



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


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






def     a(      A       :    Union[str, Any] ,     A       :    int=1.0 ,     A       :    List[Any]=None ,     A       :    Optional[int]=None       )		->       Dict:
					"""simple docstring"""





					if rng is None:
										a					     =   global_rng

					a					     =   []
					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 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=7		,       lowerCamelCase_=400		,       lowerCamelCase_=2000		,       lowerCamelCase_=1		,       lowerCamelCase_=0.0		,       lowerCamelCase_=16000		,       lowerCamelCase_=True		,       lowerCamelCase_=80		,       lowerCamelCase_=16		,       lowerCamelCase_=64		,       lowerCamelCase_="hann_window"		,       lowerCamelCase_=80		,       lowerCamelCase_=7600		,       lowerCamelCase_=1E-1_0		,       lowerCamelCase_=True		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   min_seq_length
						a					     =   max_seq_length
						a					     =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						a					     =   feature_size
						a					     =   padding_value
						a					     =   sampling_rate
						a					     =   do_normalize
						a					     =   num_mel_bins
						a					     =   hop_length
						a					     =   win_length
						a					     =   win_function
						a					     =   fmin
						a					     =   fmax
						a					     =   mel_floor
						a					     =   return_attention_mask




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						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		,       lowerCamelCase_=False		,       lowerCamelCase_=False		):
						"""simple docstring"""

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

						if equal_length:
											a					     =   floats_list((self.batch_size, self.max_seq_length)		)
						else:
											# make sure that inputs increase in size
											a					     =   [
											    _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					     =   [np.asarray(lowerCamelCase_		) for x in speech_inputs]

						return speech_inputs




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

						if equal_length:
											a					     =   [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					     =   [
											    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					     =   [np.asarray(lowerCamelCase_		) for x in speech_inputs]

						return speech_inputs




@require_torch
class 	_lowercase							(   lowerCAmelCase,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							SpeechTaFeatureExtractor




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   SpeechTaFeatureExtractionTester(self		)




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

						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		):
						"""simple docstring"""

						a					     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						# create three inputs of length 800, 1000, and 1200
						a					     =   [floats_list((1, x)		)[0] for x in range(800		,       1400		,       200		)]
						a					     =   [np.asarray(lowerCamelCase_		) for speech_input in speech_inputs]

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

						# Test batched
						a					     =   feat_extract(lowerCamelCase_		,       return_tensors="np"		).input_values
						a					     =   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		):
						"""simple docstring"""

						a					     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						a					     =   [floats_list((1, x)		)[0] for x in range(800		,       1400		,       200		)]

						a					     =   ["longest", "max_length", "do_not_pad"]
						a					     =   [None, 1600, None]
						for max_length, padding in zip(lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   feat_extract(lowerCamelCase_		,       padding=lowerCamelCase_		,       max_length=lowerCamelCase_		,       return_tensors="np"		)
											a					     =   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][:1000]		)
											self.assertTrue(input_values[0][1000:].sum() < 1E-6		)
											self._check_zero_mean_unit_variance(input_values[2][:1200]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						a					     =   range(800		,       1400		,       200		)
						a					     =   [floats_list((1, x)		)[0] for x in lengths]

						a					     =   ["longest", "max_length", "do_not_pad"]
						a					     =   [None, 1600, None]

						for max_length, padding in zip(lowerCamelCase_		,       lowerCamelCase_		):
											a					     =   feat_extract(lowerCamelCase_		,       max_length=lowerCamelCase_		,       padding=lowerCamelCase_		)
											a					     =   processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:800]		)
											self._check_zero_mean_unit_variance(input_values[1][:1000]		)
											self._check_zero_mean_unit_variance(input_values[2][:1200]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						a					     =   [floats_list((1, x)		)[0] for x in range(800		,       1400		,       200		)]
						a					     =   feat_extract(
						    lowerCamelCase_		,       truncation=lowerCamelCase_		,       max_length=1000		,       padding="max_length"		,       return_tensors="np"		)
						a					     =   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		):
						"""simple docstring"""

						a					     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						a					     =   [floats_list((1, x)		)[0] for x in range(800		,       1400		,       200		)]
						a					     =   feat_extract(
						    lowerCamelCase_		,       truncation=lowerCamelCase_		,       max_length=1000		,       padding="longest"		,       return_tensors="np"		)
						a					     =   processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]		)
						self._check_zero_mean_unit_variance(input_values[1, :1000]		)
						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, 1000)		)

						a					     =   [floats_list((1, x)		)[0] for x in range(800		,       1400		,       200		)]
						a					     =   feat_extract(
						    lowerCamelCase_		,       truncation=lowerCamelCase_		,       max_length=2000		,       padding="longest"		,       return_tensors="np"		)
						a					     =   processed.input_values

						self._check_zero_mean_unit_variance(input_values[0, :800]		)
						self._check_zero_mean_unit_variance(input_values[1, :1000]		)
						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, 1200)		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						a					     =   np.random.rand(100		).astype(np.floataa		)
						a					     =   np_speech_inputs.tolist()

