

import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


__snake_case							:	List[str]											=       random.Random()


def  _UpperCAmelCase							(		a__   ,     a__=1.0   ,     a__=None   ,     a__=None):

			'''simple docstring'''



			if rng is None:
						a_		:		str      				=      global_rng

			a_		:		Any      				=      []
			for batch_idx in range(shape[0]):
						values.append([])
						for _ in range(shape[1]):
									values[-1].append(rng.random() * scale)

			return values



@require_torch
class     A__(unittest.TestCase				):
						"""simple docstring"""






						def __init__(  self   ,	_lowercase   ,	_lowercase=7   ,	_lowercase=400   ,	_lowercase=2_000   ,	_lowercase=1   ,	_lowercase=0.0   ,	_lowercase=16_000   ,	_lowercase=True   ,	_lowercase=80   ,	_lowercase=16   ,	_lowercase=64   ,	_lowercase="hann_window"   ,	_lowercase=80   ,	_lowercase=7_600   ,	_lowercase=1e-10   ,	_lowercase=True   ,	)			->      List[Any]:
									a_		:		Union[str, Any]      				=      parent
									a_		:		str      				=      batch_size
									a_		:		List[str]      				=      min_seq_length
									a_		:		Tuple      				=      max_seq_length
									a_		:		List[str]      				=      (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									a_		:		List[str]      				=      feature_size
									a_		:		Optional[Any]      				=      padding_value
									a_		:		Any      				=      sampling_rate
									a_		:		List[str]      				=      do_normalize
									a_		:		List[str]      				=      num_mel_bins
									a_		:		str      				=      hop_length
									a_		:		Any      				=      win_length
									a_		:		int      				=      win_function
									a_		:		int      				=      fmin
									a_		:		Any      				=      fmax
									a_		:		str      				=      mel_floor
									a_		:		List[Any]      				=      return_attention_mask





						def        UpperCamelCase__ (  self  )			->      Optional[int]:
									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "do_normalize": self.do_normalize,
									    "num_mel_bins": self.num_mel_bins,
									    "hop_length": self.hop_length,
									    "win_length": self.win_length,
									    "win_function": self.win_function,
									    "fmin": self.fmin,
									    "fmax": self.fmax,
									    "mel_floor": self.mel_floor,
									    "return_attention_mask": self.return_attention_mask,
									}





						def        UpperCamelCase__ (  self   ,	_lowercase=False   ,	_lowercase=False  )			->      List[Any]:

									def _flatten(_lowercase  ):
												return list(itertools.chain(*lowerCamelCase_  )  )

									if equal_length:
												a_		:		Optional[int]      				=      floats_list((self.batch_size, self.max_seq_length)  )
									else:
												# make sure that inputs increase in size
												a_		:		Tuple      				=      [
												    _flatten(floats_list((x, self.feature_size)  )  )
												    for x in range(self.min_seq_length   ,	self.max_seq_length   ,	self.seq_length_diff  )
												]

									if numpify:
												a_		:		Dict      				=      [np.asarray(lowerCamelCase_  ) for x in speech_inputs]

									return speech_inputs





						def        UpperCamelCase__ (  self   ,	_lowercase=False   ,	_lowercase=False  )			->      Optional[int]:

									if equal_length:
												a_		:		Any      				=      [floats_list((self.max_seq_length, self.num_mel_bins)  ) for _ in range(self.batch_size  )]
									else:
												# make sure that inputs increase in size
												a_		:		Union[str, Any]      				=      [
												    floats_list((x, self.num_mel_bins)  )
												    for x in range(self.min_seq_length   ,	self.max_seq_length   ,	self.seq_length_diff  )
												]

									if numpify:
												a_		:		str      				=      [np.asarray(lowerCamelCase_  ) for x in speech_inputs]

									return speech_inputs







@require_torch
class     A__(a_,   unittest.TestCase				):
						"""simple docstring"""






