





import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
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


_SCREAMING_SNAKE_CASE   						=		random.Random()


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a=1.0 ,			__a=None ,			__a=None ):
	if rng is None:
		snake_case_		:      Optional[int]           =						global_rng

	snake_case_		:      Dict           =						[]
	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 SCREAMING_SNAKE_CASE_    (       unittest.TestCase       ):






						def __init__(   self   :   Optional[Any]		, _A   :   Any		, _A   :   Tuple=7		, _A   :   Any=400		, _A   :   Dict=2000		, _A   :   Tuple=1		, _A   :   Tuple=0.0		, _A   :   Optional[Any]=24000		, _A   :   Union[str, Any]=True		, )				->     Dict:







							"""simple docstring"""


							snake_case_		:      List[str]           =						parent
							snake_case_		:      List[Any]           =						batch_size
							snake_case_		:      Optional[int]           =						min_seq_length
							snake_case_		:      Any           =						max_seq_length
							snake_case_		:      Tuple           =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							snake_case_		:      Any           =						feature_size
							snake_case_		:      Any           =						padding_value
							snake_case_		:      List[Any]           =						sampling_rate
							snake_case_		:      str           =						return_attention_mask






						def UpperCAmelCase_					(   self   :   List[str]						)				->     str:







							"""simple docstring"""


							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}






						def UpperCAmelCase_					(   self   :   Any		, _A   :   List[Any]=False		, _A   :   int=False						)				->     int:







							"""simple docstring"""


							def _flatten(_A   :   Dict						):
								return list(itertools.chain(*_A						)						)

							if equal_length:
								snake_case_		:      List[str]           =						floats_list((self.batch_size, self.max_seq_length)						)
							else:
								# make sure that inputs increase in size
								snake_case_		:      str           =						[
								    _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:
								snake_case_		:      Tuple           =						[np.asarray(_A						) for x in audio_inputs]

							return audio_inputs
@require_torch
class SCREAMING_SNAKE_CASE_    (       snake_case_							,					unittest.TestCase       ):
						__magic_name__:      int			   =							EncodecFeatureExtractor






						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     Tuple:







							"""simple docstring"""


							snake_case_		:      Any           =						EnCodecFeatureExtractionTester(self						)






						def UpperCAmelCase_					(   self   :   Dict						)				->     int:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							# create three inputs of length 800, 1000, and 1200
							snake_case_		:      Optional[Any]           =						[floats_list((1, x)						)[0] for x in range(800		, 1400		, 200						)]
							snake_case_		:      Any           =						[np.asarray(_A						) for audio_input in audio_inputs]

							# Test not batched input
							snake_case_		:      str           =						feat_extract(audio_inputs[0]		, return_tensors='np'						).input_values
							snake_case_		:      Tuple           =						feat_extract(np_audio_inputs[0]		, return_tensors='np'						).input_values
							self.assertTrue(np.allclose(_A		, _A		, atol=1E-3						)						)

							# Test batched
							snake_case_		:      Dict           =						feat_extract(_A		, padding=_A		, return_tensors='np'						).input_values
							snake_case_		:      Optional[int]           =						feat_extract(_A		, padding=_A		, return_tensors='np'						).input_values
							for enc_seq_a, enc_seq_a in zip(_A		, _A						):
								self.assertTrue(np.allclose(_A		, _A		, atol=1E-3						)						)






						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     List[str]:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
							snake_case_		:      str           =						np.random.rand(100						).astype(np.floataa						)
							snake_case_		:      Optional[Any]           =						np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
								snake_case_		:      Optional[Any]           =						feature_extractor.pad([{'input_values': inputs}]		, return_tensors='np'						)
								self.assertTrue(np_processed.input_values.dtype == np.floataa						)
								snake_case_		:      List[str]           =						feature_extractor.pad([{'input_values': inputs}]		, return_tensors='pt'						)
								self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)






						def UpperCAmelCase_					(   self   :   Optional[Any]		, _A   :   Optional[Any]						)				->     List[Any]:







							"""simple docstring"""


							from datasets import load_dataset

							snake_case_		:      List[Any]           =						load_dataset('hf-internal-testing/librispeech_asr_dummy'		, 'clean'		, split='validation'						)
							# automatic decoding with librispeech
							snake_case_		:      Dict           =						ds.sort('id'						).select(range(_A						)						)[:num_samples]['audio']

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






						def UpperCAmelCase_					(   self   :   Union[str, Any]						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						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
							snake_case_		:      List[Any]           =						self._load_datasamples(1						)
							snake_case_		:      Dict           =						EncodecFeatureExtractor()
							snake_case_		:      str           =						feature_extractor(_A		, return_tensors='pt'						).input_values
							self.assertEquals(input_values.shape		, (1, 1, 93680)						)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]		, _A		, atol=1E-6						)						)






						def UpperCAmelCase_					(   self   :   Any						)				->     Any:







							"""simple docstring"""


							snake_case_		:      Optional[Any]           =						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
							snake_case_		:      int           =						self._load_datasamples(1						)
							snake_case_		:      List[str]           =						[np.tile(input_audio[0][None]		, reps=(2, 1)						)]
							input_audio[0][1] *= 0.5
							snake_case_		:      List[str]           =						EncodecFeatureExtractor(feature_size=2						)
							snake_case_		:      Dict           =						feature_extractor(_A		, return_tensors='pt'						).input_values
							self.assertEquals(input_values.shape		, (1, 2, 93680)						)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]		, _A		, atol=1E-6						)						)
							self.assertTrue(torch.allclose(input_values[0, 1, :30]		, EXPECTED_INPUT_VALUES * 0.5		, atol=1E-6						)						)






						def UpperCAmelCase_					(   self   :   str						)				->     List[Any]:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						self._load_datasamples(2						)
							# would be easier if the stride was like
							snake_case_		:      Dict           =						EncodecFeatureExtractor(feature_size=1		, chunk_length_s=1		, overlap=0.0_1						)

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    _A		, '^Both padding and truncation were set. Make sure you only set one.$'		, ):
								snake_case_		:      Dict           =						feature_extractor(
								    _A		, padding='max_length'		, truncation=_A		, return_tensors='pt'						).input_values

							# truncate to chunk
							snake_case_		:      Optional[int]           =						feature_extractor(_A		, truncation=_A		, return_tensors='pt'						).input_values
							self.assertEquals(truncated_outputs.shape		, (2, 1, 71520)						)  # 2 chunks

							# force truncate to max_length
							snake_case_		:      Optional[int]           =						feature_extractor(
							    _A		, truncation=_A		, max_length=48000		, return_tensors='pt'						).input_values
							self.assertEquals(truncated_outputs.shape		, (2, 1, 48000)						)

							# pad to chunk
							snake_case_		:      str           =						feature_extractor(_A		, padding=_A		, return_tensors='pt'						).input_values
							self.assertEquals(padded_outputs.shape		, (2, 1, 95280)						)

							# pad to chunk
							snake_case_		:      Dict           =						feature_extractor(_A		, return_tensors='pt'						).input_values
							self.assertEquals(truncated_outputs.shape		, (2, 1, 95280)						)

							# force pad to max length
							snake_case_		:      Tuple           =						feature_extractor(
							    _A		, padding='max_length'		, max_length=100000		, return_tensors='pt'						).input_values
							self.assertEquals(truncated_outputs.shape		, (2, 1, 100000)						)

							# force no pad
							with self.assertRaisesRegex(
							    _A		, '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'		, ):
								snake_case_		:      Optional[Any]           =						feature_extractor(_A		, padding=_A		, return_tensors='pt'						).input_values

							snake_case_		:      Optional[int]           =						feature_extractor(input_audio[0]		, padding=_A		, return_tensors='pt'						).input_values
							self.assertEquals(truncated_outputs.shape		, (1, 1, 93680)						)

							# no pad if no chunk_length_s
							snake_case_		:      List[Any]           =						None
							with self.assertRaisesRegex(
							    _A		, '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'		, ):
								snake_case_		:      Optional[int]           =						feature_extractor(_A		, padding=_A		, return_tensors='pt'						).input_values

							snake_case_		:      Optional[Any]           =						feature_extractor(input_audio[0]		, padding=_A		, return_tensors='pt'						).input_values
							self.assertEquals(truncated_outputs.shape		, (1, 1, 93680)						)

							# no pad if no overlap
							snake_case_		:      Optional[int]           =						2
							snake_case_		:      List[Any]           =						None
							with self.assertRaisesRegex(
							    _A		, '^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'		, ):
								snake_case_		:      List[str]           =						feature_extractor(_A		, padding=_A		, return_tensors='pt'						).input_values

							snake_case_		:      Any           =						feature_extractor(input_audio[0]		, padding=_A		, return_tensors='pt'						).input_values
							self.assertEquals(truncated_outputs.shape		, (1, 1, 93680)						)


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,
    )


_lowerCAmelCase        =       logging.get_logger(__name__)


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






@add_end_docstrings(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        super().__init__(*__magic_name__ ,       **__magic_name__      )

        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  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

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

        return preprocess_kwargs, {}, postprocess_kwargs
 def __call__(    self ,       __magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        return super().__call__(__magic_name__ ,       **__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

        """simple docstring"""

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

        """simple docstring"""

        A_							:       Dict	      =				model_inputs.pop('''target_size'''      )
        A_							:       Union[str, Any]	      =				self.model(**__magic_name__      )
        A_							:       Any	      =				target_size
        return model_outputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=0.9 ,       __magic_name__=0.5 ,       __magic_name__=0.5      ):

        """simple docstring"""

        A_							:       Tuple	      =				None
        if subtask in {"panoptic", None} and hasattr(self.image_processor ,       '''post_process_panoptic_segmentation'''      ):
               A_							:       Dict	      =				self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor ,       '''post_process_instance_segmentation'''      ):
               A_							:       Tuple	      =				self.image_processor.post_process_instance_segmentation

        if fn is not None:
               A_							:       Dict	      =				fn(
                   __magic_name__ ,       threshold=__magic_name__ ,       mask_threshold=__magic_name__ ,       overlap_mask_area_threshold=__magic_name__ ,       target_sizes=model_outputs['''target_size'''] ,       )[0]

               A_							:       Optional[Any]	      =				[]
               A_							:       Optional[int]	      =				outputs['''segmentation''']

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

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

               A_							:       Tuple	      =				[]
               A_							:       Any	      =				outputs.numpy()
               A_							:       Optional[Any]	      =				np.unique(__magic_name__      )

               for label in labels:
                      A_							:       Any	      =				(segmentation == label) * 255
                      A_							:       str	      =				Image.fromarray(mask.astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       List[str]	      =				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


