





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(_a )
class 	__snake_case					(      _a ):






    '''simple docstring'''





    def __init__( self    :		Optional[int]      ,    *snake_case    :		int      ,    **snake_case    :		Tuple				):

      """simple docstring"""





      super().__init__(*snake_case      ,    **snake_case				)

      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 						A		( self    :		Tuple      ,    **snake_case    :		Any				):

      """simple docstring"""





      __lowerCAmelCase								=					{}
      __lowerCAmelCase								=					{}
      if "subtask" in kwargs:
        __lowerCAmelCase								=					kwargs['''subtask''']
        __lowerCAmelCase								=					kwargs['''subtask''']
      if "threshold" in kwargs:
        __lowerCAmelCase								=					kwargs['''threshold''']
      if "mask_threshold" in kwargs:
        __lowerCAmelCase								=					kwargs['''mask_threshold''']
      if "overlap_mask_area_threshold" in kwargs:
        __lowerCAmelCase								=					kwargs['''overlap_mask_area_threshold''']

      return preprocess_kwargs, {}, postprocess_kwargs





    def __call__( self    :		str      ,    snake_case    :		Dict      ,    **snake_case    :		str				):

      """simple docstring"""





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





    def 						A		( self    :		Any      ,    snake_case    :		Optional[int]      ,    snake_case    :		Optional[Any]=None				):

      """simple docstring"""





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





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		Union[str, Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					model_inputs.pop('''target_size'''				)
      __lowerCAmelCase								=					self.model(**snake_case				)
      __lowerCAmelCase								=					target_size
      return model_outputs





    def 						A		( self    :		Tuple      ,    snake_case    :		Any      ,    snake_case    :		List[str]=None      ,    snake_case    :		List[str]=0.9      ,    snake_case    :		List[str]=0.5      ,    snake_case    :		str=0.5				):

      """simple docstring"""





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

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

        __lowerCAmelCase								=					[]
        __lowerCAmelCase								=					outputs['''segmentation''']

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

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

        __lowerCAmelCase								=					[]
        __lowerCAmelCase								=					outputs.numpy()
        __lowerCAmelCase								=					np.unique(snake_case				)

        for label in labels:
          __lowerCAmelCase								=					(segmentation == label) * 255
          __lowerCAmelCase								=					Image.fromarray(mask.astype(np.uinta				)      ,    mode='''L'''				)
          __lowerCAmelCase								=					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







import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


__lowercase				    =      (30, 487)
__lowercase				    =      (36, 1_024)
__lowercase				    =      (100, 100)
__lowercase				    =      100

__lowercase				    =      datasets.Features(
    {"""text""": ArrayaD(SHAPE_TEST_1, dtype="""float32"""), """image""": ArrayaD(SHAPE_TEST_2, dtype="""float32""")}
)

__lowercase    ,							__lowercase				    =      os.path.split(__file__)
__lowercase				    =      os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any  ,				__lowercase:		Optional[Any]  ,				__lowercase:		List[Any]):



  '''simple docstring'''






  with ArrowWriter(features=__lowercase  ,				path=os.path.join(__lowercase  ,				'''beta.arrow''')) as writer:
    for key, record in dummy_data:
      __lowerCAmelCase								=					my_features.encode_example(__lowercase)
      writer.write(__lowercase)
    __lowerCAmelCase						,    __lowerCAmelCase								=					writer.finalize()


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for _ in dataset:
    pass


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		str  ,				__lowercase:		Dict):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[Any]  ,				__lowercase:		Union[str, Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					10
  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for i in range(0  ,				len(__lowercase)  ,				__lowercase):
    __lowerCAmelCase								=					dataset[i : i + batch_size]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Union[str, Any]  ,				__lowercase:		str):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


@get_duration
def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Optional[int]  ,				__lowercase:		Optional[Any]):



  '''simple docstring'''






  __lowerCAmelCase								=					datasets.Dataset.from_file(
      filename=os.path.join(__lowercase  ,				'''beta.arrow''')  ,				info=datasets.DatasetInfo(features=__lowercase))
  dataset.set_format('''numpy''')
  for col in feats:
    __lowerCAmelCase								=					dataset[col]


def        __SCREAMING_SNAKE_CASE	(				):



  '''simple docstring'''






  __lowerCAmelCase								=					{}
  __lowerCAmelCase								=					(
      read_unformated,
      read_formatted_as_numpy,
      read_batch_unformated,
      read_batch_formatted_as_numpy,
      read_col_unformated,
      read_col_formatted_as_numpy,
  )
  with tempfile.TemporaryDirectory() as tmp_dir:
    __lowerCAmelCase								=					datasets.Features({'''image''': ArrayaD(__lowercase  ,				dtype='''float32''')})
    __lowerCAmelCase								=					generate_examples(features=__lowercase  ,				num_examples=__lowercase)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32''')))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': SPEED_TEST_SHAPE})
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with tempfile.TemporaryDirectory() as tmp_dir:
    # don't use fixed length for fair comparison
    # feats = datasets.Features(
    #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
    # )
    __lowerCAmelCase								=					datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''))})
    __lowerCAmelCase								=					generate_examples(
        features=__lowercase  ,				num_examples=__lowercase  ,				seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}  ,				)
    __lowerCAmelCase								=					write(__lowercase  ,				__lowercase  ,				__lowercase)
    for read_func in read_functions:
      __lowerCAmelCase								=					read_func(__lowercase  ,				__lowercase)

  with open(__lowercase  ,				'''wb''') as f:
    f.write(json.dumps(__lowercase).encode('''utf-8'''))


if __name__ == "__main__":  # useful to run the profiler
      benchmark_array_xd()