						_A  :							Optional[int]     =       SpeechTaFeatureExtractor





						def        UpperCamelCase__ (  self  )			->      Optional[int]:
									a_		:		List[Any]      				=      SpeechTaFeatureExtractionTester(self  )





						def        UpperCamelCase__ (  self   ,	_lowercase  )			->      List[str]:
									self.assertTrue(np.all(np.mean(lowerCamelCase_   ,	axis=0  ) < 1e-3  )  )
									self.assertTrue(np.all(np.abs(np.var(lowerCamelCase_   ,	axis=0  ) - 1  ) < 1e-3  )  )





						def        UpperCamelCase__ (  self  )			->      int:
									a_		:		Dict      				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									# create three inputs of length 800, 1000, and 1200
									a_		:		Optional[int]      				=      [floats_list((1, x)  )[0] for x in range(800   ,	1_400   ,	200  )]
									a_		:		List[str]      				=      [np.asarray(lowerCamelCase_  ) for speech_input in speech_inputs]

									# Test not batched input
									a_		:		str      				=      feat_extract(speech_inputs[0]   ,	return_tensors="""np"""  ).input_values
									a_		:		Dict      				=      feat_extract(np_speech_inputs[0]   ,	return_tensors="""np"""  ).input_values
									self.assertTrue(np.allclose(lowerCamelCase_   ,	lowerCamelCase_   ,	atol=1e-3  )  )

									# Test batched
									a_		:		str      				=      feat_extract(lowerCamelCase_   ,	return_tensors="""np"""  ).input_values
									a_		:		Union[str, Any]      				=      feat_extract(lowerCamelCase_   ,	return_tensors="""np"""  ).input_values
									for enc_seq_a, enc_seq_a in zip(lowerCamelCase_   ,	lowerCamelCase_  ):
												self.assertTrue(np.allclose(lowerCamelCase_   ,	lowerCamelCase_   ,	atol=1e-3  )  )





						def        UpperCamelCase__ (  self  )			->      List[str]:
									a_		:		Any      				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a_		:		int      				=      [floats_list((1, x)  )[0] for x in range(800   ,	1_400   ,	200  )]

									a_		:		Any      				=      ["""longest""", """max_length""", """do_not_pad"""]
									a_		:		Dict      				=      [None, 1_600, None]
									for max_length, padding in zip(lowerCamelCase_   ,	lowerCamelCase_  ):
												a_		:		int      				=      feat_extract(lowerCamelCase_   ,	padding=lowerCamelCase_   ,	max_length=lowerCamelCase_   ,	return_tensors="""np"""  )
												a_		:		Dict      				=      processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:800]  )
												self.assertTrue(input_values[0][800:].sum() < 1e-6  )
												self._check_zero_mean_unit_variance(input_values[1][:1_000]  )
												self.assertTrue(input_values[0][1_000:].sum() < 1e-6  )
												self._check_zero_mean_unit_variance(input_values[2][:1_200]  )





						def        UpperCamelCase__ (  self  )			->      Any:
									a_		:		Tuple      				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a_		:		List[str]      				=      range(800   ,	1_400   ,	200  )
									a_		:		Union[str, Any]      				=      [floats_list((1, x)  )[0] for x in lengths]

									a_		:		Optional[Any]      				=      ["""longest""", """max_length""", """do_not_pad"""]
									a_		:		str      				=      [None, 1_600, None]

									for max_length, padding in zip(lowerCamelCase_   ,	lowerCamelCase_  ):
												a_		:		str      				=      feat_extract(lowerCamelCase_   ,	max_length=lowerCamelCase_   ,	padding=lowerCamelCase_  )
												a_		:		Dict      				=      processed.input_values

												self._check_zero_mean_unit_variance(input_values[0][:800]  )
												self._check_zero_mean_unit_variance(input_values[1][:1_000]  )
												self._check_zero_mean_unit_variance(input_values[2][:1_200]  )





						def        UpperCamelCase__ (  self  )			->      int:
									a_		:		Dict      				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a_		:		int      				=      [floats_list((1, x)  )[0] for x in range(800   ,	1_400   ,	200  )]
									a_		:		List[Any]      				=      feat_extract(
									    lowerCamelCase_   ,	truncation=lowerCamelCase_   ,	max_length=1_000   ,	padding="""max_length"""   ,	return_tensors="""np"""  )
									a_		:		Any      				=      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]  )
									self._check_zero_mean_unit_variance(input_values[1]  )
									self._check_zero_mean_unit_variance(input_values[2]  )





						def        UpperCamelCase__ (  self  )			->      str:
									a_		:		str      				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a_		:		Tuple      				=      [floats_list((1, x)  )[0] for x in range(800   ,	1_400   ,	200  )]
									a_		:		Tuple      				=      feat_extract(
									    lowerCamelCase_   ,	truncation=lowerCamelCase_   ,	max_length=1_000   ,	padding="""longest"""   ,	return_tensors="""np"""  )
									a_		:		Tuple      				=      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]  )
									self._check_zero_mean_unit_variance(input_values[1, :1_000]  )
									self._check_zero_mean_unit_variance(input_values[2]  )

