



"""simple docstring"""


import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
					import jax.numpy as jnp

					from transformers.models.distilbert.modeling_flax_distilbert import (
					    FlaxDistilBertForMaskedLM,
					    FlaxDistilBertForMultipleChoice,
					    FlaxDistilBertForQuestionAnswering,
					    FlaxDistilBertForSequenceClassification,
					    FlaxDistilBertForTokenClassification,
					    FlaxDistilBertModel,
					)
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def __init__(		self     :    Union[str, Any]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    Tuple=1_3				,   __UpperCamelCase     :    Optional[Any]=7				,   __UpperCamelCase     :    Optional[Any]=True				,   __UpperCamelCase     :    Tuple=True				,   __UpperCamelCase     :    List[Any]=True				,   __UpperCamelCase     :    Any=True				,   __UpperCamelCase     :    Optional[int]=9_9				,   __UpperCamelCase     :    int=3_2				,   __UpperCamelCase     :    List[str]=5				,   __UpperCamelCase     :    Optional[Any]=4				,   __UpperCamelCase     :    List[Any]=3_7				,   __UpperCamelCase     :    Union[str, Any]="gelu"				,   __UpperCamelCase     :    List[Any]=0.1				,   __UpperCamelCase     :    Optional[Any]=0.1				,   __UpperCamelCase     :    Tuple=5_1_2				,   __UpperCamelCase     :    Optional[Any]=1_6				,   __UpperCamelCase     :    Optional[int]=2				,   __UpperCamelCase     :    int=0.0_2				,   __UpperCamelCase     :    Dict=4				,   )->int:
							_UpperCAmelCase					=    parent
							_UpperCAmelCase					=    batch_size
							_UpperCAmelCase					=    seq_length
							_UpperCAmelCase					=    is_training
							_UpperCAmelCase					=    use_attention_mask
							_UpperCAmelCase					=    use_token_type_ids
							_UpperCAmelCase					=    use_labels
							_UpperCAmelCase					=    vocab_size
							_UpperCAmelCase					=    hidden_size
							_UpperCAmelCase					=    num_hidden_layers
							_UpperCAmelCase					=    num_attention_heads
							_UpperCAmelCase					=    intermediate_size
							_UpperCAmelCase					=    hidden_act
							_UpperCAmelCase					=    hidden_dropout_prob
							_UpperCAmelCase					=    attention_probs_dropout_prob
							_UpperCAmelCase					=    max_position_embeddings
							_UpperCAmelCase					=    type_vocab_size
							_UpperCAmelCase					=    type_sequence_label_size
							_UpperCAmelCase					=    initializer_range
							_UpperCAmelCase					=    num_choices



			def  lowercase__						(		self     :    Dict   )->Optional[int]:
							_UpperCAmelCase					=    ids_tensor([self.batch_size, self.seq_length]				,   self.vocab_size   )

							_UpperCAmelCase					=    None
							if self.use_attention_mask:
											_UpperCAmelCase					=    random_attention_mask([self.batch_size, self.seq_length]   )

							_UpperCAmelCase					=    DistilBertConfig(
							    vocab_size=self.vocab_size				,   dim=self.hidden_size				,   n_layers=self.num_hidden_layers				,   n_heads=self.num_attention_heads				,   hidden_dim=self.intermediate_size				,   hidden_act=self.hidden_act				,   dropout=self.hidden_dropout_prob				,   attention_dropout=self.attention_probs_dropout_prob				,   max_position_embeddings=self.max_position_embeddings				,   initializer_range=self.initializer_range				,   tie_weights_=__UpperCamelCase				,   )

							return config, input_ids, attention_mask



			def  lowercase__						(		self     :    Tuple   )->Any:
							_UpperCAmelCase					=    self.prepare_config_and_inputs()
							_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    config_and_inputs
							_UpperCAmelCase					=    {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
							return config, inputs_dict



@require_flax
class 			_a (					lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	(
			    (
			        FlaxDistilBertModel,
			        FlaxDistilBertForMaskedLM,
			        FlaxDistilBertForMultipleChoice,
			        FlaxDistilBertForQuestionAnswering,
			        FlaxDistilBertForSequenceClassification,
			        FlaxDistilBertForTokenClassification,
			        FlaxDistilBertForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)



			def  lowercase__						(		self     :    Optional[int]   )->Optional[int]:
							_UpperCAmelCase					=    FlaxDistilBertModelTester(self   )



			@slow
			def  lowercase__						(		self     :    Any   )->Tuple:
							for model_class_name in self.all_model_classes:
											_UpperCAmelCase					=    model_class_name.from_pretrained('''distilbert-base-uncased'''   )
											_UpperCAmelCase					=    model(np.ones((1, 1)   )   )
											self.assertIsNotNone(__UpperCamelCase   )



@require_flax
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			@slow
			def  lowercase__						(		self     :    Dict   )->Union[str, Any]:
							_UpperCAmelCase					=    FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''   )
							_UpperCAmelCase					=    np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]   )
							_UpperCAmelCase					=    np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
							_UpperCAmelCase					=    model(__UpperCamelCase				,   attention_mask=__UpperCamelCase   )[0]
							_UpperCAmelCase					=    (1, 1_1, 7_6_8)
							self.assertEqual(output.shape				,   __UpperCamelCase   )
							_UpperCAmelCase					=    np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]   )

							self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]				,   __UpperCamelCase				,   atol=1e-4   )   )








"""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)



