




"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase__ :    List[Any] 					=							logging.get_logger(__name__)

UpperCamelCase__ :    int 					=							{
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}
class     A__      (      lowercase_     ):
	A_							:				Dict						     =      """mega"""




	def __init__(   self  :	int      ,    lowercase  :	Any=30_522      ,    lowercase  :	Tuple=128      ,    lowercase  :	Any=4      ,    lowercase  :	Dict=256      ,    lowercase  :	Dict=16      ,    lowercase  :	Union[str, Any]=True      ,    lowercase  :	Union[str, Any]=64      ,    lowercase  :	List[str]=False      ,    lowercase  :	List[Any]=-1      ,    lowercase  :	Optional[Any]=None      ,    lowercase  :	Tuple=True      ,    lowercase  :	Optional[int]="scalenorm"      ,    lowercase  :	int=True      ,    lowercase  :	int="silu"      ,    lowercase  :	Optional[int]="softmax"      ,    lowercase  :	Tuple=0.1      ,    lowercase  :	Any=0.1      ,    lowercase  :	Union[str, Any]=0.1      ,    lowercase  :	Tuple=False      ,    lowercase  :	str=True      ,    lowercase  :	Union[str, Any]=256      ,    lowercase  :	str=True      ,    lowercase  :	int=0.1      ,    lowercase  :	Dict=2_048      ,    lowercase  :	Dict=False      ,    lowercase  :	Dict=2      ,    lowercase  :	Optional[int]=0.02      ,    lowercase  :	Dict=0.2      ,    lowercase  :	Optional[int]=0.02      ,    lowercase  :	int=1.0      ,    lowercase  :	int=1      ,    lowercase  :	str=0      ,    lowercase  :	Union[str, Any]=2      ,    lowercase  :	Tuple="rotary"      ,    lowercase  :	List[str]=None      ,    lowercase  :	Union[str, Any]=True      ,    lowercase  :	int=True      ,    **lowercase  :	Optional[int]      ,    )					-> int:
						super().__init__(pad_token_id=lowercase      ,    bos_token_id=lowercase      ,    eos_token_id=lowercase      ,    **lowercase	)

						__snake_case          =	vocab_size
						__snake_case          =	hidden_size
						__snake_case          =	num_hidden_layers
						__snake_case          =	activation
						__snake_case          =	attention_activation
						__snake_case          =	intermediate_size
						__snake_case          =	ema_projection_size
						__snake_case          =	bidirectional
						__snake_case          =	shared_representation_size
						__snake_case          =	use_chunking
						__snake_case          =	chunk_size
						__snake_case          =	truncation
						__snake_case          =	normalize_before_mega
						__snake_case          =	normalization_type
						__snake_case          =	norm_affine
						__snake_case          =	dropout_prob
						__snake_case          =	hidden_dropout_prob
						__snake_case          =	attention_probs_dropout_prob
						__snake_case          =	use_feature_dropout
						__snake_case          =	use_normalized_ffn
						__snake_case          =	nffn_hidden_size
						__snake_case          =	normalize_before_ffn
						__snake_case          =	nffn_activation_dropout_prob
						__snake_case          =	max_positions
						__snake_case          =	add_token_type_embeddings
						__snake_case          =	type_vocab_size
						__snake_case          =	initializer_range
						__snake_case          =	ema_delta_alpha_range
						__snake_case          =	ema_beta_range
						__snake_case          =	ema_gamma_omega_range
						__snake_case          =	relative_positional_bias
						__snake_case          =	use_cache
						__snake_case          =	classifier_dropout
						__snake_case          =	add_lm_hidden_dense_layer
						__snake_case          =	1  # not used but required by Hugging Face

class     A__      (      lowercase_     ):




	@property
	def 							a_		(   self  :	Union[str, Any]	)					-> Mapping[str, Mapping[int, str]]:
						if self.task == "multiple-choice":
											__snake_case          =	{0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
											__snake_case          =	{0: """batch""", 1: """sequence"""}
						return OrderedDict(
						    [
						        ("""input_ids""", dynamic_axis),
						        ("""attention_mask""", dynamic_axis),
						    ]	)