									# make sure that if max_length < longest -> then pad to max_length
									self.assertTrue(input_values.shape == (3, 1_000)  )

									a_		:		Optional[int]      				=      [floats_list((1, x)  )[0] for x in range(800   ,	1_400   ,	200  )]
									a_		:		Any      				=      feat_extract(
									    lowerCamelCase_   ,	truncation=lowerCamelCase_   ,	max_length=2_000   ,	padding="""longest"""   ,	return_tensors="""np"""  )
									a_		:		Union[str, Any]      				=      processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]  )
									self._check_zero_mean_unit_variance(input_values[1, :1_000]  )
									self._check_zero_mean_unit_variance(input_values[2]  )

									# make sure that if max_length > longest -> then pad to longest
									self.assertTrue(input_values.shape == (3, 1_200)  )





						def        UpperCamelCase__ (  self  )			->      int:
									a_		:		Tuple      				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									a_		:		List[str]      				=      np.random.rand(100  ).astype(np.floataa  )
									a_		:		str      				=      np_speech_inputs.tolist()

									for inputs in [py_speech_inputs, np_speech_inputs]:
												a_		:		str      				=      feature_extractor.pad([{"""input_values""": inputs}]   ,	return_tensors="""np"""  )
												self.assertTrue(np_processed.input_values.dtype == np.floataa  )
												a_		:		Dict      				=      feature_extractor.pad([{"""input_values""": inputs}]   ,	return_tensors="""pt"""  )
												self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )





						def        UpperCamelCase__ (  self  )			->      List[Any]:
									a_		:		Dict      				=      self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
									# create three inputs of length 800, 1000, and 1200
									a_		:		Optional[Any]      				=      [floats_list((1, x)  )[0] for x in range(800   ,	1_400   ,	200  )]
									a_		:		List[str]      				=      [np.asarray(lowerCamelCase_  ) for speech_input in speech_inputs]

									# Test feature size
									a_		:		Tuple      				=      feature_extractor(audio_target=lowerCamelCase_   ,	padding=lowerCamelCase_   ,	return_tensors="""np"""  ).input_values
									self.assertTrue(input_values.ndim == 3  )
									self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins  )

									# Test not batched input
									a_		:		str      				=      feature_extractor(speech_inputs[0]   ,	return_tensors="""np"""  ).input_values
									a_		:		Optional[int]      				=      feature_extractor(np_speech_inputs[0]   ,	return_tensors="""np"""  ).input_values
									self.assertTrue(np.allclose(lowerCamelCase_   ,	lowerCamelCase_   ,	atol=1e-3  )  )

									# Test batched
									a_		:		Union[str, Any]      				=      feature_extractor(lowerCamelCase_   ,	return_tensors="""np"""  ).input_values
									a_		:		Tuple      				=      feature_extractor(lowerCamelCase_   ,	return_tensors="""np"""  ).input_values
									for enc_seq_a, enc_seq_a in zip(lowerCamelCase_   ,	lowerCamelCase_  ):
												self.assertTrue(np.allclose(lowerCamelCase_   ,	lowerCamelCase_   ,	atol=1e-3  )  )

