






import warnings
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


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







class  _lowercase   (   _A						):
      _a							:						Tuple										=							['input_ids', 'attention_mask']
      def __init__(     self				,			a="</s>"				,			a="<unk>"				,			a="<pad>"				,			a=1_2_5				,			a=None				,			**a				,			):
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
        snake_case__  :    Optional[Any]							       =[F"<extra_id_{i}>" for i in range(a   )]
       elif extra_ids > 0 and additional_special_tokens is not None:
        # Check that we have the right number of extra_id special tokens
        snake_case__  :    Tuple							       =len(set(filter(lambda a   : bool("""extra_id""" in str(a   )   )				,			a   )   )   )
        if extra_tokens != extra_ids:
         raise ValueError(
             F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
             """ provided to ByT5Tokenizer. In this case the additional_special_tokens must include the"""
             """ extra_ids tokens"""   )

       snake_case__  :    int							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else pad_token
       snake_case__  :    Optional[int]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else eos_token
       snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else unk_token

       super().__init__(
           eos_token=a				,			unk_token=a				,			pad_token=a				,			extra_ids=a				,			additional_special_tokens=a				,			**a				,			)

       snake_case__  :    Optional[int]							       =extra_ids

       snake_case__  :    str							       =2**8  # utf is 8 bits

       # define special tokens dict
       snake_case__  :    Dict[int, str]							       ={
           self.pad_token: 0,
           self.eos_token: 1,
           self.unk_token: 2,
       }
       snake_case__  :    List[Any]							       =len(self.special_tokens_encoder   )
       snake_case__  :    Optional[int]							       =len(a   )
       for i, token in enumerate(a   ):
        snake_case__  :    Optional[int]							       =self.vocab_size + i - n
       snake_case__  :    Dict[str, int]							       ={v: k for k, v in self.special_tokens_encoder.items()}
      @property
      def 						lowercase__					(     self   ):
       return self._utf_vocab_size + self._num_special_tokens + self._extra_ids
      def 						lowercase__					(     self				,			a				,			a = None				,			a = False   ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=a				,			token_ids_a=a				,			already_has_special_tokens=a   )

       # normal case: some special tokens
       if token_ids_a is None:
        return ([0] * len(a   )) + [1]
       return ([0] * len(a   )) + [1] + ([0] * len(a   )) + [1]
      def 						lowercase__					(     self				,			a   ):
       if len(a   ) > 0 and token_ids[-1] == self.eos_token_id:
        warnings.warn(
            F"This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"
            """ eos tokens being added."""   )
        return token_ids
       else:
        return token_ids + [self.eos_token_id]
      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    Tuple							       =[self.eos_token_id]

       if token_ids_a is None:
        return len(token_ids_a + eos   ) * [0]
       return len(token_ids_a + eos + token_ids_a + eos   ) * [0]
      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    List[Any]							       =self._add_eos_if_not_present(a   )
       if token_ids_a is None:
        return token_ids_a
       else:
        snake_case__  :    Optional[Any]							       =self._add_eos_if_not_present(a   )
        return token_ids_a + token_ids_a
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    int							       =[chr(a   ) for i in text.encode("""utf-8"""   )]
       return tokens
      def 						lowercase__					(     self				,			a   ):
       if token in self.special_tokens_encoder:
        snake_case__  :    str							       =self.special_tokens_encoder[token]
       elif token in self.added_tokens_encoder:
        snake_case__  :    List[str]							       =self.added_tokens_encoder[token]
       elif len(a   ) != 1:
        snake_case__  :    str							       =self.unk_token_id
       else:
        snake_case__  :    Tuple							       =ord(a   ) + self._num_special_tokens
       return token_id
      def 						lowercase__					(     self				,			a   ):
       if index in self.special_tokens_decoder:
        snake_case__  :    Dict							       =self.special_tokens_decoder[index]
       else:
        snake_case__  :    List[str]							       =chr(index - self._num_special_tokens   )
       return token
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Any							       =B""""""
       for token in tokens:
        if token in self.special_tokens_decoder:
         snake_case__  :    Any							       =self.special_tokens_decoder[token].encode("""utf-8"""   )
        elif token in self.added_tokens_decoder:
         snake_case__  :    Optional[int]							       =self.special_tokens_decoder[token].encode("""utf-8"""   )
        elif token in self.special_tokens_encoder:
         snake_case__  :    Tuple							       =token.encode("""utf-8"""   )
        elif token in self.added_tokens_encoder:
         snake_case__  :    List[Any]							       =token.encode("""utf-8"""   )
        else:
         snake_case__  :    Any							       =bytes([ord(a   )]   )
        bstring += tok_string
       snake_case__  :    Dict							       =bstring.decode("""utf-8"""				,			errors="""ignore"""   )
       return string



      def 						lowercase__					(     self				,			a				,			a = None   ):
       return ()













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

__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)







@add_end_docstrings(_A						)
class  _lowercase   (   _A						):
      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:
        snake_case__  :    Any							       =kwargs.pop("""text_queries"""   )

       if isinstance(a				,			(str, Image.Image)   ):
        snake_case__  :    Union[str, Any]							       ={"""image""": image, """candidate_labels""": candidate_labels}
       else:
        snake_case__  :    str							       =image
       snake_case__  :    Union[str, Any]							       =super().__call__(a				,			**a   )
       return results
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Union[str, Any]							       ={}
       if "threshold" in kwargs:
        snake_case__  :    Optional[Any]							       =kwargs["""threshold"""]
       if "top_k" in kwargs:
        snake_case__  :    Dict							       =kwargs["""top_k"""]
       return {}, {}, postprocess_params
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    List[Any]							       =load_image(inputs["""image"""]   )
       snake_case__  :    Dict							       =inputs["""candidate_labels"""]
       if isinstance(a				,			a   ):
        snake_case__  :    str							       =candidate_labels.split(""","""   )

       snake_case__  :    Optional[Any]							       =torch.tensor([[image.height, image.width]]				,			dtype=torch.intaa   )
       for i, candidate_label in enumerate(a   ):
        snake_case__  :    Tuple							       =self.tokenizer(a				,			return_tensors=self.framework   )
        snake_case__  :    int							       =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 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       =model_inputs.pop("""target_size"""   )
       snake_case__  :    Tuple							       =model_inputs.pop("""candidate_label"""   )
       snake_case__  :    Dict							       =model_inputs.pop("""is_last"""   )

       snake_case__  :    Any							       =self.model(**a   )

       snake_case__  :    List[Any]							       ={"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
       return model_outputs
      def 						lowercase__					(     self				,			a				,			a=0.1				,			a=None   ):
       snake_case__  :    Optional[int]							       =[]
       for model_output in model_outputs:
        snake_case__  :    Tuple							       =model_output["""candidate_label"""]
        snake_case__  :    Optional[int]							       =BaseModelOutput(a   )
        snake_case__  :    int							       =self.image_processor.post_process_object_detection(
            outputs=a				,			threshold=a				,			target_sizes=model_output["""target_size"""]   )[0]

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

         snake_case__  :    Tuple							       ={"""score""": score, """label""": label, """box""": box}
         results.append(a   )

       snake_case__  :    Tuple							       =sorted(a				,			key=lambda a   : x["score"]				,			reverse=a   )
       if top_k:
        snake_case__  :    Union[str, Any]							       =results[:top_k]

       return results



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





