
'''simple docstring'''
from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
						from PIL import Image

						from ..image_utils import load_image

if is_torch_available():
						from ..models.auto.modeling_auto import (
						    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
						    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
						    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
						    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
						)


lowercase__		    =	logging.get_logger(__name__)


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






@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class      snake_case__    (	__SCREAMING_SNAKE_CASE ):







		"""simple docstring"""

		def __init__(	self							:       str   ,     *UpperCamelCase__							:       Dict   ,     **UpperCamelCase__							:       str	)		->       Any:






				"""simple docstring"""







				super().__init__(*UpperCamelCase__   ,     **UpperCamelCase__	)

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

				requires_backends(self   ,     '''vision'''	)
				self.check_model_type(
				    dict(
				        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
				        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
				        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
				        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()	)	)



		def 	lowerCAmelCase							(	self							:       int   ,     **UpperCamelCase__							:       Dict	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    List[str]			       =		{}
				snake_case   :    Tuple			       =		{}
				if "subtask" in kwargs:
						snake_case   :    Any			       =		kwargs['''subtask''']
						snake_case   :    Optional[int]			       =		kwargs['''subtask''']
				if "threshold" in kwargs:
						snake_case   :    int			       =		kwargs['''threshold''']
				if "mask_threshold" in kwargs:
						snake_case   :    Any			       =		kwargs['''mask_threshold''']
				if "overlap_mask_area_threshold" in kwargs:
						snake_case   :    Any			       =		kwargs['''overlap_mask_area_threshold''']

				return preprocess_kwargs, {}, postprocess_kwargs



		def __call__(	self							:       Tuple   ,     UpperCamelCase__							:       int   ,     **UpperCamelCase__							:       int	)		->       Union[Predictions, List[Prediction]]:






				"""simple docstring"""







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



		def 	lowerCAmelCase							(	self							:       int   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       List[str]=None	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		load_image(UpperCamelCase__	)
				snake_case   :    str			       =		[(image.height, image.width)]
				if self.model.config.__class__.__name__ == "OneFormerConfig":
						if subtask is None:
								snake_case   :    Union[str, Any]			       =		{}
						else:
								snake_case   :    Any			       =		{'''task_inputs''': [subtask]}
						snake_case   :    str			       =		self.image_processor(images=[image]   ,     return_tensors='''pt'''   ,     **UpperCamelCase__	)
						snake_case   :    Union[str, Any]			       =		self.tokenizer(
						    inputs['''task_inputs''']   ,     padding='''max_length'''   ,     max_length=self.model.config.task_seq_len   ,     return_tensors=self.framework   ,     )['''input_ids''']
				else:
						snake_case   :    Union[str, Any]			       =		self.image_processor(images=[image]   ,     return_tensors='''pt'''	)
				snake_case   :    Optional[Any]			       =		target_size
				return inputs



		def 	lowerCAmelCase							(	self							:       List[str]   ,     UpperCamelCase__							:       Any	)		->       List[str]:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		model_inputs.pop('''target_size'''	)
				snake_case   :    Union[str, Any]			       =		self.model(**UpperCamelCase__	)
				snake_case   :    str			       =		target_size
				return model_outputs



		def 	lowerCAmelCase							(	self							:       Dict   ,     UpperCamelCase__							:       int   ,     UpperCamelCase__							:       Tuple=None   ,     UpperCamelCase__							:       Any=0.9   ,     UpperCamelCase__							:       List[str]=0.5   ,     UpperCamelCase__							:       Optional[int]=0.5	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Any			       =		None
				if subtask in {"panoptic", None} and hasattr(self.image_processor   ,     '''post_process_panoptic_segmentation'''	):
						snake_case   :    Optional[Any]			       =		self.image_processor.post_process_panoptic_segmentation
				elif subtask in {"instance", None} and hasattr(self.image_processor   ,     '''post_process_instance_segmentation'''	):
						snake_case   :    int			       =		self.image_processor.post_process_instance_segmentation

				if fn is not None:
						snake_case   :    Any			       =		fn(
						    UpperCamelCase__   ,     threshold=UpperCamelCase__   ,     mask_threshold=UpperCamelCase__   ,     overlap_mask_area_threshold=UpperCamelCase__   ,     target_sizes=model_outputs['''target_size''']   ,     )[0]

						snake_case   :    List[Any]			       =		[]
						snake_case   :    Union[str, Any]			       =		outputs['''segmentation''']

						for segment in outputs["segments_info"]:
								snake_case   :    Any			       =		(segmentation == segment['''id''']) * 255
								snake_case   :    Dict			       =		Image.fromarray(mask.numpy().astype(np.uinta	)   ,     mode='''L'''	)
								snake_case   :    Optional[Any]			       =		self.model.config.idalabel[segment['''label_id''']]
								snake_case   :    Dict			       =		segment['''score''']
								annotation.append({'''score''': score, '''label''': label, '''mask''': mask}	)

				elif subtask in {"semantic", None} and hasattr(self.image_processor   ,     '''post_process_semantic_segmentation'''	):
						snake_case   :    Optional[int]			       =		self.image_processor.post_process_semantic_segmentation(
						    UpperCamelCase__   ,     target_sizes=model_outputs['''target_size''']	)[0]

						snake_case   :    str			       =		[]
						snake_case   :    Dict			       =		outputs.numpy()
						snake_case   :    List[Any]			       =		np.unique(UpperCamelCase__	)

						for label in labels:
								snake_case   :    List[Any]			       =		(segmentation == label) * 255
								snake_case   :    Dict			       =		Image.fromarray(mask.astype(np.uinta	)   ,     mode='''L'''	)
								snake_case   :    List[Any]			       =		self.model.config.idalabel[label]
								annotation.append({'''score''': None, '''label''': label, '''mask''': mask}	)
				else:
						raise ValueError(f'Subtask {subtask} is not supported for model {type(self.model	)}'	)
				return annotation




