


# Lint as: python3
import itertools
import os
import re


_lowerCamelCase      			=						re.compile(r'([A-Z]+)([A-Z][a-z])')
_lowerCamelCase      			=						re.compile(r'([a-z\d])([A-Z])')

_lowerCamelCase      			=						re.compile(r'(?<!_)_(?!_)')
_lowerCamelCase      			=						re.compile(r'(_{2,})')

_lowerCamelCase      			=						r'^\w+(\.\w+)*$'

_lowerCamelCase      			=						r'<>:/\|?*'




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]				)  ->							List[str]:
    UpperCAmelCase_										=					_uppercase_uppercase_re.sub(R'''\1_\2'''	,					__UpperCamelCase				)
    UpperCAmelCase_										=					_lowercase_uppercase_re.sub(R'''\1_\2'''	,					__UpperCamelCase				)
    return name.lower()




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]				)  ->							Tuple:
    UpperCAmelCase_										=					_single_underscore_re.split(__UpperCamelCase				)
    UpperCAmelCase_										=					[_multiple_underscores_re.split(__UpperCamelCase				) for n in name]
    return "".join(n.capitalize() for n in itertools.chain.from_iterable(__UpperCamelCase				) if n != ''''''				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]				)  ->							Dict:
    if os.path.basename(__UpperCamelCase				) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'				)
    return camelcase_to_snakecase(__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						List[str]				)  ->							int:
    if os.path.basename(__UpperCamelCase				) != name:
        raise ValueError(f'Should be a dataset name, not a path: {name}'				)
    if not re.match(_split_re	,					__UpperCamelCase				):
        raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'				)
    return f'{filename_prefix_for_name(__UpperCamelCase				)}-{split}'




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Dict=None				)  ->							Dict:
    UpperCAmelCase_										=					filename_prefix_for_split(__UpperCamelCase	,					__UpperCamelCase				)
    if filetype_suffix:
        prefix += f'.{filetype_suffix}'
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)
    return f'{filepath}*'




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]=None	,					__UpperCamelCase      :						Any=None				)  ->							Optional[int]:
    UpperCAmelCase_										=					filename_prefix_for_split(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					__UpperCamelCase				)

    if shard_lengths:
        UpperCAmelCase_										=					len(__UpperCamelCase				)
        UpperCAmelCase_										=					[f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(__UpperCamelCase				)]
        if filetype_suffix:
            UpperCAmelCase_										=					[filename + f'.{filetype_suffix}' for filename in filenames]
        return filenames
    else:
        UpperCAmelCase_										=					prefix
        if filetype_suffix:
            filename += f'.{filetype_suffix}'
        return [filename]











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'''],
             ) ,   )





