"""simple docstring"""

import collections
import inspect
import unittest

from transformers import NatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import NatBackbone, NatForImageClassification, NatModel
				from transformers.models.nat.modeling_nat import NAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor






class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    Tuple     , UpperCamelCase__							:    Any     , UpperCamelCase__							:    Optional[Any]=13     , UpperCamelCase__							:    Optional[Any]=64     , UpperCamelCase__							:    str=4     , UpperCamelCase__							:    List[str]=3     , UpperCamelCase__							:    int=16     , UpperCamelCase__							:    str=[1, 2, 1]     , UpperCamelCase__							:    List[Any]=[2, 4, 8]     , UpperCamelCase__							:    Optional[Any]=3     , UpperCamelCase__							:    List[Any]=2.0     , UpperCamelCase__							:    int=True     , UpperCamelCase__							:    List[str]=0.0     , UpperCamelCase__							:    Optional[Any]=0.0     , UpperCamelCase__							:    Optional[Any]=0.1     , UpperCamelCase__							:    Tuple="gelu"     , UpperCamelCase__							:    Tuple=True     , UpperCamelCase__							:    Any=0.02     , UpperCamelCase__							:    str=1E-5     , UpperCamelCase__							:    Dict=True     , UpperCamelCase__							:    Optional[int]=None     , UpperCamelCase__							:    Any=True     , UpperCamelCase__							:    Tuple=10     , UpperCamelCase__							:    List[Any]=["stage1", "stage2"]     , UpperCamelCase__							:    Optional[int]=[1, 2]     , )				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=parent
													__UpperCamelCase							=batch_size
													__UpperCamelCase							=image_size
													__UpperCamelCase							=patch_size
													__UpperCamelCase							=num_channels
													__UpperCamelCase							=embed_dim
													__UpperCamelCase							=depths
													__UpperCamelCase							=num_heads
													__UpperCamelCase							=kernel_size
													__UpperCamelCase							=mlp_ratio
													__UpperCamelCase							=qkv_bias
													__UpperCamelCase							=hidden_dropout_prob
													__UpperCamelCase							=attention_probs_dropout_prob
													__UpperCamelCase							=drop_path_rate
													__UpperCamelCase							=hidden_act
													__UpperCamelCase							=patch_norm
													__UpperCamelCase							=layer_norm_eps
													__UpperCamelCase							=initializer_range
													__UpperCamelCase							=is_training
													__UpperCamelCase							=scope
													__UpperCamelCase							=use_labels
													__UpperCamelCase							=num_labels
													__UpperCamelCase							=out_features
													__UpperCamelCase							=out_indices





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

													__UpperCamelCase							=None
													if self.use_labels:
																				__UpperCamelCase							=ids_tensor([self.batch_size]     , self.num_labels				)

													__UpperCamelCase							=self.get_config()

													return config, pixel_values, labels





						def   UpperCAmelCase_ (				self							:    int				)				->				Union[str, Any]:
													'''simple docstring'''



													return NatConfig(
													    num_labels=self.num_labels     , image_size=self.image_size     , patch_size=self.patch_size     , num_channels=self.num_channels     , embed_dim=self.embed_dim     , depths=self.depths     , num_heads=self.num_heads     , kernel_size=self.kernel_size     , mlp_ratio=self.mlp_ratio     , qkv_bias=self.qkv_bias     , hidden_dropout_prob=self.hidden_dropout_prob     , attention_probs_dropout_prob=self.attention_probs_dropout_prob     , drop_path_rate=self.drop_path_rate     , hidden_act=self.hidden_act     , patch_norm=self.patch_norm     , layer_norm_eps=self.layer_norm_eps     , initializer_range=self.initializer_range     , out_features=self.out_features     , out_indices=self.out_indices     , )





						def   UpperCAmelCase_ (				self							:    List[Any]     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    Union[str, Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=NatModel(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													__UpperCamelCase							=__UpperCamelCase							=(config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
													__UpperCamelCase							=int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

													self.parent.assertEqual(
													    result.last_hidden_state.shape     , (self.batch_size, expected_height, expected_width, expected_dim)				)





						def   UpperCAmelCase_ (				self							:    Dict     , UpperCamelCase__							:    Union[str, Any]     , UpperCamelCase__							:    List[Any]     , UpperCamelCase__							:    Any				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=NatForImageClassification(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__     , labels=UpperCamelCase__				)
													self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)				)

													# test greyscale images
													__UpperCamelCase							=1
													__UpperCamelCase							=NatForImageClassification(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()

													__UpperCamelCase							=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
													__UpperCamelCase							=model(UpperCamelCase__				)
													self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)				)





						def   UpperCAmelCase_ (				self							:    Any     , UpperCamelCase__							:    int     , UpperCamelCase__							:    Dict     , UpperCamelCase__							:    str				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=NatBackbone(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													# verify hidden states
													self.parent.assertEqual(len(result.feature_maps				)     , len(config.out_features				)				)
													self.parent.assertListEqual(list(result.feature_maps[0].shape				)     , [self.batch_size, model.channels[0], 16, 16]				)

													# verify channels
													self.parent.assertEqual(len(model.channels				)     , len(config.out_features				)				)

													# verify backbone works with out_features=None
													__UpperCamelCase							=None
													__UpperCamelCase							=NatBackbone(config=UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()
													__UpperCamelCase							=model(UpperCamelCase__				)

													# verify feature maps
													self.parent.assertEqual(len(result.feature_maps				)     , 1				)
													self.parent.assertListEqual(list(result.feature_maps[0].shape				)     , [self.batch_size, model.channels[-1], 4, 4]				)

													# verify channels
													self.parent.assertEqual(len(model.channels				)     , 1				)





						def   UpperCAmelCase_ (				self							:    Any				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=self.prepare_config_and_inputs()
													__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase							=config_and_inputs
													__UpperCamelCase							={'''pixel_values''': pixel_values}
													return config, inputs_dict






@require_natten
@require_torch
class 		_lowercase (						__a					,					__a					,					unittest.TestCase      ):



						"""simple docstring"""




						lowercase__       =  (
						    (
						        NatModel,
						        NatForImageClassification,
						        NatBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						lowercase__       =  (
						    {'''feature-extraction''': NatModel, '''image-classification''': NatForImageClassification}
						    if is_torch_available()
						    else {}
						)
						lowercase__       =  False

						lowercase__       =  False
						lowercase__       =  False
						lowercase__       =  False
						lowercase__       =  False





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=NatModelTester(self				)
													__UpperCamelCase							=ConfigTester(self     , config_class=UpperCamelCase__     , embed_dim=37				)





						def   UpperCAmelCase_ (				self							:    Any				)				->				str:
													'''simple docstring'''



													self.create_and_test_config_common_properties()
													self.config_tester.create_and_test_config_to_json_string()
													self.config_tester.create_and_test_config_to_json_file()
													self.config_tester.create_and_test_config_from_and_save_pretrained()
													self.config_tester.create_and_test_config_with_num_labels()
													self.config_tester.check_config_can_be_init_without_params()
													self.config_tester.check_config_arguments_init()





						def   UpperCAmelCase_ (				self							:    Dict				)				->				Optional[int]:
													'''simple docstring'''



													return





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_backbone(*UpperCamelCase__				)





						@unittest.skip(reason='''Nat does not use inputs_embeds'''				)
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[str]:
													'''simple docstring'''



													pass





						@unittest.skip(reason='''Nat does not use feedforward chunking'''				)
						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Optional[Any]:
													'''simple docstring'''



													pass





						def   UpperCAmelCase_ (				self							:    Any				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(UpperCamelCase__				)
																				self.assertIsInstance(model.get_input_embeddings()     , (nn.Module)				)
																				__UpperCamelCase							=model.get_output_embeddings()
																				self.assertTrue(x is None or isinstance(UpperCamelCase__     , nn.Linear				)				)





						def   UpperCAmelCase_ (				self							:    int				)				->				str:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(UpperCamelCase__				)
																				__UpperCamelCase							=inspect.signature(model.forward				)
																				# signature.parameters is an OrderedDict => so arg_names order is deterministic
																				__UpperCamelCase							=[*signature.parameters.keys()]

																				__UpperCamelCase							=['''pixel_values''']
																				self.assertListEqual(arg_names[:1]     , UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    int				)				->				str:
													'''simple docstring'''



													self.skipTest('''Nat\'s attention operation is handled entirely by NATTEN.'''				)





						def   UpperCAmelCase_ (				self							:    Tuple     , UpperCamelCase__							:    Tuple     , UpperCamelCase__							:    Optional[Any]     , UpperCamelCase__							:    List[str]     , UpperCamelCase__							:    int				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=model_class(UpperCamelCase__				)
													model.to(UpperCamelCase__				)
													model.eval()

													with torch.no_grad():
																				__UpperCamelCase							=model(**self._prepare_for_class(UpperCamelCase__     , UpperCamelCase__				)				)

													__UpperCamelCase							=outputs.hidden_states

													__UpperCamelCase							=getattr(
													    self.model_tester     , '''expected_num_hidden_layers'''     , len(self.model_tester.depths				) + 1				)
													self.assertEqual(len(UpperCamelCase__				)     , UpperCamelCase__				)

													# Nat has a different seq_length
													__UpperCamelCase							=(
													    config.patch_size
													    if isinstance(config.patch_size     , collections.abc.Iterable				)
													    else (config.patch_size, config.patch_size)
													)

													__UpperCamelCase							=image_size[0] // patch_size[0]
													__UpperCamelCase							=image_size[1] // patch_size[1]

													self.assertListEqual(
													    list(hidden_states[0].shape[-3:]				)     , [height, width, self.model_tester.embed_dim]     , )

													if model_class.__name__ != "NatBackbone":
																				__UpperCamelCase							=outputs.reshaped_hidden_states
																				self.assertEqual(len(UpperCamelCase__				)     , UpperCamelCase__				)

																				__UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase	,      __UpperCamelCase							=reshaped_hidden_states[0].shape
																				__UpperCamelCase							=(
																				    reshaped_hidden_states[0].view(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				).permute(0     , 2     , 3     , 1				)
																				)
																				self.assertListEqual(
																				    list(reshaped_hidden_states.shape[-3:]				)     , [height, width, self.model_tester.embed_dim]     , )





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase							=(
													    self.model_tester.image_size
													    if isinstance(self.model_tester.image_size     , collections.abc.Iterable				)
													    else (self.model_tester.image_size, self.model_tester.image_size)
													)

													for model_class in self.all_model_classes:
																				__UpperCamelCase							=True
																				self.check_hidden_states_output(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)

																				# check that output_hidden_states also work using config
																				del inputs_dict["output_hidden_states"]
																				__UpperCamelCase							=True

																				self.check_hidden_states_output(UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__     , UpperCamelCase__				)





						@slow
						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Dict:
													'''simple docstring'''



													for model_name in NAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																				__UpperCamelCase							=NatModel.from_pretrained(UpperCamelCase__				)
																				self.assertIsNotNone(UpperCamelCase__				)





						def   UpperCAmelCase_ (				self							:    Dict				)				->				int:
													'''simple docstring'''



													__UpperCamelCase	,      __UpperCamelCase							=self.model_tester.prepare_config_and_inputs_for_common()

													__UpperCamelCase							=_config_zero_init(UpperCamelCase__				)
													for model_class in self.all_model_classes:
																				__UpperCamelCase							=model_class(config=UpperCamelCase__				)
																				for name, param in model.named_parameters():
																											if "embeddings" not in name and param.requires_grad:
																																		self.assertIn(
																																		    ((param.data.mean() * 1E9).round() / 1E9).item()     , [0.0, 1.0]     , msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""     , )






@require_natten
@require_vision
@require_torch
class 		_lowercase (						unittest.TestCase      ):



						"""simple docstring"""




						@cached_property
						def   UpperCAmelCase_ (				self							:    Optional[int]				)				->				Any:
													'''simple docstring'''



													return AutoImageProcessor.from_pretrained('''shi-labs/nat-mini-in1k-224'''				) if is_vision_available() else None





						@slow
						def   UpperCAmelCase_ (				self							:    str				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=NatForImageClassification.from_pretrained('''shi-labs/nat-mini-in1k-224'''				).to(UpperCamelCase__				)
													__UpperCamelCase							=self.default_image_processor

													__UpperCamelCase							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
													__UpperCamelCase							=image_processor(images=UpperCamelCase__     , return_tensors='''pt'''				).to(UpperCamelCase__				)

													# forward pass
													with torch.no_grad():
																				__UpperCamelCase							=model(**UpperCamelCase__				)

													# verify the logits
													__UpperCamelCase							=torch.Size((1, 1000)				)
													self.assertEqual(outputs.logits.shape     , UpperCamelCase__				)
													__UpperCamelCase							=torch.tensor([0.38_05, -0.86_76, -0.39_12]				).to(UpperCamelCase__				)
													self.assertTrue(torch.allclose(outputs.logits[0, :3]     , UpperCamelCase__     , atol=1E-4				)				)






