






"""simple docstring"""





from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__lowerCamelCase							=  logging.get_logger(__name__)



@add_end_docstrings(SCREAMING_SNAKE_CASE_			)
class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      Union[str, Any]     ,		**__snake_case  :      Optional[int]					)			->							int:
											super().__init__(**__snake_case					)
											requires_backends(self     ,		"""vision"""					)
											requires_backends(self     ,		"""torch"""					)

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

											self.check_model_type(__snake_case					)

					def   lowerCamelCase__   (			self  :      str     ,		**__snake_case  :      Any					)			->							Tuple:
											__magic_name__:	Optional[Any]							=       {}
											__magic_name__:	Any							=       {}
											__magic_name__:	Any							=       {}
											# preprocess args
											if "points_per_batch" in kwargs:
																	__magic_name__:	Optional[Any]							=       kwargs["""points_per_batch"""]
											if "points_per_crop" in kwargs:
																	__magic_name__:	Tuple							=       kwargs["""points_per_crop"""]
											if "crops_n_layers" in kwargs:
																	__magic_name__:	str							=       kwargs["""crops_n_layers"""]
											if "crop_overlap_ratio" in kwargs:
																	__magic_name__:	Tuple							=       kwargs["""crop_overlap_ratio"""]
											if "crop_n_points_downscale_factor" in kwargs:
																	__magic_name__:	Dict							=       kwargs["""crop_n_points_downscale_factor"""]
											# postprocess args
											if "pred_iou_thresh" in kwargs:
																	__magic_name__:	Union[str, Any]							=       kwargs["""pred_iou_thresh"""]
											if "stability_score_offset" in kwargs:
																	__magic_name__:	Tuple							=       kwargs["""stability_score_offset"""]
											if "mask_threshold" in kwargs:
																	__magic_name__:	List[str]							=       kwargs["""mask_threshold"""]
											if "stability_score_thresh" in kwargs:
																	__magic_name__:	Optional[Any]							=       kwargs["""stability_score_thresh"""]
											if "crops_nms_thresh" in kwargs:
																	__magic_name__:	Tuple							=       kwargs["""crops_nms_thresh"""]
											if "output_rle_mask" in kwargs:
																	__magic_name__:	str							=       kwargs["""output_rle_mask"""]
											if "output_bboxes_mask" in kwargs:
																	__magic_name__:	List[Any]							=       kwargs["""output_bboxes_mask"""]
											return preprocess_kwargs, forward_params, postprocess_kwargs

					def __call__(			self  :      List[str]     ,		__snake_case  :      List[Any]     ,		*__snake_case  :      int     ,		__snake_case  :      Union[str, Any]=None     ,		__snake_case  :      Optional[int]=None     ,		**__snake_case  :      Tuple					)			->							Dict:
											return super().__call__(__snake_case     ,		*__snake_case     ,		num_workers=__snake_case     ,		batch_size=__snake_case     ,		**__snake_case					)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      str     ,		__snake_case  :      int=6_4     ,		__snake_case  :      int = 0     ,		__snake_case  :      float = 5_1_2 / 1_5_0_0     ,		__snake_case  :      Optional[int] = 3_2     ,		__snake_case  :      Optional[int] = 1     ,		)			->							Any:
											__magic_name__:	Tuple							=       load_image(__snake_case					)
											__magic_name__:	Dict							=       self.image_processor.size["""longest_edge"""]
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	Dict							=       self.image_processor.generate_crop_boxes(
											    __snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case					)
											__magic_name__:	Tuple							=       self.image_processor(images=__snake_case     ,		return_tensors="""pt"""					)

											with self.device_placement():
																	if self.framework == "pt":
																							__magic_name__:	Optional[int]							=       self.get_inference_context()
																							with inference_context():
																													__magic_name__:	Dict							=       self._ensure_tensor_on_device(__snake_case     ,		device=self.device					)
																													__magic_name__:	Dict							=       self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""					)					)
																													__magic_name__:	List[Any]							=       image_embeddings

											__magic_name__:	int							=       grid_points.shape[1]
											__magic_name__:	List[str]							=       points_per_batch if points_per_batch is not None else n_points

											if points_per_batch <= 0:
																	raise ValueError(
																	    """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
																	    """To return all points at once, set points_per_batch to None"""					)

											for i in range(0     ,		__snake_case     ,		__snake_case					):
																	__magic_name__:	Optional[Any]							=       grid_points[:, i : i + points_per_batch, :, :]
																	__magic_name__:	Tuple							=       input_labels[:, i : i + points_per_batch]
																	__magic_name__:	Union[str, Any]							=       i == n_points - points_per_batch
																	yield {
																	    "input_points": batched_points,
																	    "input_labels": labels,
																	    "input_boxes": crop_boxes,
																	    "is_last": is_last,
																	    **model_inputs,
																	}

					def   lowerCamelCase__   (			self  :      Optional[int]     ,		__snake_case  :      Any     ,		__snake_case  :      List[str]=0.88     ,		__snake_case  :      int=0.95     ,		__snake_case  :      Union[str, Any]=0     ,		__snake_case  :      Tuple=1     ,		)			->							List[Any]:
											__magic_name__:	Dict							=       model_inputs.pop("""input_boxes"""					)
											__magic_name__:	str							=       model_inputs.pop("""is_last"""					)
											__magic_name__:	Dict							=       model_inputs.pop("""original_sizes"""					).tolist()
											__magic_name__:	Any							=       model_inputs.pop("""reshaped_input_sizes"""					).tolist()

