





'''simple docstring'''







import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase     :       List[Any]						=  logging.get_logger(__name__)

lowerCamelCase     :       Union[str, Any]						=  {
    "Salesforce/blip-vqa-base": "https://huggingface.co/Salesforce/blip-vqa-base/resolve/main/config.json",
    "Salesforce/blip-vqa-capfit-large": (
        "https://huggingface.co/Salesforce/blip-vqa-base-capfit/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-base": (
        "https://huggingface.co/Salesforce/blip-image-captioning-base/resolve/main/config.json"
    ),
    "Salesforce/blip-image-captioning-large": (
        "https://huggingface.co/Salesforce/blip-image-captioning-large/resolve/main/config.json"
    ),
    "Salesforce/blip-itm-base-coco": "https://huggingface.co/Salesforce/blip-itm-base-coco/resolve/main/config.json",
    "Salesforce/blip-itm-large-coco": "https://huggingface.co/Salesforce/blip-itm-large-coco/resolve/main/config.json",
    "Salesforce/blip-itm-base-flikr": "https://huggingface.co/Salesforce/blip-itm-base-flikr/resolve/main/config.json",
    "Salesforce/blip-itm-large-flikr": (
        "https://huggingface.co/Salesforce/blip-itm-large-flikr/resolve/main/config.json"
    ),
}


class 					__a      (__lowerCamelCase     ):
							__a					:   List[str]									=       '''blip_text_model'''

							def __init__(							self							:     str       ,     __magic_name__							:     List[str]=3_05_24       ,     __magic_name__							:     int=7_68       ,     __magic_name__							:     Any=7_68       ,     __magic_name__							:     Optional[Any]=30_72       ,     __magic_name__							:     Tuple=7_68       ,     __magic_name__							:     Dict=12       ,     __magic_name__							:     Union[str, Any]=8       ,     __magic_name__							:     List[str]=5_12       ,     __magic_name__							:     int="gelu"       ,     __magic_name__							:     Any=1E-12       ,     __magic_name__							:     Optional[int]=0.0       ,     __magic_name__							:     List[Any]=0.0       ,     __magic_name__							:     Optional[int]=0.0_2       ,     __magic_name__							:     Tuple=3_05_22       ,     __magic_name__							:     Tuple=2       ,     __magic_name__							:     Any=0       ,     __magic_name__							:     Any=1_02       ,     __magic_name__							:     Optional[Any]=True       ,     __magic_name__							:     Tuple=True       ,     **__magic_name__							:     Union[str, Any]       ,     )					->       Union[str, Any]:







													"""simple docstring"""
													super().__init__(
													    pad_token_id=a_       ,     bos_token_id=a_       ,     eos_token_id=a_       ,     sep_token_id=a_       ,     **a_       ,     )

													UpperCAmelCase_			:      Dict     						=  vocab_size
													UpperCAmelCase_			:      List[Any]     						=  hidden_size
													UpperCAmelCase_			:      Optional[int]     						=  encoder_hidden_size
													UpperCAmelCase_			:      List[Any]     						=  intermediate_size
													UpperCAmelCase_			:      Optional[Any]     						=  projection_dim
													UpperCAmelCase_			:      List[Any]     						=  hidden_dropout_prob
													UpperCAmelCase_			:      Tuple     						=  num_hidden_layers
													UpperCAmelCase_			:      int     						=  num_attention_heads
													UpperCAmelCase_			:      str     						=  max_position_embeddings
													UpperCAmelCase_			:      Optional[Any]     						=  layer_norm_eps
													UpperCAmelCase_			:      Union[str, Any]     						=  hidden_act
													UpperCAmelCase_			:      Tuple     						=  initializer_range
													UpperCAmelCase_			:      Optional[int]     						=  attention_probs_dropout_prob
													UpperCAmelCase_			:      int     						=  is_decoder
													UpperCAmelCase_			:      Union[str, Any]     						=  use_cache



