have a SentencePiece fixture for testing
								lowercase__    :			Optional[int]     =				PegasusTokenizer(a__							)
								tokenizer.save_pretrained(self.tmpdirname							)

	@cached_property
	def        __a      (	self							)   ->     List[Any]:





								"""simple docstring"""


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

	def        __a      (	self			, **lowerCamelCase							)   ->     PegasusTokenizer:





								"""simple docstring"""


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

	def        __a      (	self			, lowerCamelCase							)   ->     Optional[int]:





								"""simple docstring"""


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

	def        __a      (	self							)   ->     Dict:





								"""simple docstring"""


								lowercase__    :			Dict     =				"""</s>"""
								lowercase__    :			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							)   ->     List[str]:





								"""simple docstring"""


								lowercase__    :			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							)   ->     Union[str, Any]:





								"""simple docstring"""


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

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





								"""simple docstring"""


								lowercase__    :			int     =				self.rust_tokenizer_class.from_pretrained(self.tmpdirname							)
								lowercase__    :			Tuple     =				self.tokenizer_class.from_pretrained(self.tmpdirname							)
								lowercase__    :			List[Any]     =				(
								    """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>"""
								)
								lowercase__    :			Tuple     =				rust_tokenizer([raw_input_str]			, return_tensors=a__			, add_special_tokens=a__							).input_ids[0]
								lowercase__    :			Union[str, Any]     =				py_tokenizer([raw_input_str]			, return_tensors=a__			, add_special_tokens=a__							).input_ids[0]
								self.assertListEqual(a__			, a__							)

	def        __a      (	self							)   ->     Tuple:





								"""simple docstring"""


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

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





								"""simple docstring"""


								lowercase__    :			Optional[int]     =				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
								lowercase__    :			Any     =				"""To ensure a smooth flow of bank resolutions."""
								lowercase__    :			int     =				[413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
								lowercase__    :			Any     =				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							)   ->     Dict:





								"""simple docstring"""


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





								"""simple docstring"""


								lowercase__    :			Union[str, Any]     =				{"""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=a__			, model_name="google/bigbird-pegasus-large-arxiv"			, revision="ba85d0851d708441f91440d509690f1ab6353415"			, )







@require_sentencepiece
@require_tokenizers
class  UpperCAmelCase(	lowercase_       ,      unittest.TestCase	):



	"""simple docstring"""






	a      :   List[Any]			    =	PegasusTokenizer
	a      :   List[Any]			    =	PegasusTokenizerFast
	a      :   List[str]			    =	True
	a      :   List[Any]			    =	True

	def        __a      (	self							)   ->     Dict:





								"""simple docstring"""


								super().setUp()

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

	@cached_property
	def        __a      (	self							)   ->     Union[str, Any]:





								"""simple docstring"""


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

	def        __a      (	self			, **lowerCamelCase							)   ->     PegasusTokenizer:





								"""simple docstring"""


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

	def        __a      (	self			, lowerCamelCase							)   ->     str:





								"""simple docstring"""


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

	def        __a      (	self							)   ->     Optional[Any]:





								"""simple docstring"""


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

	@require_torch
	def        __a      (	self							)   ->     str:





								"""simple docstring"""


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





								"""simple docstring"""


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

								lowercase__    :			Dict     =				self._large_tokenizer(a__							).input_ids

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







from __future__ import annotations




class       snake_case__ :

       '''simple docstring'''



       def __init__(				self      ,    a__     )			->   None:






            '''simple docstring'''





            __snake_case      :str 							=    order

            # a_{0} ... a_{k}
            __snake_case      :int 							=    [1.0] + [0.0] * order
            # b_{0} ... b_{k}
            __snake_case      :Any 							=    [1.0] + [0.0] * order

            # x[n-1] ... x[n-k]
            __snake_case      :List[Any] 							=    [0.0] * self.order
            # y[n-1] ... y[n-k]
            __snake_case      :int 							=    [0.0] * self.order



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   None:






            '''simple docstring'''





            if len(a__     ) < self.order:
                 __snake_case      :Optional[int] 							=    [1.0, *a_coeffs]

            if len(a__     ) != self.order + 1:
                 __snake_case      :Dict 							=    (
                     F'''Expected a_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            if len(a__     ) != self.order + 1:
                 __snake_case      :Optional[Any] 							=    (
                     F'''Expected b_coeffs to have {self.order + 1} elements '''
                     F'''for {self.order}-order filter, got {len(a__     )}'''
                 )
                 raise ValueError(a__     )

            __snake_case      :str 							=    a_coeffs
            __snake_case      :str 							=    b_coeffs



       def        __lowercase    (				self      ,    a__     )			->   float:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    0.0

            # Start at index 1 and do index 0 at the end.
            for i in range(1      ,    self.order + 1     ):
                 result += (
                     self.b_coeffs[i] * self.input_history[i - 1]
                     - self.a_coeffs[i] * self.output_history[i - 1]
                 )

            __snake_case      :Dict 							=    (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

            __snake_case      :Optional[int] 							=    self.input_history[:-1]
            __snake_case      :Optional[Any] 							=    self.output_history[:-1]

            __snake_case      :Union[str, Any] 							=    sample
            __snake_case      :Optional[Any] 							=    result

            return result