"""simple docstring"""

from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


UpperCamelCase__ :    Union[str, Any] 					=							{
    "id": "5733be284776f41900661182",
    "title": "University_of_Notre_Dame",
    "context": "Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend \"Venite Ad Me Omnes\". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.",
    "question": "To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?",
    "answers": {"text": ["Saint Bernadette Soubirous"], "answer_start": [5_15]},
}




@pytest.mark.parametrize(
    """task_cls"""				,					[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]				,					)
def       snake_case__	(       lowerCAmelCase   :		Optional[int]		):
					__snake_case          =	task_cls()
					__snake_case          =	asdict(lowerCAmelCase		)
					__snake_case          =	task_template_from_dict(lowerCAmelCase		)
					assert task == reloaded
class     A__      :




	def 							a_		(   self  :	Tuple	)					-> int:
						__snake_case          =	LanguageModeling(text_column="""input_text"""	)
						assert {"input_text": "text"} == task.column_mapping




	def 							a_		(   self  :	Union[str, Any]	)					-> List[Any]:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						__snake_case          =	{"""text_column""": """input_text"""}
						__snake_case          =	LanguageModeling.from_dict(lowercase	)
						assert "language-modeling" == task.task
						assert input_schema == task.input_schema

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	str	)					-> Any:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	str	)					-> Union[str, Any]:
						__snake_case          =	TextClassification(text_column="""input_text"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_text""": """text""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	int	)					-> List[Any]:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{"""text_column""": """input_text""", """label_column""": """input_labels"""}
						__snake_case          =	TextClassification.from_dict(lowercase	)
						self.assertEqual("""text-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Union[str, Any]	)					-> List[Any]:
						__snake_case          =	TextClassification(text_column="""input_text"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> Tuple:
						__snake_case          =	QuestionAnsweringExtractive(
						    context_column="""input_context"""      ,    question_column="""input_question"""      ,    answers_column="""input_answers"""	)
						self.assertDictEqual(
						    {"""input_context""": """context""", """input_question""": """question""", """input_answers""": """answers"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	int	)					-> Tuple:
						__snake_case          =	Features({"""question""": Value("""string"""	), """context""": Value("""string"""	)}	)
						__snake_case          =	Features(
						    {
						        """answers""": Sequence(
						            {
						                """text""": Value("""string"""	),
						                """answer_start""": Value("""int32"""	),
						            }	)
						    }	)
						__snake_case          =	{
						    """context_column""": """input_input_context""",
						    """question_column""": """input_question""",
						    """answers_column""": """input_answers""",
						}
						__snake_case          =	QuestionAnsweringExtractive.from_dict(lowercase	)
						self.assertEqual("""question-answering-extractive"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> Union[str, Any]:
						__snake_case          =	Summarization(text_column="""input_text"""      ,    summary_column="""input_summary"""	)
						self.assertDictEqual({"""input_text""": """text""", """input_summary""": """summary"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	List[Any]	)					-> Dict:
						__snake_case          =	Features({"""text""": Value("""string"""	)}	)
						__snake_case          =	Features({"""summary""": Value("""string"""	)}	)
						__snake_case          =	{"""text_column""": """input_text""", """summary_column""": """input_summary"""}
						__snake_case          =	Summarization.from_dict(lowercase	)
						self.assertEqual("""summarization"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Any	)					-> int:
						__snake_case          =	AutomaticSpeechRecognition(audio_column="""input_audio"""      ,    transcription_column="""input_transcription"""	)
						self.assertDictEqual({"""input_audio""": """audio""", """input_transcription""": """transcription"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Dict	)					-> List[str]:
						__snake_case          =	Features({"""audio""": Audio()}	)
						__snake_case          =	Features({"""transcription""": Value("""string"""	)}	)
						__snake_case          =	{
						    """audio_column""": """input_audio""",
						    """transcription_column""": """input_transcription""",
						}
						__snake_case          =	AutomaticSpeechRecognition.from_dict(lowercase	)
						self.assertEqual("""automatic-speech-recognition"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	int	)					-> Union[str, Any]:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	Optional[int]	)					-> str:
						__snake_case          =	AudioClassification(audio_column="""input_audio"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_audio""": """audio""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Union[str, Any]	)					-> Any:
						__snake_case          =	Features({"""audio""": Audio()}	)
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{
						    """audio_column""": """input_image""",
						    """label_column""": """input_label""",
						}
						__snake_case          =	AudioClassification.from_dict(lowercase	)
						self.assertEqual("""audio-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Optional[Any]	)					-> Optional[int]:
						__snake_case          =	AudioClassification(audio_column="""input_audio"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	Optional[int]	)					-> str:
						__snake_case          =	sorted(["""pos""", """neg"""]	)




