

'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_a					:			List[Any]							=    logging.get_logger(__name__)



class 							lowercase_						(		a			):

  '''simple docstring'''





  def __init__(  self ,			a_ ,			a_ ,			a_ ,			**a_  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						feature_size
    UpperCAmelCase    				=						sampling_rate
    UpperCAmelCase    				=						padding_value

    UpperCAmelCase    				=						kwargs.pop('padding_side' ,			'right'  )
    UpperCAmelCase    				=						kwargs.pop('return_attention_mask' ,			a_  )

    super().__init__(**a_  )
  def     snake_case_      (  self ,			a_ ,			a_ = True ,			a_ = None ,			a_ = False ,			a_ = None ,			a_ = None ,			a_ = None ,			)				->					BatchFeature:


    """simple docstring"""
    # If we have a list of dicts, let's convert it in a dict of lists
    # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
    if isinstance(a_ ,			(list, tuple)  ) and isinstance(processed_features[0] ,			(dict, BatchFeature)  ):
      UpperCAmelCase    				=						{
          key: [example[key] for example in processed_features] for key in processed_features[0].keys()
      }

    # The model's main input name, usually `input_values`, has be passed for padding
    if self.model_input_names[0] not in processed_features:
      raise ValueError(
          'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
          F''' to this method that includes {self.model_input_names[0]}, but you provided'''
          F''' {list(processed_features.keys()  )}'''  )

    UpperCAmelCase    				=						processed_features[self.model_input_names[0]]
    UpperCAmelCase    				=						(
        return_attention_mask if return_attention_mask is not None else self.return_attention_mask
    )

    if len(a_  ) == 0:
      if return_attention_mask:
        UpperCAmelCase    				=						[]
      return processed_features

    # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
    # and rebuild them afterwards if no return_tensors is specified
    # Note that we lose the specific device the tensor may be on for PyTorch

    UpperCAmelCase    				=						required_input[0]
    if isinstance(a_ ,			(list, tuple)  ):
      # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
      UpperCAmelCase    				=						0
      while len(required_input[index]  ) == 0:
        index += 1
      if index < len(a_  ):
        UpperCAmelCase    				=						required_input[index][0]

    if return_tensors is None:
      if is_tf_tensor(a_  ):
        UpperCAmelCase    				=						'tf'
      elif is_torch_tensor(a_  ):
        UpperCAmelCase    				=						'pt'
      elif isinstance(a_ ,			(int, float, list, tuple, np.ndarray)  ):
        UpperCAmelCase    				=						'np'
      else:
        raise ValueError(
            F'''type of {first_element} unknown: {type(a_  )}. '''
            'Should be one of a python, numpy, pytorch or tensorflow object.'  )

    for key, value in processed_features.items():
      if isinstance(value[0] ,			(int, float)  ):
        UpperCAmelCase    				=						to_numpy(a_  )
      else:
        UpperCAmelCase    				=						[to_numpy(a_  ) for v in value]

        # Convert padding_strategy in PaddingStrategy
    UpperCAmelCase    				=						self._get_padding_strategies(padding=a_ ,			max_length=a_  )

    UpperCAmelCase    				=						processed_features[self.model_input_names[0]]

    UpperCAmelCase    				=						len(a_  )
    if not all(len(a_  ) == batch_size for v in processed_features.values()  ):
      raise ValueError('Some items in the output dictionary have a different batch size than others.'  )

    UpperCAmelCase    				=						[]
    for i in range(a_  ):
      UpperCAmelCase    				=						{k: v[i] for k, v in processed_features.items()}
      # truncation
      UpperCAmelCase    				=						self._truncate(
          a_ ,			max_length=a_ ,			pad_to_multiple_of=a_ ,			truncation=a_ ,			)
      truncated_inputs.append(a_  )

    if padding_strategy == PaddingStrategy.LONGEST:
      # make sure that `max_length` cannot be longer than the longest truncated length
      UpperCAmelCase    				=						max(len(input_slice[self.model_input_names[0]]  ) for input_slice in truncated_inputs  )
      UpperCAmelCase    				=						PaddingStrategy.MAX_LENGTH

