import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase     						=			logging.get_logger(__name__)

UpperCAmelCase     						=			{
    '''shi-labs/oneformer_ade20k_swin_tiny''': (
        '''https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'''
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Optional[Any]			      =	'oneformer'
 _lowerCamelCase  :		Dict			      =	{'hidden_size': 'hidden_dim'}

 def __init__(     self    :						List[str]    ,     lowerCamelCase    :						Optional[Dict] = None    ,     lowerCamelCase    :						int = 2_5_5    ,     lowerCamelCase    :						int = 1_5_0    ,     lowerCamelCase    :						int = 0.1    ,     lowerCamelCase    :						float = 2.0    ,     lowerCamelCase    :						float = 5.0    ,     lowerCamelCase    :						float = 5.0    ,     lowerCamelCase    :						float = 0.5    ,     lowerCamelCase    :						float = 0.0_7    ,     lowerCamelCase    :						int = 1_2_5_4_4    ,     lowerCamelCase    :						float = 3.0    ,     lowerCamelCase    :						float = 0.7_5    ,     lowerCamelCase    :						float = 0.0_2    ,     lowerCamelCase    :						float = 1.0    ,     lowerCamelCase    :						float = 1e-05    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						bool = True    ,     lowerCamelCase    :						bool = True    ,     lowerCamelCase    :						Optional[list] = [4, 8, 1_6, 3_2]    ,     lowerCamelCase    :						int = 7_7    ,     lowerCamelCase    :						int = 2_5_6    ,     lowerCamelCase    :						int = 7_7    ,     lowerCamelCase    :						int = 6    ,     lowerCamelCase    :						int = 4_9_4_0_8    ,     lowerCamelCase    :						int = 2    ,     lowerCamelCase    :						int = 1_6    ,     lowerCamelCase    :						int = 2_5_6    ,     lowerCamelCase    :						int = 2_5_6    ,     lowerCamelCase    :						int = 2_5_6    ,     lowerCamelCase    :						int = 1_0_2_4    ,     lowerCamelCase    :						str = "GN"    ,     lowerCamelCase    :						int = 6    ,     lowerCamelCase    :						int = 1_0    ,     lowerCamelCase    :						bool = True    ,     lowerCamelCase    :						int = 8    ,     lowerCamelCase    :						float = 0.1    ,     lowerCamelCase    :						int = 2_0_4_8    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						bool = False    ,     lowerCamelCase    :						int = 2    ,     lowerCamelCase    :						int = 4    ,     **lowerCamelCase    :						Dict    ,     )	->							Optional[Any]:
        if backbone_config is None:
               logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'					)
               _UpperCamelCase       :    List[str]					  =					CONFIG_MAPPING['swin'](
                   image_size=2_2_4    ,     in_channels=3    ,     patch_size=4    ,     embed_dim=9_6    ,     depths=[2, 2, 6, 2]    ,     num_heads=[3, 6, 1_2, 2_4]    ,     window_size=7    ,     drop_path_rate=0.3    ,     use_absolute_embeddings=lowerCamelCase    ,     out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,     )
        elif isinstance(lowerCamelCase    ,     lowerCamelCase					):
               _UpperCamelCase       :    List[str]					  =					backbone_config.get('model_type'					)
               _UpperCamelCase       :    Any					  =					CONFIG_MAPPING[backbone_model_type]
               _UpperCamelCase       :    Dict					  =					config_class.from_dict(lowerCamelCase					)

