



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

A__	      =       logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__       )
class 					__lowerCAmelCase (     lowerCamelCase__       ):




				def __init__(							self						,				**_snake_case		):



									"""simple docstring"""





									super().__init__(**_snake_case		)

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

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




				def __call__(							self						,				_snake_case						,				_snake_case = None						,				**_snake_case						,				):



									"""simple docstring"""





									if "text_queries" in kwargs:
														_lowerCAmelCase						=    kwargs.pop("""text_queries"""		)

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




				def 							snake_case   (							self						,				**_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    {}
									if "threshold" in kwargs:
														_lowerCAmelCase						=    kwargs["""threshold"""]
									if "top_k" in kwargs:
														_lowerCAmelCase						=    kwargs["""top_k"""]
									return {}, {}, postprocess_params




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    load_image(inputs["""image"""]		)
									_lowerCAmelCase						=    inputs["""candidate_labels"""]
									if isinstance(_snake_case						,				_snake_case		):
														_lowerCAmelCase						=    candidate_labels.split(""","""		)

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




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase						=    model_inputs.pop("""target_size"""		)
									_lowerCAmelCase						=    model_inputs.pop("""candidate_label"""		)
									_lowerCAmelCase						=    model_inputs.pop("""is_last"""		)

									_lowerCAmelCase						=    self.model(**_snake_case		)

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




				def 							snake_case   (							self						,				_snake_case						,				_snake_case=0.1						,				_snake_case=None		):



									"""simple docstring"""





									_lowerCAmelCase						=    []
									for model_output in model_outputs:
														_lowerCAmelCase						=    model_output["""candidate_label"""]
														_lowerCAmelCase						=    BaseModelOutput(_snake_case		)
														_lowerCAmelCase						=    self.image_processor.post_process_object_detection(
														    outputs=_snake_case						,				threshold=_snake_case						,				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(_snake_case		)

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

									return results




				def 							snake_case   (							self						,				_snake_case		):



									"""simple docstring"""





									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 collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_:List[Any]             =						TypeVar("""KEY""")
SCREAMING_SNAKE_CASE_:Dict             =						TypeVar("""VAL""")






@dataclass(frozen=SCREAMING_SNAKE_CASE__ ,						slots=SCREAMING_SNAKE_CASE__    )
class SCREAMING_SNAKE_CASE__     (		Generic[KEY, VAL]    ):




    '''simple docstring'''






    __lowerCamelCase			: KEY
    __lowerCamelCase			: VAL






class SCREAMING_SNAKE_CASE__     (		_Item    ):




    '''simple docstring'''






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






    def __bool__(       self		):
        return False


SCREAMING_SNAKE_CASE_:Optional[Any]             =						_DeletedItem()






class SCREAMING_SNAKE_CASE__     (		MutableMapping[KEY, VAL]    ):




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__ = 8,		lowerCamelCase__ = 0.75		):
        A :       List[str]          =   initial_block_size
        A :       list[_Item | None]          =   [None] * initial_block_size
        assert 0.0 < capacity_factor < 1.0
        A :       Dict          =   capacity_factor
        A :       Optional[Any]          =   0






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return hash(lowerCamelCase__		) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        return (ind + 1) % len(self._buckets		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       List[Any]          =   self._buckets[ind]
        if not stored:
            A :       Optional[int]          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            self._len += 1
            return True
        elif stored.key == key:
            A :       int          =   _Item(lowerCamelCase__,		lowerCamelCase__		)
            return True
        else:
            return False






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   len(self._buckets		) * self._capacity_factor
        return len(self		) >= int(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        if len(self._buckets		) <= self._initial_block_size:
            return False
        A :       Optional[Any]          =   len(self._buckets		) * self._capacity_factor / 2
        return len(self		) < limit






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Dict          =   self._buckets
        A :       Optional[Any]          =   [None] * new_size
        A :       List[str]          =   0
        for item in old_buckets:
            if item:
                self._add_item(item.key,		item.val		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) * 2		)






    def 			_lowerCAmelCase					(       self		):
        self._resize(len(self._buckets		) // 2		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Union[str, Any]          =   self._get_bucket_index(lowerCamelCase__		)
        for _ in range(len(self._buckets		)		):
            yield ind
            A :       Dict          =   self._get_next_ind(lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            if self._try_set(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
                break






    def __setitem__(       self,		lowerCamelCase__,		lowerCamelCase__		):
        if self._is_full():
            self._size_up()

        self._add_item(lowerCamelCase__,		lowerCamelCase__		)






    def __delitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       Tuple          =   self._buckets[ind]
            if item is None:
                raise KeyError(lowerCamelCase__		)
            if item is _deleted:
                continue
            if item.key == key:
                A :       int          =   _deleted
                self._len -= 1
                break
        if self._is_sparse():
            self._size_down()






    def __getitem__(       self,		lowerCamelCase__		):
        for ind in self._iterate_buckets(lowerCamelCase__		):
            A :       int          =   self._buckets[ind]
            if item is None:
                break
            if item is _deleted:
                continue
            if item.key == key:
                return item.val
        raise KeyError(lowerCamelCase__		)






    def __len__(       self		):
        return self._len






    def __iter__(       self		):
        yield from (item.key for item in self._buckets if item)






    def __repr__(       self		):
        A :       Union[str, Any]          =   """ ,""".join(
            f'''{item.key}: {item.val}''' for item in self._buckets if item		)
        return f'''HashMap({val_string})'''


