




import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__     :     List[str]		    =       get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class       a_       (     SCREAMING_SNAKE_CASE__  ,    unittest.TestCase     ):
	A    							= XLMProphetNetTokenizer
	A    							= False
	A    							= True





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				super().setUp()

				# We have a SentencePiece fixture for testing
				SCREAMING_SNAKE_CASE_								=						XLMProphetNetTokenizer(lowerCamelCase__      ,     keep_accents=lowerCamelCase__      )
				tokenizer.save_pretrained(self.tmpdirname      )





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'[PAD]'
				SCREAMING_SNAKE_CASE_								=						0

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





	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						list(self.get_tokenizer().get_vocab().keys()      )

				self.assertEqual(vocab_keys[0]      ,     '[PAD]'      )
				self.assertEqual(vocab_keys[1]      ,     '[CLS]'      )
				self.assertEqual(vocab_keys[-1]      ,     'j'      )
				self.assertEqual(len(lowerCamelCase__      )      ,     1012      )





	def 						A_(							self      )  ->					List[str]:




				"""simple docstring"""



				self.assertEqual(self.get_tokenizer().vocab_size      ,     1012      )





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						XLMProphetNetTokenizer(lowerCamelCase__      ,     keep_accents=lowerCamelCase__      )

				SCREAMING_SNAKE_CASE_								=						tokenizer.tokenize('This is a test'      )
				self.assertListEqual(lowerCamelCase__      ,     ['▁This', '▁is', '▁a', '▁t', 'est']      )

				self.assertListEqual(
				    tokenizer.convert_tokens_to_ids(lowerCamelCase__      )      ,     [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]      ,     )

				SCREAMING_SNAKE_CASE_								=						tokenizer.tokenize('I was born in 92000, and this is falsé.'      )
				self.assertListEqual(
				    lowerCamelCase__      ,     [
				        SPIECE_UNDERLINE + 'I',
				        SPIECE_UNDERLINE + 'was',
				        SPIECE_UNDERLINE + 'b',
				        'or',
				        'n',
				        SPIECE_UNDERLINE + 'in',
				        SPIECE_UNDERLINE + '',
				        '9',
				        '2',
				        '0',
				        '0',
				        '0',
				        ',',
				        SPIECE_UNDERLINE + 'and',
				        SPIECE_UNDERLINE + 'this',
				        SPIECE_UNDERLINE + 'is',
				        SPIECE_UNDERLINE + 'f',
				        'al',
				        's',
				        'é',
				        '.',
				    ]      ,     )
				SCREAMING_SNAKE_CASE_								=						tokenizer.convert_tokens_to_ids(lowerCamelCase__      )
				self.assertListEqual(
				    lowerCamelCase__      ,     [
				        value + tokenizer.fairseq_offset
				        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
				    ]      ,     )

				SCREAMING_SNAKE_CASE_								=						tokenizer.convert_ids_to_tokens(lowerCamelCase__      )
				self.assertListEqual(
				    lowerCamelCase__      ,     [
				        SPIECE_UNDERLINE + 'I',
				        SPIECE_UNDERLINE + 'was',
				        SPIECE_UNDERLINE + 'b',
				        'or',
				        'n',
				        SPIECE_UNDERLINE + 'in',
				        SPIECE_UNDERLINE + '',
				        '[UNK]',
				        '2',
				        '0',
				        '0',
				        '0',
				        ',',
				        SPIECE_UNDERLINE + 'and',
				        SPIECE_UNDERLINE + 'this',
				        SPIECE_UNDERLINE + 'is',
				        SPIECE_UNDERLINE + 'f',
				        'al',
				        's',
				        '[UNK]',
				        '.',
				    ]      ,     )





	@cached_property
	def 						A_(							self      )  ->					Optional[Any]:




				"""simple docstring"""



				return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased'      )





	@slow
	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						'Hello World!'
				SCREAMING_SNAKE_CASE_								=						[3_5389, 6672, 49, 2]
				self.assertListEqual(lowerCamelCase__      ,     self.big_tokenizer.encode(lowerCamelCase__      )      )





	@slow
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						{'input_ids': [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 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, 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, 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, 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=lowerCamelCase__      ,     model_name='microsoft/xprophetnet-large-wiki100-cased'      ,     revision='1acad1643ddd54a44df6a1b797ada8373685d90e'      ,     )






import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''image_processor''', '''tokenizer''']
			snake_case_  						=			'''OwlViTImageProcessor'''
			snake_case_  						=			('''CLIPTokenizer''', '''CLIPTokenizerFast''')


			def __init__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'     ,		lowerCamelCase__     ,		)
											__lowerCamelCase									=       kwargs.pop('feature_extractor'				)

							__lowerCamelCase									=       image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError('You need to specify an `image_processor`.'				)
							if tokenizer is None:
											raise ValueError('You need to specify a `tokenizer`.'				)

							super().__init__(lowerCamelCase__     ,		lowerCamelCase__				)


			def __call__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__="max_length"     ,		lowerCamelCase__="np"     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''

							if text is None and query_images is None and images is None:
											raise ValueError(
											    'You have to specify at least one text or query image or image. All three cannot be none.'				)

							if text is not None:
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				) or (isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and not isinstance(text[0]     ,		lowerCamelCase__				)):
															__lowerCamelCase									=       [self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)]

											elif isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and isinstance(text[0]     ,		lowerCamelCase__				):
															__lowerCamelCase									=       []

															# Maximum number of queries across batch
															__lowerCamelCase									=       max([len(lowerCamelCase__				) for t in text]				)

															# Pad all batch samples to max number of text queries
															for t in text:
																			if len(lowerCamelCase__				) != max_num_queries:
																							__lowerCamelCase									=       t + [' '] * (max_num_queries - len(lowerCamelCase__				))

																			__lowerCamelCase									=       self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)
																			encodings.append(lowerCamelCase__				)
											else:
															raise TypeError('Input text should be a string, a list of strings or a nested list of strings'				)

											if return_tensors == "np":
															__lowerCamelCase									=       np.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       np.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											elif return_tensors == "jax" and is_flax_available():
															import jax.numpy as jnp

															__lowerCamelCase									=       jnp.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       jnp.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											elif return_tensors == "pt" and is_torch_available():
															import torch

															__lowerCamelCase									=       torch.cat([encoding['input_ids'] for encoding in encodings]     ,		dim=0				)
															__lowerCamelCase									=       torch.cat([encoding['attention_mask'] for encoding in encodings]     ,		dim=0				)

											elif return_tensors == "tf" and is_tf_available():
															import tensorflow as tf

															__lowerCamelCase									=       tf.stack([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       tf.stack([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											else:
															raise ValueError('Target return tensor type could not be returned'				)

											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       input_ids
											__lowerCamelCase									=       attention_mask

							if query_images is not None:
											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       self.image_processor(
											    lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				).pixel_values
											__lowerCamelCase									=       query_pixel_values

							if images is not None:
											__lowerCamelCase									=       self.image_processor(lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)

							if text is not None and images is not None:
											__lowerCamelCase									=       image_features.pixel_values
											return encoding
							elif query_images is not None and images is not None:
											__lowerCamelCase									=       image_features.pixel_values
											return encoding
							elif text is not None or query_images is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**lowerCamelCase__				)     ,		tensor_type=lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							return self.image_processor.post_process(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							return self.image_processor.post_process_object_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			@property
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor_class






			@property
			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor


