



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     (		UpperCAmelCase__							,     unittest.TestCase      ):
					"""simple docstring"""






					A__				:		Tuple			       =    BlenderbotSmallTokenizer
					A__				:		List[Any]			       =    False



					def     _a							(     self					:       int					):






								"""simple docstring"""


								super().setUp()

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

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

								A__       =    os.path.join(self.tmpdirname				,					VOCAB_FILES_NAMES['vocab_file']					)
								A__       =    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     _a							(     self					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Any				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


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

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

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



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


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

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









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     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Union[str, Any]			       =    ["image_processor", "tokenizer"]
					A__				:		Optional[Any]			       =    "BridgeTowerImageProcessor"
					A__				:		List[Any]			       =    ("RobertaTokenizer", "RobertaTokenizerFast")



					def __init__(     self					:       List[Any]				,					_snake_case					:       Optional[Any]				,					_snake_case					:       Optional[int]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       List[Any]				,					_snake_case					:       int				,					_snake_case					:       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] = None				,					_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					:       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					:       Optional[int]				,					):






								"""simple docstring"""


								A__       =    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				,					)
								# add pixel_values + pixel_mask
								A__       =    self.image_processor(
								    _snake_case				,					return_tensors=_snake_case				,					do_normalize=_snake_case				,					do_center_crop=_snake_case				,					**_snake_case					)
								encoding.update(_snake_case					)

								return encoding



					def     _a							(     self					:       Any				,					*_snake_case					:       Tuple				,					**_snake_case					:       List[Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       Dict				,					*_snake_case					:       Dict				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


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



					@property
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


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