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
						# create three inputs of length 800, 1000, and 1200
						a					     =   [floats_list((1, x)		)[0] for x in range(800		,       1400		,       200		)]
						a					     =   [np.asarray(lowerCamelCase_		) for speech_input in speech_inputs]

						# Test feature size
						a					     =   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					     =   feature_extractor(speech_inputs[0]		,       return_tensors="np"		).input_values
						a					     =   feature_extractor(np_speech_inputs[0]		,       return_tensors="np"		).input_values
						self.assertTrue(np.allclose(lowerCamelCase_		,       lowerCamelCase_		,       atol=1E-3		)		)

						# Test batched
						a					     =   feature_extractor(lowerCamelCase_		,       return_tensors="np"		).input_values
						a					     =   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					     =   [floats_list((1, x)		)[0] for x in (800, 800, 800)]
						a					     =   np.asarray(lowerCamelCase_		)
						a					     =   feature_extractor(lowerCamelCase_		,       return_tensors="np"		).input_values
						a					     =   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		):
						"""simple docstring"""

						a					     =   self.feat_extract_tester.prepare_inputs_for_target()
						a					     =   self.feature_extraction_class(**self.feat_extract_dict		)
						a					     =   feat_extract.model_input_names[0]

						a					     =   BatchFeature({input_name: speech_inputs}		)

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

						a					     =   self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase_		)
						a					     =   BatchFeature({input_name: speech_inputs}		,       tensor_type="np"		)

						a					     =   processed_features[input_name]

						if len(batch_features_input.shape		) < 3:
											a					     =   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		):
						"""simple docstring"""

						a					     =   self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCamelCase_		)
						a					     =   self.feature_extraction_class(**self.feat_extract_dict		)
						a					     =   feat_extract.model_input_names[0]

						a					     =   BatchFeature({input_name: speech_inputs}		,       tensor_type="pt"		)

						a					     =   processed_features[input_name]

						if len(batch_features_input.shape		) < 3:
											a					     =   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		):
						"""simple docstring"""

						a					     =   self.feature_extraction_class(**self.feat_extract_dict		)
						a					     =   self.feat_extract_tester.prepare_inputs_for_target()
						a					     =   feat_extract.model_input_names[0]

						a					     =   BatchFeature({input_name: speech_inputs}		)

						a					     =   feat_extract.num_mel_bins  # hack!

						a					     =   feat_extract.pad(lowerCamelCase_		,       padding="longest"		,       return_tensors="np"		)[input_name]
						a					     =   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		):
						"""simple docstring"""

						a					     =   self.feat_extract_dict
						a					     =   True
						a					     =   self.feature_extraction_class(**lowerCamelCase_		)
						a					     =   self.feat_extract_tester.prepare_inputs_for_target()
						a					     =   [len(lowerCamelCase_		) for x in speech_inputs]
						a					     =   feat_extract.model_input_names[0]

						a					     =   BatchFeature({input_name: speech_inputs}		)

						a					     =   feat_extract.num_mel_bins  # hack!

						a					     =   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		):
						"""simple docstring"""

						a					     =   self.feat_extract_dict
						a					     =   True
						a					     =   self.feature_extraction_class(**lowerCamelCase_		)
						a					     =   self.feat_extract_tester.prepare_inputs_for_target()
						a					     =   [len(lowerCamelCase_		) for x in speech_inputs]
						a					     =   feat_extract.model_input_names[0]

						a					     =   BatchFeature({input_name: speech_inputs}		)
						a					     =   min(lowerCamelCase_		)

						a					     =   feat_extract.num_mel_bins  # hack!

						a					     =   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		,       lowerCamelCase_		):
						"""simple docstring"""

						from datasets import load_dataset

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

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




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   torch.tensor(
						    [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
						     3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
						     2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
						     4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
						     7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
						     4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]		)
						# fmt: on

						a					     =   self._load_datasamples(1		)
						a					     =   SpeechTaFeatureExtractor()
						a					     =   feature_extractor(lowerCamelCase_		,       return_tensors="pt"		).input_values
						self.assertEquals(input_values.shape		,       (1, 93680)		)
						self.assertTrue(torch.allclose(input_values[0, :30]		,       lowerCamelCase_		,       atol=1E-6		)		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   torch.tensor(
						    [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
						     -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
						     -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
						     -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]		)
						# fmt: on

						a					     =   self._load_datasamples(1		)
						a					     =   SpeechTaFeatureExtractor()
						a					     =   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}
