






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	:				List[str]      	=     get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')




@require_sentencepiece
@require_tokenizers
class 		UpperCAmelCase_    (						lowerCAmelCase__       ,	unittest.TestCase       ):


						'''simple docstring'''




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






						def    _lowerCAmelCase							(						self					)			->					List[str]:
								super().setUp()

								# We have a SentencePiece fixture for testing
								snake_case_					:				Dict    			=							PegasusTokenizer(__a					)
								tokenizer.save_pretrained(self.tmpdirname					)






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






						def    _lowerCAmelCase							(						self		,					**_SCREAMING_SNAKE_CASE					)			->					Optional[Any]:
								return PegasusTokenizer.from_pretrained(self.tmpdirname		,					**__a					)






						def    _lowerCAmelCase							(						self		,					_SCREAMING_SNAKE_CASE					)			->					str:
								return ("This is a test", "This is a test")






						def    _lowerCAmelCase							(						self					)			->					Optional[int]:
								snake_case_					:				Optional[Any]    			=							"</s>"
								snake_case_					:				Dict    			=							1

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






						def    _lowerCAmelCase							(						self					)			->					Any:
								snake_case_					:				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    _lowerCAmelCase							(						self					)			->					List[Any]:
								self.assertEqual(self.get_tokenizer().vocab_size		,					1103					)






						def    _lowerCAmelCase							(						self					)			->					Union[str, Any]:
								snake_case_					:				List[str]    			=							self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
								snake_case_					:				str    			=							self.tokenizer_class.from_pretrained(self.tmpdirname					)
								snake_case_					:				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>"
								)
								snake_case_					:				int    			=							rust_tokenizer([raw_input_str]		,					return_tensors=__a		,					add_special_tokens=__a					).input_ids[0]
								snake_case_					:				Optional[Any]    			=							py_tokenizer([raw_input_str]		,					return_tensors=__a		,					add_special_tokens=__a					).input_ids[0]
								self.assertListEqual(__a		,					__a					)






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






						def    _lowerCAmelCase							(						self					)			->					Union[str, Any]:
								snake_case_					:				List[Any]    			=							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
								snake_case_					:				Tuple    			=							"To ensure a smooth flow of bank resolutions."
								snake_case_					:				Optional[int]    			=							[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
								snake_case_					:				int    			=							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    _lowerCAmelCase							(						self					)			->					str:
								snake_case_					:				str    			=							["This is going to be way too long." * 150, "short example"]
								snake_case_					:				Optional[int]    			=							["not super long but more than 5 tokens", "tiny"]
								snake_case_					:				Union[str, Any]    			=							self._large_tokenizer(__a		,					padding=__a		,					truncation=__a		,					return_tensors="pt"					)
								snake_case_					:				Tuple    			=							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    _lowerCAmelCase							(						self					)			->					str:
								# fmt: off
								snake_case_					:				Optional[Any]    			=							{"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 		UpperCAmelCase_    (						lowerCAmelCase__       ,	unittest.TestCase       ):


						'''simple docstring'''




						A  : Tuple							=       PegasusTokenizer
						A  : str							=       PegasusTokenizerFast
						A  : str							=       True
						A  : int							=       True






						def    _lowerCAmelCase							(						self					)			->					Union[str, Any]:
								super().setUp()

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






						@cached_property
						def    _lowerCAmelCase							(						self					)			->					int:
								return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"					)






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






						def    _lowerCAmelCase							(						self		,					_SCREAMING_SNAKE_CASE					)			->					Optional[int]:
								return ("This is a test", "This is a test")






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






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

								snake_case_					:				Optional[int]    			=							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"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__						)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =field(default="""image-classification"""			,metadata={"""include_in_asdict_even_if_is_default""": True}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""image""": Image()}						)
							__UpperCAmelCase		:       ClassVar[Features]							 =Features({"""labels""": ClassLabel}						)
							__UpperCAmelCase		:       str							 ="image"
							__UpperCAmelCase		:       str							 ="labels"

							def      snake_case							(	self       ,  __a      ):
										if self.label_column not in features:
													raise ValueError(f"Column {self.label_column} is not present in features."      )
										if not isinstance(features[self.label_column]       ,  __a      ):
													raise ValueError(f"Column {self.label_column} is not a ClassLabel."      )
										__lowerCAmelCase							       =     copy.deepcopy(self      )
										__lowerCAmelCase							       =     self.label_schema.copy()
										__lowerCAmelCase							       =     features[self.label_column]
										__lowerCAmelCase							       =     label_schema
										return task_template



							@property
							def      snake_case							(	self      ):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}




