



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 							a_  (		_snake_case				):
						UpperCamelCase__      :						Any				       ="oneformer"
						UpperCamelCase__      :						int				       ={"hidden_size": "hidden_dim"}

						def __init__(    self	:Dict   ,						_lowercase	:Optional[Dict] = None   ,						_lowercase	:int = 255   ,						_lowercase	:int = 150   ,						_lowercase	:int = 0.1   ,						_lowercase	:float = 2.0   ,						_lowercase	:float = 5.0   ,						_lowercase	:float = 5.0   ,						_lowercase	:float = 0.5   ,						_lowercase	:float = 0.07   ,						_lowercase	:int = 12544   ,						_lowercase	:float = 3.0   ,						_lowercase	:float = 0.75   ,						_lowercase	:float = 0.02   ,						_lowercase	:float = 1.0   ,						_lowercase	:float = 1E-0_5   ,						_lowercase	:bool = False   ,						_lowercase	:bool = True   ,						_lowercase	:bool = True   ,						_lowercase	:Optional[list] = [4, 8, 16, 32]   ,						_lowercase	:int = 77   ,						_lowercase	:int = 256   ,						_lowercase	:int = 77   ,						_lowercase	:int = 6   ,						_lowercase	:int = 49408   ,						_lowercase	:int = 2   ,						_lowercase	:int = 16   ,						_lowercase	:int = 256   ,						_lowercase	:int = 256   ,						_lowercase	:int = 256   ,						_lowercase	:int = 1024   ,						_lowercase	:str = "GN"   ,						_lowercase	:int = 6   ,						_lowercase	:int = 10   ,						_lowercase	:bool = True   ,						_lowercase	:int = 8   ,						_lowercase	:float = 0.1   ,						_lowercase	:int = 2048   ,						_lowercase	:bool = False   ,						_lowercase	:bool = False   ,						_lowercase	:int = 2   ,						_lowercase	:int = 4   ,						**_lowercase	:Optional[int]   ,						)		->					str:
												if backbone_config is None:
																		logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.''')
																		UpperCAmelCase_     							=     CONFIG_MAPPING['''swin'''](
																		    image_size=224   ,						in_channels=3   ,						patch_size=4   ,						embed_dim=96   ,						depths=[2, 2, 6, 2]   ,						num_heads=[3, 6, 12, 24]   ,						window_size=7   ,						drop_path_rate=0.3   ,						use_absolute_embeddings=_lowercase   ,						out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']   ,						)
												elif isinstance(_lowercase   ,						_lowercase):
																		UpperCAmelCase_     							=     backbone_config.get('''model_type''')
																		UpperCAmelCase_     							=     CONFIG_MAPPING[backbone_model_type]
																		UpperCAmelCase_     							=     config_class.from_dict(_lowercase)

												UpperCAmelCase_     							=     backbone_config

												UpperCAmelCase_     							=     ignore_value
												UpperCAmelCase_     							=     num_queries
												UpperCAmelCase_     							=     no_object_weight
												UpperCAmelCase_     							=     class_weight
												UpperCAmelCase_     							=     mask_weight
												UpperCAmelCase_     							=     dice_weight
												UpperCAmelCase_     							=     contrastive_weight
												UpperCAmelCase_     							=     contrastive_temperature
												UpperCAmelCase_     							=     train_num_points
												UpperCAmelCase_     							=     oversample_ratio
												UpperCAmelCase_     							=     importance_sample_ratio
												UpperCAmelCase_     							=     init_std
												UpperCAmelCase_     							=     init_xavier_std
												UpperCAmelCase_     							=     layer_norm_eps
												UpperCAmelCase_     							=     is_training
												UpperCAmelCase_     							=     use_auxiliary_loss
												UpperCAmelCase_     							=     output_auxiliary_logits
												UpperCAmelCase_     							=     strides
												UpperCAmelCase_     							=     task_seq_len
												UpperCAmelCase_     							=     text_encoder_width
												UpperCAmelCase_     							=     text_encoder_context_length
												UpperCAmelCase_     							=     text_encoder_num_layers
												UpperCAmelCase_     							=     text_encoder_vocab_size
												UpperCAmelCase_     							=     text_encoder_proj_layers
												UpperCAmelCase_     							=     text_encoder_n_ctx
												UpperCAmelCase_     							=     conv_dim
												UpperCAmelCase_     							=     mask_dim
												UpperCAmelCase_     							=     hidden_dim
												UpperCAmelCase_     							=     encoder_feedforward_dim
												UpperCAmelCase_     							=     norm
												UpperCAmelCase_     							=     encoder_layers
												UpperCAmelCase_     							=     decoder_layers
												UpperCAmelCase_     							=     use_task_norm
												UpperCAmelCase_     							=     num_attention_heads
												UpperCAmelCase_     							=     dropout
												UpperCAmelCase_     							=     dim_feedforward
												UpperCAmelCase_     							=     pre_norm
												UpperCAmelCase_     							=     enforce_input_proj
												UpperCAmelCase_     							=     query_dec_layers
												UpperCAmelCase_     							=     common_stride
												UpperCAmelCase_     							=     decoder_layers

