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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[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     :      Optional[int]      )     ->   Dict:
							return PegasusTokenizer.from_pretrained('google/pegasus-large'      )


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


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


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   str:
							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     :      int      )     ->   Dict:
							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     :      Union[str, Any]      )     ->   List[str]:
							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     :      List[Any]      )     ->   Any:
							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]      )     ->   str:
							# 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:    Any      =    True
						_lowerCamelCase:    Dict      =    True


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


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


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


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

"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{
    '''hustvl/yolos-small''': '''https://huggingface.co/hustvl/yolos-small/resolve/main/config.json''',
    # See all YOLOS models at https://huggingface.co/models?filter=yolos
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[str]      =    '''yolos'''


						def __init__( self     :      Dict  ,A_     :      Optional[Any]=768  ,A_     :      int=12  ,A_     :      List[str]=12  ,A_     :      str=3072  ,A_     :      Tuple="gelu"  ,A_     :      Dict=0.0  ,A_     :      List[Any]=0.0  ,A_     :      Any=0.02  ,A_     :      str=1e-12  ,A_     :      List[Any]=[512, 864]  ,A_     :      Union[str, Any]=16  ,A_     :      List[str]=3  ,A_     :      Optional[int]=True  ,A_     :      Tuple=100  ,A_     :      str=True  ,A_     :      Optional[Any]=False  ,A_     :      Any=1  ,A_     :      Optional[Any]=5  ,A_     :      Optional[Any]=2  ,A_     :      Optional[int]=5  ,A_     :      List[Any]=2  ,A_     :      Union[str, Any]=0.1  ,**A_     :      Tuple  ,)     ->   Any:
							super().__init__(**A_      )

							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   hidden_act
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   initializer_range
							A						   =   layer_norm_eps
							A						   =   image_size
							A						   =   patch_size
							A						   =   num_channels
							A						   =   qkv_bias
							A						   =   num_detection_tokens
							A						   =   use_mid_position_embeddings
							A						   =   auxiliary_loss
							# Hungarian matcher
							A						   =   class_cost
							A						   =   bbox_cost
							A						   =   giou_cost
							# Loss coefficients
							A						   =   bbox_loss_coefficient
							A						   =   giou_loss_coefficient
							A						   =   eos_coefficient







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Any      =    version.parse('''1.11'''    )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   float:
							return 1e-4




						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   int:
							return 12