






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 ViTImageProcessor



class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     def __init__(     self       :      Optional[int] ,   __snake_case       :      List[Any] ,   __snake_case       :      List[Any]=13 ,   __snake_case       :      str=3 ,   __snake_case       :      int=2_24 ,   __snake_case       :      Dict=30 ,   __snake_case       :      int=4_00 ,   __snake_case       :      str=True ,   __snake_case       :      List[str]=None ,   __snake_case       :      Any=True ,   __snake_case       :      str=[0.5, 0.5, 0.5] ,   __snake_case       :      int=[0.5, 0.5, 0.5] ,   ):
         UpperCAmelCase_										=					size if 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_normalize
         UpperCAmelCase_										=					image_mean
         UpperCAmelCase_										=					image_std




     def   lowerCamelCase_       (     self       :      List[Any]    ):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
         }



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

     '''simple docstring'''





     lowerCAmelCase    :	int       		=  ViTImageProcessor if is_vision_available() else None
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					EfficientFormerImageProcessorTester(self    )
     @property
     def   lowerCamelCase_       (     self       :      Any    ):
         return self.image_proc_tester.prepare_image_processor_dict()
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(UpperCamelCase_ ,   '''image_mean'''    )    )
         self.assertTrue(hasattr(UpperCamelCase_ ,   '''image_std'''    )    )
         self.assertTrue(hasattr(UpperCamelCase_ ,   '''do_normalize'''    )    )
         self.assertTrue(hasattr(UpperCamelCase_ ,   '''do_resize'''    )    )
         self.assertTrue(hasattr(UpperCamelCase_ ,   '''size'''    )    )
     def   lowerCamelCase_       (     self       :      int    ):
         pass
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_										=					prepare_image_inputs(self.image_proc_tester ,   equal_resolution=UpperCamelCase_    )
         for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_ ,   Image.Image    )

         # Test not batched input
         UpperCAmelCase_										=					image_processor(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processor(UpperCamelCase_ ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             ) ,   )
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_proc_tester ,   equal_resolution=UpperCamelCase_ ,   numpify=UpperCamelCase_    )
         for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_ ,   np.ndarray    )

         # Test not batched input
         UpperCAmelCase_										=					image_processor(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processor(UpperCamelCase_ ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             ) ,   )




     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_										=					prepare_image_inputs(self.image_proc_tester ,   equal_resolution=UpperCamelCase_ ,   torchify=UpperCamelCase_    )
         for image in image_inputs:
             self.assertIsInstance(UpperCamelCase_ ,   torch.Tensor    )

         # Test not batched input
         UpperCAmelCase_										=					image_processor(image_inputs[0] ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 1,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             ) ,   )

         # Test batched
         UpperCAmelCase_										=					image_processor(UpperCamelCase_ ,   return_tensors='''pt'''    ).pixel_values
         self.assertEqual(
             encoded_images.shape ,   (
                 self.image_proc_tester.batch_size,
                 self.image_proc_tester.num_channels,
                 self.image_proc_tester.size['''height'''],
                 self.image_proc_tester.size['''width'''],
             ) ,   )











from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  ['input_values', 'padding_mask']
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      int = 1 ,   __snake_case       :      int = 2_40_00 ,   __snake_case       :      float = 0.0 ,   __snake_case       :      float = None ,   __snake_case       :      float = None ,   **__snake_case       :      Dict ,   ):
         super().__init__(feature_size=__snake_case ,   sampling_rate=__snake_case ,   padding_value=__snake_case ,   **__snake_case    )
         UpperCAmelCase_										=					chunk_length_s
         UpperCAmelCase_										=					overlap
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None or self.overlap is None:
             return None
         else:
             return max(1 ,   int((1.0 - self.overlap) * self.chunk_length    )    )




     def __call__(     self       :      List[str] ,   __snake_case       :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,   __snake_case       :      Optional[Union[bool, str, PaddingStrategy]] = None ,   __snake_case       :      Optional[bool] = False ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   __snake_case       :      Optional[int] = None ,   ):
         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                     F' {self.sampling_rate} and not {sampling_rate}.'    )
         else:
             logger.warning(
                 '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                 '''Failing to do so can result in silent errors that might be hard to debug.'''    )

         if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''    )
         elif padding is None:
             # by default let's pad the inputs
             UpperCAmelCase_										=					True

         UpperCAmelCase_										=					bool(
             isinstance(__snake_case ,   (list, tuple)    ) and (isinstance(raw_audio[0] ,   (np.ndarray, tuple, list)    ))    )

         if is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case ,   dtype=np.floataa    ).T for audio in raw_audio]
         elif not is_batched and not isinstance(__snake_case ,   np.ndarray    ):
             UpperCAmelCase_										=					np.asarray(__snake_case ,   dtype=np.floataa    )
         elif isinstance(__snake_case ,   np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
             UpperCAmelCase_										=					raw_audio.astype(np.floataa    )

         # always return batch
         if not is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case    ).T]

         # verify inputs are valid
         for idx, example in enumerate(__snake_case    ):
             if example.ndim > 2:
                 raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'    )
             if self.feature_size == 1 and example.ndim != 1:
                 raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'    )
             if self.feature_size == 2 and example.shape[-1] != 2:
                 raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'    )

         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					BatchFeature({'''input_values''': raw_audio}    )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                 UpperCAmelCase_										=					min(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.floor(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                 UpperCAmelCase_										=					max(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.ceil(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
                 UpperCAmelCase_										=					'''max_length'''
             else:
                 UpperCAmelCase_										=					input_values

        # normal padding on batch
         if padded_inputs is None:
             UpperCAmelCase_										=					self.pad(
                 __snake_case ,   max_length=__snake_case ,   truncation=__snake_case ,   padding=__snake_case ,   return_attention_mask=__snake_case ,   )
             if padding:
                 UpperCAmelCase_										=					padded_inputs.pop('''attention_mask'''    )

         UpperCAmelCase_										=					[]
         for example in padded_inputs.pop('''input_values'''    ):
             if self.feature_size == 1:
                 UpperCAmelCase_										=					example[..., None]
             input_values.append(example.T    )

         UpperCAmelCase_										=					input_values
         if return_tensors is not None:
             UpperCAmelCase_										=					padded_inputs.convert_to_tensors(__snake_case    )

         return padded_inputs





