


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(	_a  ,				unittest.TestCase ):
					snake_case_		:					str    					=						PegasusTokenizer
					snake_case_		:					Optional[int]    					=						PegasusTokenizerFast
					snake_case_		:					str    					=						True
					snake_case_		:					List[str]    					=						True


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Tuple:




											'''simple docstring'''

											super().setUp()

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


					@cached_property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						str:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple  ,     **SCREAMING_SNAKE_CASE						:		str    )      ->						PegasusTokenizer:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any  ,     SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						List[str]:




											'''simple docstring'''

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


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




											'''simple docstring'''

											__snake_case				=		"</s>"
											__snake_case				=		1

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						str:




											'''simple docstring'''

											__snake_case				=		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(SCREAMING_SNAKE_CASE    )  ,     1_1_0_3    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						int:




											'''simple docstring'''

											self.assertEqual(self.get_tokenizer().vocab_size  ,     1_1_0_3    )


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




											'''simple docstring'''

											__snake_case				=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
											__snake_case				=		self.tokenizer_class.from_pretrained(self.tmpdirname    )
											__snake_case				=		(
											    "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				=		rust_tokenizer([raw_input_str]  ,     return_tensors=SCREAMING_SNAKE_CASE  ,     add_special_tokens=SCREAMING_SNAKE_CASE    ).input_ids[0]
											__snake_case				=		py_tokenizer([raw_input_str]  ,     return_tensors=SCREAMING_SNAKE_CASE  ,     add_special_tokens=SCREAMING_SNAKE_CASE    ).input_ids[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self._large_tokenizer
											# <mask_1> masks whole sentence while <mask_2> masks single word
											__snake_case				=		"<mask_1> To ensure a <mask_2> flow of bank resolutions."
											__snake_case				=		[2, 4_1_3, 6_1_5, 1_1_4, 3, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
											__snake_case				=		tokenizer([raw_input_str]  ,     return_tensors=SCREAMING_SNAKE_CASE    ).input_ids[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self._large_tokenizer
											# The tracebacks for the following asserts are **better** without messages or self.assertEqual
											assert tokenizer.vocab_size == 9_6_1_0_3
											assert tokenizer.pad_token_id == 0
											assert tokenizer.eos_token_id == 1
											assert tokenizer.offset == 1_0_3
											assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_0_5
											assert tokenizer.unk_token == "<unk>"
											assert tokenizer.model_max_length == 1_0_2_4
											__snake_case				=		"To ensure a smooth flow of bank resolutions."
											__snake_case				=		[4_1_3, 6_1_5, 1_1_4, 2_2_9_1, 1_9_7_1, 1_1_3, 1_6_7_9, 1_0_7_1_0, 1_0_7, 1]
											__snake_case				=		tokenizer([raw_input_str]  ,     return_tensors=SCREAMING_SNAKE_CASE    ).input_ids[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_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]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		["This is going to be way too long." * 1_5_0, "short example"]
											__snake_case				=		["not super long but more than 5 tokens", "tiny"]
											__snake_case				=		self._large_tokenizer(SCREAMING_SNAKE_CASE  ,     padding=SCREAMING_SNAKE_CASE  ,     truncation=SCREAMING_SNAKE_CASE  ,     return_tensors="pt"    )
											__snake_case				=		self._large_tokenizer(
											    text_target=SCREAMING_SNAKE_CASE  ,     max_length=5  ,     padding=SCREAMING_SNAKE_CASE  ,     truncation=SCREAMING_SNAKE_CASE  ,     return_tensors="pt"    )

											assert batch.input_ids.shape == (2, 1_0_2_4)
											assert batch.attention_mask.shape == (2, 1_0_2_4)
											assert targets["input_ids"].shape == (2, 5)
											assert len(SCREAMING_SNAKE_CASE    ) == 2  # input_ids, attention_mask.