    UpperCAmelCase    				=						{}
    for i in range(a_  ):
      # padding
      UpperCAmelCase    				=						self._pad(
          truncated_inputs[i] ,			max_length=a_ ,			padding_strategy=a_ ,			pad_to_multiple_of=a_ ,			return_attention_mask=a_ ,			)

      for key, value in outputs.items():
        if key not in batch_outputs:
          UpperCAmelCase    				=						[]
        if value.dtype is np.dtype(np.floataa  ):
          UpperCAmelCase    				=						value.astype(np.floataa  )
        batch_outputs[key].append(a_  )

    return BatchFeature(a_ ,			tensor_type=a_  )
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = PaddingStrategy.DO_NOT_PAD ,			a_ = None ,			a_ = None ,			)				->					dict:


    """simple docstring"""
    UpperCAmelCase    				=						processed_features[self.model_input_names[0]]

    if padding_strategy == PaddingStrategy.LONGEST:
      UpperCAmelCase    				=						len(a_  )

    if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
      UpperCAmelCase    				=						((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

    UpperCAmelCase    				=						padding_strategy != PaddingStrategy.DO_NOT_PAD and len(a_  ) < max_length

    if return_attention_mask and "attention_mask" not in processed_features:
      UpperCAmelCase    				=						np.ones(len(a_  ) ,			dtype=np.intaa  )

    if needs_to_be_padded:
      UpperCAmelCase    				=						max_length - len(a_  )
      if self.padding_side == "right":
        if return_attention_mask:
          UpperCAmelCase    				=						np.pad(
              processed_features['attention_mask'] ,			(0, difference)  )
        UpperCAmelCase    				=						((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
        UpperCAmelCase    				=						np.pad(
            a_ ,			a_ ,			'constant' ,			constant_values=self.padding_value  )
      elif self.padding_side == "left":
        if return_attention_mask:
          UpperCAmelCase    				=						np.pad(
              processed_features['attention_mask'] ,			(difference, 0)  )
        UpperCAmelCase    				=						((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
        UpperCAmelCase    				=						np.pad(
            a_ ,			a_ ,			'constant' ,			constant_values=self.padding_value  )
      else:
        raise ValueError('Invalid padding strategy:' + str(self.padding_side  )  )

    return processed_features
  def     snake_case_      (  self ,			a_ ,			a_ = None ,			a_ = None ,			a_ = None ,			)				->					Tuple:


    """simple docstring"""
    if not truncation:
      return processed_features
    elif truncation and max_length is None:
      raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'  )

    UpperCAmelCase    				=						processed_features[self.model_input_names[0]]

    # find `max_length` that fits `pad_to_multiple_of`
    if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
      UpperCAmelCase    				=						((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

    UpperCAmelCase    				=						len(a_  ) > max_length

    if needs_to_be_truncated:
      UpperCAmelCase    				=						processed_features[self.model_input_names[0]][:max_length]
      if "attention_mask" in processed_features:
        UpperCAmelCase    				=						processed_features['attention_mask'][:max_length]

    return processed_features





  def     snake_case_      (  self ,			a_=False ,			a_=None  )				->					int:


    """simple docstring"""

    # Get padding strategy
    if padding is not False:
      if padding is True:
        UpperCAmelCase    				=						PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
      elif not isinstance(a_ ,			a_  ):
        UpperCAmelCase    				=						PaddingStrategy(a_  )
      elif isinstance(a_ ,			a_  ):
        UpperCAmelCase    				=						padding
    else:
      UpperCAmelCase    				=						PaddingStrategy.DO_NOT_PAD

    # Set max length if needed
    if max_length is None:
      if padding_strategy == PaddingStrategy.MAX_LENGTH:
        raise ValueError(
            F'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined'''  )

        # Test if we have a padding value
    if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
      raise ValueError(
          'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
          ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'  )

    return padding_strategy





'''simple docstring'''




import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
  from PIL import Image
else:



  class 							lowercase_						:

    '''simple docstring'''





    @staticmethod
    def     snake_case_      (  *a_ ,			**a_  )				->					Tuple:


      """simple docstring"""
      pass


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						hashlib.mda(image.tobytes()    )
  return m.hexdigest()[:10]


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Image    ):
  UpperCAmelCase    				=						np.array(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						npimg.shape
  return {"hash": hashimage(SCREAMING_SNAKE_CASE    ), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  __lowerCAmelCase       :			Tuple     	= dict(
      (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  __lowerCAmelCase       :			Tuple     	= dict(
      (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()			) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])			)
  def     snake_case_      (  self ,			a_ ,			a_ ,			a_  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						MaskGenerationPipeline(model=a_ ,			image_processor=a_  )
    return image_segmenter, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]
  def     snake_case_      (  self ,			a_ ,			a_  )				->					Optional[Any]:


    """simple docstring"""
    pass
  @require_tf
  @unittest.skip('Image segmentation not implemented in TF'  )
  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    pass
  @slow
  @require_torch
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model='facebook/sam-vit-huge'  )

    UpperCAmelCase    				=						image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg' ,			points_per_batch=2_5_6  )

    # Shortening by hashing
    UpperCAmelCase    				=						[]
    for i, o in enumerate(outputs['masks']  ):
      new_outupt += [{"mask": mask_to_test_readable(a_  ), "scores": outputs["scores"][i]}]

    # fmt: off
    self.assertEqual(
        nested_simplify(a_ ,			decimals=4  ) ,			[
            {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.021},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
            {'mask': {'hash': 'e2d0b7a0b7', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9967},
            {'mask': {'hash': '453c7844bd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.993},
            {'mask': {'hash': '3d44f2926d', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9909},
            {'mask': {'hash': '64033ddc3f', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9879},
            {'mask': {'hash': '801064ff79', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9834},
            {'mask': {'hash': '6172f276ef', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9716},
            {'mask': {'hash': 'b49e60e084', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9612},
            {'mask': {'hash': 'a811e775fd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9599},
            {'mask': {'hash': 'a6a8ebcf4b', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9552},
            {'mask': {'hash': '9d8257e080', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9532},
            {'mask': {'hash': '32de6454a8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9516},
            {'mask': {'hash': 'af3d4af2c8', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9499},
            {'mask': {'hash': '3c6db475fb', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9483},
            {'mask': {'hash': 'c290813fb9', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9464},
            {'mask': {'hash': 'b6f0b8f606', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': '92ce16bfdf', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.943},
            {'mask': {'hash': 'c749b25868', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9408},
            {'mask': {'hash': 'efb6cab859', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9335},
            {'mask': {'hash': '1ff2eafb30', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9326},
            {'mask': {'hash': '788b798e24', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.9262},
            {'mask': {'hash': 'abea804f0e', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8999},
            {'mask': {'hash': '7b9e8ddb73', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8986},
            {'mask': {'hash': 'cd24047c8a', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8984},
            {'mask': {'hash': '6943e6bcbd', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8873},
            {'mask': {'hash': 'b5f47c9191', 'shape': (4_8_0, 6_4_0)}, 'scores': 0.8871}
        ] ,			)
    # fmt: on





  @require_torch
  @slow
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						'facebook/sam-vit-huge'
    UpperCAmelCase    				=						pipeline('mask-generation' ,			model=a_  )

    UpperCAmelCase    				=						image_segmenter(
        'http://images.cocodataset.org/val2017/000000039769.jpg' ,			pred_iou_thresh=1 ,			points_per_batch=2_5_6  )

    # Shortening by hashing
    UpperCAmelCase    				=						[]
    for i, o in enumerate(outputs['masks']  ):
      new_outupt += [{"mask": mask_to_test_readable(a_  ), "scores": outputs["scores"][i]}]

    self.assertEqual(
        nested_simplify(a_ ,			decimals=4  ) ,			[
            {'mask': {'hash': '115ad19f5f', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0444},
            {'mask': {'hash': '6affa964c6', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0210},
            {'mask': {'hash': 'dfe28a0388', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0167},
            {'mask': {'hash': 'c0a5f4a318', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0132},
            {'mask': {'hash': 'fe8065c197', 'shape': (4_8_0, 6_4_0)}, 'scores': 1.0053},
        ] ,			)


