



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):
			"""simple docstring"""



			def __init__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Union[str, Any]=7				,   __UpperCamelCase     :    Optional[int]=3				,   __UpperCamelCase     :    str=3_0				,   __UpperCamelCase     :    List[str]=4_0_0				,   __UpperCamelCase     :    Optional[Any]=True				,   __UpperCamelCase     :    str=None				,   __UpperCamelCase     :    Tuple=0.9				,   __UpperCamelCase     :    Union[str, Any]=None				,   __UpperCamelCase     :    int=True				,   __UpperCamelCase     :    List[Any]=[0.5, 0.5, 0.5]				,   __UpperCamelCase     :    str=[0.5, 0.5, 0.5]				,   )->Dict:
							_UpperCAmelCase					=    size if size is not None else {'''shortest_edge''': 3_0}
							_UpperCAmelCase					=    crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
							_UpperCAmelCase					=    parent
							_UpperCAmelCase					=    batch_size
							_UpperCAmelCase					=    num_channels
							_UpperCAmelCase					=    min_resolution
							_UpperCAmelCase					=    max_resolution
							_UpperCAmelCase					=    do_resize_and_center_crop
							_UpperCAmelCase					=    size
							_UpperCAmelCase					=    crop_pct
							_UpperCAmelCase					=    crop_size
							_UpperCAmelCase					=    do_normalize
							_UpperCAmelCase					=    image_mean
							_UpperCAmelCase					=    image_std



			def  lowercase__						(		self     :    Optional[Any]   )->Union[str, Any]:
							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 (					lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	PoolFormerImageProcessor if is_vision_available() else None



			def  lowercase__						(		self     :    Any   )->str:
							_UpperCAmelCase					=    PoolFormerImageProcessingTester(self   )



			@property
			def  lowercase__						(		self     :    str   )->Optional[int]:
							return self.image_processor_tester.prepare_image_processor_dict()



			def  lowercase__						(		self     :    str   )->List[Any]:
							_UpperCAmelCase					=    self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''do_resize_and_center_crop'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''size'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''crop_pct'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''do_normalize'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''image_mean'''   )   )
							self.assertTrue(hasattr(__UpperCamelCase				,   '''image_std'''   )   )



			def  lowercase__						(		self     :    List[Any]   )->Union[str, Any]:
							_UpperCAmelCase					=    self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size				,   {'''shortest_edge''': 3_0}   )
							self.assertEqual(image_processor.crop_size				,   {'''height''': 3_0, '''width''': 3_0}   )

							_UpperCAmelCase					=    self.image_processing_class.from_dict(self.image_processor_dict				,   size=4_2				,   crop_size=8_4   )
							self.assertEqual(image_processor.size				,   {'''shortest_edge''': 4_2}   )
							self.assertEqual(image_processor.crop_size				,   {'''height''': 8_4, '''width''': 8_4}   )



			def  lowercase__						(		self     :    Dict   )->List[str]:
							pass



			def  lowercase__						(		self     :    List[Any]   )->List[str]:
							# 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=__UpperCamelCase   )
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase				,   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(__UpperCamelCase				,   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  lowercase__						(		self     :    Optional[int]   )->List[Any]:
							# 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=__UpperCamelCase				,   numpify=__UpperCamelCase   )
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase				,   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(__UpperCamelCase				,   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  lowercase__						(		self     :    List[Any]   )->List[Any]:
							# 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=__UpperCamelCase				,   torchify=__UpperCamelCase   )
							for image in image_inputs:
											self.assertIsInstance(__UpperCamelCase				,   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(__UpperCamelCase				,   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'''],
							    )				,   )








"""simple docstring"""


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

import yaml
class 			_a (					yaml.SafeLoader):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Any   )->List[Any]:
							_UpperCAmelCase					=    [self.constructed_objects[key_node] for key_node, _ in node.value]
							_UpperCAmelCase					=    [tuple(__UpperCamelCase   ) if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else key for key in keys]
							_UpperCAmelCase					=    Counter(__UpperCamelCase   )
							_UpperCAmelCase					=    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
											raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    str=False   )->Dict:
							_UpperCAmelCase					=    super().construct_mapping(__UpperCamelCase				,   deep=__UpperCamelCase   )
							self._check_no_duplicates_on_constructed_node(__UpperCamelCase   )
							return mapping







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



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

				return None, "\n".join(_SCREAMING_SNAKE_CASE				)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			# class attributes
			UpperCamelCase__			      =	{"""train_eval_index"""}  # train-eval-index in the YAML metadata



			@classmethod
			def  lowercase__						(		cls     :    List[Any]				,   __UpperCamelCase     :    Path   )->"DatasetMetadata":
							with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(readme_file.read()   )
							if yaml_string is not None:
											return cls.from_yaml_string(__UpperCamelCase   )
							else:
											return cls()



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Path   )->List[Any]:
							if path.exists():
											with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
															_UpperCAmelCase					=    readme_file.read()
							else:
											_UpperCAmelCase					=    None
							_UpperCAmelCase					=    self._to_readme(__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''w'''				,   encoding='''utf-8'''   ) as readme_file:
											readme_file.write(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[str] = None   )->str:
							if readme_content is not None:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(__UpperCamelCase   )
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n''' + content
							else:
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n'''
							return full_content



			@classmethod
			def  lowercase__						(		cls     :    str				,   __UpperCamelCase     :    str   )->"DatasetMetadata":
							_UpperCAmelCase					=    yaml.load(__UpperCamelCase				,   Loader=_NoDuplicateSafeLoader   ) or {}

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



			def  lowercase__						(		self     :    str   )->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=__UpperCamelCase				,   allow_unicode=__UpperCamelCase				,   encoding='''utf-8'''				,   ).decode('''utf-8'''   )



__A   :							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

					__A   :							str										=   ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
					ap.add_argument("readme_filepath")
					__A   :							Union[str, Any]										=   ap.parse_args()

					__A   :							Dict										=   Path(args.readme_filepath)
					__A   :							Tuple										=   DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)