@require_torch
@require_natten
class 		_lowercase (						unittest.TestCase					,					__a      ):



						"""simple docstring"""




						lowercase__       =  (NatBackbone,) if is_torch_available() else ()
						lowercase__       =  NatConfig





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=NatModelTester(self				)




"""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


__lowercase    =				{
    '''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    lowerCAmelCase			(__UpperCamelCase							:    List[str]			):
							"""simple docstring"""


							__UpperCamelCase							=task_cls()
							__UpperCamelCase							=asdict(__UpperCamelCase			)
							__UpperCamelCase							=task_template_from_dict(__UpperCamelCase			)
							assert task == reloaded






class 		_lowercase :



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=LanguageModeling(text_column='''input_text'''				)
													assert {"input_text": "text"} == task.column_mapping





						def   UpperCAmelCase_ (				self							:    str				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''text''': Value('''string'''				)}				)
													__UpperCamelCase							={'''text_column''': '''input_text'''}
													__UpperCamelCase							=LanguageModeling.from_dict(UpperCamelCase__				)
													assert "language-modeling" == task.task
													assert input_schema == task.input_schema






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=sorted(['''pos''', '''neg''']				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=TextClassification(text_column='''input_text'''     , label_column='''input_label'''				)
													self.assertDictEqual({'''input_text''': '''text''', '''input_label''': '''labels'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				Union[str, Any]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''text''': Value('''string'''				)}				)
													# Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
													__UpperCamelCase							=Features({'''labels''': ClassLabel}				)
													__UpperCamelCase							={'''text_column''': '''input_text''', '''label_column''': '''input_labels'''}
													__UpperCamelCase							=TextClassification.from_dict(UpperCamelCase__				)
													self.assertEqual('''text-classification'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=TextClassification(text_column='''input_text'''     , label_column='''input_label'''				)
													self.assertEqual(task.label_schema['''labels''']     , UpperCamelCase__				)
													__UpperCamelCase							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
													self.assertEqual(task.label_schema['''labels''']     , ClassLabel(names=self.labels				)				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Dict				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=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   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				Optional[Any]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''question''': Value('''string'''				), '''context''': Value('''string'''				)}				)
													__UpperCamelCase							=Features(
													    {
													        '''answers''': Sequence(
													            {
													                '''text''': Value('''string'''				),
													                '''answer_start''': Value('''int32'''				),
													            }				)
													    }				)
													__UpperCamelCase							={
													    '''context_column''': '''input_input_context''',
													    '''question_column''': '''input_question''',
													    '''answers_column''': '''input_answers''',
													}
													__UpperCamelCase							=QuestionAnsweringExtractive.from_dict(UpperCamelCase__				)
													self.assertEqual('''question-answering-extractive'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=Summarization(text_column='''input_text'''     , summary_column='''input_summary'''				)
													self.assertDictEqual({'''input_text''': '''text''', '''input_summary''': '''summary'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    int				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''text''': Value('''string'''				)}				)
													__UpperCamelCase							=Features({'''summary''': Value('''string'''				)}				)
													__UpperCamelCase							={'''text_column''': '''input_text''', '''summary_column''': '''input_summary'''}
													__UpperCamelCase							=Summarization.from_dict(UpperCamelCase__				)
													self.assertEqual('''summarization'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[Any]				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=AutomaticSpeechRecognition(audio_column='''input_audio'''     , transcription_column='''input_transcription'''				)
													self.assertDictEqual({'''input_audio''': '''audio''', '''input_transcription''': '''transcription'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''audio''': Audio()}				)
													__UpperCamelCase							=Features({'''transcription''': Value('''string'''				)}				)
													__UpperCamelCase							={
													    '''audio_column''': '''input_audio''',
													    '''transcription_column''': '''input_transcription''',
													}
													__UpperCamelCase							=AutomaticSpeechRecognition.from_dict(UpperCamelCase__				)
													self.assertEqual('''automatic-speech-recognition'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    List[str]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=sorted(['''pos''', '''neg''']				)





						def   UpperCAmelCase_ (				self							:    Union[str, Any]				)				->				int:
													'''simple docstring'''



