
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        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: List[str]	=		BlenderbotSmallTokenizer
						UpperCamelCase_							: List[Any]	=		False

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								super().setUp()

								_UpperCamelCase						    =	["""__start__""", """adapt""", """act""", """ap@@""", """te""", """__end__""", """__unk__"""]
								_UpperCamelCase						    =	dict(zip(a       ,							range(len(a				)				)				)				)

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

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

						def  A_				(			self       ,							**a				)					->      Union[str, Any]:


								'''simple docstring'''

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

						def  A_				(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	"""adapt act apte"""
								_UpperCamelCase						    =	"""adapt act apte"""
								return input_text, output_text

						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	BlenderbotSmallTokenizer(self.vocab_file       ,							self.merges_file       ,							**self.special_tokens_map				)
								_UpperCamelCase						    =	"""adapt act apte"""
								_UpperCamelCase						    =	["""adapt""", """act""", """ap@@""", """te"""]
								_UpperCamelCase						    =	tokenizer.tokenize(a				)
								self.assertListEqual(a       ,							a				)

								_UpperCamelCase						    =	[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								_UpperCamelCase						    =	[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(a				)       ,							a				)

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

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







						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								_UpperCamelCase						    =	BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""				)
								_UpperCamelCase						    =	"""I am a small frog ."""
								_UpperCamelCase						    =	"""."""
								_UpperCamelCase						    =	tok(a				)["""input_ids"""]
								_UpperCamelCase						    =	tok(a				)["""input_ids"""]

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





import warnings
from typing import List, Optional, Union

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







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Dict	=		["image_processor", "tokenizer"]
						UpperCamelCase_							: Union[str, Any]	=		"ViltImageProcessor"
						UpperCamelCase_							: List[str]	=		("BertTokenizer", "BertTokenizerFast")

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

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

								_UpperCamelCase						    =	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__(a       ,							a				)
								_UpperCamelCase						    =	self.image_processor

						def __call__(			self       ,							a       ,							a = None       ,							a = True       ,							a = False       ,							a = None       ,							a = None       ,							a = 0       ,							a = None       ,							a = None       ,							a = None       ,							a = False       ,							a = False       ,							a = False       ,							a = False       ,							a = True       ,							a = None       ,							**a       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer(
								    text=a       ,							add_special_tokens=a       ,							padding=a       ,							truncation=a       ,							max_length=a       ,							stride=a       ,							pad_to_multiple_of=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							return_overflowing_tokens=a       ,							return_special_tokens_mask=a       ,							return_offsets_mapping=a       ,							return_length=a       ,							verbose=a       ,							return_tensors=a       ,							**a       ,							)
								# add pixel_values + pixel_mask
								_UpperCamelCase						    =	self.image_processor(a       ,							return_tensors=a				)
								encoding.update(a				)

								return encoding

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

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

						def  A_				(			self       ,							*a       ,							**a				)					->      int:


								'''simple docstring'''

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

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer.model_input_names
								_UpperCamelCase						    =	self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

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







						@property
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

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



