






"""simple docstring"""
import json
import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Optional[Any]    =				CTRLTokenizer
	_A       :				List[Any]    =				False
	_A       :				Union[str, Any]    =				False





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

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

		A_		:						Optional[Any]	    =   os.path.join(self.tmpdirname     ,     VOCAB_FILES_NAMES["""vocab_file"""]							)
		A_		:						str	    =   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(lowerCAmelCase_							) + """\n"""							)
		with open(self.merges_file     ,     """w"""     ,     encoding="""utf-8"""							) as fp:
			fp.write("""\n""".join(lowerCAmelCase_							)							)





	def    lowerCamelCase(self     ,     **lowerCAmelCase_							):
		kwargs.update(self.special_tokens_map							)
		return CTRLTokenizer.from_pretrained(self.tmpdirname     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   """adapt react readapt apt"""
		A_		:						Tuple	    =   """adapt react readapt apt"""
		return input_text, output_text





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   CTRLTokenizer(self.vocab_file     ,     self.merges_file     ,     **self.special_tokens_map							)
		A_		:						int	    =   """adapt react readapt apt"""
		A_		:						Tuple	    =   """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
		A_		:						Tuple	    =   tokenizer.tokenize(lowerCAmelCase_							)
		self.assertListEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)

		A_		:						Optional[int]	    =   tokens + [tokenizer.unk_token]

		A_		:						List[str]	    =   [0, 1, 2, 4, 5, 1, 0, 3, 6]
		self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)     ,     lowerCAmelCase_							)









"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				str    =				"""AutoImageProcessor"""
	_A       :				Union[str, Any]    =				"""AutoTokenizer"""





	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		super().__init__(lowerCAmelCase_     ,     lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   self.image_processor





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

		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:
			A_		:						Optional[int]	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						List[Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

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





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		return ["input_ids", "attention_mask", "pixel_values"]

