



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml



class     lowerCAmelCase_		(  yaml.SafeLoader							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          UpperCamelCase   :  Optional[int]						     =     [self.constructed_objects[key_node] for key_node, _ in node.value]
          UpperCamelCase   :  str						     =     [tuple(SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	) else key for key in keys]
          UpperCamelCase   :  Any						     =     Counter(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     [key for key in counter if counter[key] > 1]
          if duplicate_keys:
             raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=False	)					->		List[Any]:
          UpperCamelCase   :  Tuple						     =     super().construct_mapping(SCREAMING_SNAKE_CASE_,      deep=SCREAMING_SNAKE_CASE_	)
          self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE_	)
          return mapping






def 		UpperCamelCase     (       snake_case__	:   str     )     ->					Tuple[Optional[str], str]:
   UpperCamelCase   :  Union[str, Any]						     =     list(readme_content.splitlines()     )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      UpperCamelCase   :  int						     =     full_content[1:].index('---'     ) + 1
      UpperCamelCase   :  Optional[Any]						     =     '\n'.join(full_content[1:sep_idx]     )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :]     )

   return None, "\n".join(snake_case__     )



class     lowerCAmelCase_		(  a__							):
       # class attributes
       UpperCAmelCase__    : List[Any]       					=  {"train_eval_index"}  # train-eval-index in the YAML metadata
       @classmethod
       def  snake_case_	(      cls,      SCREAMING_SNAKE_CASE_	)					->		"DatasetMetadata":
          with open(SCREAMING_SNAKE_CASE_,      encoding='utf-8'	) as readme_file:
             UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     _split_yaml_from_readme(readme_file.read()	)
          if yaml_string is not None:
             return cls.from_yaml_string(SCREAMING_SNAKE_CASE_	)
          else:
             return cls()
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          if path.exists():
             with open(SCREAMING_SNAKE_CASE_,      encoding='utf-8'	) as readme_file:
                UpperCamelCase   :  str						     =     readme_file.read()
          else:
             UpperCamelCase   :  str						     =     None
          UpperCamelCase   :  Dict						     =     self._to_readme(SCREAMING_SNAKE_CASE_	)
          with open(SCREAMING_SNAKE_CASE_,      'w',      encoding='utf-8'	) as readme_file:
             readme_file.write(SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_ = None	)					->		str:
          if readme_content is not None:
             UpperCamelCase     ,					UpperCamelCase   :  Union[str, Any]						     =     _split_yaml_from_readme(SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Union[str, Any]						     =     '---\n' + self.to_yaml_string() + '---\n' + content
          else:
             UpperCamelCase   :  Any						     =     '---\n' + self.to_yaml_string() + '---\n'
          return full_content
       @classmethod
       def  snake_case_	(      cls,      SCREAMING_SNAKE_CASE_	)					->		"DatasetMetadata":
          UpperCamelCase   :  int						     =     yaml.load(SCREAMING_SNAKE_CASE_,      Loader=_NoDuplicateSafeLoader	) or {}

          # Convert the YAML keys to DatasetMetadata fields
          UpperCamelCase   :  Optional[Any]						     =     {
              (key.replace('-',      '_'	) if key.replace('-',      '_'	) in cls._FIELDS_WITH_DASHES else key): value
              for key, value in metadata_dict.items()
          }
          return cls(**SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self	)					->		str:
          return yaml.safe_dump(
              {
                  (key.replace('_',      '-'	) if key in self._FIELDS_WITH_DASHES else key): value
                  for key, value in self.items()
              },      sort_keys=SCREAMING_SNAKE_CASE_,      allow_unicode=SCREAMING_SNAKE_CASE_,      encoding='utf-8',      ).decode('utf-8'	)