													__UpperCamelCase							=AudioClassification(audio_column='''input_audio'''     , label_column='''input_label'''				)
													self.assertDictEqual({'''input_audio''': '''audio''', '''input_label''': '''labels'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    Dict				)				->				Any:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''audio''': Audio()}				)
													__UpperCamelCase							=Features({'''labels''': ClassLabel}				)
													__UpperCamelCase							={
													    '''audio_column''': '''input_image''',
													    '''label_column''': '''input_label''',
													}
													__UpperCamelCase							=AudioClassification.from_dict(UpperCamelCase__				)
													self.assertEqual('''audio-classification'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)





						def   UpperCAmelCase_ (				self							:    Tuple				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=AudioClassification(audio_column='''input_audio'''     , label_column='''input_label'''				)
													self.assertEqual(task.label_schema['''labels''']     , UpperCamelCase__				)
													__UpperCamelCase							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
													self.assertEqual(task.label_schema['''labels''']     , ClassLabel(names=self.labels				)				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				str:
													'''simple docstring'''



													__UpperCamelCase							=sorted(['''pos''', '''neg''']				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Tuple:
													'''simple docstring'''



													__UpperCamelCase							=ImageClassification(image_column='''input_image'''     , label_column='''input_label'''				)
													self.assertDictEqual({'''input_image''': '''image''', '''input_label''': '''labels'''}     , task.column_mapping				)





						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				List[str]:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''image''': Image()}				)
													__UpperCamelCase							=Features({'''labels''': ClassLabel}				)
													__UpperCamelCase							={
													    '''image_column''': '''input_image''',
													    '''label_column''': '''input_label''',
													}
													__UpperCamelCase							=ImageClassification.from_dict(UpperCamelCase__				)
													self.assertEqual('''image-classification'''     , task.task				)
													self.assertEqual(UpperCamelCase__     , task.input_schema				)
													self.assertEqual(UpperCamelCase__     , task.label_schema				)





						def   UpperCAmelCase_ (				self							:    str				)				->				Optional[int]:
													'''simple docstring'''



													__UpperCamelCase							=ImageClassification(image_column='''input_image'''     , label_column='''input_label'''				)
													self.assertEqual(task.label_schema['''labels''']     , UpperCamelCase__				)
													__UpperCamelCase							=task.align_with_features(Features({'''input_label''': ClassLabel(names=self.labels				)}				)				)
													self.assertEqual(task.label_schema['''labels''']     , ClassLabel(names=self.labels				)				)






class 		_lowercase (						__a      ):



						"""simple docstring"""




						def   UpperCAmelCase_ (				self							:    Optional[Any]				)				->				List[Any]:
													'''simple docstring'''



													__UpperCamelCase							=DatasetInfo(task_templates=QuestionAnsweringExtractive()				)
													__UpperCamelCase							=Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}     , info=UpperCamelCase__				)
													assert isinstance(dataset.info.task_templates     , UpperCamelCase__				)
													assert len(dataset.info.task_templates				) == 1

													def keep_task(UpperCamelCase__							:    Optional[int]				):
																				return x

													def dont_keep_task(UpperCamelCase__							:    Optional[int]				):
																				__UpperCamelCase							=deepcopy(UpperCamelCase__				)
																				__UpperCamelCase							=0
																				return out

													__UpperCamelCase							=dataset.map(UpperCamelCase__				)
													assert mapped_dataset.info.task_templates == dataset.info.task_templates
													# reload from cache
													__UpperCamelCase							=dataset.map(UpperCamelCase__				)
													assert mapped_dataset.info.task_templates == dataset.info.task_templates

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





						def   UpperCAmelCase_ (				self							:    List[str]				)				->				Dict:
													'''simple docstring'''



													__UpperCamelCase							=Features({'''text''': Value('''string'''				), '''label''': ClassLabel(names=('''pos''', '''neg''')				)}				)
													__UpperCamelCase							=TextClassification(text_column='''text'''     , label_column='''label'''				)
													__UpperCamelCase							=DatasetInfo(features=UpperCamelCase__     , task_templates=UpperCamelCase__				)
													__UpperCamelCase							=Dataset.from_dict({'''text''': ['''A sentence.'''], '''label''': ['''pos''']}     , info=UpperCamelCase__				)

													def process(UpperCamelCase__							:    Any				):
																				return example

													__UpperCamelCase							=dataset.remove_columns('''label'''				)
													__UpperCamelCase							=modified_dataset.map(UpperCamelCase__				)
													assert mapped_dataset.info.task_templates == []



