

"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
				from huggingface_hub import snapshot_download
				from pyctcdecode import BeamSearchDecoderCTC

				from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
				from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
				from transformers import WavaVecaForCTC
@require_pyctcdecode
class      _UpperCAmelCase   (							unittest.TestCase       ):



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= '''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
		__UpperCAmelCase      		= dict(zip(_lowercase			,    range(len(_lowercase						)						)						)						)

		__UpperCAmelCase      		= {
		    '''unk_token''': '''<unk>''',
		    '''bos_token''': '''<s>''',
		    '''eos_token''': '''</s>''',
		}
		__UpperCAmelCase      		= {
		    '''feature_size''': 1,
		    '''padding_value''': 0.0,
		    '''sampling_rate''': 1_60_00,
		    '''return_attention_mask''': False,
		    '''do_normalize''': True,
		}

		__UpperCAmelCase      		= tempfile.mkdtemp()
		__UpperCAmelCase      		= os.path.join(self.tmpdirname			,    VOCAB_FILES_NAMES['''vocab_file''']						)
		__UpperCAmelCase      		= os.path.join(self.tmpdirname			,    _lowercase						)
		with open(self.vocab_file			,    '''w'''			,    encoding='''utf-8'''						) as fp:
			fp.write(json.dumps(_lowercase						) + '''\n'''						)

		with open(self.feature_extraction_file			,    '''w'''			,    encoding='''utf-8'''						) as fp:
			fp.write(json.dumps(_lowercase						) + '''\n'''						)

		# load decoder from hub
		__UpperCAmelCase      		= '''hf-internal-testing/ngram-beam-search-decoder'''



	def        a  (  self     :       Any			,    **_lowercase     :       Optional[int]						):
		__UpperCAmelCase      		= self.add_kwargs_tokens_map.copy()
		kwargs.update(_lowercase						)
		return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Optional[Any]			,    **_lowercase     :       Any						):
		return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Optional[Any]			,    **_lowercase     :       Optional[Any]						):
		return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name			,    **_lowercase						)



	def        a  (  self     :       Optional[Any]						):
		shutil.rmtree(self.tmpdirname						)



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		processor.save_pretrained(self.tmpdirname						)
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname						)

		# tokenizer
		self.assertEqual(processor.tokenizer.get_vocab()			,    tokenizer.get_vocab()						)
		self.assertIsInstance(processor.tokenizer			,    _lowercase						)

		# feature extractor
		self.assertEqual(processor.feature_extractor.to_json_string()			,    feature_extractor.to_json_string()						)
		self.assertIsInstance(processor.feature_extractor			,    _lowercase						)

		# decoder
		self.assertEqual(processor.decoder._alphabet.labels			,    decoder._alphabet.labels						)
		self.assertEqual(
		    processor.decoder.model_container[decoder._model_key]._unigram_set			,    decoder.model_container[decoder._model_key]._unigram_set			,    )
		self.assertIsInstance(processor.decoder			,    _lowercase						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM(
		    tokenizer=self.get_tokenizer()			,    feature_extractor=self.get_feature_extractor()			,    decoder=self.get_decoder()						)
		processor.save_pretrained(self.tmpdirname						)

		# make sure that error is thrown when decoder alphabet doesn't match
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained(
		    self.tmpdirname			,    alpha=5.0			,    beta=3.0			,    score_boundary=-7.0			,    unk_score_offset=3						)

		# decoder
		self.assertEqual(processor.language_model.alpha			,    5.0						)
		self.assertEqual(processor.language_model.beta			,    3.0						)
		self.assertEqual(processor.language_model.score_boundary			,    -7.0						)
		self.assertEqual(processor.language_model.unk_score_offset			,    3						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= self.get_tokenizer()
		# add token to trigger raise
		tokenizer.add_tokens(['''xx''']						)
		with self.assertRaisesRegex(_lowercase			,    '''include'''						):
			WavaVecaProcessorWithLM(
			    tokenizer=_lowercase			,    feature_extractor=self.get_feature_extractor()			,    decoder=self.get_decoder()						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= floats_list((3, 10_00)						)