									# Test 2-D numpy arrays are batched.
									a_		:		Optional[int]      				=      [floats_list((1, x)  )[0] for x in (800, 800, 800)]
									a_		:		str      				=      np.asarray(lowerCamelCase_  )
									a_		:		Dict      				=      feature_extractor(lowerCamelCase_   ,	return_tensors="""np"""  ).input_values
									a_		:		Union[str, Any]      				=      feature_extractor(lowerCamelCase_   ,	return_tensors="""np"""  ).input_values
									for enc_seq_a, enc_seq_a in zip(lowerCamelCase_   ,	lowerCamelCase_  ):
												self.assertTrue(np.allclose(lowerCamelCase_   ,	lowerCamelCase_   ,	atol=1e-3  )  )





						def        UpperCamelCase__ (  self  )			->      int:
									a_		:		Tuple      				=      self.feat_extract_tester.prepare_inputs_for_target()
									a_		:		Tuple      				=      self.feature_extraction_class(**self.feat_extract_dict  )
									a_		:		Optional[int]      				=      feat_extract.model_input_names[0]

									a_		:		str      				=      BatchFeature({input_name: speech_inputs}  )

									self.assertTrue(all(len(lowerCamelCase_  ) == len(lowerCamelCase_  ) for x, y in zip(lowerCamelCase_   ,	processed_features[input_name]  )  )  )

									a_		:		Tuple      				=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase_  )
									a_		:		Optional[Any]      				=      BatchFeature({input_name: speech_inputs}   ,	tensor_type="""np"""  )

									a_		:		List[str]      				=      processed_features[input_name]

									if len(batch_features_input.shape  ) < 3:
												a_		:		Optional[Any]      				=      batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.num_mel_bins)  )





						@require_torch
						def        UpperCamelCase__ (  self  )			->      Any:
									a_		:		Dict      				=      self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase_  )
									a_		:		int      				=      self.feature_extraction_class(**self.feat_extract_dict  )
									a_		:		Any      				=      feat_extract.model_input_names[0]

									a_		:		List[Any]      				=      BatchFeature({input_name: speech_inputs}   ,	tensor_type="""pt"""  )

									a_		:		Dict      				=      processed_features[input_name]

									if len(batch_features_input.shape  ) < 3:
												a_		:		Tuple      				=      batch_features_input[:, :, None]

									self.assertTrue(
									    batch_features_input.shape
									    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]  ), self.feat_extract_tester.num_mel_bins)  )





						@require_torch
						def        UpperCamelCase__ (  self  )			->      Union[str, Any]:
									a_		:		Tuple      				=      self.feature_extraction_class(**self.feat_extract_dict  )
									a_		:		Any      				=      self.feat_extract_tester.prepare_inputs_for_target()
									a_		:		List[str]      				=      feat_extract.model_input_names[0]

									a_		:		Tuple      				=      BatchFeature({input_name: speech_inputs}  )

									a_		:		str      				=      feat_extract.num_mel_bins  # hack!

									a_		:		Optional[Any]      				=      feat_extract.pad(lowerCamelCase_   ,	padding="""longest"""   ,	return_tensors="""np"""  )[input_name]
									a_		:		Dict      				=      feat_extract.pad(lowerCamelCase_   ,	padding="""longest"""   ,	return_tensors="""pt"""  )[input_name]

									self.assertTrue(abs(input_np.astype(np.floataa  ).sum() - input_pt.numpy().astype(np.floataa  ).sum()  ) < 1e-2  )





						def        UpperCamelCase__ (  self  )			->      Union[str, Any]:
									a_		:		Any      				=      self.feat_extract_dict
									a_		:		Tuple      				=      True
									a_		:		Any      				=      self.feature_extraction_class(**lowerCamelCase_  )
									a_		:		int      				=      self.feat_extract_tester.prepare_inputs_for_target()
									a_		:		Any      				=      [len(lowerCamelCase_  ) for x in speech_inputs]
									a_		:		List[str]      				=      feat_extract.model_input_names[0]

									a_		:		Union[str, Any]      				=      BatchFeature({input_name: speech_inputs}  )

									a_		:		Tuple      				=      feat_extract.num_mel_bins  # hack!

