
import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class    a_ (			_a					,			unittest.TestCase    ):
			a	:			str					   =							CTRLTokenizer
			a	:			Tuple					   =							False
			a	:			List[str]					   =							False






			def    UpperCamelCase_				(      self  ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									_lowercase					       =					["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
									_lowercase					       =					dict(zip(__UpperCamelCase				,			range(len(__UpperCamelCase  )  )  )  )
									_lowercase					       =					["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
									_lowercase					       =					{"""unk_token""": """<unk>"""}

									_lowercase					       =					os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""vocab_file"""]  )
									_lowercase					       =					os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""merges_file"""]  )
									with open(self.vocab_file				,			"""w"""				,			encoding="""utf-8"""  ) as fp:
															fp.write(json.dumps(__UpperCamelCase  ) + """\n"""  )
									with open(self.merges_file				,			"""w"""				,			encoding="""utf-8"""  ) as fp:
															fp.write("""\n""".join(__UpperCamelCase  )  )






			def    UpperCamelCase_				(      self				,			**__UpperCamelCase  ):
									kwargs.update(self.special_tokens_map  )
									return CTRLTokenizer.from_pretrained(self.tmpdirname				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_lowercase					       =					"""adapt react readapt apt"""
									_lowercase					       =					"""adapt react readapt apt"""
									return input_text, output_text






			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					CTRLTokenizer(self.vocab_file				,			self.merges_file				,			**self.special_tokens_map  )
									_lowercase					       =					"""adapt react readapt apt"""
									_lowercase					       =					"""adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
									_lowercase					       =					tokenizer.tokenize(__UpperCamelCase  )
									self.assertListEqual(__UpperCamelCase				,			__UpperCamelCase  )

									_lowercase					       =					tokens + [tokenizer.unk_token]

									_lowercase					       =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase  )				,			__UpperCamelCase  )

import warnings

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



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					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__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

									if text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

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






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class