


import inspect
import re
from hashlib import shaaaa
from typing import Dict, List

from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql  # noqa F401
from .text import text




def       a_ (       SCREAMING_SNAKE_CASE__   : List[str]	):
       '''simple docstring'''





       _lowerCamelCase			:						Any            =[]
       for line in lines:
              _lowerCamelCase			:						Tuple            =re.sub(r'#.*'						,     ''						,     SCREAMING_SNAKE_CASE__	)  # remove comments
              if line:
                     filtered_lines.append(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						Optional[Any]            ='\n'.join(SCREAMING_SNAKE_CASE__	)

       # Make a hash from all this code
       _lowerCamelCase			:						str            =full_str.encode('utf-8'	)
       return shaaaa(SCREAMING_SNAKE_CASE__	).hexdigest()


# get importable module names and hash for caching
lowerCamelCase		   =			{
    'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
    'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
    'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
    'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
    'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
    'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
    'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
    'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}

# Used to infer the module to use based on the data files extensions
lowerCamelCase		   =			{
    '.csv': ('csv', {}),
    '.tsv': ('csv', {'sep': '\t'}),
    '.json': ('json', {}),
    '.jsonl': ('json', {}),
    '.parquet': ('parquet', {}),
    '.arrow': ('arrow', {}),
    '.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCamelCase		   =			{'imagefolder', 'audiofolder'}

# Used to filter data files based on extensions given a module name
lowerCamelCase		   =			{}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
 _MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)

_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')


_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')






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 PoolFormerImageProcessor




class A    (  unittest.TestCase	):






      def __init__(   self		:	Any     ,  lowercase_		:	List[str]     ,  lowercase_		:	Union[str, Any]=7     ,  lowercase_		:	Dict=3     ,  lowercase_		:	List[Any]=30     ,  lowercase_		:	int=400     ,  lowercase_		:	List[Any]=True     ,  lowercase_		:	str=None     ,  lowercase_		:	str=0.9     ,  lowercase_		:	Union[str, Any]=None     ,  lowercase_		:	List[str]=True     ,  lowercase_		:	List[str]=[0.5, 0.5, 0.5]     ,  lowercase_		:	int=[0.5, 0.5, 0.5]     ,  )   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =size if size is not None else {'shortest_edge': 30}
             _lowerCamelCase			:						Optional[Any]            =crop_size if crop_size is not None else {'height': 30, 'width': 30}
             _lowerCamelCase			:						List[Any]            =parent
             _lowerCamelCase			:						Union[str, Any]            =batch_size
             _lowerCamelCase			:						Dict            =num_channels
             _lowerCamelCase			:						Any            =min_resolution
             _lowerCamelCase			:						Union[str, Any]            =max_resolution
             _lowerCamelCase			:						Any            =do_resize_and_center_crop
             _lowerCamelCase			:						Any            =size
             _lowerCamelCase			:						Optional[Any]            =crop_pct
             _lowerCamelCase			:						Optional[Any]            =crop_size
             _lowerCamelCase			:						List[Any]            =do_normalize
             _lowerCamelCase			:						int            =image_mean
             _lowerCamelCase			:						Optional[int]            =image_std






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Tuple:
             """simple docstring"""




             return {
                 "size": self.size,
                 "do_resize_and_center_crop": self.do_resize_and_center_crop,
                 "crop_pct": self.crop_pct,
                 "crop_size": self.crop_size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
             }




@require_torch
@require_vision
class A    (  UpperCamelCase_       ,				unittest.TestCase	):
      UpperCamelCase__				:    Dict =PoolFormerImageProcessor if is_vision_available() else None






      def 			lowerCamelCase			(   self		:	int					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =PoolFormerImageProcessingTester(self					)






      @property
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 			lowerCamelCase			(   self		:	Union[str, Any]					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.image_processing_class(**self.image_processor_dict					)
             self.assertTrue(hasattr(lowercase_     ,  'do_resize_and_center_crop'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'size'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'crop_pct'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'do_normalize'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_mean'					)					)
             self.assertTrue(hasattr(lowercase_     ,  'image_std'					)					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Any            =self.image_processing_class.from_dict(self.image_processor_dict					)
             self.assertEqual(image_processor.size     ,  {'shortest_edge': 30}					)
             self.assertEqual(image_processor.crop_size     ,  {'height': 30, 'width': 30}					)

             _lowerCamelCase			:						List[Any]            =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		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	str					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            =self.image_processing_class(**self.image_processor_dict					)
             # create random PIL images
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  Image.Image					)

             # Test not batched input
             _lowerCamelCase			:						int            =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
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  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		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.image_processing_class(**self.image_processor_dict					)
             # create random numpy tensors
             _lowerCamelCase			:						Optional[Any]            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  numpify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  np.ndarray					)

             # Test not batched input
             _lowerCamelCase			:						Dict            =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
             _lowerCamelCase			:						Optional[int]            =image_processing(lowercase_     ,  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		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.image_processing_class(**self.image_processor_dict					)
             # create random PyTorch tensors
             _lowerCamelCase			:						Dict            =prepare_image_inputs(self.image_processor_tester     ,  equal_resolution=lowercase_     ,  torchify=lowercase_					)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_     ,  torch.Tensor					)

             # Test not batched input
             _lowerCamelCase			:						str            =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
             _lowerCamelCase			:						Tuple            =image_processing(lowercase_     ,  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'],
                 )     ,  )


