

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_       : Optional[Any]         =			logging.get_logger(__name__)



@add_end_docstrings(__magic_name__				)
class     _a						(__magic_name__				):


					'''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__							:    str        =		kwargs.pop("""text_queries"""			)

						if isinstance(A__    ,       (str, Image.Image)			):
							A__							:    Dict        =		{"""image""": image, """candidate_labels""": candidate_labels}
						else:
							A__							:    List[str]        =		image
						A__							:    Optional[Any]        =		super().__call__(A__    ,       **A__			)
						return results






					def   __A			( self    ,       **A__			):
						A__							:    str        =		{}
						if "threshold" in kwargs:
							A__							:    Any        =		kwargs["""threshold"""]
						if "top_k" in kwargs:
							A__							:    Dict        =		kwargs["""top_k"""]
						return {}, {}, postprocess_params






					def   __A			( self    ,       A__			):
						A__							:    str        =		load_image(inputs["""image"""]			)
						A__							:    Union[str, Any]        =		inputs["""candidate_labels"""]
						if isinstance(A__    ,       A__			):
							A__							:    str        =		candidate_labels.split(""","""			)

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

						A__							:    Tuple        =		self.model(**A__			)

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






					def   __A			( self    ,       A__    ,       A__=0.1    ,       A__=None			):
						A__							:    Optional[Any]        =		[]
						for model_output in model_outputs:
							A__							:    int        =		model_output["""candidate_label"""]
							A__							:    int        =		BaseModelOutput(A__			)
							A__							:    Union[str, Any]        =		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__							:    Any        =		outputs["""scores"""][index].item()
								A__							:    Any        =		self._get_bounding_box(outputs["""boxes"""][index][0]			)

								A__							:    Any        =		{"""score""": score, """label""": label, """box""": box}
								results.append(A__			)

						A__							:    List[str]        =		sorted(A__    ,       key=lambda A__			: x["score"]    ,       reverse=A__			)
						if top_k:
							A__							:    Tuple        =		results[:top_k]

						return results






					def   __A			( self    ,       A__			):
						if self.framework != "pt":
							raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""			)
						A__   ,   A__   ,   A__   ,   A__							:    Union[str, Any]        =		box.int().tolist()
						A__							:    List[Any]        =		{
						    """xmin""": xmin,
						    """ymin""": ymin,
						    """xmax""": xmax,
						    """ymax""": ymax,
						}
						return bbox






import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    """files"""    ,      [
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]    ,      )
def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: Optional[int]					)     ->      List[str]:
	A__							:    Tuple        =		tmp_path_factory.mktemp("""dset_infos_dir"""					)
	if "full:README.md" in files:
		with open(dataset_infos_dir / """README.md"""    ,      """w"""					) as f:
			f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""					)
	if "empty:README.md" in files:
		with open(dataset_infos_dir / """README.md"""    ,      """w"""					) as f:
			f.write(""""""					)
    # we want to support dataset_infos.json for backward compatibility
	if "dataset_infos.json" in files:
		with open(dataset_infos_dir / """dataset_infos.json"""    ,      """w"""					) as f:
			f.write("""{\"default\": {\"dataset_size\": 42}}"""					)
	A__							:    List[Any]        =		DatasetInfosDict.from_directory(lowercase_					)
	assert dataset_infos
	assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    """dataset_info"""    ,      [
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""    ,      features=Features({"""a""": Value("""int32"""					)}					)    ,      builder_name="""builder"""    ,      config_name="""config"""    ,      version="""1.0.0"""    ,      splits=[{"""name""": """train"""}]    ,      download_size=42    ,      ),
    ]    ,      )
def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: DatasetInfo					)     ->      List[Any]:
	A__							:    Union[str, Any]        =		str(lowercase_					)
	dataset_info.write_to_directory(lowercase_					)
	A__							:    List[Any]        =		DatasetInfo.from_directory(lowercase_					)
	assert dataset_info == reloaded
	assert os.path.exists(os.path.join(lowercase_    ,      """dataset_info.json"""					)					)

def 						UpperCamelCase					()     ->      List[Any]:
	A__							:    Union[str, Any]        =		DatasetInfo(
	    description="""foo"""    ,      citation="""bar"""    ,      homepage="""https://foo.bar"""    ,      license="""CC0"""    ,      features=Features({"""a""": Value("""int32"""					)}					)    ,      post_processed={}    ,      supervised_keys=()    ,      task_templates=[]    ,      builder_name="""builder"""    ,      config_name="""config"""    ,      version="""1.0.0"""    ,      splits=[{"""name""": """train""", """num_examples""": 42}]    ,      download_checksums={}    ,      download_size=1337    ,      post_processing_size=442    ,      dataset_size=1234    ,      size_in_bytes=1337 + 442 + 1234    ,      )
	A__							:    Dict        =		dataset_info._to_yaml_dict()
	assert sorted(lowercase_					) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML					)
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		assert key in dataset_info_yaml_dict
		assert isinstance(dataset_info_yaml_dict[key]    ,      (list, dict, int, str)					)
	A__							:    Union[str, Any]        =		yaml.safe_dump(lowercase_					)
	A__							:    List[Any]        =		yaml.safe_load(lowercase_					)
	assert dataset_info_yaml_dict == reloaded

def 						UpperCamelCase					()     ->      List[str]:
	A__							:    Optional[int]        =		DatasetInfo()
	A__							:    List[Any]        =		dataset_info._to_yaml_dict()
	assert dataset_info_yaml_dict == {}


@pytest.mark.parametrize(
    """dataset_infos_dict"""    ,      [
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}					),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}					),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""    ,      features=Features({"""a""": Value("""int32"""					)}					)    ,      builder_name="""builder"""    ,      config_name="""config"""    ,      version="""1.0.0"""    ,      splits=[{"""name""": """train"""}]    ,      download_size=42    ,      )
            }					),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42					),
                """v2""": DatasetInfo(dataset_size=1337					),
            }					),
    ]    ,      )
def 						UpperCamelCase					(lowercase_: Tuple    ,      lowercase_: DatasetInfosDict					)     ->      Optional[Any]:
	A__							:    List[Any]        =		str(lowercase_					)
	dataset_infos_dict.write_to_directory(lowercase_					)
	A__							:    Dict        =		DatasetInfosDict.from_directory(lowercase_					)

	# the config_name of the dataset_infos_dict take over the attribute
	for config_name, dataset_info in dataset_infos_dict.items():
		A__							:    Optional[int]        =		config_name
		# the yaml representation doesn't include fields like description or citation
		# so we just test that we can recover what we can from the yaml
		A__							:    List[str]        =		DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()					)
	assert dataset_infos_dict == reloaded

	if dataset_infos_dict:
		assert os.path.exists(os.path.join(lowercase_    ,      """README.md"""					)					)



