


"""simple docstring"""






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							OpenAIGPTTokenizer
		A    =							OpenAIGPTTokenizerFast
		A    =							True
		A    =							False
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					_UpperCamelCase					:Optional[int]					     =	[
					    '''l''',
					    '''o''',
					    '''w''',
					    '''e''',
					    '''r''',
					    '''s''',
					    '''t''',
					    '''i''',
					    '''d''',
					    '''n''',
					    '''w</w>''',
					    '''r</w>''',
					    '''t</w>''',
					    '''lo''',
					    '''low''',
					    '''er</w>''',
					    '''low</w>''',
					    '''lowest</w>''',
					    '''newer</w>''',
					    '''wider</w>''',
					    '''<unk>''',
					]
					_UpperCamelCase					:Optional[Any]					     =	dict(zip(SCREAMING_SNAKE_CASE__					,				range(len(SCREAMING_SNAKE_CASE__   )   )   )   )
					_UpperCamelCase					:Union[str, Any]					     =	['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

					_UpperCamelCase					:Optional[int]					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''vocab_file''']   )
					_UpperCamelCase					:Optional[int]					     =	os.path.join(self.tmpdirname					,				VOCAB_FILES_NAMES['''merges_file''']   )
					with open(self.vocab_file					,				'''w'''   ) as fp:
								fp.write(json.dumps(SCREAMING_SNAKE_CASE__   )   )
					with open(self.merges_file					,				'''w'''   ) as fp:
								fp.write('''\n'''.join(SCREAMING_SNAKE_CASE__   )   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return "lower newer", "lower newer"
		def   _UpperCamelCase( self   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	OpenAIGPTTokenizer(self.vocab_file					,				self.merges_file   )

					_UpperCamelCase					:Dict					     =	'''lower'''
					_UpperCamelCase					:Optional[int]					     =	['''low''', '''er</w>''']
					_UpperCamelCase					:List[str]					     =	tokenizer.tokenize(SCREAMING_SNAKE_CASE__   )
					self.assertListEqual(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Any					     =	tokens + ['''<unk>''']
					_UpperCamelCase					:str					     =	[14, 15, 20]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				SCREAMING_SNAKE_CASE__=15   )      ->	Optional[int]:

					"""simple docstring"""



					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"   ):
											_UpperCamelCase					:int					     =	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

											# Simple input
											_UpperCamelCase					:Optional[Any]					     =	'''This is a simple input'''
											_UpperCamelCase					:Dict					     =	['''This is a simple input 1''', '''This is a simple input 2''']
											_UpperCamelCase					:Any					     =	('''This is a simple input''', '''This is a pair''')
											_UpperCamelCase					:Dict					     =	[
											    ('''This is a simple input 1''', '''This is a simple input 2'''),
											    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
											]

											# Simple input tests
											self.assertRaises(SCREAMING_SNAKE_CASE__					,				tokenizer_r.encode					,				SCREAMING_SNAKE_CASE__					,				max_length=SCREAMING_SNAKE_CASE__					,				padding='''max_length'''   )

											# Simple input
											self.assertRaises(SCREAMING_SNAKE_CASE__					,				tokenizer_r.encode_plus					,				SCREAMING_SNAKE_CASE__					,				max_length=SCREAMING_SNAKE_CASE__					,				padding='''max_length'''   )

											# Simple input
											self.assertRaises(
											    SCREAMING_SNAKE_CASE__					,				tokenizer_r.batch_encode_plus					,				SCREAMING_SNAKE_CASE__					,				max_length=SCREAMING_SNAKE_CASE__					,				padding='''max_length'''					,				)

											# Pair input
											self.assertRaises(SCREAMING_SNAKE_CASE__					,				tokenizer_r.encode					,				SCREAMING_SNAKE_CASE__					,				max_length=SCREAMING_SNAKE_CASE__					,				padding='''max_length'''   )

											# Pair input
											self.assertRaises(SCREAMING_SNAKE_CASE__					,				tokenizer_r.encode_plus					,				SCREAMING_SNAKE_CASE__					,				max_length=SCREAMING_SNAKE_CASE__					,				padding='''max_length'''   )

											# Pair input
											self.assertRaises(
											    SCREAMING_SNAKE_CASE__					,				tokenizer_r.batch_encode_plus					,				SCREAMING_SNAKE_CASE__					,				max_length=SCREAMING_SNAKE_CASE__					,				padding='''max_length'''					,				)





		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					pass






@require_ftfy
@require_spacy
@require_tokenizers
class      A(  lowerCamelCase__		):

		"""simple docstring"""





		pass





"""simple docstring"""






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class      A(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							["image_processor", "tokenizer"]
		A    =							"ViTImageProcessor"
		A    =							("CLIPTokenizer", "CLIPTokenizerFast")
		def __init__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
								    ''' instead.'''					,				SCREAMING_SNAKE_CASE__					,				)
								_UpperCamelCase					:List[str]					     =	kwargs.pop('''feature_extractor'''   )

					_UpperCamelCase					:Optional[Any]					     =	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__(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
		def __call__( self					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				SCREAMING_SNAKE_CASE__=None					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					if text is None and visual_prompt is None and images is None:
								raise ValueError('''You have to specify either text, visual prompt or images.'''   )

					if text is not None and visual_prompt is not None:
								raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

					if text is not None:
								_UpperCamelCase					:Dict					     =	self.tokenizer(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if images is not None:
								_UpperCamelCase					:Union[str, Any]					     =	self.image_processor(SCREAMING_SNAKE_CASE__					,				return_tensors=SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )

					if visual_prompt is not None and images is not None:
								_UpperCamelCase					:List[str]					     =	{
								    '''pixel_values''': image_features.pixel_values,
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					elif text is not None and images is not None:
								_UpperCamelCase					:List[Any]					     =	image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					elif visual_prompt is not None:
								_UpperCamelCase					:Optional[Any]					     =	{
								    '''conditional_pixel_values''': prompt_features.pixel_values,
								}
								return encoding
					else:
								return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__   )					,				tensor_type=SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Any:

					"""simple docstring"""



					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self					,				*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )      ->	Tuple:

					"""simple docstring"""



					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__					,				**SCREAMING_SNAKE_CASE__   )
		@property
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



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





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

					"""simple docstring"""



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