							@classmethod
							def     UpperCAmelCase__	(							cls							:     Tuple       ,     __magic_name__							:     Tuple       ,     **__magic_name__							:     Optional[Any]   )					->       str:







													"""simple docstring"""
													cls._set_token_in_kwargs(a_   )

													UpperCAmelCase_			:      Optional[int]     						=  cls.get_config_dict(a_       ,     **a_   )

													# get the text config dict if we are loading from BlipConfig
													if config_dict.get('''model_type'''   ) == "blip":
																			UpperCAmelCase_			:      str     						=  config_dict["text_config"]

													if "model_type" in config_dict and hasattr(cls       ,     '''model_type'''   ) and config_dict["model_type"] != cls.model_type:
																			logger.warning(
																			    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																			    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

													return cls.from_dict(a_       ,     **a_   )





class 					__a      (__lowerCamelCase     ):
							__a					:   Any									=       '''blip_vision_model'''

							def __init__(							self							:     Optional[Any]       ,     __magic_name__							:     List[Any]=7_68       ,     __magic_name__							:     Union[str, Any]=30_72       ,     __magic_name__							:     Union[str, Any]=5_12       ,     __magic_name__							:     List[str]=12       ,     __magic_name__							:     Dict=12       ,     __magic_name__							:     Any=3_84       ,     __magic_name__							:     Optional[Any]=16       ,     __magic_name__							:     List[str]="gelu"       ,     __magic_name__							:     Any=1E-5       ,     __magic_name__							:     Tuple=0.0       ,     __magic_name__							:     Optional[Any]=1E-10       ,     **__magic_name__							:     List[Any]       ,     )					->       List[str]:







													"""simple docstring"""
													super().__init__(**a_   )

													UpperCAmelCase_			:      List[Any]     						=  hidden_size
													UpperCAmelCase_			:      int     						=  intermediate_size
													UpperCAmelCase_			:      str     						=  projection_dim
													UpperCAmelCase_			:      List[str]     						=  num_hidden_layers
													UpperCAmelCase_			:      Dict     						=  num_attention_heads
													UpperCAmelCase_			:      Dict     						=  patch_size
													UpperCAmelCase_			:      Optional[int]     						=  image_size
													UpperCAmelCase_			:      Dict     						=  initializer_range
													UpperCAmelCase_			:      int     						=  attention_dropout
													UpperCAmelCase_			:      Tuple     						=  layer_norm_eps
													UpperCAmelCase_			:      Optional[Any]     						=  hidden_act



							@classmethod
							def     UpperCAmelCase__	(							cls							:     Union[str, Any]       ,     __magic_name__							:     Optional[int]       ,     **__magic_name__							:     Dict   )					->       Dict:







													"""simple docstring"""
													cls._set_token_in_kwargs(a_   )

													UpperCAmelCase_			:      List[Any]     						=  cls.get_config_dict(a_       ,     **a_   )

													# get the vision config dict if we are loading from BlipConfig
													if config_dict.get('''model_type'''   ) == "blip":
																			UpperCAmelCase_			:      List[Any]     						=  config_dict["vision_config"]

													if "model_type" in config_dict and hasattr(cls       ,     '''model_type'''   ) and config_dict["model_type"] != cls.model_type:
																			logger.warning(
																			    F"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																			    F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""   )

													return cls.from_dict(a_       ,     **a_   )





class 					__a      (__lowerCamelCase     ):
							__a					:   Optional[int]									=       '''blip'''
							__a					:   Optional[int]									=       True

							def __init__(							self							:     int       ,     __magic_name__							:     int=None       ,     __magic_name__							:     List[str]=None       ,     __magic_name__							:     Optional[int]=5_12       ,     __magic_name__							:     Any=2.6_5_9_2       ,     __magic_name__							:     Dict=2_56       ,     **__magic_name__							:     Dict       ,     )					->       Tuple:







													"""simple docstring"""
													super().__init__(**a_   )

													if text_config is None:
																			UpperCAmelCase_			:      Tuple     						=  {}
																			logger.info('''`text_config` is `None`. Initializing the `BlipTextConfig` with default values.'''   )

													if vision_config is None:
																			UpperCAmelCase_			:      List[str]     						=  {}
																			logger.info('''`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.'''   )

													UpperCAmelCase_			:      Dict     						=  BlipTextConfig(**a_   )
													UpperCAmelCase_			:      str     						=  BlipVisionConfig(**a_   )

													UpperCAmelCase_			:      int     						=  self.vision_config.hidden_size

													UpperCAmelCase_			:      List[str]     						=  projection_dim
													UpperCAmelCase_			:      Any     						=  logit_scale_init_value
													UpperCAmelCase_			:      int     						=  1.0
													UpperCAmelCase_			:      List[Any]     						=  0.0_2
													UpperCAmelCase_			:      int     						=  image_text_hidden_size

							@classmethod
							def     UpperCAmelCase__	(							cls							:     Optional[Any]       ,     __magic_name__							:     Any       ,     __magic_name__							:     List[str]       ,     **__magic_name__							:     int   )					->       int:







													"""simple docstring"""
													return cls(text_config=text_config.to_dict()       ,     vision_config=vision_config.to_dict()       ,     **a_   )



							def     UpperCAmelCase__	(							self							:     Dict   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      Union[str, Any]     						=  copy.deepcopy(self.__dict__   )
													UpperCAmelCase_			:      Tuple     						=  self.text_config.to_dict()
													UpperCAmelCase_			:      Tuple     						=  self.vision_config.to_dict()
													UpperCAmelCase_			:      int     						=  self.__class__.model_type
													return output












'''simple docstring'''







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      (lowerCamelCase       ,		unittest.TestCase     ):
							__a					:   List[str]									=       BlenderbotSmallTokenizer
							__a					:   List[Any]									=       False

							def     UpperCAmelCase__	(							self							:     str   )					->       str:







													"""simple docstring"""
													super().setUp()

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

													UpperCAmelCase_			:      int     						=  ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
													UpperCAmelCase_			:      Optional[Any]     						=  {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

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

							def     UpperCAmelCase__	(							self							:     List[Any]       ,     **__magic_name__							:     Dict   )					->       Tuple:







													"""simple docstring"""
													kwargs.update(self.special_tokens_map   )
													return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,     **__magic_name__   )

							def     UpperCAmelCase__	(							self							:     Optional[int]       ,     __magic_name__							:     List[str]   )					->       List[str]:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  '''adapt act apte'''
													UpperCAmelCase_			:      Tuple     						=  '''adapt act apte'''
													return input_text, output_text

							def     UpperCAmelCase__	(							self							:     str   )					->       Any:







													"""simple docstring"""
													UpperCAmelCase_			:      str     						=  BlenderbotSmallTokenizer(self.vocab_file       ,     self.merges_file       ,     **self.special_tokens_map   )
													UpperCAmelCase_			:      List[Any]     						=  '''adapt act apte'''
													UpperCAmelCase_			:      Dict     						=  ['''adapt''', '''act''', '''ap@@''', '''te''']
													UpperCAmelCase_			:      Dict     						=  tokenizer.tokenize(__magic_name__   )
													self.assertListEqual(__magic_name__       ,     __magic_name__   )

													UpperCAmelCase_			:      Tuple     						=  [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

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

							def     UpperCAmelCase__	(							self							:     int   )					->       List[str]:







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



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







													"""simple docstring"""
													UpperCAmelCase_			:      int     						=  BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
													UpperCAmelCase_			:      List[Any]     						=  '''I am a small frog .'''
													UpperCAmelCase_			:      Any     						=  '''.'''
													UpperCAmelCase_			:      List[Any]     						=  tok(__magic_name__   )['''input_ids''']
													UpperCAmelCase_			:      Optional[int]     						=  tok(__magic_name__   )['''input_ids''']

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





