



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

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    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_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_UpperCAmelCase   =	logging.get_logger(__name__)
@add_end_docstrings(__A				)
class     a		(     __A				):



  def __init__(  self       :    List[Any]				,						*lowerCAmelCase       :    List[str]				,						**lowerCAmelCase       :    Dict						)   ->    Optional[Any]:


     '''simple docstring'''




     super().__init__(*lowerCAmelCase_				,						**lowerCAmelCase_						)
     requires_backends(self				,						"""vision"""						)
     self.check_model_type(
         TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == """tf""" else MODEL_FOR_VISION_2_SEQ_MAPPING						)



  def 		lowerCamelCase__				(  self       :    Tuple				,						lowerCAmelCase       :    List[str]=None				,						lowerCAmelCase       :    int=None				,						lowerCAmelCase       :    str=None						)   ->    str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Any   	={}
     SCREAMING_SNAKE_CASE_:       List[Any]   	={}

     if prompt is not None:
        SCREAMING_SNAKE_CASE_:       List[Any]   	=prompt

     if generate_kwargs is not None:
        SCREAMING_SNAKE_CASE_:       str   	=generate_kwargs
     if max_new_tokens is not None:
        if "generate_kwargs" not in forward_kwargs:
           SCREAMING_SNAKE_CASE_:       List[Any]   	={}
        if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
           raise ValueError(
               """'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"""
               """ please use only one"""						)
        SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=max_new_tokens
     return preprocess_params, forward_kwargs, {}



  def __call__(  self       :    Tuple				,						lowerCAmelCase       :    Union[str, List[str], "Image.Image", List["Image.Image"]]				,						**lowerCAmelCase       :    int						)   ->    List[Any]:


     '''simple docstring'''




     return super().__call__(lowerCAmelCase_				,						**lowerCAmelCase_						)



  def 		lowerCamelCase__				(  self       :    Dict				,						lowerCAmelCase       :    Union[str, Any]				,						lowerCAmelCase       :    List[str]=None						)   ->    Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=load_image(lowerCAmelCase_						)

     if prompt is not None:
        if not isinstance(lowerCAmelCase_				,						lowerCAmelCase_						):
           raise ValueError(
               f'''Received an invalid text input, got - {type(lowerCAmelCase_						)} - but expected a single string. '''
               """Note also that one single text can be provided for conditional image to text generation."""						)

        SCREAMING_SNAKE_CASE_:       List[str]   	=self.model.config.model_type

        if model_type == "git":
           SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.image_processor(images=lowerCAmelCase_				,						return_tensors=self.framework						)
           SCREAMING_SNAKE_CASE_:       Any   	=self.tokenizer(text=lowerCAmelCase_				,						add_special_tokens=lowerCAmelCase_						).input_ids
           SCREAMING_SNAKE_CASE_:       Optional[int]   	=[self.tokenizer.cls_token_id] + input_ids
           SCREAMING_SNAKE_CASE_:       Dict   	=torch.tensor(lowerCAmelCase_						).unsqueeze(0						)
           model_inputs.update({"""input_ids""": input_ids}						)

        elif model_type == "pix2struct":
           SCREAMING_SNAKE_CASE_:       Union[str, Any]   	=self.image_processor(images=lowerCAmelCase_				,						header_text=lowerCAmelCase_				,						return_tensors=self.framework						)

        elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           SCREAMING_SNAKE_CASE_:       Optional[int]   	=self.image_processor(images=lowerCAmelCase_				,						return_tensors=self.framework						)
           SCREAMING_SNAKE_CASE_:       str   	=self.tokenizer(lowerCAmelCase_				,						return_tensors=self.framework						)
           model_inputs.update(lowerCAmelCase_						)

        else:
           raise ValueError(f'''Model type {model_type} does not support conditional text generation'''						)

     else:
        SCREAMING_SNAKE_CASE_:       Dict   	=self.image_processor(images=lowerCAmelCase_				,						return_tensors=self.framework						)

     if self.model.config.model_type == "git" and prompt is None:
        SCREAMING_SNAKE_CASE_:       Optional[Any]   	=None

     return model_inputs



  def 		lowerCamelCase__				(  self       :    Any				,						lowerCAmelCase       :    Union[str, Any]				,						lowerCAmelCase       :    List[str]=None						)   ->    Dict:


     '''simple docstring'''




     if (
         "input_ids" in model_inputs
         and isinstance(model_inputs["""input_ids"""]				,						lowerCAmelCase_						)
         and all(x is None for x in model_inputs["""input_ids"""]						)
     ):
        SCREAMING_SNAKE_CASE_:       str   	=None

     if generate_kwargs is None:
        SCREAMING_SNAKE_CASE_:       Any   	={}
     # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
     #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
     #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
     #  in the `_prepare_model_inputs` method.
     SCREAMING_SNAKE_CASE_:       Optional[Any]   	=model_inputs.pop(self.model.main_input_name						)
     SCREAMING_SNAKE_CASE_:       List[Any]   	=self.model.generate(lowerCAmelCase_				,						**lowerCAmelCase_				,						**lowerCAmelCase_						)
     return model_outputs





  def 		lowerCamelCase__				(  self       :    Tuple				,						lowerCAmelCase       :    List[Any]						)   ->    int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:       List[Any]   	=[]
     for output_ids in model_outputs:
        SCREAMING_SNAKE_CASE_:       int   	={
            "generated_text": self.tokenizer.decode(
                lowerCAmelCase_				,						skip_special_tokens=lowerCAmelCase_				,						)
        }
        records.append(lowerCAmelCase_						)
     return records