											__magic_name__:	str							=       self.model(**__snake_case					)

											# post processing happens here in order to avoid CPU GPU copies of ALL the masks
											__magic_name__:	Union[str, Any]							=       model_outputs["""pred_masks"""]
											__magic_name__:	Optional[int]							=       self.image_processor.post_process_masks(
											    __snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		binarize=__snake_case					)
											__magic_name__:	str							=       model_outputs["""iou_scores"""]
											__magic_name__,			__magic_name__,			__magic_name__:	int							=       self.image_processor.filter_masks(
											    masks[0]     ,		iou_scores[0]     ,		original_sizes[0]     ,		input_boxes[0]     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		)
											return {
											    "masks": masks,
											    "is_last": is_last,
											    "boxes": boxes,
											    "iou_scores": iou_scores,
											}






					def   lowerCamelCase__   (			self  :      Union[str, Any]     ,		__snake_case  :      Tuple     ,		__snake_case  :      int=False     ,		__snake_case  :      str=False     ,		__snake_case  :      List[str]=0.7     ,		)			->							Any:
											__magic_name__:	List[Any]							=       []
											__magic_name__:	Optional[int]							=       []
											__magic_name__:	Dict							=       []
											for model_output in model_outputs:
																	all_scores.append(model_output.pop("""iou_scores"""					)					)
																	all_masks.extend(model_output.pop("""masks"""					)					)
																	all_boxes.append(model_output.pop("""boxes"""					)					)

											__magic_name__:	Tuple							=       torch.cat(__snake_case					)
											__magic_name__:	List[str]							=       torch.cat(__snake_case					)
											__magic_name__,			__magic_name__,			__magic_name__,			__magic_name__:	int							=       self.image_processor.post_process_for_mask_generation(
											    __snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case					)

											__magic_name__:	Tuple							=       defaultdict(__snake_case					)
											for output in model_outputs:
																	for k, v in output.items():
																							extra[k].append(__snake_case					)

											__magic_name__:	int							=       {}
											if output_rle_mask:
																	__magic_name__:	int							=       rle_mask

											if output_bboxes_mask:
																	__magic_name__:	str							=       bounding_boxes

											return {"masks": output_masks, "scores": iou_scores, **optional, **extra}











"""simple docstring"""





import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def   a    (     __UpperCAmelCase      :    bytes      ,   __UpperCAmelCase      :    int  )    ->							np.array:
						__magic_name__:	Optional[int]							=       f'{sampling_rate}'
						__magic_name__:	Union[str, Any]							=       """1"""
						__magic_name__:	Tuple							=       """f32le"""
						__magic_name__:	Optional[Any]							=       [
						    """ffmpeg""",
						    """-i""",
						    """pipe:0""",
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]

						try:
												with subprocess.Popen(__UpperCAmelCase      ,   stdin=subprocess.PIPE      ,   stdout=subprocess.PIPE  ) as ffmpeg_process:
																		__magic_name__:	Any							=       ffmpeg_process.communicate(__UpperCAmelCase  )
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to load audio files from filename"""  ) from error
						__magic_name__:	Optional[Any]							=       output_stream[0]
						__magic_name__:	Optional[int]							=       np.frombuffer(__UpperCAmelCase      ,   np.floataa  )
						if audio.shape[0] == 0:
												raise ValueError("""Malformed soundfile"""  )
						return audio



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    str = "f32le"      ,   )    ->							Optional[int]:
						__magic_name__:	List[Any]							=       f'{sampling_rate}'
						__magic_name__:	Optional[int]							=       """1"""
						if format_for_conversion == "s16le":
												__magic_name__:	Dict							=       2
						elif format_for_conversion == "f32le":
												__magic_name__:	List[str]							=       4
						else:
												raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'  )

						__magic_name__:	str							=       platform.system()
						if system == "Linux":
												__magic_name__:	Optional[Any]							=       """alsa"""
												__magic_name__:	Optional[Any]							=       """default"""
						elif system == "Darwin":
												__magic_name__:	Union[str, Any]							=       """avfoundation"""
												__magic_name__:	Dict							=       """:0"""
						elif system == "Windows":
												__magic_name__:	Any							=       """dshow"""
												__magic_name__:	Union[str, Any]							=       """default"""

