

import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class 	lowercase    (				_lowercase    ,							unittest.TestCase							):


  """simple docstring"""




  a__					       =  CTRLTokenizer
  a__					       =  False
  a__					       =  False


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

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _UpperCamelCase   :		Union[str, Any]			=						['adapt', 're@@', 'a@@', 'apt', 'c@@', 't', '<unk>']
   _UpperCamelCase   :		int			=						dict(zip(__snake_case	,       range(len(__snake_case))))
   _UpperCamelCase   :		List[Any]			=						['#version: 0.2', 'a p', 'ap t</w>', 'r e', 'a d', 'ad apt</w>', '']
   _UpperCamelCase   :		int			=						{'unk_token': '<unk>'}

   _UpperCamelCase   :		Tuple			=						os.path.join(self.tmpdirname	,       VOCAB_FILES_NAMES['vocab_file'])
   _UpperCamelCase   :		List[Any]			=						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(__snake_case) + '\n')
   with open(self.merges_file	,       'w'	,       encoding='utf-8') as fp:
    fp.write('\n'.join(__snake_case))


  def 		A__					(				self	,       **__snake_case):
   kwargs.update(self.special_tokens_map)
   return CTRLTokenizer.from_pretrained(self.tmpdirname	,       **__snake_case)


  def 		A__					(				self	,       __snake_case):
   _UpperCamelCase   :		Union[str, Any]			=						'adapt react readapt apt'
   _UpperCamelCase   :		Optional[int]			=						'adapt react readapt apt'
   return input_text, output_text






  def 		A__					(				self):
   _UpperCamelCase   :		List[Any]			=						CTRLTokenizer(self.vocab_file	,       self.merges_file	,       **self.special_tokens_map)
   _UpperCamelCase   :		Optional[Any]			=						'adapt react readapt apt'
   _UpperCamelCase   :		Dict			=						'adapt re@@ a@@ c@@ t re@@ adapt apt'.split()
   _UpperCamelCase   :		List[str]			=						tokenizer.tokenize(__snake_case)
   self.assertListEqual(__snake_case	,       __snake_case)

   _UpperCamelCase   :		int			=						tokens + [tokenizer.unk_token]

   _UpperCamelCase   :		Union[str, Any]			=						[0, 1, 2, 4, 5, 1, 0, 3, 6]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case)	,       __snake_case)







from ...processing_utils import ProcessorMixin







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  ["image_processor", "feature_extractor"]
		a__					       =  "TvltImageProcessor"
		a__					       =  "TvltFeatureExtractor"


		def __init__(				self	,       __snake_case	,       __snake_case):
			super().__init__(image_processor=__snake_case	,       feature_extractor=__snake_case)

			_UpperCamelCase   :		List[str]			=						image_processor
			_UpperCamelCase   :		Dict			=						feature_extractor


		def __call__(				self	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=None	,       __snake_case=False	,       __snake_case=False	,       *__snake_case	,       **__snake_case	,       ):

			if images is None and audio is None:
				raise ValueError('You need to specify either an `images` or `audio` input to process.')

			_UpperCamelCase   :		Union[str, Any]			=						None
			if images is not None:
				_UpperCamelCase   :		Tuple			=						self.image_processor(__snake_case	,       mask_pixel=__snake_case	,       *__snake_case	,       **__snake_case)
			if images_mixed is not None:
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor(__snake_case	,       is_mixed=__snake_case	,       *__snake_case	,       **__snake_case)
			if audio is not None:
				_UpperCamelCase   :		Tuple			=						self.feature_extractor(
				    __snake_case	,       *__snake_case	,       sampling_rate=__snake_case	,       mask_audio=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		Tuple			=						{}
			if audio is not None:
				output_dict.update(__snake_case)
			if images is not None:
				output_dict.update(__snake_case)
			if images_mixed_dict is not None:
				output_dict.update(__snake_case)
			return output_dict






		@property
		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						self.image_processor.model_input_names
			_UpperCamelCase   :		List[Any]			=						self.feature_extractor.model_input_names
			return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))




