





from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


UpperCAmelCase     :     Optional[Any]							=	logging.get_logger(__name__)


# General docstring
UpperCAmelCase     :     Optional[int]							=	"""MobileNetV1Config"""

# Base docstring
UpperCAmelCase     :     List[Any]							=	"""google/mobilenet_v1_1.0_224"""
UpperCAmelCase     :     List[str]							=	[1, 1024, 7, 7]

# Image classification docstring
UpperCAmelCase     :     Any							=	"""google/mobilenet_v1_1.0_224"""
UpperCAmelCase     :     List[Any]							=	"""tabby, tabby cat"""


UpperCAmelCase     :     List[str]							=	[
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]=None						):





  """simple docstring"""



  a__     : Dict  ={}

  if isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : str  =model.mobilenet_va
  else:
    a__     : int  =model

  a__     : Optional[int]  ="MobilenetV1/Conv2d_0/"
  a__     : str  =backbone.conv_stem.convolution.weight
  a__     : List[Any]  =backbone.conv_stem.normalization.bias
  a__     : Union[str, Any]  =backbone.conv_stem.normalization.weight
  a__     : int  =backbone.conv_stem.normalization.running_mean
  a__     : Any  =backbone.conv_stem.normalization.running_var

  for i in range(13						):
    a__     : int  =i + 1
    a__     : int  =i * 2

    a__     : Union[str, Any]  =backbone.layer[pt_index]
    a__     : Union[str, Any]  =f'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
    a__     : Union[str, Any]  =pointer.convolution.weight
    a__     : Optional[Any]  =pointer.normalization.bias
    a__     : str  =pointer.normalization.weight
    a__     : str  =pointer.normalization.running_mean
    a__     : str  =pointer.normalization.running_var

    a__     : List[Any]  =backbone.layer[pt_index + 1]
    a__     : str  =f'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
    a__     : Any  =pointer.convolution.weight
    a__     : Optional[Any]  =pointer.normalization.bias
    a__     : Tuple  =pointer.normalization.weight
    a__     : List[Any]  =pointer.normalization.running_mean
    a__     : List[Any]  =pointer.normalization.running_var

  if isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : List[str]  ="MobilenetV1/Logits/Conv2d_1c_1x1/"
    a__     : str  =model.classifier.weight
    a__     : List[Any]  =model.classifier.bias

  return tf_to_pt_map
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       List[str]						):





  """simple docstring"""



  try:
    import numpy as np
    import tensorflow as tf
  except ImportError:
    logger.error(
        "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
        "https://www.tensorflow.org/install/ for installation instructions."						)
    raise

  # Load weights from TF model
  a__     : Tuple  =tf.train.list_variables(SCREAMING_SNAKE_CASE						)
  a__     : Optional[Any]  ={}
  for name, shape in init_vars:
    logger.info(f'''Loading TF weight {name} with shape {shape}'''						)
    a__     : List[Any]  =tf.train.load_variable(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
    a__     : Tuple  =array

  # Build TF to PyTorch weights loading map
  a__     : Optional[Any]  =_build_tf_to_pytorch_map(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

  for name, pointer in tf_to_pt_map.items():
    logger.info(f'''Importing {name}'''						)
    if name not in tf_weights:
      logger.info(f'''{name} not in tf pre-trained weights, skipping'''						)
      continue

    a__     : Optional[int]  =tf_weights[name]

    if "depthwise_weights" in name:
      logger.info("Transposing depthwise"						)
      a__     : List[str]  =np.transpose(SCREAMING_SNAKE_CASE	,	(2, 3, 0, 1)						)
    elif "weights" in name:
      logger.info("Transposing"						)
      if len(pointer.shape						) == 2:  # copying into linear layer
        a__     : Optional[Any]  =array.squeeze().transpose()
      else:
        a__     : Union[str, Any]  =np.transpose(SCREAMING_SNAKE_CASE	,	(3, 2, 0, 1)						)