        _UpperCamelCase       :    Optional[Any]					  =					backbone_config

        _UpperCamelCase       :    Any					  =					ignore_value
        _UpperCamelCase       :    str					  =					num_queries
        _UpperCamelCase       :    Dict					  =					no_object_weight
        _UpperCamelCase       :    Dict					  =					class_weight
        _UpperCamelCase       :    Optional[Any]					  =					mask_weight
        _UpperCamelCase       :    Tuple					  =					dice_weight
        _UpperCamelCase       :    Tuple					  =					contrastive_weight
        _UpperCamelCase       :    Optional[int]					  =					contrastive_temperature
        _UpperCamelCase       :    Union[str, Any]					  =					train_num_points
        _UpperCamelCase       :    Optional[Any]					  =					oversample_ratio
        _UpperCamelCase       :    int					  =					importance_sample_ratio
        _UpperCamelCase       :    Union[str, Any]					  =					init_std
        _UpperCamelCase       :    str					  =					init_xavier_std
        _UpperCamelCase       :    Union[str, Any]					  =					layer_norm_eps
        _UpperCamelCase       :    Tuple					  =					is_training
        _UpperCamelCase       :    int					  =					use_auxiliary_loss
        _UpperCamelCase       :    Tuple					  =					output_auxiliary_logits
        _UpperCamelCase       :    str					  =					strides
        _UpperCamelCase       :    Optional[int]					  =					task_seq_len
        _UpperCamelCase       :    Optional[Any]					  =					text_encoder_width
        _UpperCamelCase       :    Any					  =					text_encoder_context_length
        _UpperCamelCase       :    Optional[Any]					  =					text_encoder_num_layers
        _UpperCamelCase       :    Optional[Any]					  =					text_encoder_vocab_size
        _UpperCamelCase       :    int					  =					text_encoder_proj_layers
        _UpperCamelCase       :    List[str]					  =					text_encoder_n_ctx
        _UpperCamelCase       :    Union[str, Any]					  =					conv_dim
        _UpperCamelCase       :    Tuple					  =					mask_dim
        _UpperCamelCase       :    Tuple					  =					hidden_dim
        _UpperCamelCase       :    str					  =					encoder_feedforward_dim
        _UpperCamelCase       :    Tuple					  =					norm
        _UpperCamelCase       :    Optional[int]					  =					encoder_layers
        _UpperCamelCase       :    List[str]					  =					decoder_layers
        _UpperCamelCase       :    List[Any]					  =					use_task_norm
        _UpperCamelCase       :    Tuple					  =					num_attention_heads
        _UpperCamelCase       :    Any					  =					dropout
        _UpperCamelCase       :    List[Any]					  =					dim_feedforward
        _UpperCamelCase       :    Union[str, Any]					  =					pre_norm
        _UpperCamelCase       :    List[Any]					  =					enforce_input_proj
        _UpperCamelCase       :    Dict					  =					query_dec_layers
        _UpperCamelCase       :    Union[str, Any]					  =					common_stride
        _UpperCamelCase       :    Dict					  =					decoder_layers

        super().__init__(**lowerCamelCase					)

 def    __snake_case   (     self    :						Tuple					)	->							Dict[str, any]:
        _UpperCamelCase       :    Union[str, Any]					  =					copy.deepcopy(self.__dict__					)
        _UpperCamelCase       :    List[Any]					  =					self.backbone_config.to_dict()
        _UpperCamelCase       :    Any					  =					self.__class__.model_type
        return output




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     						=			{
    '''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 SCREAMING_SNAKE_CASE			(       lowercase_:		Optional[int]			):
       _UpperCamelCase       :    int					  =					task_cls()
       _UpperCamelCase       :    List[Any]					  =					asdict(lowercase_			)
       _UpperCamelCase       :    Union[str, Any]					  =					task_template_from_dict(lowercase_			)
       assert task == reloaded




class 				__lowerCAmelCase					:



 '''simple docstring'''

 def    __snake_case   (     self    :						Dict					)	->							Any:
        _UpperCamelCase       :    List[Any]					  =					LanguageModeling(text_column='input_text'					)
        assert {"input_text": "text"} == task.column_mapping

 def    __snake_case   (     self    :						List[Any]					)	->							List[Any]:
        _UpperCamelCase       :    List[str]					  =					Features({'text': Value('string'					)}					)
        _UpperCamelCase       :    Optional[int]					  =					{'text_column': 'input_text'}
        _UpperCamelCase       :    Optional[Any]					  =					LanguageModeling.from_dict(lowerCamelCase					)
        assert "language-modeling" == task.task
        assert input_schema == task.input_schema







class 				__lowerCAmelCase					(    a_  ):