									a_		:		Union[str, Any]      				=      feat_extract.pad(lowerCamelCase_   ,	padding="""longest"""   ,	return_tensors="""np"""  )
									self.assertIn("""attention_mask"""   ,	lowerCamelCase_  )
									self.assertListEqual(list(processed.attention_mask.shape  )   ,	list(processed[input_name].shape[:2]  )  )
									self.assertListEqual(processed.attention_mask.sum(-1  ).tolist()   ,	lowerCamelCase_  )





						def        UpperCamelCase__ (  self  )			->      Optional[Any]:
									a_		:		Union[str, Any]      				=      self.feat_extract_dict
									a_		:		Any      				=      True
									a_		:		List[str]      				=      self.feature_extraction_class(**lowerCamelCase_  )
									a_		:		Dict      				=      self.feat_extract_tester.prepare_inputs_for_target()
									a_		:		Union[str, Any]      				=      [len(lowerCamelCase_  ) for x in speech_inputs]
									a_		:		Dict      				=      feat_extract.model_input_names[0]

									a_		:		Union[str, Any]      				=      BatchFeature({input_name: speech_inputs}  )
									a_		:		Any      				=      min(lowerCamelCase_  )

									a_		:		Any      				=      feat_extract.num_mel_bins  # hack!

									a_		:		str      				=      feat_extract.pad(
									    lowerCamelCase_   ,	padding="""max_length"""   ,	max_length=lowerCamelCase_   ,	truncation=lowerCamelCase_   ,	return_tensors="""np"""  )
									self.assertIn("""attention_mask"""   ,	lowerCamelCase_  )
									self.assertListEqual(
									    list(processed_pad.attention_mask.shape  )   ,	[processed_pad[input_name].shape[0], max_length]  )
									self.assertListEqual(
									    processed_pad.attention_mask[:, :max_length].sum(-1  ).tolist()   ,	[max_length for x in speech_inputs]  )





						def        UpperCamelCase__ (  self   ,	_lowercase  )			->      int:
									from datasets import load_dataset

									a_		:		Optional[Any]      				=      load_dataset("""hf-internal-testing/librispeech_asr_dummy"""   ,	"""clean"""   ,	split="""validation"""  )
									# automatic decoding with librispeech
									a_		:		Any      				=      ds.sort("""id"""  ).select(range(lowerCamelCase_  )  )[:num_samples]["""audio"""]

									return [x["array"] for x in speech_samples]





						def        UpperCamelCase__ (  self  )			->      List[str]:
									a_		:		List[str]      				=      torch.tensor(
									    [2.3804e-03, 2.0752e-03, 1.9836e-03, 2.1057e-03, 1.6174e-03,
									     3.0518e-04, 9.1553e-05, 3.3569e-04, 9.7656e-04, 1.8311e-03,
									     2.0142e-03, 2.1057e-03, 1.7395e-03, 4.5776e-04, -3.9673e-04,
									     4.5776e-04, 1.0071e-03, 9.1553e-05, 4.8828e-04, 1.1597e-03,
									     7.3242e-04, 9.4604e-04, 1.8005e-03, 1.8311e-03, 8.8501e-04,
									     4.2725e-04, 4.8828e-04, 7.3242e-04, 1.0986e-03, 2.1057e-03]  )
									# fmt: on

									a_		:		Tuple      				=      self._load_datasamples(1  )
									a_		:		int      				=      SpeechTaFeatureExtractor()
									a_		:		Dict      				=      feature_extractor(lowerCamelCase_   ,	return_tensors="""pt"""  ).input_values
									self.assertEquals(input_values.shape   ,	(1, 93_680)  )
									self.assertTrue(torch.allclose(input_values[0, :30]   ,	lowerCamelCase_   ,	atol=1e-6  )  )





						def        UpperCamelCase__ (  self  )			->      Optional[Any]:
									a_		:		List[Any]      				=      torch.tensor(
									    [-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
									     -3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
									     -3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
									     -3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8]  )
									# fmt: on

									a_		:		Tuple      				=      self._load_datasamples(1  )
									a_		:		Any      				=      SpeechTaFeatureExtractor()
									a_		:		int      				=      feature_extractor(audio_target=lowerCamelCase_   ,	return_tensors="""pt"""  ).input_values
									self.assertEquals(input_values.shape   ,	(1, 366, 80)  )
									self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,	lowerCamelCase_   ,	atol=1e-4  )  )







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

_lowercase: Any      			=		logging.get_logger(__name__)