'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
						from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

						from .test_feature_extraction_speechta import floats_list


lowercase__		    =	get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")






@require_torch
class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		tempfile.mkdtemp()

				snake_case   :    Optional[int]			       =		SpeechTaTokenizer(UpperCamelCase__	)
				tokenizer.save_pretrained(self.tmpdirname	)

				snake_case   :    Any			       =		{
				    '''feature_size''': 1,
				    '''padding_value''': 0.0,
				    '''sampling_rate''': 1_6000,
				    '''do_normalize''': False,
				    '''num_mel_bins''': 80,
				    '''hop_length''': 16,
				    '''win_length''': 64,
				    '''win_function''': '''hann_window''',
				    '''fmin''': 80,
				    '''fmax''': 7600,
				    '''mel_floor''': 1e-1_0,
				    '''reduction_factor''': 2,
				    '''return_attention_mask''': True,
				}

				snake_case   :    Tuple			       =		os.path.join(self.tmpdirname   ,     UpperCamelCase__	)
				with open(self.feature_extraction_file   ,     '''w'''   ,     encoding='''utf-8'''	) as fp:
						fp.write(json.dumps(UpperCamelCase__	) + '''\n'''	)



		def 	lowerCAmelCase							(	self							:       Dict   ,     **UpperCamelCase__							:       Any	)		->       str:






				"""simple docstring"""







				return SpeechTaTokenizer.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Union[str, Any]   ,     **UpperCamelCase__							:       int	)		->       Optional[Any]:






				"""simple docstring"""







				return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname   ,     **UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Any:






				"""simple docstring"""







				shutil.rmtree(self.tmpdirname	)



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Dict:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_tokenizer()
				snake_case   :    List[str]			       =		self.get_feature_extractor()

				snake_case   :    Dict			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				processor.save_pretrained(self.tmpdirname	)
				snake_case   :    Dict			       =		SpeechTaProcessor.from_pretrained(self.tmpdirname	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       str	)		->       Any:






				"""simple docstring"""







				snake_case   :    int			       =		SpeechTaProcessor(tokenizer=self.get_tokenizer()   ,     feature_extractor=self.get_feature_extractor()	)
				processor.save_pretrained(self.tmpdirname	)

				snake_case   :    int			       =		self.get_tokenizer(bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''	)
				snake_case   :    str			       =		self.get_feature_extractor(do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				snake_case   :    Tuple			       =		SpeechTaProcessor.from_pretrained(
				    self.tmpdirname   ,     bos_token='''(BOS)'''   ,     eos_token='''(EOS)'''   ,     do_normalize=UpperCamelCase__   ,     padding_value=1.0	)

				self.assertEqual(processor.tokenizer.get_vocab()   ,     tokenizer_add_kwargs.get_vocab()	)
				self.assertIsInstance(processor.tokenizer   ,     UpperCamelCase__	)

				self.assertEqual(processor.feature_extractor.to_json_string()   ,     feature_extractor_add_kwargs.to_json_string()	)
				self.assertIsInstance(processor.feature_extractor   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    List[str]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Union[str, Any]			       =		floats_list((3, 1000)	)

				snake_case   :    List[str]			       =		feature_extractor(audio=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    Any			       =		processor(audio=UpperCamelCase__   ,     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 	lowerCAmelCase							(	self							:       Dict	)		->       str:






				"""simple docstring"""







				snake_case   :    str			       =		self.get_feature_extractor()
				snake_case   :    Optional[int]			       =		self.get_tokenizer()

				snake_case   :    Union[str, Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		floats_list((3, 1000)	)

				snake_case   :    str			       =		feature_extractor(audio_target=UpperCamelCase__   ,     return_tensors='''np'''	)
				snake_case   :    List[Any]			       =		processor(audio_target=UpperCamelCase__   ,     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 	lowerCAmelCase							(	self							:       List[Any]	)		->       int:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    Union[str, Any]			       =		self.get_tokenizer()

				snake_case   :    List[str]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[Any]			       =		'''This is a test string'''

				snake_case   :    Dict			       =		processor(text=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

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



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Dict:






				"""simple docstring"""







				snake_case   :    Optional[int]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    str			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Dict			       =		'''This is a test string'''

				snake_case   :    Any			       =		processor(text_target=UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer(UpperCamelCase__	)

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



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    List[str]			       =		self.get_feature_extractor()
				snake_case   :    str			       =		self.get_tokenizer()

				snake_case   :    Optional[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

				snake_case   :    Optional[int]			       =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				snake_case   :    List[str]			       =		processor.batch_decode(UpperCamelCase__	)
				snake_case   :    List[str]			       =		tokenizer.batch_decode(UpperCamelCase__	)

				self.assertListEqual(UpperCamelCase__   ,     UpperCamelCase__	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    List[Any]			       =		self.get_feature_extractor()
				snake_case   :    Tuple			       =		self.get_tokenizer()

				snake_case   :    List[Any]			       =		SpeechTaProcessor(tokenizer=UpperCamelCase__   ,     feature_extractor=UpperCamelCase__	)

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