    if pointer.shape != array.shape:
      raise ValueError(f'''Pointer shape {pointer.shape} and array shape {array.shape} mismatched'''						)

    logger.info(f'''Initialize PyTorch weight {name} {array.shape}'''						)
    a__     : List[str]  =torch.from_numpy(SCREAMING_SNAKE_CASE						)

    tf_weights.pop(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
    tf_weights.pop(name + "/RMSProp"	,	SCREAMING_SNAKE_CASE						)
    tf_weights.pop(name + "/RMSProp_1"	,	SCREAMING_SNAKE_CASE						)
    tf_weights.pop(name + "/ExponentialMovingAverage"	,	SCREAMING_SNAKE_CASE						)

  logger.info(f'''Weights not copied to PyTorch model: {", ".join(tf_weights.keys()						)}'''						)
  return model
def 					_A	(		SCREAMING_SNAKE_CASE					:       torch.Tensor	,	SCREAMING_SNAKE_CASE					:       nn.Convad						):





  """simple docstring"""



  a__ ,							a__     : Optional[int]  =features.shape[-2:]
  a__ ,							a__     : List[str]  =conv_layer.stride
  a__ ,							a__     : List[str]  =conv_layer.kernel_size

  if in_height % stride_height == 0:
    a__     : Dict  =max(kernel_height - stride_height	,	0						)
  else:
    a__     : Dict  =max(kernel_height - (in_height % stride_height)	,	0						)

  if in_width % stride_width == 0:
    a__     : Dict  =max(kernel_width - stride_width	,	0						)
  else:
    a__     : int  =max(kernel_width - (in_width % stride_width)	,	0						)

  a__     : List[Any]  =pad_along_width // 2
  a__     : Optional[int]  =pad_along_width - pad_left
  a__     : Tuple  =pad_along_height // 2
  a__     : Dict  =pad_along_height - pad_top

  a__     : List[str]  =(pad_left, pad_right, pad_top, pad_bottom)
  return nn.functional.pad(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"constant"	,	0.0						)







class     __lowerCAmelCase	(   nn.Module):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = 1		,				lowerCAmelCase__ = 1		,				lowerCAmelCase__ = False		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = True		,				)       ->						None:



        '''simple docstring'''




        super().__init__()
        a__     : Union[str, Any]  =config

        if in_channels % groups != 0:
          raise ValueError(F'''Input channels ({in_channels}) are not divisible by {groups} groups.'''							)
        if out_channels % groups != 0:
          raise ValueError(F'''Output channels ({out_channels}) are not divisible by {groups} groups.'''							)

        a__     : Dict  =0 if config.tf_padding else int((kernel_size - 1) / 2							)

        a__     : List[Any]  =nn.Convad(
            in_channels=lowerCAmelCase__		,				out_channels=lowerCAmelCase__		,				kernel_size=lowerCAmelCase__		,				stride=lowerCAmelCase__		,				padding=lowerCAmelCase__		,				groups=lowerCAmelCase__		,				bias=lowerCAmelCase__		,				padding_mode="zeros"		,				)

        if use_normalization:
          a__     : str  =nn.BatchNormad(
              num_features=lowerCAmelCase__		,				eps=config.layer_norm_eps		,				momentum=0.99_97		,				affine=lowerCAmelCase__		,				track_running_stats=lowerCAmelCase__		,				)
        else:
          a__     : Optional[int]  =None

        if use_activation:
          if isinstance(lowerCAmelCase__		,				lowerCAmelCase__							):
            a__     : Optional[int]  =ACTaFN[use_activation]
          elif isinstance(config.hidden_act		,				lowerCAmelCase__							):
            a__     : Optional[Any]  =ACTaFN[config.hidden_act]
          else:
            a__     : List[str]  =config.hidden_act
        else:
          a__     : List[str]  =None







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						torch.Tensor:



        '''simple docstring'''




        if self.config.tf_padding:
          a__     : Optional[int]  =apply_tf_padding(lowerCAmelCase__		,				self.convolution							)
        a__     : Tuple  =self.convolution(lowerCAmelCase__							)
        if self.normalization is not None:
          a__     : Dict  =self.normalization(lowerCAmelCase__							)
        if self.activation is not None:
          a__     : int  =self.activation(lowerCAmelCase__							)
        return features







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Optional[int]            =      MobileNetVaConfig
      _lowercase    : Optional[int]            =      load_tf_weights_in_mobilenet_va
      _lowercase    : str            =      """mobilenet_v1"""
      _lowercase    : str            =      """pixel_values"""
      _lowercase    : Dict            =      False







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						None:



        '''simple docstring'''




        if isinstance(lowerCAmelCase__		,				(nn.Linear, nn.Convad)							):
          module.weight.data.normal_(mean=0.0		,				std=self.config.initializer_range							)
          if module.bias is not None:
            module.bias.data.zero_()
        elif isinstance(lowerCAmelCase__		,				nn.BatchNormad							):
          module.bias.data.zero_()
          module.weight.data.fill_(1.0							)
UpperCAmelCase     :     Union[str, Any]							=	r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

UpperCAmelCase     :     List[str]							=	r"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""







@add_start_docstrings(
    """The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."""						,    UpperCamelCase__						,    )
class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = True							)       ->						str:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__							)
        a__     : Tuple  =config

        a__     : Dict  =3_2
        a__     : Union[str, Any]  =max(int(depth * config.depth_multiplier							)		,				config.min_depth							)

        a__     : Union[str, Any]  =MobileNetVaConvLayer(
            lowerCAmelCase__		,				in_channels=config.num_channels		,				out_channels=lowerCAmelCase__		,				kernel_size=3		,				stride=2		,				)

        a__     : Dict  =[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

        a__     : Tuple  =nn.ModuleList()
        for i in range(1_3							):
          a__     : Optional[int]  =out_channels

          if strides[i] == 2 or i == 0:
            depth *= 2
            a__     : Tuple  =max(int(depth * config.depth_multiplier							)		,				config.min_depth							)

          self.layer.append(
              MobileNetVaConvLayer(
                  lowerCAmelCase__		,				in_channels=lowerCAmelCase__		,				out_channels=lowerCAmelCase__		,				kernel_size=3		,				stride=strides[i]		,				groups=lowerCAmelCase__		,				)							)

          self.layer.append(
              MobileNetVaConvLayer(
                  lowerCAmelCase__		,				in_channels=lowerCAmelCase__		,				out_channels=lowerCAmelCase__		,				kernel_size=1		,				)							)

        a__     : str  =nn.AdaptiveAvgPoolad((1, 1)							) if add_pooling_layer else None

        # Initialize weights and apply final processing
        self.post_init()







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						List[str]:



        '''simple docstring'''




        raise NotImplementedError







      @add_start_docstrings_to_model_forward(lowerCAmelCase__							)
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC		,				output_type=lowerCAmelCase__		,				config_class=_CONFIG_FOR_DOC		,				modality="vision"		,				expected_output=_EXPECTED_OUTPUT_SHAPE		,				)
      def        _lowercase  (   self		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				)       ->						Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:



        '''simple docstring'''




        a__     : str  =(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        a__     : Union[str, Any]  =return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
          raise ValueError("You have to specify pixel_values"							)

        a__     : Optional[int]  =self.conv_stem(lowerCAmelCase__							)

        a__     : Dict  =() if output_hidden_states else None

        for i, layer_module in enumerate(self.layer							):
          a__     : List[str]  =layer_module(lowerCAmelCase__							)

          if output_hidden_states:
            a__     : Optional[int]  =all_hidden_states + (hidden_states,)

        a__     : int  =hidden_states

        if self.pooler is not None:
          a__     : List[str]  =torch.flatten(self.pooler(lowerCAmelCase__							)		,				start_dim=1							)
        else:
          a__     : Optional[int]  =None

        if not return_dict:
          return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None							)

        return BaseModelOutputWithPoolingAndNoAttention(
            last_hidden_state=lowerCAmelCase__		,				pooler_output=lowerCAmelCase__		,				hidden_states=lowerCAmelCase__		,				)