		__UpperCAmelCase      		= feature_extractor(_lowercase			,    return_tensors='''np'''						)
		__UpperCAmelCase      		= processor(_lowercase			,    return_tensors='''np'''						)

		for key in input_feat_extract.keys():
			self.assertAlmostEqual(input_feat_extract[key].sum()			,    input_processor[key].sum()			,    delta=1E-2						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= '''This is a test string'''

		__UpperCAmelCase      		= processor(text=_lowercase						)

		__UpperCAmelCase      		= tokenizer(_lowercase						)

		for key in encoded_tok.keys():
			self.assertListEqual(encoded_tok[key]			,    encoded_processor[key]						)



	def        a  (  self     :       Optional[int]			,    _lowercase     :       Any=(2, 10, 16)			,    _lowercase     :       str=77						):
		np.random.seed(_lowercase						)
		return np.random.rand(*_lowercase						)



	def        a  (  self     :       Union[str, Any]						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= self._get_dummy_logits(shape=(10, 16)			,    seed=13						)

		__UpperCAmelCase      		= processor.decode(_lowercase						)

		__UpperCAmelCase      		= decoder.decode_beams(_lowercase						)[0]

		self.assertEqual(decoded_decoder[0]			,    decoded_processor.text						)
		self.assertEqual('''</s> <s> </s>'''			,    decoded_processor.text						)
		self.assertEqual(decoded_decoder[-2]			,    decoded_processor.logit_score						)
		self.assertEqual(decoded_decoder[-1]			,    decoded_processor.lm_score						)



	@parameterized.expand([[None], ['''fork'''], ['''spawn''']]						)
	def        a  (  self     :       int			,    _lowercase     :       Dict						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= self._get_dummy_logits()

		# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
		#       otherwise, the LM won't be available to the pool's sub-processes.
		# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
		if pool_context is None:
			__UpperCAmelCase      		= processor.batch_decode(_lowercase						)
		else:
			with get_context(_lowercase						).Pool() as pool:
				__UpperCAmelCase      		= processor.batch_decode(_lowercase			,    _lowercase						)

		__UpperCAmelCase      		= list(_lowercase						)

		with get_context('''fork'''						).Pool() as p:
			__UpperCAmelCase      		= decoder.decode_beams_batch(_lowercase			,    _lowercase						)

		__UpperCAmelCase		,							__UpperCAmelCase		,							__UpperCAmelCase      		= [], [], []
		for beams in decoded_beams:
			texts_decoder.append(beams[0][0]						)
			logit_scores_decoder.append(beams[0][-2]						)
			lm_scores_decoder.append(beams[0][-1]						)

		self.assertListEqual(_lowercase			,    decoded_processor.text						)
		self.assertListEqual(['''<s> <s> </s>''', '''<s> <s> <s>''']			,    decoded_processor.text						)
		self.assertListEqual(_lowercase			,    decoded_processor.logit_score						)
		self.assertListEqual(_lowercase			,    decoded_processor.lm_score						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= self._get_dummy_logits()

		__UpperCAmelCase      		= 15
		__UpperCAmelCase      		= -20.0
		__UpperCAmelCase      		= -4.0

		__UpperCAmelCase      		= processor.batch_decode(
		    _lowercase			,    beam_width=_lowercase			,    beam_prune_logp=_lowercase			,    token_min_logp=_lowercase			,    )
		__UpperCAmelCase      		= decoded_processor_out.text

		__UpperCAmelCase      		= list(_lowercase						)

		with get_context('''fork'''						).Pool() as pool:
			__UpperCAmelCase      		= decoder.decode_beams_batch(
			    _lowercase			,    _lowercase			,    beam_width=_lowercase			,    beam_prune_logp=_lowercase			,    token_min_logp=_lowercase			,    )

