




import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					["""image_processor""", """tokenizer"""]
						UpperCamelCase     						=					"""FlavaImageProcessor"""
						UpperCamelCase     						=					("""BertTokenizer""", """BertTokenizerFast""")

						def __init__(		self   :Optional[Any]    , __snake_case   :str=None    , __snake_case   :str=None    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	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."""    , __snake_case    , )
														__magic_name__	:	str								=kwargs.pop("""feature_extractor""" )

										__magic_name__	:	str								=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__(__snake_case    , __snake_case )
										__magic_name__	:	List[Any]								=self.image_processor

						def __call__(		self   :Tuple    , __snake_case   :Optional[ImageInput] = None    , __snake_case   :Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None    , __snake_case   :bool = True    , __snake_case   :Union[bool, str, PaddingStrategy] = False    , __snake_case   :Union[bool, str, TruncationStrategy] = False    , __snake_case   :Optional[int] = None    , __snake_case   :int = 0    , __snake_case   :Optional[int] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :bool = False    , __snake_case   :bool = False    , __snake_case   :bool = False    , __snake_case   :bool = False    , __snake_case   :bool = True    , __snake_case   :Optional[Union[str, TensorType]] = None    , **__snake_case   :Tuple    , ):
										'''simple docstring'''

										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:
														__magic_name__	:	Any								=self.tokenizer(
														    text=__snake_case    , add_special_tokens=__snake_case    , padding=__snake_case    , truncation=__snake_case    , max_length=__snake_case    , stride=__snake_case    , pad_to_multiple_of=__snake_case    , return_token_type_ids=__snake_case    , return_attention_mask=__snake_case    , return_overflowing_tokens=__snake_case    , return_special_tokens_mask=__snake_case    , return_offsets_mapping=__snake_case    , return_length=__snake_case    , verbose=__snake_case    , return_tensors=__snake_case    , **__snake_case    , )
										if images is not None:
														__magic_name__	:	List[Any]								=self.image_processor(
														    __snake_case    , return_image_mask=__snake_case    , return_codebook_pixels=__snake_case    , return_tensors=__snake_case    , **__snake_case    , )

										if text is not None and images is not None:
														encoding.update(__snake_case )
														return encoding
										elif text is not None:
														return encoding
										else:
														return BatchEncoding(data=dict(**__snake_case )    , tensor_type=__snake_case )

						def     A__						(		self   :List[str]    , *__snake_case   :Any    , **__snake_case   :Any ):
										'''simple docstring'''

										return self.tokenizer.batch_decode(*__snake_case    , **__snake_case )

						def     A__						(		self   :Optional[int]    , *__snake_case   :List[Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										return self.tokenizer.decode(*__snake_case    , **__snake_case )

						@property
						def     A__						(		self   :Any ):
										'''simple docstring'''

										__magic_name__	:	str								=self.tokenizer.model_input_names
										__magic_name__	:	Union[str, Any]								=self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )

						@property
						def     A__						(		self   :Union[str, Any] ):
										'''simple docstring'''

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


						@property
						def     A__						(		self   :int ):
										'''simple docstring'''

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










import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin




class 		__A      (			UpperCamelCase__					,						unittest.TestCase			):
						UpperCamelCase     						=					BlenderbotSmallTokenizer
						UpperCamelCase     						=					False

						def     A__						(		self   :Dict ):
										'''simple docstring'''

										super().setUp()

										__magic_name__	:	Tuple								=["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
										__magic_name__	:	Optional[Any]								=dict(zip(__snake_case    , range(len(__snake_case ) ) ) )

										__magic_name__	:	List[Any]								=["""#version: 0.2""", """a p""", """t e</w>""", """ap t</w>""", """a d""", """ad apt</w>""", """a c""", """ac t</w>""", """"""]
										__magic_name__	:	int								={"""unk_token""": """__unk__""", """bos_token""": """__start__""", """eos_token""": """__end__"""}

										__magic_name__	:	Dict								=os.path.join(self.tmpdirname    , VOCAB_FILES_NAMES["""vocab_file"""] )
										__magic_name__	:	int								=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   :Union[str, Any]    , **__snake_case   :Optional[int] ):
										'''simple docstring'''

										kwargs.update(self.special_tokens_map )
										return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname    , **__snake_case )

						def     A__						(		self   :str    , __snake_case   :List[Any] ):
										'''simple docstring'''

										__magic_name__	:	Any								="""adapt act apte"""
										__magic_name__	:	str								="""adapt act apte"""
										return input_text, output_text

						def     A__						(		self   :List[str] ):
										'''simple docstring'''

										__magic_name__	:	Tuple								=BlenderbotSmallTokenizer(self.vocab_file    , self.merges_file    , **self.special_tokens_map )
										__magic_name__	:	List[str]								="""adapt act apte"""
										__magic_name__	:	Optional[int]								=["""adapt""", """act""", """ap@@""", """te"""]
										__magic_name__	:	List[Any]								=tokenizer.tokenize(__snake_case )
										self.assertListEqual(__snake_case    , __snake_case )

										__magic_name__	:	int								=[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

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

						def     A__						(		self   :Optional[int] ):
										'''simple docstring'''

										__magic_name__	:	Any								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										assert tok("""sam""" ).input_ids == [13_84]
										__magic_name__	:	Tuple								="""I am a small frog."""
										__magic_name__	:	Tuple								=tok([src_text]    , padding=__snake_case    , truncation=__snake_case )["""input_ids"""]
										__magic_name__	:	Optional[Any]								=tok.batch_decode(__snake_case    , skip_special_tokens=__snake_case    , clean_up_tokenization_spaces=__snake_case )[0]
										assert src_text != decoded  # I wish it did!
										assert decoded == "i am a small frog ."


						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M""" )
										__magic_name__	:	int								="""I am a small frog ."""
										__magic_name__	:	List[Any]								="""."""
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]
										__magic_name__	:	List[str]								=tok(__snake_case )["""input_ids"""]

										assert encoded[-1] == encoded_dot[0]




