




'''simple docstring'''


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {
    "Salesforce/instruct-blip-flan-t5": "https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json",
}





class    __lowercase						(  __magic_name__ ):
   _a							=      """instructblip_vision_model"""







   def __init__(	self      ,							UpperCamelCase=1408      ,							UpperCamelCase=6144      ,							UpperCamelCase=39      ,							UpperCamelCase=16      ,							UpperCamelCase=224      ,							UpperCamelCase=14      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=1e-6      ,							UpperCamelCase=0.0      ,							UpperCamelCase=1e-10      ,							UpperCamelCase=True      ,							**UpperCamelCase      ,							)				->    Any:
     super().__init__(**UpperCamelCase      )

     __a			=					hidden_size
     __a			=					intermediate_size
     __a			=					num_hidden_layers
     __a			=					num_attention_heads
     __a			=					patch_size
     __a			=					image_size
     __a			=					initializer_range
     __a			=					attention_dropout
     __a			=					layer_norm_eps
     __a			=					hidden_act
     __a			=					qkv_bias







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    "PretrainedConfig":
     cls._set_token_in_kwargs(UpperCamelCase      )

     __a	,       __a			=					cls.get_config_dict(UpperCamelCase      ,							**UpperCamelCase      )

     # get the vision config dict if we are loading from InstructBlipConfig
     if config_dict.get('model_type'      ) == "instructblip":
       __a			=					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(UpperCamelCase      ,							**UpperCamelCase      )





class    __lowercase						(  __magic_name__ ):
   _a							=      """instructblip_qformer"""







   def __init__(	self      ,							UpperCamelCase=3_0522      ,							UpperCamelCase=768      ,							UpperCamelCase=12      ,							UpperCamelCase=12      ,							UpperCamelCase=3072      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase=512      ,							UpperCamelCase=0.02      ,							UpperCamelCase=1e-12      ,							UpperCamelCase=0      ,							UpperCamelCase="absolute"      ,							UpperCamelCase=2      ,							UpperCamelCase=1408      ,							**UpperCamelCase      ,							)				->    List[Any]:
     super().__init__(pad_token_id=UpperCamelCase      ,							**UpperCamelCase      )

     __a			=					vocab_size
     __a			=					hidden_size
     __a			=					num_hidden_layers
     __a			=					num_attention_heads
     __a			=					hidden_act
     __a			=					intermediate_size
     __a			=					hidden_dropout_prob
     __a			=					attention_probs_dropout_prob
     __a			=					max_position_embeddings
     __a			=					initializer_range
     __a			=					layer_norm_eps
     __a			=					position_embedding_type
     __a			=					cross_attention_frequency
     __a			=					encoder_hidden_size







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    "PretrainedConfig":
     cls._set_token_in_kwargs(UpperCamelCase      )

     __a	,       __a			=					cls.get_config_dict(UpperCamelCase      ,							**UpperCamelCase      )

     # get the qformer config dict if we are loading from InstructBlipConfig
     if config_dict.get('model_type'      ) == "instructblip":
       __a			=					config_dict['qformer_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(UpperCamelCase      ,							**UpperCamelCase      )





class    __lowercase						(  __magic_name__ ):
   _a							=      """instructblip"""
   _a							=      True







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=32      ,							**UpperCamelCase      )				->    Dict:
     super().__init__(**UpperCamelCase      )

     if vision_config is None:
       __a			=					{}
       logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'      )

     if qformer_config is None:
       __a			=					{}
       logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'      )

     if text_config is None:
       __a			=					{}
       logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'      )

     __a			=					InstructBlipVisionConfig(**UpperCamelCase      )
     __a			=					InstructBlipQFormerConfig(**UpperCamelCase      )
     __a			=					text_config['model_type'] if 'model_type' in text_config else 'opt'
     __a			=					CONFIG_MAPPING[text_model_type](**UpperCamelCase      )

     __a			=					self.text_config.tie_word_embeddings
     __a			=					self.text_config.is_encoder_decoder

     __a			=					num_query_tokens
     __a			=					self.vision_config.hidden_size
     __a			=					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
     __a			=					1.0
     __a			=					0.02







   @classmethod
   def 		UpperCamelCase__			(	cls      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							**UpperCamelCase      ,							)				->    List[str]:
     return cls(
         vision_config=vision_config.to_dict()      ,							qformer_config=qformer_config.to_dict()      ,							text_config=text_config.to_dict()      ,							**UpperCamelCase      ,							)







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					copy.deepcopy(self.__dict__      )
     __a			=					self.vision_config.to_dict()
     __a			=					self.qformer_config.to_dict()
     __a			=					self.text_config.to_dict()
     __a			=					self.__class__.model_type
     return output









'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_ 		=      {
    "vocab_file": {"mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"},
    "tokenizer_file": {
        "mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase_ 		=      {"mobilebert-uncased": 5_12}


UpperCAmelCase_ 		=      {}





class    __lowercase						(  __magic_name__ ):
   _a							=      VOCAB_FILES_NAMES
   _a							=      PRETRAINED_VOCAB_FILES_MAP
   _a							=      PRETRAINED_INIT_CONFIGURATION
   _a							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _a							=      MobileBertTokenizer







   def __init__(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=True      ,							UpperCamelCase="[UNK]"      ,							UpperCamelCase="[SEP]"      ,							UpperCamelCase="[PAD]"      ,							UpperCamelCase="[CLS]"      ,							UpperCamelCase="[MASK]"      ,							UpperCamelCase=True      ,							UpperCamelCase=None      ,							**UpperCamelCase      ,							)				->    List[Any]:
     super().__init__(
         UpperCamelCase      ,							tokenizer_file=UpperCamelCase      ,							do_lower_case=UpperCamelCase      ,							unk_token=UpperCamelCase      ,							sep_token=UpperCamelCase      ,							pad_token=UpperCamelCase      ,							cls_token=UpperCamelCase      ,							mask_token=UpperCamelCase      ,							tokenize_chinese_chars=UpperCamelCase      ,							strip_accents=UpperCamelCase      ,							**UpperCamelCase      ,							)

     __a			=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
     if (
         normalizer_state.get('lowercase'      ,							UpperCamelCase      ) != do_lower_case
         or normalizer_state.get('strip_accents'      ,							UpperCamelCase      ) != strip_accents
         or normalizer_state.get('handle_chinese_chars'      ,							UpperCamelCase      ) != tokenize_chinese_chars
     ):
       __a			=					getattr(UpperCamelCase      ,							normalizer_state.pop('type'      )      )
       __a			=					do_lower_case
       __a			=					strip_accents
       __a			=					tokenize_chinese_chars
       __a			=					normalizer_class(**UpperCamelCase      )

     __a			=					do_lower_case







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      )				->    Tuple:
     __a			=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

     if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

     return output







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[int]:
     __a			=					[self.sep_token_id]
     __a			=					[self.cls_token_id]
     if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
     return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple[str]:
     __a			=					self._tokenizer.model.save(UpperCamelCase      ,							name=UpperCamelCase      )
     return tuple(UpperCamelCase      )



