
"""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


_lowercase							      = get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')







@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase_ (					_lowercase						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			List[str]					=			PegasusTokenizer
						_lowerCamelCase:			Tuple					=			PegasusTokenizerFast
						_lowerCamelCase:			List[Any]					=			True
						_lowerCamelCase:			List[Any]					=			True


						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Any:
							super().setUp()

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


						@cached_property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple					)   ->      List[str]:
							return PegasusTokenizer.from_pretrained('google/pegasus-large'					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,**A_   :					int					)   ->      PegasusTokenizer:
							return PegasusTokenizer.from_pretrained(self.tmpdirname     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]     ,A_   :					Dict					)   ->      str:
							return ("This is a test", "This is a test")


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]					)   ->      Tuple:
							A        =      '</s>'
							A        =      1

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


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]					)   ->      Tuple:
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Tuple:
							self.assertEqual(self.get_tokenizer().vocab_size     ,1103					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[Any]					)   ->      Any:
							A        =      self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
							A        =      self.tokenizer_class.from_pretrained(self.tmpdirname					)
							A        =      (
							    '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        =      rust_tokenizer([raw_input_str]     ,return_tensors=A_     ,add_special_tokens=A_					).input_ids[0]
							A        =      py_tokenizer([raw_input_str]     ,return_tensors=A_     ,add_special_tokens=A_					).input_ids[0]
							self.assertListEqual(A_     ,A_					)


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


						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Union[str, Any]:
							A        =      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
							A        =      'To ensure a smooth flow of bank resolutions.'
							A        =      [413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Dict					)   ->      Dict:
							A        =      ['This is going to be way too long.' * 150, 'short example']
							A        =      ['not super long but more than 5 tokens', 'tiny']
							A        =      self._large_tokenizer(A_     ,padding=A_     ,truncation=A_     ,return_tensors='pt'					)
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      Dict:
							# fmt: off
							A        =      {'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=A_     ,model_name='google/bigbird-pegasus-large-arxiv'     ,revision='ba85d0851d708441f91440d509690f1ab6353415'     ,)







@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase_ (					_lowercase						,    unittest.TestCase			):



						'''simple docstring'''


						_lowerCamelCase:			Dict					=			PegasusTokenizer
						_lowerCamelCase:			Dict					=			PegasusTokenizerFast
						_lowerCamelCase:			Tuple					=			True
						_lowerCamelCase:			Any					=			True


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

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


						@cached_property
						def   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Optional[Any]:
							return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv'					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					Dict     ,**A_   :					List[Any]					)   ->      PegasusTokenizer:
							return PegasusTokenizer.from_pretrained(self.tmpdirname     ,**A_					)


						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]     ,A_   :					List[Any]					)   ->      int:
							return ("This is a test", "This is a test")


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


						@require_torch
						def   _SCREAMING_SNAKE_CASE					(       self   :					int					)   ->      Any:
							A        =      ['This is going to be way too long.' * 1000, 'short example']
							A        =      ['not super long but more than 5 tokens', 'tiny']
							A        =      self._large_tokenizer(A_     ,padding=A_     ,truncation=A_     ,return_tensors='pt'					)
							A        =      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   _SCREAMING_SNAKE_CASE					(       self   :					Any					)   ->      Tuple:
							A        =      (
							    'This is an example string that is used to test the original TF implementation against the HF'
							    ' implementation'
							)

							A        =      self._large_tokenizer(A_					).input_ids

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





import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


_lowerCamelCase	:		List[Any]							       =   """sshleifer/bart-tiny-random"""
_lowerCamelCase	:		List[Any]							       =   """patrickvonplaten/t5-tiny-random"""


@require_torch
class 				UpperCamelCase_    ( unittest.TestCase     ):

    '''simple docstring'''






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





           '''simple docstring'''

           return AutoConfig.from_pretrained(UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.num_hidden_layers					, 1)


    def        SCREAMING_SNAKE_CASE							(			self      :	int)						->Any:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)


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





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=UpperCAmelCase__)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, self.teacher_config.encoder_layers)


    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->int:





           '''simple docstring'''

           A__ ,							*A__       =    create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=1					, d=1)
           self.assertEqual(student.config.encoder_layers					, 1)
           self.assertEqual(student.config.decoder_layers					, 1)





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





           '''simple docstring'''

           with self.assertRaises(UpperCAmelCase__):
                  create_student_by_copying_alternating_layers(UpperCAmelCase__					, tempfile.mkdtemp()					, e=UpperCAmelCase__					, d=UpperCAmelCase__)


