
'''simple docstring'''
from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

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







@add_end_docstrings(_SCREAMING_SNAKE_CASE			)
class  __A				( _SCREAMING_SNAKE_CASE			):
 def __init__(   self			:    Union[str, Any]				,   *lowerCamelCase			:    List[str]				,   **lowerCamelCase			:    List[str]       ):



   """simple docstring"""





   super().__init__(*lowerCamelCase				,   **lowerCamelCase       )
   requires_backends(self				,   """vision"""       )
   self.check_model_type(lowerCamelCase       )
 def __call__(   self			:    Tuple				,   lowerCamelCase			:    Union[str, List[str], "Image.Image", List["Image.Image"]]				,   **lowerCamelCase			:    Tuple       ):



   """simple docstring"""





   return super().__call__(lowerCamelCase				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Tuple				,   **lowerCamelCase			:    str       ):



   """simple docstring"""





   return {}, {}, {}
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    str       ):



   """simple docstring"""





   __A		:     int												=		load_image(lowerCamelCase       )
   __A		:     Optional[Any]												=		image.size
   __A		:     Optional[int]												=		self.image_processor(images=lowerCamelCase				,   return_tensors=self.framework       )
   return model_inputs
 def 		lowercase_(   self			:    List[str]				,   lowerCamelCase			:    int       ):



   """simple docstring"""





   __A		:     List[str]												=		self.model(**lowerCamelCase       )
   return model_outputs






 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    int       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		model_outputs.predicted_depth
   __A		:     Optional[int]												=		torch.nn.functional.interpolate(
       predicted_depth.unsqueeze(1       )				,   size=self.image_size[::-1]				,   mode="""bicubic"""				,   align_corners=lowerCamelCase       )
   __A		:     Optional[int]												=		prediction.squeeze().cpu().numpy()
   __A		:     int												=		(output * 2_55 / np.max(lowerCamelCase       )).astype("""uint8"""       )
   __A		:     Optional[int]												=		Image.fromarray(lowerCamelCase       )
   __A		:     Dict												=		{}
   __A		:     List[str]												=		predicted_depth
   __A		:     Optional[int]												=		depth
   return output_dict





'''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


A__   :		Any 		=logging.get_logger(__name__)

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







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''instructblip_vision_model'''
 def __init__(   self			:    Tuple				,   lowerCamelCase			:    Optional[int]=14_08				,   lowerCamelCase			:    str=61_44				,   lowerCamelCase			:    List[Any]=39				,   lowerCamelCase			:    Optional[Any]=16				,   lowerCamelCase			:    Optional[int]=2_24				,   lowerCamelCase			:    Any=14				,   lowerCamelCase			:    str="gelu"				,   lowerCamelCase			:    str=1e-6				,   lowerCamelCase			:    Dict=0.0				,   lowerCamelCase			:    Dict=1e-1_0				,   lowerCamelCase			:    Optional[Any]=True				,   **lowerCamelCase			:    List[str]				,   ):



   """simple docstring"""





   super().__init__(**lowerCamelCase       )

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






 @classmethod
 def 		lowercase_(   cls			:    Union[str, Any]				,   lowerCamelCase			:    Union[str, os.PathLike]				,   **lowerCamelCase			:    str       ):



   """simple docstring"""





   cls._set_token_in_kwargs(lowerCamelCase       )

   __A							,					__A		:     int												=		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		:     Tuple												=		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  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''instructblip_qformer'''
 def __init__(   self			:    Tuple				,   lowerCamelCase			:    int=3_05_22				,   lowerCamelCase			:    Tuple=7_68				,   lowerCamelCase			:    Optional[Any]=12				,   lowerCamelCase			:    Tuple=12				,   lowerCamelCase			:    str=30_72				,   lowerCamelCase			:    int="gelu"				,   lowerCamelCase			:    Union[str, Any]=0.1				,   lowerCamelCase			:    List[str]=0.1				,   lowerCamelCase			:    int=5_12				,   lowerCamelCase			:    Tuple=0.02				,   lowerCamelCase			:    List[str]=1e-1_2				,   lowerCamelCase			:    int=0				,   lowerCamelCase			:    List[str]="absolute"				,   lowerCamelCase			:    Optional[Any]=2				,   lowerCamelCase			:    List[Any]=14_08				,   **lowerCamelCase			:    List[Any]				,   ):



   """simple docstring"""





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

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






 @classmethod
 def 		lowercase_(   cls			:    Any				,   lowerCamelCase			:    Union[str, os.PathLike]				,   **lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   cls._set_token_in_kwargs(lowerCamelCase       )

   __A							,					__A		:     List[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  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       ='''instructblip'''
 lowerCamelCase				       =True
 def __init__(   self			:    Any				,   lowerCamelCase			:    Optional[Any]=None				,   lowerCamelCase			:    List[str]=None				,   lowerCamelCase			:    Any=None				,   lowerCamelCase			:    Any=32				,   **lowerCamelCase			:    int       ):



   """simple docstring"""





   super().__init__(**lowerCamelCase       )

   if vision_config is None:
     __A		:     int												=		{}
     logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""       )

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

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

   __A		:     List[Any]												=		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		:     Optional[int]												=		self.text_config.tie_word_embeddings
   __A		:     Dict												=		self.text_config.is_encoder_decoder

   __A		:     Optional[int]												=		num_query_tokens
   __A		:     int												=		self.vision_config.hidden_size
   __A		:     str												=		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
   __A		:     Optional[Any]												=		1.0
   __A		:     Optional[int]												=		0.02
 @classmethod
 def 		lowercase_(   cls			:    List[str]				,   lowerCamelCase			:    InstructBlipVisionConfig				,   lowerCamelCase			:    InstructBlipQFormerConfig				,   lowerCamelCase			:    PretrainedConfig				,   **lowerCamelCase			:    Optional[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			:    List[Any]       ):



   """simple docstring"""





   __A		:     Tuple												=		copy.deepcopy(self.__dict__       )
   __A		:     Optional[int]												=		self.vision_config.to_dict()
   __A		:     Optional[Any]												=		self.qformer_config.to_dict()
   __A		:     List[Any]												=		self.text_config.to_dict()
   __A		:     Optional[int]												=		self.__class__.model_type
   return output



