



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

import yaml



class 			lowerCamelCase__     (				yaml.SafeLoader):




		'''simple docstring'''



		def 						lowerCAmelCase__ (self						,__lowerCamelCase					)	->     Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__	:							Any				=	[self.constructed_objects[key_node] for key_node, _ in node.value]
						lowerCAmelCase__	:							Optional[Any]				=	[tuple(lowerCamelCase__					) if isinstance(lowerCamelCase__						,lowerCamelCase__					) else key for key in keys]
						lowerCAmelCase__	:							Any				=	Counter(lowerCamelCase__					)
						lowerCAmelCase__	:							Any				=	[key for key in counter if counter[key] > 1]
						if duplicate_keys:
										raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""					)



		def 						lowerCAmelCase__ (self						,__lowerCamelCase						,__lowerCamelCase=False					)	->     str:

						"""simple docstring"""






						lowerCAmelCase__	:							int				=	super().construct_mapping(lowerCamelCase__						,deep=lowerCamelCase__					)
						self._check_no_duplicates_on_constructed_node(lowerCamelCase__					)
						return mapping






def       lowerCAmelCase__						(       lowerCamelCase_						:						List[str]):





				'''simple docstring'''

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

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



class 			lowerCamelCase__     (				__lowerCamelCase):




		'''simple docstring'''



		snake_case_      ={'train_eval_index'}  # train-eval-index in the YAML metadata



		@classmethod
		def 						lowerCAmelCase__ (cls						,__lowerCamelCase					)	->     "DatasetMetadata":

						"""simple docstring"""






						with open(lowerCamelCase__						,encoding='''utf-8'''					) as readme_file:
										lowerCAmelCase__		,						lowerCAmelCase__	:							str				=	_split_yaml_from_readme(readme_file.read()					)
						if yaml_string is not None:
										return cls.from_yaml_string(lowerCamelCase__					)
						else:
										return cls()



		def 						lowerCAmelCase__ (self						,__lowerCamelCase					)	->     Tuple:

						"""simple docstring"""






						if path.exists():
										with open(lowerCamelCase__						,encoding='''utf-8'''					) as readme_file:
														lowerCAmelCase__	:							Dict				=	readme_file.read()
						else:
										lowerCAmelCase__	:							str				=	None
						lowerCAmelCase__	:							List[str]				=	self._to_readme(lowerCamelCase__					)
						with open(lowerCamelCase__						,'''w'''						,encoding='''utf-8'''					) as readme_file:
										readme_file.write(lowerCamelCase__					)



		def 						lowerCAmelCase__ (self						,__lowerCamelCase = None					)	->     str:

						"""simple docstring"""






						if readme_content is not None:
										lowerCAmelCase__		,						lowerCAmelCase__	:							List[Any]				=	_split_yaml_from_readme(lowerCamelCase__					)
										lowerCAmelCase__	:							Any				=	'''---\n''' + self.to_yaml_string() + '''---\n''' + content
						else:
										lowerCAmelCase__	:							int				=	'''---\n''' + self.to_yaml_string() + '''---\n'''
						return full_content



		@classmethod
		def 						lowerCAmelCase__ (cls						,__lowerCamelCase					)	->     "DatasetMetadata":

						"""simple docstring"""






						lowerCAmelCase__	:							int				=	yaml.load(lowerCamelCase__						,Loader=_NoDuplicateSafeLoader					) or {}

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



		def 						lowerCAmelCase__ (self					)	->     str:

						"""simple docstring"""






