



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


_lowercase    	=		logging.get_logger(__name__)

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



class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''instructblip_vision_model'''




     def __init__(					self			,    a__=1_4_0_8			,    a__=6_1_4_4			,    a__=3_9			,    a__=1_6			,    a__=2_2_4			,    a__=1_4			,    a__="gelu"			,    a__=1e-6			,    a__=0.0			,    a__=1e-10			,    a__=True			,    **a__			,    ):
         super().__init__(**a__)

         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 		snake_case_		(					cls			,    a__			,    **a__):
         cls._set_token_in_kwargs(a__)

         A__			,						A__					=       cls.get_config_dict(a__			,    **a__)

         # 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(a__			,    **a__)







class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''instructblip_qformer'''




     def __init__(					self			,    a__=3_0_5_2_2			,    a__=7_6_8			,    a__=1_2			,    a__=1_2			,    a__=3_0_7_2			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=0.0_2			,    a__=1e-12			,    a__=0			,    a__="absolute"			,    a__=2			,    a__=1_4_0_8			,    **a__			,    ):
         super().__init__(pad_token_id=a__			,    **a__)

         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 		snake_case_		(					cls			,    a__			,    **a__):
         cls._set_token_in_kwargs(a__)

         A__			,						A__					=       cls.get_config_dict(a__			,    **a__)

         # 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(a__			,    **a__)







class      _UpperCAmelCase      (							A__  ):
     UpperCamelCase__		     =					'''instructblip'''
     UpperCamelCase__		     =					True




     def __init__(					self			,    a__=None			,    a__=None			,    a__=None			,    a__=3_2			,    **a__):
         super().__init__(**a__)

         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(**a__)
         A__					=       InstructBlipQFormerConfig(**a__)
         A__					=       text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
         A__					=       CONFIG_MAPPING[text_model_type](**a__)

         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__					=       0.0_2




     @classmethod
     def 		snake_case_		(					cls			,    a__			,    a__			,    a__			,    **a__			,    ):
         return cls(
             vision_config=vision_config.to_dict()			,    qformer_config=qformer_config.to_dict()			,    text_config=text_config.to_dict()			,    **a__			,    )




     def 		snake_case_		(					self):
         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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowercase    	=		{
    "configuration_layoutlmv2": ["LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMv2Config"],
    "processing_layoutlmv2": ["LayoutLMv2Processor"],
    "tokenization_layoutlmv2": ["LayoutLMv2Tokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		["LayoutLMv2TokenizerFast"]

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		["LayoutLMv2FeatureExtractor"]
   _lowercase    	=		["LayoutLMv2ImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase    	=		[
       "LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST",
       "LayoutLMv2ForQuestionAnswering",
       "LayoutLMv2ForSequenceClassification",
       "LayoutLMv2ForTokenClassification",
       "LayoutLMv2Layer",
       "LayoutLMv2Model",
       "LayoutLMv2PreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
   from .processing_layoutlmva import LayoutLMvaProcessor
   from .tokenization_layoutlmva import LayoutLMvaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_layoutlmva import (
          LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMvaForQuestionAnswering,
          LayoutLMvaForSequenceClassification,
          LayoutLMvaForTokenClassification,
          LayoutLMvaLayer,
          LayoutLMvaModel,
          LayoutLMvaPreTrainedModel,
      )
else:
   import sys

   _lowercase    	=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
