



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

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =	{
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''
						a__ =	"mbart"
						a__ =	["past_key_values"]
						a__ =	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







						def __init__(						self   :      int						,						__lowerCamelCase   :      Optional[int]=5_02_65						,						__lowerCamelCase   :      str=10_24						,						__lowerCamelCase   :      str=12						,						__lowerCamelCase   :      Dict=40_96						,						__lowerCamelCase   :      str=16						,						__lowerCamelCase   :      int=12						,						__lowerCamelCase   :      Optional[Any]=40_96						,						__lowerCamelCase   :      Any=16						,						__lowerCamelCase   :      Optional[Any]=0.0						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      Dict=True						,						__lowerCamelCase   :      List[str]=True						,						__lowerCamelCase   :      Optional[int]="gelu"						,						__lowerCamelCase   :      List[Any]=10_24						,						__lowerCamelCase   :      Dict=0.1						,						__lowerCamelCase   :      Tuple=0.0						,						__lowerCamelCase   :      List[str]=0.0						,						__lowerCamelCase   :      Union[str, Any]=0.02						,						__lowerCamelCase   :      List[Any]=0.0						,						__lowerCamelCase   :      List[Any]=False						,						__lowerCamelCase   :      Optional[Any]=1						,						__lowerCamelCase   :      str=0						,						__lowerCamelCase   :      Optional[Any]=2						,						__lowerCamelCase   :      Dict=2						,						**__lowerCamelCase   :      int						,						)   ->					Union[str, Any]:
									A				:       Union[str, Any]														=    vocab_size
									A				:       List[str]														=    max_position_embeddings
									A				:       Optional[int]														=    d_model
									A				:       Any														=    encoder_ffn_dim
									A				:       Union[str, Any]														=    encoder_layers
									A				:       int														=    encoder_attention_heads
									A				:       Union[str, Any]														=    decoder_ffn_dim
									A				:       Any														=    decoder_layers
									A				:       Tuple														=    decoder_attention_heads
									A				:       Any														=    dropout
									A				:       Union[str, Any]														=    attention_dropout
									A				:       str														=    activation_dropout
									A				:       Dict														=    activation_function
									A				:       Optional[int]														=    init_std
									A				:       Optional[Any]														=    encoder_layerdrop
									A				:       Union[str, Any]														=    decoder_layerdrop
									A				:       Tuple														=    classifier_dropout
									A				:       Union[str, Any]														=    use_cache
									A				:       List[str]														=    encoder_layers
									A				:       List[str]														=    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__lowerCamelCase						,						bos_token_id=__lowerCamelCase						,						eos_token_id=__lowerCamelCase						,						is_encoder_decoder=__lowerCamelCase						,						forced_eos_token_id=__lowerCamelCase						,						**__lowerCamelCase						,						)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[str]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)

												if self.use_past:
															A				:       str														=    {0: "batch"}
															A				:       Optional[int]														=    {0: "batch", 1: "past_decoder_sequence + sequence"}
												else:
															A				:       Optional[int]														=    {0: "batch", 1: "decoder_sequence"}
															A				:       Union[str, Any]														=    {0: "batch", 1: "decoder_sequence"}

												if self.use_past:
															self.fill_with_past_key_values_(__lowerCamelCase						,						direction="inputs"			)
									elif self.task == "causal-lm":
												# TODO: figure this case out.
												A				:       Dict														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												    ]			)
												if self.use_past:
															A							,			A				:       Union[str, Any]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[str]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       int														=    {0: "batch", 2: "past_sequence + sequence"}
									else:
												A				:       Optional[int]														=    OrderedDict(
												    [
												        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
												        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
												        ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
												        ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
												    ]			)

									return common_inputs







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					Mapping[str, Mapping[int, str]]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       str														=    super().outputs
									else:
												A				:       int														=    super(__lowerCamelCase						,						self			).outputs
												if self.use_past:
															A							,			A				:       Optional[int]														=    self.num_layers
															for i in range(__lowerCamelCase			):
																		A				:       List[Any]														=    {0: "batch", 2: "past_sequence + sequence"}
																		A				:       Tuple														=    {0: "batch", 2: "past_sequence + sequence"}
									return common_outputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      int						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       Tuple														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									# Generate decoder inputs
									A				:       Optional[int]														=    seq_length if not self.use_past else 1
									A				:       str														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									A				:       Tuple														=    {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									A				:       Union[str, Any]														=    dict(**__lowerCamelCase						,						**__lowerCamelCase			)

									if self.use_past:
												if not is_torch_available():
															raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
												else:
															import torch
												A							,			A				:       List[Any]														=    common_inputs["input_ids"].shape
												A				:       Any														=    common_inputs["decoder_input_ids"].shape[1]
												A							,			A				:       Union[str, Any]														=    self.num_attention_heads
												A				:       Tuple														=    (
												    batch,
												    num_encoder_attention_heads,
												    encoder_seq_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)
												A				:       Dict														=    decoder_seq_length + 3
												A				:       Union[str, Any]														=    (
												    batch,
												    num_decoder_attention_heads,
												    decoder_past_length,
												    self._config.hidden_size // num_decoder_attention_heads,
												)