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


__snake_case							:						str      ={
    '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

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

							__snake_case							:						Optional[Any]      =Path(args.readme_filepath)
							__snake_case							:						Optional[int]      =DatasetMetadata.from_readme(readme_filepath)
							print(dataset_metadata)
							dataset_metadata.to_readme(readme_filepath)




import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor

class    _lowercase				(					unittest.TestCase			):

   def __init__(				self	:    List[str]			,						lowerCamelCase__	:    List[str]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Dict[str, int] = None			,						lowerCamelCase__	:    int = 3_2			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Union[int, float] = 1 / 2_5_5			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]			,						lowerCamelCase__	:    Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]			,						lowerCamelCase__	:    bool = True			,						lowerCamelCase__	:    List[str]=7			,						lowerCamelCase__	:    int=3_0			,						lowerCamelCase__	:    List[Any]=4_0_0			,						lowerCamelCase__	:    int=3			,						) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		do_resize
         A_							       =		size if size is not None else {'''shortest_edge''': 2_8_8}
         A_							       =		size_divisor
         A_							       =		do_rescale
         A_							       =		rescale_factor
         A_							       =		do_normalize
         A_							       =		do_center_crop
         A_							       =		image_mean
         A_							       =		image_std
         A_							       =		do_pad
         A_							       =		batch_size
         A_							       =		num_channels
         A_							       =		min_resolution
         A_							       =		max_resolution

   def     UpperCamelCase					(				self	:    str       ) ->			int:
         """simple docstring"""





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }





   def     UpperCamelCase					(				self	:    List[str]			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    int=False       ) ->			Dict:
         """simple docstring"""





         if not batched:
               A_							       =		self.size['''shortest_edge''']
               A_							       =		image_inputs[0]
               if isinstance(lowerCamelCase__			,						Image.Image       ):
                     A_       ,A_							       =		image.size
               else:
                     A_       ,A_							       =		image.shape[1], image.shape[2]
               A_							       =		size / min(lowerCamelCase__			,						lowerCamelCase__       )
               if h < w:
                     A_       ,A_							       =		size, scale * w
               else:
                     A_       ,A_							       =		scale * h, size

               A_							       =		int((1_3_3_3 / 8_0_0) * size       )
               if max(lowerCamelCase__			,						lowerCamelCase__       ) > max_size:
                     A_							       =		max_size / max(lowerCamelCase__			,						lowerCamelCase__       )
                     A_							       =		newh * scale
                     A_							       =		neww * scale

               A_       ,A_							       =		int(newh + 0.5       ), int(neww + 0.5       )
               A_       ,A_							       =		(
                   newh // self.size_divisor * self.size_divisor,
                   neww // self.size_divisor * self.size_divisor,
               )

         else:
               A_							       =		[]
               for image in image_inputs:
                     A_       ,A_							       =		self.get_expected_values([image]       )
                     expected_values.append((expected_height, expected_width)       )
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[0]       )[0]
               A_							       =		max(lowerCamelCase__			,						key=lambda lowerCamelCase__       : item[1]       )[1]

         return expected_height, expected_width



@require_torch
@require_vision
class    _lowercase				(					__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       List[str]  					=    BridgeTowerImageProcessor if is_vision_available() else None

   def     UpperCamelCase					(				self	:    int       ) ->			Tuple:
         """simple docstring"""





         A_							       =		BridgeTowerImageProcessingTester(self       )

   @property
   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         return self.image_processor_tester.prepare_image_processor_dict()

   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_mean'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''image_std'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_normalize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''do_resize'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size'''       )       )
         self.assertTrue(hasattr(lowerCamelCase__			,						'''size_divisor'''       )       )

   def     UpperCamelCase					(				self	:    Optional[int]       ) ->			str:
         """simple docstring"""





         pass

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





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						Image.Image       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)

   def     UpperCamelCase					(				self	:    str       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         A_							       =		prepare_image_inputs(self.image_processor_tester			,						equal_resolution=lowerCamelCase__			,						numpify=lowerCamelCase__       )
         for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__			,						np.ndarray       )

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





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





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

         # Test not batched input
         A_							       =		image_processing(image_inputs[0]			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,						)

         # Test batched
         A_							       =		image_processing(lowerCamelCase__			,						return_tensors='''pt'''       ).pixel_values

         A_       ,A_							       =		self.image_processor_tester.get_expected_values(lowerCamelCase__			,						batched=lowerCamelCase__       )
         self.assertEqual(
             encoded_images.shape			,						(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )			,						)