 '''simple docstring'''

 def    __snake_case   (     self    :						List[Any]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Union[str, Any]					  =					sorted(['pos', 'neg']					)

 def    __snake_case   (     self    :						List[str]					)	->							Dict:
        _UpperCamelCase       :    List[str]					  =					TextClassification(text_column='input_text'    ,     label_column='input_label'					)
        self.assertDictEqual({'input_text': 'text', 'input_label': 'labels'}    ,     task.column_mapping					)

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

 def    __snake_case   (     self    :						str					)	->							Optional[int]:
        _UpperCamelCase       :    Any					  =					TextClassification(text_column='input_text'    ,     label_column='input_label'					)
        self.assertEqual(task.label_schema['labels']    ,     lowerCamelCase					)
        _UpperCamelCase       :    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    __snake_case   (     self    :						Dict					)	->							str:
        _UpperCamelCase       :    Dict					  =					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    __snake_case   (     self    :						Union[str, Any]					)	->							Tuple:
        _UpperCamelCase       :    Optional[int]					  =					Features({'question': Value('string'					), 'context': Value('string'					)}					)
        _UpperCamelCase       :    Any					  =					Features(
            {
                'answers': Sequence(
                    {
                        'text': Value('string'					),
                        'answer_start': Value('int32'					),
                    }					)
            }					)
        _UpperCamelCase       :    str					  =					{
            'context_column': 'input_input_context',
            'question_column': 'input_question',
            'answers_column': 'input_answers',
        }
        _UpperCamelCase       :    Optional[Any]					  =					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    __snake_case   (     self    :						Optional[Any]					)	->							List[str]:
        _UpperCamelCase       :    List[str]					  =					Summarization(text_column='input_text'    ,     summary_column='input_summary'					)
        self.assertDictEqual({'input_text': 'text', 'input_summary': 'summary'}    ,     task.column_mapping					)

 def    __snake_case   (     self    :						int					)	->							Dict:
        _UpperCamelCase       :    Tuple					  =					Features({'text': Value('string'					)}					)
        _UpperCamelCase       :    List[Any]					  =					Features({'summary': Value('string'					)}					)
        _UpperCamelCase       :    Any					  =					{'text_column': 'input_text', 'summary_column': 'input_summary'}
        _UpperCamelCase       :    Any					  =					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    __snake_case   (     self    :						Any					)	->							Optional[int]:
        _UpperCamelCase       :    Dict					  =					AutomaticSpeechRecognition(audio_column='input_audio'    ,     transcription_column='input_transcription'					)
        self.assertDictEqual({'input_audio': 'audio', 'input_transcription': 'transcription'}    ,     task.column_mapping					)

 def    __snake_case   (     self    :						List[Any]					)	->							int:
        _UpperCamelCase       :    Union[str, Any]					  =					Features({'audio': Audio()}					)
        _UpperCamelCase       :    Any					  =					Features({'transcription': Value('string'					)}					)
        _UpperCamelCase       :    Optional[int]					  =					{
            'audio_column': 'input_audio',
            'transcription_column': 'input_transcription',
        }
        _UpperCamelCase       :    int					  =					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    __snake_case   (     self    :						Union[str, Any]					)	->							Dict:
        _UpperCamelCase       :    Dict					  =					sorted(['pos', 'neg']					)

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

 def    __snake_case   (     self    :						Tuple					)	->							Dict:
        _UpperCamelCase       :    List[str]					  =					Features({'audio': Audio()}					)
        _UpperCamelCase       :    Union[str, Any]					  =					Features({'labels': ClassLabel}					)
        _UpperCamelCase       :    Union[str, Any]					  =					{
            'audio_column': 'input_image',
            'label_column': 'input_label',
        }
        _UpperCamelCase       :    Tuple					  =					AudioClassification.from_dict(lowerCamelCase					)
        self.assertEqual('audio-classification'    ,     task.task					)
        self.assertEqual(lowerCamelCase    ,     task.input_schema					)
        self.assertEqual(lowerCamelCase    ,     task.label_schema					)