__UpperCAmelCase	       =	{
    '''image-classification''': [],
    '''translation''': [],
    '''image-segmentation''': [],
    '''fill-mask''': [],
    '''automatic-speech-recognition''': [],
    '''token-classification''': [],
    '''sentence-similarity''': [],
    '''audio-classification''': [],
    '''question-answering''': [],
    '''summarization''': [],
    '''zero-shot-classification''': [],
    '''table-to-text''': [],
    '''feature-extraction''': [],
    '''other''': [],
    '''multiple-choice''': [],
    '''text-classification''': [],
    '''text-to-image''': [],
    '''text2text-generation''': [],
    '''zero-shot-image-classification''': [],
    '''tabular-classification''': [],
    '''tabular-regression''': [],
    '''image-to-image''': [],
    '''tabular-to-text''': [],
    '''unconditional-image-generation''': [],
    '''text-retrieval''': [],
    '''text-to-speech''': [],
    '''object-detection''': [],
    '''audio-to-audio''': [],
    '''text-generation''': [],
    '''conversational''': [],
    '''table-question-answering''': [],
    '''visual-question-answering''': [],
    '''image-to-text''': [],
    '''reinforcement-learning''': [],
    '''voice-activity-detection''': [],
    '''time-series-forecasting''': [],
    '''document-question-answering''': [],
}


if __name__ == "__main__":
    from argparse import ArgumentParser

    __UpperCAmelCase	       =	ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
    ap.add_argument('''readme_filepath''')
    __UpperCAmelCase	       =	ap.parse_args()

    __UpperCAmelCase	       =	Path(args.readme_filepath)
    __UpperCAmelCase	       =	DatasetMetadata.from_readme(readme_filepath)
    print(dataset_metadata)
    dataset_metadata.to_readme(readme_filepath)








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     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=224,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      )					->		List[str]:
          UpperCamelCase   :  Optional[int]						     =     size if size is not None else {'height': 18, 'width': 18}
          UpperCamelCase   :  List[Any]						     =     parent
          UpperCamelCase   :  List[Any]						     =     batch_size
          UpperCamelCase   :  int						     =     num_channels
          UpperCamelCase   :  int						     =     image_size
          UpperCamelCase   :  List[Any]						     =     min_resolution
          UpperCamelCase   :  int						     =     max_resolution
          UpperCamelCase   :  Any						     =     do_resize
          UpperCamelCase   :  Optional[int]						     =     size
          UpperCamelCase   :  List[str]						     =     do_normalize
          UpperCamelCase   :  Optional[Any]						     =     image_mean
          UpperCamelCase   :  Tuple						     =     image_std






       def  snake_case_	(      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     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Optional[Any]       					=  ViTImageProcessor if is_vision_available() else None
       def  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  Dict						     =     EfficientFormerImageProcessorTester(self	)
       @property
       def  snake_case_	(      self	)					->		List[Any]:
          return self.image_proc_tester.prepare_image_processor_dict()
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Optional[int]						     =     self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_mean'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'image_std'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_normalize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'do_resize'	)	)
          self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,      'size'	)	)
       def  snake_case_	(      self	)					->		Any:
          pass
       def  snake_case_	(      self	)					->		int:
          # Initialize image_processor
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          UpperCamelCase   :  List[str]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      Image.Image	)

          # Test not batched input
          UpperCamelCase   :  str						     =     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   :  Optional[Any]						     =     image_processor(SCREAMING_SNAKE_CASE_,      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  snake_case_	(      self	)					->		str:
          # Initialize image_processor
          UpperCamelCase   :  List[str]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          UpperCamelCase   :  Union[str, Any]						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      numpify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      np.ndarray	)

          # Test not batched input
          UpperCamelCase   :  Dict						     =     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   :  Dict						     =     image_processor(SCREAMING_SNAKE_CASE_,      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  snake_case_	(      self	)					->		Tuple:
          # Initialize image_processor
          UpperCamelCase   :  Any						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          UpperCamelCase   :  int						     =     prepare_image_inputs(self.image_proc_tester,      equal_resolution=SCREAMING_SNAKE_CASE_,      torchify=SCREAMING_SNAKE_CASE_	)
          for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_,      torch.Tensor	)

          # Test not batched input
          UpperCamelCase   :  Optional[int]						     =     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   :  int						     =     image_processor(SCREAMING_SNAKE_CASE_,      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'],
              ),      )



