






"""simple docstring"""





import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
  from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


UpperCamelCase					=     get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_torch
@require_torchaudio
@require_sentencepiece
class 						_a  (		unittest.TestCase       ):





   '''simple docstring'''






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




          '''simple docstring'''




          lowercase__:							Tuple         =     tempfile.mkdtemp()

          lowercase__:							List[str]         =     ["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
          lowercase__:							Any         =     dict(zip(UpperCAmelCase_					, range(len(UpperCAmelCase_))))
          lowercase__:							Tuple         =     Path(self.tmpdirname)
          save_json(UpperCAmelCase_					, save_dir / VOCAB_FILES_NAMES["vocab_file"])
          if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
                 copyfile(UpperCAmelCase_					, save_dir / VOCAB_FILES_NAMES["spm_file"])

          lowercase__:							List[Any]         =     SpeechaTextTokenizer.from_pretrained(self.tmpdirname)
          tokenizer.save_pretrained(self.tmpdirname)

          lowercase__:							int         =     {
              "feature_size": 24,
              "num_mel_bins": 24,
              "padding_value": 0.0,
              "sampling_rate": 16_000,
              "return_attention_mask": False,
              "do_normalize": True,
          }
          save_json(UpperCAmelCase_					, save_dir / FEATURE_EXTRACTOR_NAME)





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




          '''simple docstring'''




          return SpeechaTextTokenizer.from_pretrained(self.tmpdirname					, **UpperCAmelCase_)





   def __lowercase							(       self					, **UpperCAmelCase_)			->   Dict:




          '''simple docstring'''




          return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname					, **UpperCAmelCase_)





   def __lowercase							(       self)			->   Optional[int]:




          '''simple docstring'''




          shutil.rmtree(self.tmpdirname)





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     self.get_tokenizer()
          lowercase__:							Union[str, Any]         =     self.get_feature_extractor()

          lowercase__:							str         =     SpeechaTextProcessor(tokenizer=UpperCAmelCase_					, feature_extractor=UpperCAmelCase_)

          processor.save_pretrained(self.tmpdirname)
          lowercase__:							List[Any]         =     SpeechaTextProcessor.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 __lowercase							(       self)			->   Tuple:




          '''simple docstring'''




          lowercase__:							Dict         =     SpeechaTextProcessor(
              tokenizer=self.get_tokenizer()					, feature_extractor=self.get_feature_extractor())
          processor.save_pretrained(self.tmpdirname)

          lowercase__:							Tuple         =     self.get_tokenizer(bos_token="(BOS)"					, eos_token="(EOS)")
          lowercase__:							List[str]         =     self.get_feature_extractor(do_normalize=UpperCAmelCase_					, padding_value=1.0)

          lowercase__:							str         =     SpeechaTextProcessor.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 __lowercase							(       self)			->   Any:




          '''simple docstring'''




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

          lowercase__:							List[Any]         =     SpeechaTextProcessor(tokenizer=UpperCAmelCase_					, feature_extractor=UpperCAmelCase_)

          lowercase__:							Any         =     floats_list((3, 1_000))

          lowercase__:							List[Any]         =     feature_extractor(UpperCAmelCase_					, return_tensors="np")
          lowercase__:							Optional[Any]         =     processor(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 __lowercase							(       self)			->   Tuple:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.get_feature_extractor()
          lowercase__:							Optional[int]         =     self.get_tokenizer()

          lowercase__:							Dict         =     SpeechaTextProcessor(tokenizer=UpperCAmelCase_					, feature_extractor=UpperCAmelCase_)

          lowercase__:							int         =     "This is a test string"

          lowercase__:							List[Any]         =     processor(text=UpperCAmelCase_)

          lowercase__:							Optional[Any]         =     tokenizer(UpperCAmelCase_)

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





   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.get_feature_extractor()
          lowercase__:							Optional[Any]         =     self.get_tokenizer()

          lowercase__:							Tuple         =     SpeechaTextProcessor(tokenizer=UpperCAmelCase_					, feature_extractor=UpperCAmelCase_)

          lowercase__:							Union[str, Any]         =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase__:							List[Any]         =     processor.batch_decode(UpperCAmelCase_)
          lowercase__:							Any         =     tokenizer.batch_decode(UpperCAmelCase_)

          self.assertListEqual(UpperCAmelCase_					, UpperCAmelCase_)





   def __lowercase							(       self)			->   Tuple:




          '''simple docstring'''




          lowercase__:							str         =     self.get_feature_extractor()
          lowercase__:							Union[str, Any]         =     self.get_tokenizer()

          lowercase__:							List[str]         =     SpeechaTextProcessor(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"					, )







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


