


'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import MobileViTImageProcessor




class 	a       (       unittest.TestCase    ):
 def __init__(	self      ,			__magic_name__      ,			__magic_name__=7      ,			__magic_name__=3      ,			__magic_name__=18      ,			__magic_name__=30      ,			__magic_name__=4_00      ,			__magic_name__=True      ,			__magic_name__=None      ,			__magic_name__=True      ,			__magic_name__=None      ,			__magic_name__=True      ,			)   ->							str:
      _a      =							size if size is not None else {"shortest_edge": 20}
      _a      =							crop_size if crop_size is not None else {"height": 18, "width": 18}
      _a      =							parent
      _a      =							batch_size
      _a      =							num_channels
      _a      =							image_size
      _a      =							min_resolution
      _a      =							max_resolution
      _a      =							do_resize
      _a      =							size
      _a      =							do_center_crop
      _a      =							crop_size
      _a      =							do_flip_channel_order




 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_flip_channel_order": self.do_flip_channel_order,
      }





@require_torch
@require_vision
class 	a       (       _SCREAMING_SNAKE_CASE					,			unittest.TestCase    ):
 _lowerCAmelCase	   =		MobileViTImageProcessor if is_vision_available() else None
 def 							__UpperCAmelCase						(	self  )   ->							Optional[int]:
      _a      =							MobileViTImageProcessingTester(self  )
 @property
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      return self.image_processor_tester.prepare_image_processor_dict()
 def 							__UpperCAmelCase						(	self  )   ->							int:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(lowerCAmelCase__      ,			'do_resize'  )  )
      self.assertTrue(hasattr(lowerCAmelCase__      ,			'size'  )  )
      self.assertTrue(hasattr(lowerCAmelCase__      ,			'do_center_crop'  )  )
      self.assertTrue(hasattr(lowerCAmelCase__      ,			'center_crop'  )  )
      self.assertTrue(hasattr(lowerCAmelCase__      ,			'do_flip_channel_order'  )  )
 def 							__UpperCAmelCase						(	self  )   ->							Tuple:
      _a      =							self.image_processing_class.from_dict(self.image_processor_dict  )
      self.assertEqual(image_processor.size      ,			{'shortest_edge': 20}  )
      self.assertEqual(image_processor.crop_size      ,			{'height': 18, 'width': 18}  )

      _a      =							self.image_processing_class.from_dict(self.image_processor_dict      ,			size=42      ,			crop_size=84  )
      self.assertEqual(image_processor.size      ,			{'shortest_edge': 42}  )
      self.assertEqual(image_processor.crop_size      ,			{'height': 84, 'width': 84}  )
 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      pass
 def 							__UpperCAmelCase						(	self  )   ->							List[Any]:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _a      =							prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=lowerCAmelCase__  )
      for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase__      ,			Image.Image  )

      # Test not batched input
      _a      =							image_processing(image_inputs[0]      ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,			)

      # Test batched
      _a      =							image_processing(lowerCAmelCase__      ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,			)
 def 							__UpperCAmelCase						(	self  )   ->							str:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _a      =							prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=lowerCAmelCase__      ,			numpify=lowerCAmelCase__  )
      for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase__      ,			np.ndarray  )

      # Test not batched input
      _a      =							image_processing(image_inputs[0]      ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,			)

      # Test batched
      _a      =							image_processing(lowerCAmelCase__      ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,			)




 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      _a      =							self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _a      =							prepare_image_inputs(self.image_processor_tester      ,			equal_resolution=lowerCAmelCase__      ,			torchify=lowerCAmelCase__  )
      for image in image_inputs:
           self.assertIsInstance(lowerCAmelCase__      ,			torch.Tensor  )

      # Test not batched input
      _a      =							image_processing(image_inputs[0]      ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,			(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,			)

      # Test batched
      _a      =							image_processing(lowerCAmelCase__      ,			return_tensors='pt'  ).pixel_values
      self.assertEqual(
          encoded_images.shape      ,			(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )      ,			)





'''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[str]				    =      logging.get_logger(__name__)




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			**__magic_name__  )   ->							Any:
      _a      =							feature_size
      _a      =							sampling_rate
      _a      =							padding_value

      _a      =							kwargs.pop('padding_side' ,			'right'  )
      _a      =							kwargs.pop('return_attention_mask' ,			__magic_name__  )

      super().__init__(**__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = None ,			__magic_name__ = False ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							BatchFeature:
      # 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(__magic_name__ ,			(list, tuple)  ) and isinstance(processed_features[0] ,			(dict, BatchFeature)  ):
           _a      =							{
               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()  )}'  )

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

      if len(__magic_name__  ) == 0:
           if return_attention_mask:
                _a      =							[]
           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

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

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

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

        # Convert padding_strategy in PaddingStrategy
      _a      =							self._get_padding_strategies(padding=__magic_name__ ,			max_length=__magic_name__  )

      _a      =							processed_features[self.model_input_names[0]]

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

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

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

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

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

      return BatchFeature(__magic_name__ ,			tensor_type=__magic_name__  )
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = PaddingStrategy.DO_NOT_PAD ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							dict:
      _a      =							processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
           _a      =							len(__magic_name__  )

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

      _a      =							padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__magic_name__  ) < max_length

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

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

      return processed_features
 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			)   ->							List[str]:
      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.'  )

      _a      =							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):
           _a      =							((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      _a      =							len(__magic_name__  ) > max_length

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

      return processed_features




 def 							__UpperCAmelCase						(	self ,			__magic_name__=False ,			__magic_name__=None  )   ->							Dict:

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