		__UpperCAmelCase      		= [d[0][0] for d in decoded_decoder_out]
		__UpperCAmelCase      		= [d[0][2] for d in decoded_decoder_out]
		__UpperCAmelCase      		= [d[0][3] for d in decoded_decoder_out]

		self.assertListEqual(_lowercase			,    _lowercase						)
		self.assertListEqual(['''</s> <s> <s>''', '''<s> <s> <s>''']			,    _lowercase						)

		self.assertTrue(np.array_equal(_lowercase			,    decoded_processor_out.logit_score						)						)
		self.assertTrue(np.allclose([-20.054, -18.447]			,    _lowercase			,    atol=1E-3						)						)

		self.assertTrue(np.array_equal(_lowercase			,    decoded_processor_out.lm_score						)						)
		self.assertTrue(np.allclose([-15.554, -13.9_474]			,    _lowercase			,    atol=1E-3						)						)



	def        a  (  self     :       int						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		__UpperCAmelCase      		= self._get_dummy_logits()

		__UpperCAmelCase      		= 2.0
		__UpperCAmelCase      		= 5.0
		__UpperCAmelCase      		= -20.0
		__UpperCAmelCase      		= True

		__UpperCAmelCase      		= processor.batch_decode(
		    _lowercase			,    alpha=_lowercase			,    beta=_lowercase			,    unk_score_offset=_lowercase			,    lm_score_boundary=_lowercase			,    )
		__UpperCAmelCase      		= decoded_processor_out.text

		__UpperCAmelCase      		= list(_lowercase						)
		decoder.reset_params(
		    alpha=_lowercase			,    beta=_lowercase			,    unk_score_offset=_lowercase			,    lm_score_boundary=_lowercase			,    )

		with get_context('''fork'''						).Pool() as pool:
			__UpperCAmelCase      		= decoder.decode_beams_batch(
			    _lowercase			,    _lowercase			,    )

		__UpperCAmelCase      		= [d[0][0] for d in decoded_decoder_out]

		self.assertListEqual(_lowercase			,    _lowercase						)
		self.assertListEqual(['''<s> </s> <s> </s> </s>''', '''</s> </s> <s> </s> </s>''']			,    _lowercase						)
		__UpperCAmelCase      		= processor.decoder.model_container[processor.decoder._model_key]
		self.assertEqual(lm_model.alpha			,    2.0						)
		self.assertEqual(lm_model.beta			,    5.0						)
		self.assertEqual(lm_model.unk_score_offset			,    -20.0						)
		self.assertEqual(lm_model.score_boundary			,    _lowercase						)



	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

		__UpperCAmelCase      		= processor.decoder.model_container[processor.decoder._model_key]
		__UpperCAmelCase      		= Path(language_model._kenlm_model.path.decode('''utf-8'''						)						).parent.parent.absolute()

		__UpperCAmelCase      		= os.listdir(_lowercase						)
		__UpperCAmelCase      		= ['''alphabet.json''', '''language_model''']

		downloaded_decoder_files.sort()
		expected_decoder_files.sort()

		# test that only decoder relevant files from
		# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
		# are downloaded and none of the rest (e.g. README.md, ...)
		self.assertListEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       List[Any]						):
		__UpperCAmelCase      		= snapshot_download('''hf-internal-testing/processor_with_lm'''						)

		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained(_lowercase						)

		__UpperCAmelCase      		= processor.decoder.model_container[processor.decoder._model_key]
		__UpperCAmelCase      		= Path(language_model._kenlm_model.path.decode('''utf-8'''						)						).parent.parent.absolute()

		__UpperCAmelCase      		= os.listdir(_lowercase						)
		__UpperCAmelCase      		= os.listdir(_lowercase						)

		local_decoder_files.sort()
		expected_decoder_files.sort()

		# test that both decoder form hub and local files in cache are the same
		self.assertListEqual(_lowercase			,    _lowercase						)



	def        a  (  self     :       int						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
		__UpperCAmelCase      		= AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