					@slow
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		{"input_ids": [[3_8_9_7_9, 1_4_3, 1_8_4_8_5, 6_0_6, 1_3_0, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 5_4_1_8_9, 1_1_2_9, 1_1_1, 2_6_6_6_9, 8_7_6_8_6, 1_2_1, 9_1_1_4, 1_4_7_8_7, 1_2_1, 1_3_2_4_9, 1_5_8, 5_9_2, 9_5_6, 1_2_1, 1_4_6_2_1, 3_1_5_7_6, 1_4_3, 6_2_6_1_3, 1_0_8, 9_6_8_8, 9_3_0, 4_3_4_3_0, 1_1_5_6_2, 6_2_6_1_3, 3_0_4, 1_0_8, 1_1_4_4_3, 8_9_7, 1_0_8, 9_3_1_4, 1_7_4_1_5, 6_3_3_9_9, 1_0_8, 1_1_4_4_3, 7_6_1_4, 1_8_3_1_6, 1_1_8, 4_2_8_4, 7_1_4_8, 1_2_4_3_0, 1_4_3, 1_4_0_0, 2_5_7_0_3, 1_5_8, 1_1_1, 4_2_8_4, 7_1_4_8, 1_1_7_7_2, 1_4_3, 2_1_2_9_7, 1_0_6_4, 1_5_8, 1_2_2, 2_0_4, 3_5_0_6, 1_7_5_4, 1_1_3_3, 1_4_7_8_7, 1_5_8_1, 1_1_5, 3_3_2_2_4, 4_4_8_2, 1_1_1, 1_3_5_5, 1_1_0, 2_9_1_7_3, 3_1_7, 5_0_8_3_3, 1_0_8, 2_0_1_4_7, 9_4_6_6_5, 1_1_1, 7_7_1_9_8, 1_0_7, 1], [1_1_0, 6_2_6_1_3, 1_1_7, 6_3_8, 1_1_2, 1_1_3_3, 1_2_1, 2_0_0_9_8, 1_3_5_5, 7_9_0_5_0, 1_3_8_7_2, 1_3_5, 1_5_9_6, 5_3_5_4_1, 1_3_5_2, 1_4_1, 1_3_0_3_9, 5_5_4_2, 1_2_4, 3_0_2, 5_1_8, 1_1_1, 2_6_8, 2_9_5_6, 1_1_5, 1_4_9, 4_4_2_7, 1_0_7, 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_3_9, 1_2_3_5, 2_7_9_9, 1_8_2_8_9, 1_7_7_8_0, 2_0_4, 1_0_9, 9_4_7_4, 1_2_9_6, 1_0_7, 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=SCREAMING_SNAKE_CASE  ,     model_name="google/bigbird-pegasus-large-arxiv"  ,     revision="ba85d0851d708441f91440d509690f1ab6353415"  ,     )






@require_sentencepiece
@require_tokenizers
class      UpperCamelCase(	_a  ,				unittest.TestCase ):
					snake_case_		:					Any    					=						PegasusTokenizer
					snake_case_		:					int    					=						PegasusTokenizerFast
					snake_case_		:					Dict    					=						True
					snake_case_		:					Tuple    					=						True


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						List[str]:




											'''simple docstring'''

											super().setUp()

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


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




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     **SCREAMING_SNAKE_CASE						:		Tuple    )      ->						PegasusTokenizer:




											'''simple docstring'''

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


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




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.rust_tokenizer_class.from_pretrained(self.tmpdirname    )
											__snake_case				=		self.tokenizer_class.from_pretrained(self.tmpdirname    )
											__snake_case				=		(
											    "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				=		rust_tokenizer([raw_input_str]  ,     return_tensors=SCREAMING_SNAKE_CASE  ,     add_special_tokens=SCREAMING_SNAKE_CASE    ).input_ids[0]
											__snake_case				=		py_tokenizer([raw_input_str]  ,     return_tensors=SCREAMING_SNAKE_CASE  ,     add_special_tokens=SCREAMING_SNAKE_CASE    ).input_ids[0]
											self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )


					@require_torch
					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		["This is going to be way too long." * 1_0_0_0, "short example"]
											__snake_case				=		["not super long but more than 5 tokens", "tiny"]
											__snake_case				=		self._large_tokenizer(SCREAMING_SNAKE_CASE  ,     padding=SCREAMING_SNAKE_CASE  ,     truncation=SCREAMING_SNAKE_CASE  ,     return_tensors="pt"    )
											__snake_case				=		self._large_tokenizer(
											    text_target=SCREAMING_SNAKE_CASE  ,     max_length=5  ,     padding=SCREAMING_SNAKE_CASE  ,     truncation=SCREAMING_SNAKE_CASE  ,     return_tensors="pt"    )

											assert batch.input_ids.shape == (2, 4_0_9_6)
											assert batch.attention_mask.shape == (2, 4_0_9_6)
											assert targets["input_ids"].shape == (2, 5)
											assert len(SCREAMING_SNAKE_CASE    ) == 2  # input_ids, attention_mask.






					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Optional[Any]:




											'''simple docstring'''

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

											__snake_case				=		self._large_tokenizer(SCREAMING_SNAKE_CASE    ).input_ids

											self.assertListEqual(
											    SCREAMING_SNAKE_CASE  ,     [1_8_2, 1_1_7, 1_4_2, 5_8_7, 4_2_1_1, 1_2_0, 1_1_7, 2_6_3, 1_1_2, 8_0_4, 1_0_9, 8_5_6, 2_5_0_1_6, 3_1_3_7, 4_6_4, 1_0_9, 2_6_9_5_5, 3_1_3_7, 1]  ,     )









import inspect
import unittest






class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						List[Any]:




											'''simple docstring'''

											try:
																	import diffusers  # noqa: F401
											except ImportError:
																	assert False






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Any:




											'''simple docstring'''

											import diffusers
											from diffusers.dependency_versions_table import deps

											__snake_case				=		inspect.getmembers(SCREAMING_SNAKE_CASE  ,     inspect.isclass    )

											for cls_name, cls_module in all_classes:
																	if "dummy_" in cls_module.__module__:
																							for backend in cls_module._backends:
																													if backend == "k_diffusion":
																																			__snake_case				=		"k-diffusion"
																													elif backend == "invisible_watermark":
																																			__snake_case				=		"invisible-watermark"
																													assert backend in deps, f'''{backend} is not in the deps table!'''





