






"""simple docstring"""



import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


A_      =							importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
						from .safilesystem import SaFileSystem  # noqa: F401

A_      =							[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
						if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
												warnings.warn(F'A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.')
						fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)




def        _lowerCAmelCase						(							UpperCAmelCase__    :   str )						->str:
						if "://" in dataset_path:
												A__  :					int											=					dataset_path.split("""://""" )[1]
						return dataset_path




def        _lowerCAmelCase						(							UpperCAmelCase__    :   fsspec.AbstractFileSystem )						->bool:
						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False




def        _lowerCAmelCase						(							UpperCAmelCase__    :   fsspec.AbstractFileSystem,  UpperCAmelCase__    :   str,  UpperCAmelCase__    :   str )						->Any:
						A__  :					Union[str, Any]											=					not is_remote_filesystem(UpperCAmelCase__ )
						if is_local:
												# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
												shutil.move(fs._strip_protocol(UpperCAmelCase__ ),  fs._strip_protocol(UpperCAmelCase__ ) )
						else:
												fs.mv(UpperCAmelCase__,  UpperCAmelCase__,  recursive=UpperCAmelCase__ )




def        _lowerCAmelCase						(							)						->None:
						if hasattr(fsspec.asyn,  """reset_lock""" ):
												# for future fsspec>2022.05.0
												fsspec.asyn.reset_lock()
						else:
												A__  :					int											=					None
												A__  :					List[str]											=					None
												A__  :					Tuple											=					threading.Lock()














"""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, Pipeline


if is_vision_available():
						from ..image_utils import load_image


if is_torch_available():
						import torch

						from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

A_      =							logging.get_logger(__name__)


A_      =							Dict[str, Any]
A_      =							List[Prediction]






@add_end_docstrings(UpperCamelCase						)
class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase						):


							def __init__(     self :  str     ,      *snake_case :  Tuple     ,      **snake_case :  Tuple							):
													'''simple docstring'''





													super().__init__(*snake_case     ,      **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(
													    dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()							)							)


							def   _UpperCamelCase    (     self :  List[Any]     ,      **snake_case :  Optional[int]							):
													'''simple docstring'''





													A__  :					Dict											=					{}
													if "threshold" in kwargs:
																			A__  :					int											=					kwargs["""threshold"""]
													return {}, {}, postprocess_kwargs


							def __call__(     self :  Tuple     ,      *snake_case :  Union[str, Any]     ,      **snake_case :  Union[str, Any]							):
													'''simple docstring'''





													return super().__call__(*snake_case     ,      **snake_case							)


							def   _UpperCamelCase    (     self :  str     ,      snake_case :  int							):
													'''simple docstring'''





													A__  :					List[str]											=					load_image(snake_case							)
													A__  :					int											=					torch.IntTensor([[image.height, image.width]]							)
													A__  :					Union[str, Any]											=					self.image_processor(images=[image]     ,      return_tensors="""pt"""							)
													if self.tokenizer is not None:
																			A__  :					str											=					self.tokenizer(text=inputs["""words"""]     ,      boxes=inputs["""boxes"""]     ,      return_tensors="""pt"""							)
													A__  :					List[str]											=					target_size
													return inputs


							def   _UpperCamelCase    (     self :  Optional[int]     ,      snake_case :  List[Any]							):
													'''simple docstring'''





													A__  :					str											=					model_inputs.pop("""target_size"""							)
													A__  :					Dict											=					self.model(**snake_case							)
													A__  :					Optional[Any]											=					outputs.__class__({"""target_size""": target_size, **outputs}							)
													if self.tokenizer is not None:
																			A__  :					str											=					model_inputs["""bbox"""]
													return model_outputs


							def   _UpperCamelCase    (     self :  Tuple     ,      snake_case :  Optional[int]     ,      snake_case :  int=0.9							):
													'''simple docstring'''





													A__  :					Any											=					model_outputs["""target_size"""]
													if self.tokenizer is not None:
																			# This is a LayoutLMForTokenClassification variant.
																			# The OCR got the boxes and the model classified the words.
																			A__					,					A__  :					Tuple											=					target_size[0].tolist()

																			def unnormalize(snake_case :  Optional[int]							):
																									return self._get_bounding_box(
																									    torch.Tensor(
																									        [
																									            (width * bbox[0] / 1000),
																									            (height * bbox[1] / 1000),
																									            (width * bbox[2] / 1000),
																									            (height * bbox[3] / 1000),
																									        ]							)							)

																			A__					,					A__  :					Optional[int]											=					model_outputs["""logits"""].squeeze(0							).softmax(dim=-1							).max(dim=-1							)
																			A__  :					Optional[Any]											=					[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
																			A__  :					List[str]											=					[unnormalize(snake_case							) for bbox in model_outputs["""bbox"""].squeeze(0							)]
																			A__  :					Tuple											=					["""score""", """label""", """box"""]
																			A__  :					Any											=					[dict(zip(snake_case     ,      snake_case							)							) for vals in zip(scores.tolist()     ,      snake_case     ,      snake_case							) if vals[0] > threshold]
													else:
																			# This is a regular ForObjectDetectionModel
																			A__  :					Union[str, Any]											=					self.image_processor.post_process_object_detection(snake_case     ,      snake_case     ,      snake_case							)
																			A__  :					str											=					raw_annotations[0]
																			A__  :					str											=					raw_annotation["""scores"""]
																			A__  :					List[Any]											=					raw_annotation["""labels"""]
																			A__  :					int											=					raw_annotation["""boxes"""]

																			A__  :					str											=					scores.tolist()
																			A__  :					Any											=					[self.model.config.idalabel[label.item()] for label in labels]
																			A__  :					int											=					[self._get_bounding_box(snake_case							) for box in boxes]

																			# {"scores": [...], ...} --> [{"score":x, ...}, ...]
																			A__  :					str											=					["""score""", """label""", """box"""]
																			A__  :					Dict											=					[
																			    dict(zip(snake_case     ,      snake_case							)							)
																			    for vals in zip(raw_annotation["""scores"""]     ,      raw_annotation["""labels"""]     ,      raw_annotation["""boxes"""]							)
																			]

													return annotation


							def   _UpperCamelCase    (     self :  Union[str, Any]     ,      snake_case :  "torch.Tensor"							):
													'''simple docstring'''





													if self.framework != "pt":
																			raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch."""							)
													A__					,					A__					,					A__					,					A__  :					Any											=					box.int().tolist()
													A__  :					Any											=					{
													    """xmin""": xmin,
													    """ymin""": ymin,
													    """xmax""": xmax,
													    """ymax""": ymax,
													}
													return bbox