		__UpperCAmelCase      		= floats_list((3, 10_00)						)

		__UpperCAmelCase      		= processor_wavaveca(_lowercase			,    return_tensors='''np'''						)
		__UpperCAmelCase      		= processor_auto(_lowercase			,    return_tensors='''np'''						)

		for key in input_wavaveca.keys():
			self.assertAlmostEqual(input_wavaveca[key].sum()			,    input_auto[key].sum()			,    delta=1E-2						)

		__UpperCAmelCase      		= self._get_dummy_logits()

		__UpperCAmelCase      		= processor_wavaveca.batch_decode(_lowercase						)
		__UpperCAmelCase      		= processor_auto.batch_decode(_lowercase						)

		self.assertListEqual(decoded_wavaveca.text			,    decoded_auto.text						)



	def        a  (  self     :       Optional[int]						):
		__UpperCAmelCase      		= self.get_feature_extractor()
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= self.get_decoder()

		__UpperCAmelCase      		= WavaVecaProcessorWithLM(tokenizer=_lowercase			,    feature_extractor=_lowercase			,    decoder=_lowercase						)

		self.assertListEqual(
		    processor.model_input_names			,    feature_extractor.model_input_names			,    msg='''`processor` and `feature_extractor` model input names do not match'''			,    )



	@staticmethod
	def        a  (  _lowercase     :       List[Any]			,    _lowercase     :       Optional[Any]						):
		__UpperCAmelCase      		= [d[key] for d in offsets]
		return retrieved_list



	def        a  (  self     :       Tuple						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
		__UpperCAmelCase      		= self._get_dummy_logits()[0]

		__UpperCAmelCase      		= processor.decode(_lowercase			,    output_word_offsets=_lowercase						)
		# check Wav2Vec2CTCTokenizerOutput keys for word
		self.assertEqual(len(outputs.keys()						)			,    4						)
		self.assertTrue('''text''' in outputs						)
		self.assertTrue('''word_offsets''' in outputs						)
		self.assertTrue(isinstance(_lowercase			,    _lowercase						)						)

		self.assertEqual(''' '''.join(self.get_from_offsets(outputs['''word_offsets''']			,    '''word'''						)						)			,    outputs.text						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,    '''word'''						)			,    ['''<s>''', '''<s>''', '''</s>''']						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,    '''start_offset'''						)			,    [0, 2, 4]						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets''']			,    '''end_offset'''						)			,    [1, 3, 5]						)



	def        a  (  self     :       str						):
		__UpperCAmelCase      		= WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
		__UpperCAmelCase      		= self._get_dummy_logits()

		__UpperCAmelCase      		= processor.batch_decode(_lowercase			,    output_word_offsets=_lowercase						)

		# check Wav2Vec2CTCTokenizerOutput keys for word
		self.assertEqual(len(outputs.keys()						)			,    4						)
		self.assertTrue('''text''' in outputs						)
		self.assertTrue('''word_offsets''' in outputs						)
		self.assertTrue(isinstance(_lowercase			,    _lowercase						)						)

		self.assertListEqual(
		    [''' '''.join(self.get_from_offsets(_lowercase			,    '''word'''						)						) for o in outputs['''word_offsets''']]			,    outputs.text						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,    '''word'''						)			,    ['''<s>''', '''<s>''', '''</s>''']						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,    '''start_offset'''						)			,    [0, 2, 4]						)
		self.assertListEqual(self.get_from_offsets(outputs['''word_offsets'''][0]			,    '''end_offset'''						)			,    [1, 3, 5]						)



	@slow
	@require_torch
	@require_torchaudio
	def        a  (  self     :       Union[str, Any]						):
		import torch