												A				:       int														=    torch.cat(
												    [common_inputs["decoder_attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase			)]						,						dim=1			)

												A				:       Optional[int]														=    []
												# If the number of encoder and decoder layers are present in the model configuration, both are considered
												A							,			A				:       Dict														=    self.num_layers
												A				:       Dict														=    min(__lowerCamelCase						,						__lowerCamelCase			)
												A				:       Optional[Any]														=    max(__lowerCamelCase						,						__lowerCamelCase			) - min_num_layers
												A				:       Tuple														=    "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

												for _ in range(__lowerCamelCase			):
															common_inputs["past_key_values"].append(
															    (
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															        torch.zeros(__lowerCamelCase			),
															    )			)
												# TODO: test this.
												A				:       List[str]														=    encoder_shape if remaining_side_name == "encoder" else decoder_shape
												for _ in range(__lowerCamelCase						,						__lowerCamelCase			):
															common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			))			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      str						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									A				:       List[Any]														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)

									if self.use_past:
												if not is_torch_available():
															raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
												else:
															import torch
												A							,			A				:       List[Any]														=    common_inputs["input_ids"].shape
												# Not using the same length for past_key_values
												A				:       Any														=    seqlen + 2
												A							,			A				:       List[Any]														=    self.num_layers
												A							,			A				:       int														=    self.num_attention_heads
												A				:       int														=    (
												    batch,
												    num_encoder_attention_heads,
												    past_key_values_length,
												    self._config.hidden_size // num_encoder_attention_heads,
												)

												A				:       Tuple														=    common_inputs["attention_mask"].dtype
												A				:       Dict														=    torch.cat(
												    [common_inputs["attention_mask"], torch.ones(__lowerCamelCase						,						__lowerCamelCase						,						dtype=__lowerCamelCase			)]						,						dim=1			)
												A				:       Optional[Any]														=    [
												    (torch.zeros(__lowerCamelCase			), torch.zeros(__lowerCamelCase			)) for _ in range(__lowerCamelCase			)
												]
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									A				:       Any														=    compute_effective_axis_dimension(
									    __lowerCamelCase						,						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
									A				:       Any														=    tokenizer.num_special_tokens_to_add(__lowerCamelCase			)
									A				:       Tuple														=    compute_effective_axis_dimension(
									    __lowerCamelCase						,						fixed_dimension=OnnxConfig.default_fixed_sequence						,						num_token_to_add=__lowerCamelCase			)

									# Generate dummy inputs according to compute batch and sequence
									A				:       Dict														=    [" ".join([tokenizer.unk_token]			) * seq_length] * batch_size
									A				:       Tuple														=    dict(tokenizer(__lowerCamelCase						,						return_tensors=__lowerCamelCase			)			)
									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      PreTrainedTokenizer						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      int = -1						,						__lowerCamelCase   :      bool = False						,						__lowerCamelCase   :      Optional[TensorType] = None						,						)   ->					Mapping[str, Any]:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       List[Any]														=    self._generate_dummy_inputs_for_default_and_seqaseq_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)

									elif self.task == "causal-lm":
												A				:       Tuple														=    self._generate_dummy_inputs_for_causal_lm(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)
									else:
												A				:       List[Any]														=    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
												    __lowerCamelCase						,						batch_size=__lowerCamelCase						,						seq_length=__lowerCamelCase						,						is_pair=__lowerCamelCase						,						framework=__lowerCamelCase			)

									return common_inputs







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]						,						__lowerCamelCase   :      str						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      Any						,						__lowerCamelCase   :      List[Any]			)   ->					Dict:
									if self.task in ["default", "seq2seq-lm"]:
												A				:       Any														=    super()._flatten_past_key_values_(__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)
									else:
												A				:       Optional[Any]														=    super(__lowerCamelCase						,						self			)._flatten_past_key_values_(
												    __lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase						,						__lowerCamelCase			)




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


__SCREAMING_SNAKE_CASE     =	{
    """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""": [515]},
}




@pytest.mark.parametrize(
    "task_cls"			,     [
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,     )
def 						UpperCAmelCase (    _lowerCamelCase     ):
			A				:       Optional[int]														=    task_cls()
			A				:       int														=    asdict(_lowerCamelCase     )
			A				:       int														=    task_template_from_dict(_lowerCamelCase     )
			assert task == reloaded

class 						lowerCamelCase_     :






