




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


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      List[Any] ,   __snake_case       :      int ,   __snake_case       :      int ,   __snake_case       :      float ,   **__snake_case       :      Tuple    ):
         UpperCAmelCase_										=					feature_size
         UpperCAmelCase_										=					sampling_rate
         UpperCAmelCase_										=					padding_value

         UpperCAmelCase_										=					kwargs.pop('''padding_side''' ,   '''right'''    )
         UpperCAmelCase_										=					kwargs.pop('''return_attention_mask''' ,   __snake_case    )

         super().__init__(**__snake_case    )
     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      Union[
             BatchFeature,
             List[BatchFeature],
             Dict[str, BatchFeature],
             Dict[str, List[BatchFeature]],
             List[Dict[str, BatchFeature]],
         ] ,   __snake_case       :      Union[bool, str, PaddingStrategy] = True ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      bool = False ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[bool] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   ):
         # 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(__snake_case ,   (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(__snake_case    ) == 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(__snake_case ,   (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(__snake_case    ):
                 UpperCAmelCase_										=					required_input[index][0]

         if return_tensors is None:
             if is_tf_tensor(__snake_case    ):
                 UpperCAmelCase_										=					'''tf'''
             elif is_torch_tensor(__snake_case    ):
                 UpperCAmelCase_										=					'''pt'''
             elif isinstance(__snake_case ,   (int, float, list, tuple, np.ndarray)    ):
                 UpperCAmelCase_										=					'''np'''
             else:
                 raise ValueError(
                     F'type of {first_element} unknown: {type(__snake_case    )}. '
                     '''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(__snake_case    )
             else:
                 UpperCAmelCase_										=					[to_numpy(__snake_case    ) for v in value]

        # Convert padding_strategy in PaddingStrategy
         UpperCAmelCase_										=					self._get_padding_strategies(padding=__snake_case ,   max_length=__snake_case    )

         UpperCAmelCase_										=					processed_features[self.model_input_names[0]]

         UpperCAmelCase_										=					len(__snake_case    )
         if not all(len(__snake_case    ) == 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(__snake_case    ):
             UpperCAmelCase_										=					{k: v[i] for k, v in processed_features.items()}
             # truncation
             UpperCAmelCase_										=					self._truncate(
                 __snake_case ,   max_length=__snake_case ,   pad_to_multiple_of=__snake_case ,   truncation=__snake_case ,   )
             truncated_inputs.append(__snake_case    )

         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(__snake_case    ):
             # padding
             UpperCAmelCase_										=					self._pad(
                 truncated_inputs[i] ,   max_length=__snake_case ,   padding_strategy=__snake_case ,   pad_to_multiple_of=__snake_case ,   return_attention_mask=__snake_case ,   )

             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(__snake_case    )

         return BatchFeature(__snake_case ,   tensor_type=__snake_case    )
     def   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      Union[Dict[str, np.ndarray], BatchFeature] ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[bool] = None ,   ):
         UpperCAmelCase_										=					processed_features[self.model_input_names[0]]

         if padding_strategy == PaddingStrategy.LONGEST:
             UpperCAmelCase_										=					len(__snake_case    )

         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(__snake_case    ) < max_length

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

         if needs_to_be_padded:
             UpperCAmelCase_										=					max_length - len(__snake_case    )
             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(
                     __snake_case ,   __snake_case ,   '''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(
                     __snake_case ,   __snake_case ,   '''constant''' ,   constant_values=self.padding_value    )
             else:
                 raise ValueError('''Invalid padding strategy:''' + str(self.padding_side    )    )

         return processed_features
     def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      Union[Dict[str, np.ndarray], BatchFeature] ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[bool] = None ,   ):
         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(__snake_case    ) > 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   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      List[str]=False ,   __snake_case       :      Dict=None    ):

         # 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(__snake_case ,   __snake_case    ):
                 UpperCAmelCase_										=					PaddingStrategy(__snake_case    )
             elif isinstance(__snake_case ,   __snake_case    ):
                 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











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 MobileNetVaImageProcessor



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      int ,   __snake_case       :      List[Any]=7 ,   __snake_case       :      Any=3 ,   __snake_case       :      Any=18 ,   __snake_case       :      str=30 ,   __snake_case       :      Any=4_00 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      str=None ,   __snake_case       :      Any=True ,   __snake_case       :      List[Any]=None ,   ):
         UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 20}
         UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					num_channels
         UpperCAmelCase_										=					image_size
         UpperCAmelCase_										=					min_resolution
         UpperCAmelCase_										=					max_resolution
         UpperCAmelCase_										=					do_resize
         UpperCAmelCase_										=					size
         UpperCAmelCase_										=					do_center_crop
         UpperCAmelCase_										=					crop_size




     def   lowerCamelCase_       (     self       :      Any    ):
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  MobileNetVaImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					MobileNetVaImageProcessingTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         return self.image_processor_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(__snake_case ,   '''do_resize'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''size'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''do_center_crop'''    )    )
         self.assertTrue(hasattr(__snake_case ,   '''crop_size'''    )    )
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					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}    )

         UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Optional[int]    ):
         pass
     def   lowerCamelCase_       (     self       :      Tuple    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   Image.Image    )

         # Test not batched input
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					image_processing(__snake_case ,   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   lowerCamelCase_       (     self       :      str    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   numpify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   np.ndarray    )

         # Test not batched input
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					image_processing(__snake_case ,   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   lowerCamelCase_       (     self       :      int    ):
         # Initialize image_processing
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case ,   torchify=__snake_case    )
         for image in image_inputs:
             self.assertIsInstance(__snake_case ,   torch.Tensor    )

         # Test not batched input
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					image_processing(__snake_case ,   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'''],
             ) ,   )