@add_start_docstrings(
    """
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """						,    UpperCamelCase__						,    )
class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				lowerCAmelCase__							)       ->						None:



        '''simple docstring'''




        super().__init__(lowerCAmelCase__							)

        a__     : List[str]  =config.num_labels
        a__     : Union[str, Any]  =MobileNetVaModel(lowerCAmelCase__							)

        a__     : Tuple  =self.mobilenet_va.layer[-1].convolution.out_channels

        # Classifier head
        a__     : Optional[Any]  =nn.Dropout(config.classifier_dropout_prob		,				inplace=lowerCAmelCase__							)
        a__     : List[str]  =nn.Linear(lowerCAmelCase__		,				config.num_labels							) if config.num_labels > 0 else nn.Identity()

        # Initialize weights and apply final processing
        self.post_init()







      @add_start_docstrings_to_model_forward(lowerCAmelCase__							)
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT		,				output_type=lowerCAmelCase__		,				config_class=_CONFIG_FOR_DOC		,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT		,				)
      def        _lowercase  (   self		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				)       ->						Union[tuple, ImageClassifierOutputWithNoAttention]:



        '''simple docstring'''




        a__     : Optional[int]  =return_dict if return_dict is not None else self.config.use_return_dict

        a__     : str  =self.mobilenet_va(lowerCAmelCase__		,				output_hidden_states=lowerCAmelCase__		,				return_dict=lowerCAmelCase__							)

        a__     : Any  =outputs.pooler_output if return_dict else outputs[1]

        a__     : int  =self.classifier(self.dropout(lowerCAmelCase__							)							)

        a__     : Any  =None
        if labels is not None:
          if self.config.problem_type is None:
            if self.num_labels == 1:
              a__     : Dict  ="regression"
            elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
              a__     : int  ="single_label_classification"
            else:
              a__     : Tuple  ="multi_label_classification"

          if self.config.problem_type == "regression":
            a__     : List[str]  =MSELoss()
            if self.num_labels == 1:
              a__     : Tuple  =loss_fct(logits.squeeze()		,				labels.squeeze()							)
            else:
              a__     : Dict  =loss_fct(lowerCAmelCase__		,				lowerCAmelCase__							)
          elif self.config.problem_type == "single_label_classification":
            a__     : str  =CrossEntropyLoss()
            a__     : Optional[Any]  =loss_fct(logits.view(-1		,				self.num_labels							)		,				labels.view(-1							)							)
          elif self.config.problem_type == "multi_label_classification":
            a__     : int  =BCEWithLogitsLoss()
            a__     : Optional[int]  =loss_fct(lowerCAmelCase__		,				lowerCAmelCase__							)

        if not return_dict:
          a__     : Dict  =(logits,) + outputs[2:]
          return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(
            loss=lowerCAmelCase__		,				logits=lowerCAmelCase__		,				hidden_states=outputs.hidden_states		,				)













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

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







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip_vision_model"""







      def __init__(   self		,				lowerCAmelCase__=1_4_0_8		,				lowerCAmelCase__=6_1_4_4		,				lowerCAmelCase__=3_9		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=2_2_4		,				lowerCAmelCase__=1_4		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=1E-6		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=1E-10		,				lowerCAmelCase__=True		,				**lowerCAmelCase__		,				)       ->						Optional[Any]:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        a__     : Tuple  =hidden_size
        a__     : Any  =intermediate_size
        a__     : Union[str, Any]  =num_hidden_layers
        a__     : Optional[Any]  =num_attention_heads
        a__     : List[str]  =patch_size
        a__     : int  =image_size
        a__     : Tuple  =initializer_range
        a__     : Any  =attention_dropout
        a__     : List[Any]  =layer_norm_eps
        a__     : Optional[Any]  =hidden_act
        a__     : Optional[Any]  =qkv_bias