						__magic_name__:	Union[str, Any]							=       [
						    """ffmpeg""",
						    """-f""",
						    format_,
						    """-i""",
						    input_,
						    """-ac""",
						    ac,
						    """-ar""",
						    ar,
						    """-f""",
						    format_for_conversion,
						    """-fflags""",
						    """nobuffer""",
						    """-hide_banner""",
						    """-loglevel""",
						    """quiet""",
						    """pipe:1""",
						]
						__magic_name__:	Tuple							=       int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
						__magic_name__:	Union[str, Any]							=       _ffmpeg_stream(__UpperCAmelCase      ,   __UpperCAmelCase  )
						for item in iterator:
												yield item



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    float      ,   __UpperCAmelCase      :    Optional[int] = None      ,   __UpperCAmelCase      :    Optional[Union[Tuple[float, float], float]] = None      ,   __UpperCAmelCase      :    str = "f32le"      ,   )    ->							List[Any]:
						if stream_chunk_s is not None:
												__magic_name__:	Optional[int]							=       stream_chunk_s
						else:
												__magic_name__:	List[str]							=       chunk_length_s

						__magic_name__:	Optional[int]							=       ffmpeg_microphone(__UpperCAmelCase      ,   __UpperCAmelCase      ,   format_for_conversion=__UpperCAmelCase  )
						if format_for_conversion == "s16le":
												__magic_name__:	str							=       np.intaa
												__magic_name__:	str							=       2
						elif format_for_conversion == "f32le":
												__magic_name__:	Optional[Any]							=       np.floataa
												__magic_name__:	Dict							=       4
						else:
												raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'  )

						if stride_length_s is None:
												__magic_name__:	List[Any]							=       chunk_length_s / 6
						__magic_name__:	Optional[Any]							=       int(round(sampling_rate * chunk_length_s  )  ) * size_of_sample
						if isinstance(__UpperCAmelCase      ,   (int, float)  ):
												__magic_name__:	Union[str, Any]							=       [stride_length_s, stride_length_s]

						__magic_name__:	List[str]							=       int(round(sampling_rate * stride_length_s[0]  )  ) * size_of_sample
						__magic_name__:	List[Any]							=       int(round(sampling_rate * stride_length_s[1]  )  ) * size_of_sample
						__magic_name__:	List[str]							=       datetime.datetime.now()
						__magic_name__:	Optional[Any]							=       datetime.timedelta(seconds=__UpperCAmelCase  )
						for item in chunk_bytes_iter(__UpperCAmelCase      ,   __UpperCAmelCase      ,   stride=(stride_left, stride_right)      ,   stream=__UpperCAmelCase  ):
												# Put everything back in numpy scale
												__magic_name__:	int							=       np.frombuffer(item["""raw"""]      ,   dtype=__UpperCAmelCase  )
												__magic_name__:	int							=       (
												    item["""stride"""][0] // size_of_sample,
												    item["""stride"""][1] // size_of_sample,
												)
												__magic_name__:	Union[str, Any]							=       sampling_rate
												audio_time += delta
												if datetime.datetime.now() > audio_time + 1_0 * delta:
																		# We're late !! SKIP
																		continue
												yield item



def   a    (     __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    int      ,   __UpperCAmelCase      :    Tuple[int, int]      ,   __UpperCAmelCase      :    bool = False  )    ->							Union[str, Any]:
						__magic_name__:	Tuple							=       B""""""
						__magic_name__,			__magic_name__:	int							=       stride
						if stride_left + stride_right >= chunk_len:
												raise ValueError(
												    f'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'  )
						__magic_name__:	int							=       0
						for raw in iterator:
												acc += raw
												if stream and len(__UpperCAmelCase  ) < chunk_len:
																		__magic_name__:	Optional[int]							=       (_stride_left, 0)
																		yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
												else:
																		while len(__UpperCAmelCase  ) >= chunk_len:
																								# We are flushing the accumulator
																								__magic_name__:	Union[str, Any]							=       (_stride_left, stride_right)
																								__magic_name__:	Any							=       {"""raw""": acc[:chunk_len], """stride""": stride}
																								if stream:
																														__magic_name__:	str							=       False
																								yield item
																								__magic_name__:	str							=       stride_left
																								__magic_name__:	Optional[Any]							=       acc[chunk_len - stride_left - stride_right :]
    # Last chunk
						if len(__UpperCAmelCase  ) > stride_left:
												__magic_name__:	List[Any]							=       {"""raw""": acc, """stride""": (_stride_left, 0)}
												if stream:
																		__magic_name__:	Union[str, Any]							=       False
												yield item



def   a    (     __UpperCAmelCase      :    Tuple      ,   __UpperCAmelCase      :    int  )    ->							List[Any]:
						__magic_name__:	str							=       2**2_4  # 16Mo
						try:
												with subprocess.Popen(__UpperCAmelCase      ,   stdout=subprocess.PIPE      ,   bufsize=__UpperCAmelCase  ) as ffmpeg_process:
																		while True:
																								__magic_name__:	Dict							=       ffmpeg_process.stdout.read(__UpperCAmelCase  )
																								if raw == b"":
																														break
																								yield raw
						except FileNotFoundError as error:
												raise ValueError("""ffmpeg was not found but is required to stream audio files from filename"""  ) from error