		__UpperCAmelCase      		= load_dataset('''common_voice'''			,    '''en'''			,    split='''train'''			,    streaming=_lowercase						)
		__UpperCAmelCase      		= ds.cast_column('''audio'''			,    datasets.Audio(sampling_rate=1_60_00						)						)
		__UpperCAmelCase      		= iter(_lowercase						)
		__UpperCAmelCase      		= next(_lowercase						)

		__UpperCAmelCase      		= AutoProcessor.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''						)
		__UpperCAmelCase      		= WavaVecaForCTC.from_pretrained('''patrickvonplaten/wav2vec2-base-100h-with-lm'''						)

		# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
		__UpperCAmelCase      		= processor(sample['''audio''']['''array''']			,    return_tensors='''pt'''						).input_values

		with torch.no_grad():
			__UpperCAmelCase      		= model(_lowercase						).logits.cpu().numpy()

		__UpperCAmelCase      		= processor.decode(logits[0]			,    output_word_offsets=_lowercase						)

		__UpperCAmelCase      		= model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
		__UpperCAmelCase      		= [
		    {
		        '''start_time''': d['''start_offset'''] * time_offset,
		        '''end_time''': d['''end_offset'''] * time_offset,
		        '''word''': d['''word'''],
		    }
		    for d in output['''word_offsets''']
		]

		__UpperCAmelCase      		= '''WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'''

		# output words
		self.assertEqual(''' '''.join(self.get_from_offsets(_lowercase			,    '''word'''						)						)			,    _lowercase						)
		self.assertEqual(''' '''.join(self.get_from_offsets(_lowercase			,    '''word'''						)						)			,    output.text						)

		# output times
		__UpperCAmelCase      		= torch.tensor(self.get_from_offsets(_lowercase			,    '''start_time'''						)						)
		__UpperCAmelCase      		= torch.tensor(self.get_from_offsets(_lowercase			,    '''end_time'''						)						)

		# fmt: off
		__UpperCAmelCase      		= torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]						)
		__UpperCAmelCase      		= torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]						)
		# fmt: on

		self.assertTrue(torch.allclose(_lowercase			,    _lowercase			,    atol=0.01						)						)
		self.assertTrue(torch.allclose(_lowercase			,    _lowercase			,    atol=0.01						)						)








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

UpperCAmelCase__      			=						logging.get_logger(__name__)




@add_end_docstrings(__UpperCamelCase      )
class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						def __init__(self:					int ,					**__UpperCAmelCase:					Optional[Any]   )    ->     str:




											'''simple docstring'''



											super().__init__(**__UpperCAmelCase   )
											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(__UpperCAmelCase   )



						def        UpperCAmelCase__					(self:					Optional[int] ,					**__UpperCAmelCase:					Optional[int]   )    ->     List[Any]:




											'''simple docstring'''



											__a					:						Any      =					{}
											__a					:						Union[str, Any]      =					{}
											__a					:						List[str]      =					{}
											# preprocess args
											if "points_per_batch" in kwargs:
																__a					:						Dict      =					kwargs["points_per_batch"]
											if "points_per_crop" in kwargs:
																__a					:						Tuple      =					kwargs["points_per_crop"]
											if "crops_n_layers" in kwargs:
																__a					:						Dict      =					kwargs["crops_n_layers"]
											if "crop_overlap_ratio" in kwargs:
																__a					:						int      =					kwargs["crop_overlap_ratio"]
											if "crop_n_points_downscale_factor" in kwargs:
																__a					:						Optional[int]      =					kwargs["crop_n_points_downscale_factor"]
											# postprocess args
											if "pred_iou_thresh" in kwargs:
																__a					:						int      =					kwargs["pred_iou_thresh"]
											if "stability_score_offset" in kwargs:
																__a					:						Any      =					kwargs["stability_score_offset"]
											if "mask_threshold" in kwargs:
																__a					:						int      =					kwargs["mask_threshold"]
											if "stability_score_thresh" in kwargs:
																__a					:						Any      =					kwargs["stability_score_thresh"]
											if "crops_nms_thresh" in kwargs:
																__a					:						int      =					kwargs["crops_nms_thresh"]
											if "output_rle_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_rle_mask"]
											if "output_bboxes_mask" in kwargs:
																__a					:						Optional[Any]      =					kwargs["output_bboxes_mask"]
											return preprocess_kwargs, forward_params, postprocess_kwargs



						def __call__(self:					Union[str, Any] ,					__UpperCAmelCase:					str ,					*__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					str=None ,					__UpperCAmelCase:					Union[str, Any]=None ,					**__UpperCAmelCase:					Optional[int]   )    ->     Tuple:




											'''simple docstring'''



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



						def        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					List[str] ,					__UpperCAmelCase:					int=64 ,					__UpperCAmelCase:					int = 0 ,					__UpperCAmelCase:					float = 512 / 1500 ,					__UpperCAmelCase:					Optional[int] = 32 ,					__UpperCAmelCase:					Optional[int] = 1 ,					)    ->     str:




											'''simple docstring'''



											__a					:						Union[str, Any]      =					load_image(__UpperCAmelCase   )
											__a					:						List[str]      =					self.image_processor.size["longest_edge"]
											__a      ,			__a      ,			__a      ,			__a					:						int      =					self.image_processor.generate_crop_boxes(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					self.image_processor(images=__UpperCAmelCase ,					return_tensors="pt"   )

											with self.device_placement():
																if self.framework == "pt":
																					__a					:						str      =					self.get_inference_context()
																					with inference_context():
																										__a					:						int      =					self._ensure_tensor_on_device(__UpperCAmelCase ,					device=self.device   )
																										__a					:						Union[str, Any]      =					self.model.get_image_embeddings(model_inputs.pop("pixel_values"   )   )
																										__a					:						Optional[Any]      =					image_embeddings

											__a					:						Dict      =					grid_points.shape[1]
											__a					:						Any      =					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 ,					__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[str]      =					grid_points[:, i : i + points_per_batch, :, :]
																__a					:						str      =					input_labels[:, i : i + points_per_batch]
																__a					:						str      =					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:					Any ,					__UpperCAmelCase:					Optional[Any] ,					__UpperCAmelCase:					Any=0.88 ,					__UpperCAmelCase:					Optional[int]=0.95 ,					__UpperCAmelCase:					Optional[int]=0 ,					__UpperCAmelCase:					int=1 ,					)    ->     List[str]:




											'''simple docstring'''



											__a					:						List[Any]      =					model_inputs.pop("input_boxes"   )
											__a					:						Optional[Any]      =					model_inputs.pop("is_last"   )
											__a					:						Dict      =					model_inputs.pop("original_sizes"   ).tolist()
											__a					:						int      =					model_inputs.pop("reshaped_input_sizes"   ).tolist()

											__a					:						Optional[Any]      =					self.model(**__UpperCAmelCase   )

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



						def        UpperCAmelCase__					(self:					Any ,					__UpperCAmelCase:					str ,					__UpperCAmelCase:					List[str]=False ,					__UpperCAmelCase:					int=False ,					__UpperCAmelCase:					List[str]=0.7 ,					)    ->     Dict:




											'''simple docstring'''



											__a					:						Dict      =					[]
											__a					:						Optional[Any]      =					[]
											__a					:						Optional[Any]      =					[]
											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					:						Any      =					torch.cat(__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					torch.cat(__UpperCAmelCase   )
											__a      ,			__a      ,			__a      ,			__a					:						Tuple      =					self.image_processor.post_process_for_mask_generation(
											    __UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Optional[int]      =					defaultdict(__UpperCAmelCase   )
											for output in model_outputs:
																for k, v in output.items():
																					extra[k].append(__UpperCAmelCase   )

											__a					:						int      =					{}
											if output_rle_mask:
																__a					:						Optional[int]      =					rle_mask

											if output_bboxes_mask:
																__a					:						List[str]      =					bounding_boxes

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