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						"PretrainedConfig":



        '''simple docstring'''




        cls._set_token_in_kwargs(lowerCAmelCase__							)

        a__ ,							a__     : Optional[Any]  =cls.get_config_dict(lowerCAmelCase__		,				**lowerCAmelCase__							)

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







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip_qformer"""







      def __init__(   self		,				lowerCAmelCase__=3_0_5_2_2		,				lowerCAmelCase__=7_6_8		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=3_0_7_2		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=5_1_2		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=1E-12		,				lowerCAmelCase__=0		,				lowerCAmelCase__="absolute"		,				lowerCAmelCase__=2		,				lowerCAmelCase__=1_4_0_8		,				**lowerCAmelCase__		,				)       ->						Dict:



        '''simple docstring'''




        super().__init__(pad_token_id=lowerCAmelCase__		,				**lowerCAmelCase__							)

        a__     : Optional[int]  =vocab_size
        a__     : Optional[Any]  =hidden_size
        a__     : str  =num_hidden_layers
        a__     : Optional[int]  =num_attention_heads
        a__     : Dict  =hidden_act
        a__     : Optional[int]  =intermediate_size
        a__     : Union[str, Any]  =hidden_dropout_prob
        a__     : Optional[int]  =attention_probs_dropout_prob
        a__     : List[Any]  =max_position_embeddings
        a__     : Union[str, Any]  =initializer_range
        a__     : Optional[int]  =layer_norm_eps
        a__     : int  =position_embedding_type
        a__     : int  =cross_attention_frequency
        a__     : Tuple  =encoder_hidden_size







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						"PretrainedConfig":



        '''simple docstring'''




        cls._set_token_in_kwargs(lowerCAmelCase__							)

        a__ ,							a__     : str  =cls.get_config_dict(lowerCAmelCase__		,				**lowerCAmelCase__							)

        # get the qformer config dict if we are loading from InstructBlipConfig
        if config_dict.get("model_type"							) == "instructblip":
          a__     : Optional[int]  =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(lowerCAmelCase__		,				**lowerCAmelCase__							)







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : Dict            =      """instructblip"""
      _lowercase    : List[Any]            =      True







      def __init__(   self		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=None		,				lowerCAmelCase__=3_2		,				**lowerCAmelCase__							)       ->						str:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        if vision_config is None:
          a__     : List[Any]  ={}
          logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."							)

        if qformer_config is None:
          a__     : Tuple  ={}
          logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."							)

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

        a__     : Dict  =InstructBlipVisionConfig(**lowerCAmelCase__							)
        a__     : Union[str, Any]  =InstructBlipQFormerConfig(**lowerCAmelCase__							)
        a__     : Tuple  =text_config["model_type"] if "model_type" in text_config else "opt"
        a__     : List[str]  =CONFIG_MAPPING[text_model_type](**lowerCAmelCase__							)

        a__     : Union[str, Any]  =self.text_config.tie_word_embeddings
        a__     : Optional[Any]  =self.text_config.is_encoder_decoder

        a__     : str  =num_query_tokens
        a__     : List[Any]  =self.vision_config.hidden_size
        a__     : str  =self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        a__     : List[Any]  =1.0
        a__     : List[str]  =0.02







      @classmethod
      def        _lowercase  (   cls		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				**lowerCAmelCase__		,				)       ->						int:



        '''simple docstring'''




        return cls(
            vision_config=vision_config.to_dict()		,				qformer_config=qformer_config.to_dict()		,				text_config=text_config.to_dict()		,				**lowerCAmelCase__		,				)







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



        '''simple docstring'''




        a__     : int  =copy.deepcopy(self.__dict__							)
        a__     : int  =self.vision_config.to_dict()
        a__     : str  =self.qformer_config.to_dict()
        a__     : str  =self.text_config.to_dict()
        a__     : List[str]  =self.__class__.model_type
        return output