"""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


lowerCamelCase_					=      logging.get_logger(__name__)

lowerCamelCase_					=      {
    '''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 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				'''detr'''
	__magic_name__	      =				['''past_key_values''']
	__magic_name__	      =				{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	}





	def __init__(      self					: Dict	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: Union[str, Any]=None	,	lowerCAmelCase_					: Dict=3	,	lowerCAmelCase_					: List[Any]=100	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: int=8	,	lowerCAmelCase_					: int=6	,	lowerCAmelCase_					: Union[str, Any]=2_048	,	lowerCAmelCase_					: Any=8	,	lowerCAmelCase_					: Dict=0.0	,	lowerCAmelCase_					: Union[str, Any]=0.0	,	lowerCAmelCase_					: Dict=True	,	lowerCAmelCase_					: List[Any]="relu"	,	lowerCAmelCase_					: List[Any]=256	,	lowerCAmelCase_					: Tuple=0.1	,	lowerCAmelCase_					: Optional[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0	,	lowerCAmelCase_					: List[Any]=0.0_2	,	lowerCAmelCase_					: Optional[int]=1.0	,	lowerCAmelCase_					: str=False	,	lowerCAmelCase_					: Tuple="sine"	,	lowerCAmelCase_					: str="resnet50"	,	lowerCAmelCase_					: List[Any]=True	,	lowerCAmelCase_					: int=False	,	lowerCAmelCase_					: Optional[Any]=1	,	lowerCAmelCase_					: int=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: List[Any]=1	,	lowerCAmelCase_					: Dict=5	,	lowerCAmelCase_					: Optional[Any]=2	,	lowerCAmelCase_					: Union[str, Any]=0.1	,	**lowerCAmelCase_					: Dict	,	)						->			int:
				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_     : Tuple			     =					CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
							elif isinstance(lowerCAmelCase_	,	lowerCAmelCase_						):
										UpperCAmelCase_     : Dict			     =					backbone_config.get("model_type"						)
										UpperCAmelCase_     : Dict			     =					CONFIG_MAPPING[backbone_model_type]
										UpperCAmelCase_     : Tuple			     =					config_class.from_dict(lowerCAmelCase_						)
							# set timm attributes to None
							UpperCAmelCase_     ,	UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					None, None, None

				UpperCAmelCase_     : str			     =					use_timm_backbone
				UpperCAmelCase_     : Optional[Any]			     =					backbone_config
				UpperCAmelCase_     : Tuple			     =					num_channels
				UpperCAmelCase_     : Dict			     =					num_queries
				UpperCAmelCase_     : str			     =					d_model
				UpperCAmelCase_     : Any			     =					encoder_ffn_dim
				UpperCAmelCase_     : Union[str, Any]			     =					encoder_layers
				UpperCAmelCase_     : Optional[int]			     =					encoder_attention_heads
				UpperCAmelCase_     : List[str]			     =					decoder_ffn_dim
				UpperCAmelCase_     : Tuple			     =					decoder_layers
				UpperCAmelCase_     : Optional[int]			     =					decoder_attention_heads
				UpperCAmelCase_     : List[Any]			     =					dropout
				UpperCAmelCase_     : Union[str, Any]			     =					attention_dropout
				UpperCAmelCase_     : int			     =					activation_dropout
				UpperCAmelCase_     : List[str]			     =					activation_function
				UpperCAmelCase_     : Optional[int]			     =					init_std
				UpperCAmelCase_     : Union[str, Any]			     =					init_xavier_std
				UpperCAmelCase_     : List[str]			     =					encoder_layerdrop
				UpperCAmelCase_     : Tuple			     =					decoder_layerdrop
				UpperCAmelCase_     : str			     =					encoder_layers
				UpperCAmelCase_     : Any			     =					auxiliary_loss
				UpperCAmelCase_     : Optional[int]			     =					position_embedding_type
				UpperCAmelCase_     : List[str]			     =					backbone
				UpperCAmelCase_     : int			     =					use_pretrained_backbone
				UpperCAmelCase_     : Any			     =					dilation
				# Hungarian matcher
				UpperCAmelCase_     : str			     =					class_cost
				UpperCAmelCase_     : Any			     =					bbox_cost
				UpperCAmelCase_     : int			     =					giou_cost
				# Loss coefficients
				UpperCAmelCase_     : List[str]			     =					mask_loss_coefficient
				UpperCAmelCase_     : Dict			     =					dice_loss_coefficient
				UpperCAmelCase_     : Any			     =					bbox_loss_coefficient
				UpperCAmelCase_     : Union[str, Any]			     =					giou_loss_coefficient
				UpperCAmelCase_     : int			     =					eos_coefficient
				super().__init__(is_encoder_decoder=lowerCAmelCase_	,	**lowerCAmelCase_						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			int:
				return self.encoder_attention_heads





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			int:
				return self.d_model





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: Union[str, Any]	,	lowerCAmelCase_					: PretrainedConfig	,	**lowerCAmelCase_					: Tuple						)						->			List[Any]:
				return cls(backbone_config=lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Dict[str, any]:
				UpperCAmelCase_     : Tuple			     =					copy.deepcopy(self.__dict__						)
				if output["backbone_config"] is not None:
							UpperCAmelCase_     : Union[str, Any]			     =					self.backbone_config.to_dict()
				UpperCAmelCase_     : Any			     =					self.__class__.model_type
				return output







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				version.parse('''1.11'''     )





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Dict						)						->			Mapping[str, Mapping[int, str]]:
				return OrderedDict(
				    [
				        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
				        ("pixel_mask", {0: "batch"}),
				    ]						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			float:
				return 1e-5





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			int:
				return 12



