


from typing import Any, Dict, List, Union

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


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from transformers.modeling_outputs import BaseModelOutput

  from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

SCREAMING_SNAKE_CASE							    =							logging.get_logger(__name__)


@add_end_docstrings(__A							)
class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       **__A    ):
        super().__init__(**__A    )

        if self.framework == "tf":
           raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''    )

        requires_backends(self  ,       """vision"""    )
        self.check_model_type(__A    )


     def __call__(	self  ,       __A  ,       __A = None  ,       **__A  ,       ):
        if "text_queries" in kwargs:
           __a					=  kwargs.pop("""text_queries"""    )

        if isinstance(__A  ,       (str, Image.Image)    ):
           __a					=  {"""image""": image, """candidate_labels""": candidate_labels}
        else:
           __a					=  image
        __a					=  super().__call__(__A  ,       **__A    )
        return results


     def 	snake_case_  (	self  ,       **__A    ):
        __a					=  {}
        if "threshold" in kwargs:
           __a					=  kwargs["""threshold"""]
        if "top_k" in kwargs:
           __a					=  kwargs["""top_k"""]
        return {}, {}, postprocess_params


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  load_image(inputs["""image"""]    )
        __a					=  inputs["""candidate_labels"""]
        if isinstance(__A  ,       __A    ):
           __a					=  candidate_labels.split(""","""    )

        __a					=  torch.tensor([[image.height, image.width]]  ,       dtype=torch.intaa    )
        for i, candidate_label in enumerate(__A    ):
           __a					=  self.tokenizer(__A  ,       return_tensors=self.framework    )
           __a					=  self.image_processor(__A  ,       return_tensors=self.framework    )
           yield {
               "is_last": i == len(__A    ) - 1,
               "target_size": target_size,
               "candidate_label": candidate_label,
               **text_inputs,
               **image_features,
           }


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  model_inputs.pop("""target_size"""    )
        __a					=  model_inputs.pop("""candidate_label"""    )
        __a					=  model_inputs.pop("""is_last"""    )

        __a					=  self.model(**__A    )

        __a					=  {"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
        return model_outputs


     def 	snake_case_  (	self  ,       __A  ,       __A=0.1  ,       __A=None    ):
        __a					=  []
        for model_output in model_outputs:
           __a					=  model_output["""candidate_label"""]
           __a					=  BaseModelOutput(__A    )
           __a					=  self.image_processor.post_process_object_detection(
               outputs=__A  ,       threshold=__A  ,       target_sizes=model_output["""target_size"""]    )[0]

           for index in outputs["scores"].nonzero():
              __a					=  outputs["""scores"""][index].item()
              __a					=  self._get_bounding_box(outputs["""boxes"""][index][0]    )

              __a					=  {"""score""": score, """label""": label, """box""": box}
              results.append(__A    )

        __a					=  sorted(__A  ,       key=lambda __A    : x["score"]  ,       reverse=__A    )
        if top_k:
           __a					=  results[:top_k]

        return results


     def 	snake_case_  (	self  ,       __A    ):
        if self.framework != "pt":
           raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""    )
        __a	,    __a	,    __a	,    __a					=  box.int().tolist()
        __a					=  {
            """xmin""": xmin,
            """ymin""": ymin,
            """xmax""": xmax,
            """ymax""": ymax,
        }
        return bbox




'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						  =		logging.get_logger(__name__)

__snake_case						  =		{
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						'''mra'''




  def __init__(		self      :       Any   ,				lowercase_      :       List[str]=50_265   ,				lowercase_      :       Tuple=768   ,				lowercase_      :       Optional[Any]=12   ,				lowercase_      :       str=12   ,				lowercase_      :       Optional[int]=3_072   ,				lowercase_      :       Union[str, Any]="gelu"   ,				lowercase_      :       str=0.1   ,				lowercase_      :       Tuple=0.1   ,				lowercase_      :       str=512   ,				lowercase_      :       Optional[int]=1   ,				lowercase_      :       Tuple=0.0_2   ,				lowercase_      :       List[Any]=1e-5   ,				lowercase_      :       Optional[Any]="absolute"   ,				lowercase_      :       int=4   ,				lowercase_      :       int="full"   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       str=0   ,				lowercase_      :       List[str]=1   ,				lowercase_      :       Tuple=0   ,				lowercase_      :       Optional[Any]=2   ,				**lowercase_      :       List[str]   ,				):







    '''simple docstring'''


    super().__init__(pad_token_id=lowercase_   ,				bos_token_id=lowercase_   ,				eos_token_id=lowercase_   ,				**lowercase_      )

    lowercase_     =       vocab_size
    lowercase_     =       max_position_embeddings
    lowercase_     =       hidden_size
    lowercase_     =       num_hidden_layers
    lowercase_     =       num_attention_heads
    lowercase_     =       intermediate_size
    lowercase_     =       hidden_act
    lowercase_     =       hidden_dropout_prob
    lowercase_     =       attention_probs_dropout_prob
    lowercase_     =       initializer_range
    lowercase_     =       type_vocab_size
    lowercase_     =       layer_norm_eps
    lowercase_     =       position_embedding_type
    lowercase_     =       block_per_row
    lowercase_     =       approx_mode
    lowercase_     =       initial_prior_first_n_blocks
    lowercase_     =       initial_prior_diagonal_n_blocks

