


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

import yaml

class   A__ (   yaml.SafeLoader     ):







    """simple docstring"""

    def 		a_      (	self      , __snake_case   ):
         snake_case					=    [self.constructed_objects[key_node] for key_node, _ in node.value]
         snake_case					=    [tuple(__snake_case   ) if isinstance(__snake_case      , __snake_case   ) else key for key in keys]
         snake_case					=    Counter(__snake_case   )
         snake_case					=    [key for key in counter if counter[key] > 1]
         if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''   )




    def 		a_      (	self      , __snake_case      , __snake_case=False   ):
         snake_case					=    super().construct_mapping(__snake_case      , deep=__snake_case   )
         self._check_no_duplicates_on_constructed_node(__snake_case   )
         return mapping






def       UpperCAmelCase__						(UpperCamelCase_    ):



     """simple docstring"""






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

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

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		{'train_eval_index'}  # train-eval-index in the YAML metadata
    @classmethod
    def 		a_      (	cls      , __snake_case   ):
         with open(__snake_case      , encoding='''utf-8'''   ) as readme_file:
              snake_case			,  snake_case					=    _split_yaml_from_readme(readme_file.read()   )
         if yaml_string is not None:
              return cls.from_yaml_string(__snake_case   )
         else:
              return cls()
    def 		a_      (	self      , __snake_case   ):
         if path.exists():
              with open(__snake_case      , encoding='''utf-8'''   ) as readme_file:
                   snake_case					=    readme_file.read()
         else:
              snake_case					=    None
         snake_case					=    self._to_readme(__snake_case   )
         with open(__snake_case      , '''w'''      , encoding='''utf-8'''   ) as readme_file:
              readme_file.write(__snake_case   )
    def 		a_      (	self      , __snake_case = None   ):
         if readme_content is not None:
              snake_case			,  snake_case					=    _split_yaml_from_readme(__snake_case   )
              snake_case					=    '''---\n''' + self.to_yaml_string() + '''---\n''' + content
         else:
              snake_case					=    '''---\n''' + self.to_yaml_string() + '''---\n'''
         return full_content
    @classmethod
    def 		a_      (	cls      , __snake_case   ):
         snake_case					=    yaml.load(__snake_case      , Loader=_NoDuplicateSafeLoader   ) or {}

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




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






_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {
    "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

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

 _SCREAMING_SNAKE_CASE      :			str      				=   Path(args.readme_filepath)
 _SCREAMING_SNAKE_CASE      :			List[str]      				=   DatasetMetadata.from_readme(readme_filepath)
 print(dataset_metadata)
 dataset_metadata.to_readme(readme_filepath)








from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE      :			List[str]      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[int]      				=   {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'efficientnet'
    def __init__(	self      , __snake_case = 3      , __snake_case = 6_0_0      , __snake_case = 2.0      , __snake_case = 3.1      , __snake_case = 8      , __snake_case = [3, 3, 5, 3, 5, 5, 3]      , __snake_case = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]      , __snake_case = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]      , __snake_case = []      , __snake_case = [1, 2, 2, 2, 1, 2, 1]      , __snake_case = [1, 2, 2, 3, 3, 4, 1]      , __snake_case = [1, 6, 6, 6, 6, 6, 6]      , __snake_case = 0.25      , __snake_case = "swish"      , __snake_case = 2_5_6_0      , __snake_case = "mean"      , __snake_case = 0.02      , __snake_case = 0.001      , __snake_case = 0.99      , __snake_case = 0.5      , __snake_case = 0.2      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    num_channels
         snake_case					=    image_size
         snake_case					=    width_coefficient
         snake_case					=    depth_coefficient
         snake_case					=    depth_divisor
         snake_case					=    kernel_sizes
         snake_case					=    in_channels
         snake_case					=    out_channels
         snake_case					=    depthwise_padding
         snake_case					=    strides
         snake_case					=    num_block_repeats
         snake_case					=    expand_ratios
         snake_case					=    squeeze_expansion_ratio
         snake_case					=    hidden_act
         snake_case					=    hidden_dim
         snake_case					=    pooling_type
         snake_case					=    initializer_range
         snake_case					=    batch_norm_eps
         snake_case					=    batch_norm_momentum
         snake_case					=    dropout_rate
         snake_case					=    drop_connect_rate
         snake_case					=    sum(__snake_case   ) * 4






class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		version.parse('1.11'     )
    @property
    def 		a_      (	self   ):
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]   )




    @property
    def 		a_      (	self   ):
         return 1E-5




