
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


SCREAMING_SNAKE_CASE							:int										=    get_tests_dir('fixtures/test_sentencepiece_no_bos.model')





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




	'''simple docstring'''





	snake_case_    =					PegasusTokenizer
	snake_case_    =					PegasusTokenizerFast
	snake_case_    =					True
	snake_case_    =					True






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					super().setUp()

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






	@cached_property
	def     UpperCamelCase_  (	self :			Tuple			):
					return PegasusTokenizer.from_pretrained("google/pegasus-large"			)






	def     UpperCamelCase_  (	self :			str							,**A :			Any			):
					return PegasusTokenizer.from_pretrained(self.tmpdirname							,**A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Dict			):
					return ("This is a test", "This is a test")






	def     UpperCamelCase_  (	self :			str			):
					__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     UpperCamelCase_  (	self :			Union[str, Any]			):
					__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			)							,11_03			)






	def     UpperCamelCase_  (	self :			int			):
					self.assertEqual(self.get_tokenizer().vocab_size							,11_03			)






	def     UpperCamelCase_  (	self :			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     UpperCamelCase_  (	self :			Tuple			):
					__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, 4_13, 6_15, 1_14, 3, 19_71, 1_13, 16_79, 1_07_10, 1_07, 1]
					__A             =			tokenizer([raw_input_str]							,return_tensors=A			).input_ids[0]
					self.assertListEqual(A							,A			)






	def     UpperCamelCase_  (	self :			List[Any]			):
					__A             =			self._large_tokenizer
					# The tracebacks for the following asserts are **better** without messages or self.assertEqual
					assert tokenizer.vocab_size == 9_61_03
					assert tokenizer.pad_token_id == 0
					assert tokenizer.eos_token_id == 1
					assert tokenizer.offset == 1_03
					assert tokenizer.unk_token_id == tokenizer.offset + 2 == 1_05
					assert tokenizer.unk_token == "<unk>"
					assert tokenizer.model_max_length == 10_24
					__A             =			"To ensure a smooth flow of bank resolutions."
					__A             =			[4_13, 6_15, 1_14, 22_91, 19_71, 1_13, 16_79, 1_07_10, 1_07, 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     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			["This is going to be way too long." * 1_50, "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, 10_24)
					assert batch.attention_mask.shape == (2, 10_24)
					assert targets["input_ids"].shape == (2, 5)
					assert len(A			) == 2  # input_ids, attention_mask.







	@slow
	def     UpperCamelCase_  (	self :			Dict			):
					# fmt: off
					__A             =			{"input_ids": [[3_89_79, 1_43, 1_84_85, 6_06, 1_30, 2_66_69, 8_76_86, 1_21, 5_41_89, 11_29, 1_11, 2_66_69, 8_76_86, 1_21, 91_14, 1_47_87, 1_21, 1_32_49, 1_58, 5_92, 9_56, 1_21, 1_46_21, 3_15_76, 1_43, 6_26_13, 1_08, 96_88, 9_30, 4_34_30, 1_15_62, 6_26_13, 3_04, 1_08, 1_14_43, 8_97, 1_08, 93_14, 1_74_15, 6_33_99, 1_08, 1_14_43, 76_14, 1_83_16, 1_18, 42_84, 71_48, 1_24_30, 1_43, 14_00, 2_57_03, 1_58, 1_11, 42_84, 71_48, 1_17_72, 1_43, 2_12_97, 10_64, 1_58, 1_22, 2_04, 35_06, 17_54, 11_33, 1_47_87, 15_81, 1_15, 3_32_24, 44_82, 1_11, 13_55, 1_10, 2_91_73, 3_17, 5_08_33, 1_08, 2_01_47, 9_46_65, 1_11, 7_71_98, 1_07, 1], [1_10, 6_26_13, 1_17, 6_38, 1_12, 11_33, 1_21, 2_00_98, 13_55, 7_90_50, 1_38_72, 1_35, 15_96, 5_35_41, 13_52, 1_41, 1_30_39, 55_42, 1_24, 3_02, 5_18, 1_11, 2_68, 29_56, 1_15, 1_49, 44_27, 1_07, 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_39, 12_35, 27_99, 1_82_89, 1_77_80, 2_04, 1_09, 94_74, 12_96, 1_07, 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				(  __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					PegasusTokenizer
	snake_case_    =					PegasusTokenizerFast
	snake_case_    =					True
	snake_case_    =					True






	def     UpperCamelCase_  (	self :			Union[str, 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     UpperCamelCase_  (	self :			Any			):
					return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv"			)






	def     UpperCamelCase_  (	self :			int							,**A :			str			):
					return PegasusTokenizer.from_pretrained(self.tmpdirname							,**A			)






	def     UpperCamelCase_  (	self :			List[str]							,A :			str			):
					return ("This is a test", "This is a test")






	def     UpperCamelCase_  (	self :			Dict			):
					__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     UpperCamelCase_  (	self :			Dict			):
					__A             =			["This is going to be way too long." * 10_00, "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, 40_96)
					assert batch.attention_mask.shape == (2, 40_96)
					assert targets["input_ids"].shape == (2, 5)
					assert len(A			) == 2  # input_ids, attention_mask.







	def     UpperCamelCase_  (	self :			List[Any]			):
					__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							,[1_82, 1_17, 1_42, 5_87, 42_11, 1_20, 1_17, 2_63, 1_12, 8_04, 1_09, 8_56, 2_50_16, 31_37, 4_64, 1_09, 2_69_55, 31_37, 1]							,)


import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE							:Any										=    logging.get_logger(__name__)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			int							,*A :			Dict							,**A :			Dict			):
					warnings.warn(
					    "The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
					    " use CLIPImageProcessor instead."							,A							,)
					super().__init__(*A							,**A			)
