





"""simple docstring"""





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(_UpperCAmelCase					)
class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						**lowercase )		->     Optional[int]:
            super().__init__(**lowercase )

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

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





      def __call__(       self      ,						lowercase      ,						lowercase = None      ,						**lowercase      ,						)		->     List[str]:
            if "text_queries" in kwargs:
                  lowerCAmelCase  						=       kwargs.pop("""text_queries""" )

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





      def     _snake_case			(       self      ,						**lowercase )		->     List[str]:
            lowerCAmelCase  						=       {}
            if "threshold" in kwargs:
                  lowerCAmelCase  						=       kwargs["""threshold"""]
            if "top_k" in kwargs:
                  lowerCAmelCase  						=       kwargs["""top_k"""]
            return {}, {}, postprocess_params





      def     _snake_case			(       self      ,						lowercase )		->     List[str]:
            lowerCAmelCase  						=       load_image(inputs["""image"""] )
            lowerCAmelCase  						=       inputs["""candidate_labels"""]
            if isinstance(lowercase      ,						lowercase ):
                  lowerCAmelCase  						=       candidate_labels.split(""",""" )

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





      def     _snake_case			(       self      ,						lowercase )		->     str:
            lowerCAmelCase  						=       model_inputs.pop("""target_size""" )
            lowerCAmelCase  						=       model_inputs.pop("""candidate_label""" )
            lowerCAmelCase  						=       model_inputs.pop("""is_last""" )

            lowerCAmelCase  						=       self.model(**lowercase )

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





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=0.1      ,						lowercase=None )		->     List[str]:
            lowerCAmelCase  						=       []
            for model_output in model_outputs:
                  lowerCAmelCase  						=       model_output["""candidate_label"""]
                  lowerCAmelCase  						=       BaseModelOutput(lowercase )
                  lowerCAmelCase  						=       self.image_processor.post_process_object_detection(
                      outputs=lowercase      ,						threshold=lowercase      ,						target_sizes=model_output["""target_size"""] )[0]

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

                        lowerCAmelCase  						=       {"""score""": score, """label""": label, """box""": box}
                        results.append(lowercase )

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

            return results





      def     _snake_case			(       self      ,						lowercase )		->     Dict[str, int]:
            if self.framework != "pt":
                  raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch.""" )
            lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       box.int().tolist()
            lowerCAmelCase  						=       {
                """xmin""": xmin,
                """ymin""": ymin,
                """xmax""": xmax,
                """ymax""": ymax,
            }
            return bbox













from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput







@dataclass
class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None
       lowerCAmelCase__      		=     None







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       def __init__(  self :    Any							,				A :    List[str]=1							,				A :    str=0							,				A :    List[Any]=2							,				A :    Union[str, Any]=512							,				A :    Tuple="cls"							,				A :    Union[str, Any]=False							,				A :    Optional[Any]=True							,				**A :    Optional[int]							,				):
         super().__init__(pad_token_id=A							,				bos_token_id=A							,				eos_token_id=A							,				**A		)
         __snake_case:						str   =						project_dim
         __snake_case:						Optional[int]   =						pooler_fn
         __snake_case:						Dict   =						learn_encoder
         __snake_case:						str   =						use_attention_mask







class     __snake_case       (							__lowerCamelCase			):





       '''simple docstring'''







       lowerCAmelCase__      		=     [R"""pooler""", R"""logit_scale"""]
       lowerCAmelCase__      		=     [R"""position_ids""", R"""predictions.decoder.bias"""]
       lowerCAmelCase__      		=     """roberta"""
       lowerCAmelCase__      		=     RobertaSeriesConfig




       def __init__(  self :    Dict							,				A :    Dict		):
         super().__init__(A		)
         __snake_case:						Optional[Any]   =						XLMRobertaModel(A		)
         __snake_case:						List[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
         __snake_case:						Optional[int]   =						getattr(A							,				"""has_pre_transformation"""							,				A		)
         if self.has_pre_transformation:
           __snake_case:						Optional[Any]   =						nn.Linear(config.hidden_size							,				config.project_dim		)
           __snake_case:						Optional[Any]   =						nn.LayerNorm(config.hidden_size							,				eps=config.layer_norm_eps		)
         self.post_init()






       def 							UpperCAmelCase__ (  self :    Optional[Any]							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[torch.Tensor] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				A :    Optional[bool] = None							,				):
         __snake_case:						Any   =						return_dict if return_dict is not None else self.config.use_return_dict

         __snake_case:						Optional[int]   =						self.base_model(
             input_ids=A							,				attention_mask=A							,				token_type_ids=A							,				position_ids=A							,				head_mask=A							,				inputs_embeds=A							,				encoder_hidden_states=A							,				encoder_attention_mask=A							,				output_attentions=A							,				output_hidden_states=True if self.has_pre_transformation else output_hidden_states							,				return_dict=A							,				)

         if self.has_pre_transformation:
           __snake_case:						int   =						outputs["""hidden_states"""][-2]
           __snake_case:						List[str]   =						self.pre_LN(A		)
           __snake_case:						List[str]   =						self.transformation_pre(A		)

           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)
         else:
           __snake_case:						Optional[int]   =						self.transformation(outputs.last_hidden_state		)
           return TransformationModelOutput(
               projection_state=A							,				last_hidden_state=outputs.last_hidden_state							,				hidden_states=outputs.hidden_states							,				attentions=outputs.attentions							,				)