	def 							a_		(   self  :	Optional[int]	)					-> List[str]:
						__snake_case          =	ImageClassification(image_column="""input_image"""      ,    label_column="""input_label"""	)
						self.assertDictEqual({"""input_image""": """image""", """input_label""": """labels"""}      ,    task.column_mapping	)




	def 							a_		(   self  :	Optional[Any]	)					-> int:
						__snake_case          =	Features({"""image""": Image()}	)
						__snake_case          =	Features({"""labels""": ClassLabel}	)
						__snake_case          =	{
						    """image_column""": """input_image""",
						    """label_column""": """input_label""",
						}
						__snake_case          =	ImageClassification.from_dict(lowercase	)
						self.assertEqual("""image-classification"""      ,    task.task	)
						self.assertEqual(lowercase      ,    task.input_schema	)
						self.assertEqual(lowercase      ,    task.label_schema	)




	def 							a_		(   self  :	Optional[int]	)					-> Any:
						__snake_case          =	ImageClassification(image_column="""input_image"""      ,    label_column="""input_label"""	)
						self.assertEqual(task.label_schema["""labels"""]      ,    lowercase	)
						__snake_case          =	task.align_with_features(Features({"""input_label""": ClassLabel(names=self.labels	)}	)	)
						self.assertEqual(task.label_schema["""labels"""]      ,    ClassLabel(names=self.labels	)	)

class     A__      (      lowercase_     ):




	def 							a_		(   self  :	str	)					-> str:
						__snake_case          =	DatasetInfo(task_templates=QuestionAnsweringExtractive()	)
						__snake_case          =	Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}      ,    info=lowercase	)
						assert isinstance(dataset.info.task_templates      ,    lowercase	)
						assert len(dataset.info.task_templates	) == 1

						def keep_task(lowercase  :	Tuple	):
											return x

						def dont_keep_task(lowercase  :	List[str]	):
											__snake_case          =	deepcopy(lowercase	)
											__snake_case          =	0
											return out

						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == dataset.info.task_templates
						# reload from cache
						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == dataset.info.task_templates

						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == []
						# reload from cache
						__snake_case          =	dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == []




	def 							a_		(   self  :	Any	)					-> int:
						__snake_case          =	Features({"""text""": Value("""string"""	), """label""": ClassLabel(names=("""pos""", """neg""")	)}	)
						__snake_case          =	TextClassification(text_column="""text"""      ,    label_column="""label"""	)
						__snake_case          =	DatasetInfo(features=lowercase      ,    task_templates=lowercase	)
						__snake_case          =	Dataset.from_dict({"""text""": ["""A sentence."""], """label""": ["""pos"""]}      ,    info=lowercase	)

						def process(lowercase  :	List[Any]	):
											return example

						__snake_case          =	dataset.remove_columns("""label"""	)
						__snake_case          =	modified_dataset.map(lowercase	)
						assert mapped_dataset.info.task_templates == []