 def    __snake_case   (     self    :						Any					)	->							List[Any]:
        _UpperCamelCase       :    Any					  =					AudioClassification(audio_column='input_audio'    ,     label_column='input_label'					)
        self.assertEqual(task.label_schema['labels']    ,     lowerCamelCase					)
        _UpperCamelCase       :    Union[str, 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    __snake_case   (     self    :						int					)	->							str:
        _UpperCamelCase       :    Tuple					  =					sorted(['pos', 'neg']					)

 def    __snake_case   (     self    :						Optional[Any]					)	->							Any:
        _UpperCamelCase       :    str					  =					ImageClassification(image_column='input_image'    ,     label_column='input_label'					)
        self.assertDictEqual({'input_image': 'image', 'input_label': 'labels'}    ,     task.column_mapping					)

 def    __snake_case   (     self    :						int					)	->							Dict:
        _UpperCamelCase       :    Dict					  =					Features({'image': Image()}					)
        _UpperCamelCase       :    Tuple					  =					Features({'labels': ClassLabel}					)
        _UpperCamelCase       :    Optional[int]					  =					{
            'image_column': 'input_image',
            'label_column': 'input_label',
        }
        _UpperCamelCase       :    List[str]					  =					ImageClassification.from_dict(lowerCamelCase					)
        self.assertEqual('image-classification'    ,     task.task					)
        self.assertEqual(lowerCamelCase    ,     task.input_schema					)
        self.assertEqual(lowerCamelCase    ,     task.label_schema					)

 def    __snake_case   (     self    :						List[str]					)	->							Union[str, Any]:
        _UpperCamelCase       :    Any					  =					ImageClassification(image_column='input_image'    ,     label_column='input_label'					)
        self.assertEqual(task.label_schema['labels']    ,     lowerCamelCase					)
        _UpperCamelCase       :    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    __snake_case   (     self    :						List[str]					)	->							Dict:
        _UpperCamelCase       :    List[str]					  =					DatasetInfo(task_templates=QuestionAnsweringExtractive()					)
        _UpperCamelCase       :    Tuple					  =					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    :						Dict					):
               return x

        def dont_keep_task(lowerCamelCase    :						List[Any]					):
               _UpperCamelCase       :    Union[str, Any]					  =					deepcopy(lowerCamelCase					)
               _UpperCamelCase       :    List[str]					  =					0
               return out

        _UpperCamelCase       :    Any					  =					dataset.map(lowerCamelCase					)
        assert mapped_dataset.info.task_templates == dataset.info.task_templates
        # reload from cache
        _UpperCamelCase       :    Any					  =					dataset.map(lowerCamelCase					)
        assert mapped_dataset.info.task_templates == dataset.info.task_templates

        _UpperCamelCase       :    int					  =					dataset.map(lowerCamelCase					)
        assert mapped_dataset.info.task_templates == []
        # reload from cache
        _UpperCamelCase       :    int					  =					dataset.map(lowerCamelCase					)
        assert mapped_dataset.info.task_templates == []

 def    __snake_case   (     self    :						Union[str, Any]					)	->							Any:
        _UpperCamelCase       :    List[str]					  =					Features({'text': Value('string'					), 'label': ClassLabel(names=('pos', 'neg')					)}					)
        _UpperCamelCase       :    List[str]					  =					TextClassification(text_column='text'    ,     label_column='label'					)
        _UpperCamelCase       :    List[str]					  =					DatasetInfo(features=lowerCamelCase    ,     task_templates=lowerCamelCase					)
        _UpperCamelCase       :    Any					  =					Dataset.from_dict({'text': ['A sentence.'], 'label': ['pos']}    ,     info=lowerCamelCase					)

        def process(lowerCamelCase    :						Any					):
               return example

        _UpperCamelCase       :    Dict					  =					dataset.remove_columns('label'					)
        _UpperCamelCase       :    Union[str, Any]					  =					modified_dataset.map(lowerCamelCase					)
        assert mapped_dataset.info.task_templates == []



