'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__a: Optional[int]          =   logging.get_logger(__name__)

__a: Tuple          =   {
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}



class 			UpperCAmelCase  (						A__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    "perceiver"

       def __init__(						self	,   __lowerCAmelCase=256	,   __lowerCAmelCase=1280	,   __lowerCAmelCase=768	,   __lowerCAmelCase=1	,   __lowerCAmelCase=26	,   __lowerCAmelCase=8	,   __lowerCAmelCase=8	,   __lowerCAmelCase=None	,   __lowerCAmelCase=None	,   __lowerCAmelCase="kv"	,   __lowerCAmelCase=1	,   __lowerCAmelCase=1	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.0_2	,   __lowerCAmelCase=1E-12	,   __lowerCAmelCase=True	,   __lowerCAmelCase=262	,   __lowerCAmelCase=2048	,   __lowerCAmelCase=56	,   __lowerCAmelCase=[368, 496]	,   __lowerCAmelCase=16	,   __lowerCAmelCase=1920	,   __lowerCAmelCase=16	,   __lowerCAmelCase=[1, 16, 224, 224]	,   **__lowerCAmelCase	,   )  ->  str:
        super().__init__(**_A )

        lowercase__				:    Dict  =					num_latents
        lowercase__				:    List[Any]  =					d_latents
        lowercase__				:    Union[str, Any]  =					d_model
        lowercase__				:    Optional[int]  =					num_blocks
        lowercase__				:    List[Any]  =					num_self_attends_per_block
        lowercase__				:    Tuple  =					num_self_attention_heads
        lowercase__				:    List[str]  =					num_cross_attention_heads
        lowercase__				:    List[Any]  =					qk_channels
        lowercase__				:    Any  =					v_channels
        lowercase__				:    Any  =					cross_attention_shape_for_attention
        lowercase__				:    List[str]  =					self_attention_widening_factor
        lowercase__				:    Any  =					cross_attention_widening_factor
        lowercase__				:    List[Any]  =					hidden_act
        lowercase__				:    List[str]  =					attention_probs_dropout_prob
        lowercase__				:    Any  =					initializer_range
        lowercase__				:    List[Any]  =					layer_norm_eps
        lowercase__				:    Tuple  =					use_query_residual
        # masked language modeling attributes
        lowercase__				:    List[str]  =					vocab_size
        lowercase__				:    Union[str, Any]  =					max_position_embeddings
        # image classification attributes
        lowercase__				:    Dict  =					image_size
        # flow attributes
        lowercase__				:    List[Any]  =					train_size
        # multimodal autoencoding attributes
        lowercase__				:    str  =					num_frames
        lowercase__				:    Any  =					audio_samples_per_frame
        lowercase__				:    Tuple  =					samples_per_patch
        lowercase__				:    Optional[Any]  =					output_shape



class 			UpperCAmelCase  (						A__					):





       '''simple docstring'''



       @property
       def _lowerCAmelCase(						self )  ->  Optional[int]:






        if self.task == "multiple-choice":
         lowercase__				:    List[str]  =					{0: "batch", 1: "choice", 2: "sequence"}
        else:
         lowercase__				:    str  =					{0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ('''inputs''', dynamic_axis),
                ('''attention_mask''', dynamic_axis),
            ] )

       @property
       def _lowerCAmelCase(						self )  ->  int:
        return 1E-4


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = -1	,   __lowerCAmelCase = -1	,   __lowerCAmelCase = -1	,   __lowerCAmelCase = False	,   __lowerCAmelCase = None	,   __lowerCAmelCase = 3	,   __lowerCAmelCase = 40	,   __lowerCAmelCase = 40	,   )  ->  Tuple:






        if isinstance(_A	,   _A ):
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         lowercase__				:    Tuple  =					compute_effective_axis_dimension(
             _A	,   fixed_dimension=OnnxConfig.default_fixed_batch	,   num_token_to_add=0 )
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         lowercase__				:    Tuple  =					preprocessor.num_special_tokens_to_add(_A )
         lowercase__				:    Any  =					compute_effective_axis_dimension(
             _A	,   fixed_dimension=OnnxConfig.default_fixed_sequence	,   num_token_to_add=_A )
         # Generate dummy inputs according to compute batch and sequence
         lowercase__				:    Optional[Any]  =					[" ".join(['''a'''] ) * seq_length] * batch_size
         lowercase__				:    str  =					dict(preprocessor(_A	,   return_tensors=_A ) )
         lowercase__				:    List[str]  =					inputs.pop('''input_ids''' )
         return inputs
        elif isinstance(_A	,   _A ) and preprocessor.model_input_names[0] == "pixel_values":
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         lowercase__				:    Any  =					compute_effective_axis_dimension(_A	,   fixed_dimension=OnnxConfig.default_fixed_batch )
         lowercase__				:    Optional[int]  =					self._generate_dummy_images(_A	,   _A	,   _A	,   _A )
         lowercase__				:    Any  =					dict(preprocessor(images=_A	,   return_tensors=_A ) )
         lowercase__				:    Any  =					inputs.pop('''pixel_values''' )
         return inputs
        else:
         raise ValueError(
             '''Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.''' )


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

import yaml






class 		a__						(					yaml.SafeLoader					):

					def 				__UpperCamelCase   (    self      :       str,_A      :       List[str]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any				      =    [self.constructed_objects[key_node] for key_node, _ in node.value]
							SCREAMING_SNAKE_CASE_  :		List[str]				      =    [tuple(_A    ) if isinstance(_A,_A    ) else key for key in keys]
							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    Counter(_A    )
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
									raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'    )






					def 				__UpperCamelCase   (    self      :       Tuple,_A      :       Dict,_A      :       List[Any]=False    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    super().construct_mapping(_A,deep=_A    )
							self._check_no_duplicates_on_constructed_node(_A    )
							return mapping







def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







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

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






class 		a__						(					A__					):
					# class attributes
					A	       =							{'train_eval_index'}  # train-eval-index in the YAML metadata

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Any,_A      :       Path    ):







							"""simple docstring"""






							with open(_A,encoding="utf-8"    ) as readme_file:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Dict				      =    _split_yaml_from_readme(readme_file.read()    )
							if yaml_string is not None:
									return cls.from_yaml_string(_A    )
							else:
									return cls()

					def 				__UpperCamelCase   (    self      :       Dict,_A      :       Path    ):







							"""simple docstring"""






							if path.exists():
									with open(_A,encoding="utf-8"    ) as readme_file:
											SCREAMING_SNAKE_CASE_  :		int				      =    readme_file.read()
							else:
									SCREAMING_SNAKE_CASE_  :		Any				      =    None
							SCREAMING_SNAKE_CASE_  :		int				      =    self._to_readme(_A    )
							with open(_A,"w",encoding="utf-8"    ) as readme_file:
									readme_file.write(_A    )

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       Optional[str] = None    ):







							"""simple docstring"""






							if readme_content is not None:
									SCREAMING_SNAKE_CASE_      ,    SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    _split_yaml_from_readme(_A    )
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    "---\n" + self.to_yaml_string() + "---\n" + content
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    "---\n" + self.to_yaml_string() + "---\n"
							return full_content

					@classmethod
					def 				__UpperCamelCase   (    cls      :       Dict,_A      :       str    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str				      =    yaml.load(_A,Loader=_NoDuplicateSafeLoader    ) or {}

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






					def 				__UpperCamelCase   (    self      :       Dict    ):







							"""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=_A,allow_unicode=_A,encoding="utf-8",).decode("utf-8"    )







__lowerCamelCase					:	List[Any]       =  {
    '''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

		__lowerCamelCase					:	List[Any]       =  ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
		ap.add_argument('''readme_filepath''')
		__lowerCamelCase					:	Dict       =  ap.parse_args()

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