
'''simple docstring'''


import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case_ :  Dict     =					logging.get_logger(__name__)

snake_case_ :  Any     =					{
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							'''detr'''
   _snake_case		   =							['''past_key_values''']
   _snake_case		   =							{
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }







   def __init__(			self ,   lowerCamelCase__=True ,   lowerCamelCase__=None ,   lowerCamelCase__=3 ,   lowerCamelCase__=1_0_0 ,   lowerCamelCase__=6 ,   lowerCamelCase__=2_0_4_8 ,   lowerCamelCase__=8 ,   lowerCamelCase__=6 ,   lowerCamelCase__=2_0_4_8 ,   lowerCamelCase__=8 ,   lowerCamelCase__=0.0 ,   lowerCamelCase__=0.0 ,   lowerCamelCase__=True ,   lowerCamelCase__="relu" ,   lowerCamelCase__=2_5_6 ,   lowerCamelCase__=0.1 ,   lowerCamelCase__=0.0 ,   lowerCamelCase__=0.0 ,   lowerCamelCase__=0.02 ,   lowerCamelCase__=1.0 ,   lowerCamelCase__=False ,   lowerCamelCase__="sine" ,   lowerCamelCase__="resnet50" ,   lowerCamelCase__=True ,   lowerCamelCase__=False ,   lowerCamelCase__=1 ,   lowerCamelCase__=5 ,   lowerCamelCase__=2 ,   lowerCamelCase__=1 ,   lowerCamelCase__=1 ,   lowerCamelCase__=5 ,   lowerCamelCase__=2 ,   lowerCamelCase__=0.1 ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      if backbone_config is not None and use_timm_backbone:
         raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''   )

      if not use_timm_backbone:
         if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
            UpperCamelCase       =  CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']   )
         elif isinstance(lowerCamelCase__ ,   lowerCamelCase__   ):
            UpperCamelCase       =  backbone_config.get('''model_type'''   )
            UpperCamelCase       =  CONFIG_MAPPING[backbone_model_type]
            UpperCamelCase       =  config_class.from_dict(lowerCamelCase__   )
         # set timm attributes to None
         UpperCamelCase     , UpperCamelCase     , UpperCamelCase       =  None, None, None

      UpperCamelCase       =  use_timm_backbone
      UpperCamelCase       =  backbone_config
      UpperCamelCase       =  num_channels
      UpperCamelCase       =  num_queries
      UpperCamelCase       =  d_model
      UpperCamelCase       =  encoder_ffn_dim
      UpperCamelCase       =  encoder_layers
      UpperCamelCase       =  encoder_attention_heads
      UpperCamelCase       =  decoder_ffn_dim
      UpperCamelCase       =  decoder_layers
      UpperCamelCase       =  decoder_attention_heads
      UpperCamelCase       =  dropout
      UpperCamelCase       =  attention_dropout
      UpperCamelCase       =  activation_dropout
      UpperCamelCase       =  activation_function
      UpperCamelCase       =  init_std
      UpperCamelCase       =  init_xavier_std
      UpperCamelCase       =  encoder_layerdrop
      UpperCamelCase       =  decoder_layerdrop
      UpperCamelCase       =  encoder_layers
      UpperCamelCase       =  auxiliary_loss
      UpperCamelCase       =  position_embedding_type
      UpperCamelCase       =  backbone
      UpperCamelCase       =  use_pretrained_backbone
      UpperCamelCase       =  dilation
      # Hungarian matcher
      UpperCamelCase       =  class_cost
      UpperCamelCase       =  bbox_cost
      UpperCamelCase       =  giou_cost
      # Loss coefficients
      UpperCamelCase       =  mask_loss_coefficient
      UpperCamelCase       =  dice_loss_coefficient
      UpperCamelCase       =  bbox_loss_coefficient
      UpperCamelCase       =  giou_loss_coefficient
      UpperCamelCase       =  eos_coefficient
      super().__init__(is_encoder_decoder=lowerCamelCase__ ,   **lowerCamelCase__   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.encoder_attention_heads







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return self.d_model







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return cls(backbone_config=lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  copy.deepcopy(self.__dict__   )
      if output["backbone_config"] is not None:
         UpperCamelCase       =  self.backbone_config.to_dict()
      UpperCamelCase       =  self.__class__.model_type
      return output




class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							version.parse('''1.11'''   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
              ('''pixel_mask''', {0: '''batch'''}),
          ]   )







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return 1e-5







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      return 1_2







'''simple docstring'''


import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							['''image_processor''', '''tokenizer''']
   _snake_case		   =							'''BlipImageProcessor'''
   _snake_case		   =							'''AutoTokenizer'''







   def __init__(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      super().__init__(lowerCamelCase__ ,   lowerCamelCase__   )

      # add QFormer tokenizer
      UpperCamelCase       =  qformer_tokenizer







   def __call__(			self ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = True ,   lowerCamelCase__ = False ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = 0 ,   lowerCamelCase__ = None ,   lowerCamelCase__ = None ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = False ,   lowerCamelCase__ = True ,   lowerCamelCase__ = None ,   **lowerCamelCase__ ,   ):

      '''simple docstring'''







      if images is None and text is None:
         raise ValueError('''You have to specify at least images or text.'''   )

      UpperCamelCase       =  BatchFeature()

      if text is not None:
         UpperCamelCase       =  self.tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         encoding.update(lowerCamelCase__   )
         UpperCamelCase       =  self.qformer_tokenizer(
             text=lowerCamelCase__ ,   add_special_tokens=lowerCamelCase__ ,   padding=lowerCamelCase__ ,   truncation=lowerCamelCase__ ,   max_length=lowerCamelCase__ ,   stride=lowerCamelCase__ ,   pad_to_multiple_of=lowerCamelCase__ ,   return_attention_mask=lowerCamelCase__ ,   return_overflowing_tokens=lowerCamelCase__ ,   return_special_tokens_mask=lowerCamelCase__ ,   return_offsets_mapping=lowerCamelCase__ ,   return_token_type_ids=lowerCamelCase__ ,   return_length=lowerCamelCase__ ,   verbose=lowerCamelCase__ ,   return_tensors=lowerCamelCase__ ,   **lowerCamelCase__ ,   )
         UpperCamelCase       =  qformer_text_encoding.pop('''input_ids'''   )
         UpperCamelCase       =  qformer_text_encoding.pop('''attention_mask'''   )

      if images is not None:
         UpperCamelCase       =  self.image_processor(lowerCamelCase__ ,   return_tensors=lowerCamelCase__   )
         encoding.update(lowerCamelCase__   )

      return encoding







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.batch_decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   def       UpperCAmelCase				(			self ,   *lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      return self.tokenizer.decode(*lowerCamelCase__ ,   **lowerCamelCase__   )







   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.tokenizer.model_input_names
      UpperCamelCase       =  self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      if os.path.isfile(lowerCamelCase__   ):
         raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'   )
      os.makedirs(lowerCamelCase__ ,   exist_ok=lowerCamelCase__   )
      UpperCamelCase       =  os.path.join(lowerCamelCase__ ,   '''qformer_tokenizer'''   )
      self.qformer_tokenizer.save_pretrained(lowerCamelCase__   )
      return super().save_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__ ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  AutoTokenizer.from_pretrained(lowerCamelCase__ ,   subfolder='''qformer_tokenizer'''   )
      UpperCamelCase       =  cls._get_arguments_from_pretrained(lowerCamelCase__ ,   **lowerCamelCase__   )
      args.append(lowerCamelCase__   )
      return cls(*lowerCamelCase__   )