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       str														=    LanguageModeling(text_column="input_text"			)
									assert {"input_text": "text"} == task.column_mapping







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					str:
									A				:       Tuple														=    Features({"text": Value("string"			)}			)
									A				:       Dict														=    {"text_column": "input_text"}
									A				:       Optional[int]														=    LanguageModeling.from_dict(__lowerCamelCase			)
									assert "language-modeling" == task.task
									assert input_schema == task.input_schema







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					str:
									A				:       List[Any]														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Optional[int]:
									A				:       List[Any]														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertDictEqual({"input_text": "text", "input_label": "labels"}						,						task.column_mapping			)







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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       Dict														=    TextClassification(text_column="input_text"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Any														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      str			)   ->					Dict:
									A				:       Optional[int]														=    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       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					Tuple:
									A				:       Dict														=    Features({"question": Value("string"			), "context": Value("string"			)}			)
									A				:       int														=    Features(
									    {
									        "answers": Sequence(
									            {
									                "text": Value("string"			),
									                "answer_start": Value("int32"			),
									            }			)
									    }			)
									A				:       Optional[int]														=    {
									    "context_column": "input_input_context",
									    "question_column": "input_question",
									    "answers_column": "input_answers",
									}
									A				:       Tuple														=    QuestionAnsweringExtractive.from_dict(__lowerCamelCase			)
									self.assertEqual("question-answering-extractive"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    Summarization(text_column="input_text"						,						summary_column="input_summary"			)
									self.assertDictEqual({"input_text": "text", "input_summary": "summary"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Any:
									A				:       Union[str, Any]														=    Features({"text": Value("string"			)}			)
									A				:       Tuple														=    Features({"summary": Value("string"			)}			)
									A				:       Optional[Any]														=    {"text_column": "input_text", "summary_column": "input_summary"}
									A				:       str														=    Summarization.from_dict(__lowerCamelCase			)
									self.assertEqual("summarization"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Any:
									A				:       Union[str, Any]														=    AutomaticSpeechRecognition(audio_column="input_audio"						,						transcription_column="input_transcription"			)
									self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}						,						task.column_mapping			)







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







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Tuple:
									A				:       Optional[Any]														=    sorted(["pos", "neg"]			)







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







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







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					Any:
									A				:       str														=    AudioClassification(audio_column="input_audio"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       List[str]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									A				:       int														=    sorted(["pos", "neg"]			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Tuple			)   ->					int:
									A				:       List[Any]														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertDictEqual({"input_image": "image", "input_label": "labels"}						,						task.column_mapping			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					int:
									A				:       Optional[int]														=    Features({"image": Image()}			)
									A				:       str														=    Features({"labels": ClassLabel}			)
									A				:       List[str]														=    {
									    "image_column": "input_image",
									    "label_column": "input_label",
									}
									A				:       Tuple														=    ImageClassification.from_dict(__lowerCamelCase			)
									self.assertEqual("image-classification"						,						task.task			)
									self.assertEqual(__lowerCamelCase						,						task.input_schema			)
									self.assertEqual(__lowerCamelCase						,						task.label_schema			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					Tuple:
									A				:       str														=    ImageClassification(image_column="input_image"						,						label_column="input_label"			)
									self.assertEqual(task.label_schema["labels"]						,						__lowerCamelCase			)
									A				:       Optional[Any]														=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels			)}			)			)
									self.assertEqual(task.label_schema["labels"]						,						ClassLabel(names=self.labels			)			)







class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Optional[int]:
									A				:       Dict														=    DatasetInfo(task_templates=QuestionAnsweringExtractive()			)
									A				:       List[Any]														=    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}						,						info=__lowerCamelCase			)
									assert isinstance(dataset.info.task_templates						,						__lowerCamelCase			)
									assert len(dataset.info.task_templates			) == 1

									def keep_task(__lowerCamelCase   :      List[str]			):
												return x

									def dont_keep_task(__lowerCamelCase   :      Any			):
												A				:       Optional[Any]														=    deepcopy(__lowerCamelCase			)
												A				:       List[Any]														=    0
												return out

									A				:       Optional[int]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates
									# reload from cache
									A				:       List[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates

									A				:       Union[str, Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []
									# reload from cache
									A				:       Optional[Any]														=    dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []







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

									def process(__lowerCamelCase   :      Dict			):
												return example

									A				:       List[Any]														=    dataset.remove_columns("label"			)
									A				:       Dict														=    modified_dataset.map(__lowerCamelCase			)
									assert mapped_dataset.info.task_templates == []