@add_end_docstrings(lowerCAmelCase	)
class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""




	def __init__(self		,       **lowerCamelCase_		):
						"""simple docstring"""

						super().__init__(**lowerCamelCase_		)
						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(lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       **lowerCamelCase_		):
						"""simple docstring"""

						a					     =   {}
						a					     =   {}
						a					     =   {}
						# preprocess args
						if "points_per_batch" in kwargs:
											a					     =   kwargs["points_per_batch"]
						if "points_per_crop" in kwargs:
											a					     =   kwargs["points_per_crop"]
						if "crops_n_layers" in kwargs:
											a					     =   kwargs["crops_n_layers"]
						if "crop_overlap_ratio" in kwargs:
											a					     =   kwargs["crop_overlap_ratio"]
						if "crop_n_points_downscale_factor" in kwargs:
											a					     =   kwargs["crop_n_points_downscale_factor"]
						# postprocess args
						if "pred_iou_thresh" in kwargs:
											a					     =   kwargs["pred_iou_thresh"]
						if "stability_score_offset" in kwargs:
											a					     =   kwargs["stability_score_offset"]
						if "mask_threshold" in kwargs:
											a					     =   kwargs["mask_threshold"]
						if "stability_score_thresh" in kwargs:
											a					     =   kwargs["stability_score_thresh"]
						if "crops_nms_thresh" in kwargs:
											a					     =   kwargs["crops_nms_thresh"]
						if "output_rle_mask" in kwargs:
											a					     =   kwargs["output_rle_mask"]
						if "output_bboxes_mask" in kwargs:
											a					     =   kwargs["output_bboxes_mask"]
						return preprocess_kwargs, forward_params, postprocess_kwargs




	def __call__(self		,       lowerCamelCase_		,       *lowerCamelCase_		,       lowerCamelCase_=None		,       lowerCamelCase_=None		,       **lowerCamelCase_		):
						"""simple docstring"""

						return super().__call__(lowerCamelCase_		,       *lowerCamelCase_		,       num_workers=lowerCamelCase_		,       batch_size=lowerCamelCase_		,       **lowerCamelCase_		)




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=64		,       lowerCamelCase_ = 0		,       lowerCamelCase_ = 512 / 1500		,       lowerCamelCase_ = 32		,       lowerCamelCase_ = 1		,       ):
						"""simple docstring"""

						a					     =   load_image(lowerCamelCase_		)
						a					     =   self.image_processor.size["longest_edge"]
						a     ,				a     ,				a     ,				a					     =   self.image_processor.generate_crop_boxes(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)
						a					     =   self.image_processor(images=lowerCamelCase_		,       return_tensors="pt"		)

						with self.device_placement():
											if self.framework == "pt":
																a					     =   self.get_inference_context()
																with inference_context():
																					a					     =   self._ensure_tensor_on_device(lowerCamelCase_		,       device=self.device		)
																					a					     =   self.model.get_image_embeddings(model_inputs.pop("pixel_values"		)		)
																					a					     =   image_embeddings

						a					     =   grid_points.shape[1]
						a					     =   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		,       lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   grid_points[:, i : i + points_per_batch, :, :]
											a					     =   input_labels[:, i : i + points_per_batch]
											a					     =   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      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=0.88		,       lowerCamelCase_=0.95		,       lowerCamelCase_=0		,       lowerCamelCase_=1		,       ):
						"""simple docstring"""

						a					     =   model_inputs.pop("input_boxes"		)
						a					     =   model_inputs.pop("is_last"		)
						a					     =   model_inputs.pop("original_sizes"		).tolist()
						a					     =   model_inputs.pop("reshaped_input_sizes"		).tolist()

						a					     =   self.model(**lowerCamelCase_		)

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




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_=False		,       lowerCamelCase_=False		,       lowerCamelCase_=0.7		,       ):
						"""simple docstring"""

						a					     =   []
						a					     =   []
						a					     =   []
						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"		)		)

						a					     =   torch.cat(lowerCamelCase_		)
						a					     =   torch.cat(lowerCamelCase_		)
						a     ,				a     ,				a     ,				a					     =   self.image_processor.post_process_for_mask_generation(
						    lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		,       lowerCamelCase_		)

						a					     =   defaultdict(lowerCamelCase_		)
						for output in model_outputs:
											for k, v in output.items():
																extra[k].append(lowerCamelCase_		)

						a					     =   {}
						if output_rle_mask:
											a					     =   rle_mask

						if output_bboxes_mask:
											a					     =   bounding_boxes

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