												super().__init__(**_lowercase)




						def 	__a       (    self	:str)		->					Dict[str, any]:
												UpperCAmelCase_     							=     copy.deepcopy(self.__dict__)
												UpperCAmelCase_     							=     self.backbone_config.to_dict()
												UpperCAmelCase_     							=     self.__class__.model_type
												return output










import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCamelCase_	       =   "\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n"

UpperCamelCase_	       =   "\nThis metric wrap the official scoring script for version 2 of the Stanford Question\nAnswering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n\nSQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions\nwritten adversarially by crowdworkers to look similar to answerable ones.\nTo do well on SQuAD2.0, systems must not only answer questions when possible, but also\ndetermine when no answer is supported by the paragraph and abstain from answering.\n"

UpperCamelCase_	       =   "\nComputes SQuAD v2 scores (F1 and EM).\nArgs:\n    predictions: List of triple for question-answers to score with the following elements:\n        - the question-answer 'id' field as given in the references (see below)\n        - the text of the answer\n        - the probability that the question has no answer\n    references: List of question-answers dictionaries with the following key-values:\n            - 'id': id of the question-answer pair (see above),\n            - 'answers': a list of Dict {'text': text of the answer as a string}\n    no_answer_threshold: float\n        Probability threshold to decide that a question has no answer.\nReturns:\n    'exact': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\n    'total': Number of score considered\n    'HasAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'HasAns_f1': The F-score of predicted tokens versus the gold answer\n    'HasAns_total': Number of score considered\n    'NoAns_exact': Exact match (the normalized answer exactly match the gold answer)\n    'NoAns_f1': The F-score of predicted tokens versus the gold answer\n    'NoAns_total': Number of score considered\n    'best_exact': Best exact match (with varying threshold)\n    'best_exact_thresh': No-answer probability threshold associated to the best exact match\n    'best_f1': Best F1 (with varying threshold)\n    'best_f1_thresh': No-answer probability threshold associated to the best F1\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22', 'no_answer_probability': 0.}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_v2_metric = datasets.load_metric(\"squad_v2\")\n    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact': 100.0, 'f1': 100.0, 'total': 1, 'HasAns_exact': 100.0, 'HasAns_f1': 100.0, 'HasAns_total': 1, 'best_exact': 100.0, 'best_exact_thresh': 0.0, 'best_f1': 100.0, 'best_f1_thresh': 0.0}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					, _KWARGS_DESCRIPTION				)
class 							a_  (		datasets.Metric				):

						def 	__a       (    self	:List[Any])		->					List[str]:
												return datasets.MetricInfo(
												    description=_DESCRIPTION   ,						citation=_CITATION   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
												        {
												            '''predictions''': {
												                '''id''': datasets.Value('''string'''),
												                '''prediction_text''': datasets.Value('''string'''),
												                '''no_answer_probability''': datasets.Value('''float32'''),
												            },
												            '''references''': {
												                '''id''': datasets.Value('''string'''),
												                '''answers''': datasets.features.Sequence(
												                    {'''text''': datasets.Value('''string'''), '''answer_start''': datasets.Value('''int32''')}),
												            },
												        })   ,						codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']   ,						reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']   ,						)




						def 	__a       (    self	:int   ,						_lowercase	:Tuple   ,						_lowercase	:List[str]   ,						_lowercase	:Optional[Any]=1.0)		->					str:
												UpperCAmelCase_     							=     {p['''id''']: p['''no_answer_probability'''] for p in predictions}
												UpperCAmelCase_     							=     [{'''paragraphs''': [{'''qas''': references}]}]
												UpperCAmelCase_     							=     {p['''id''']: p['''prediction_text'''] for p in predictions}

												UpperCAmelCase_     							=     make_qid_to_has_ans(_lowercase)  # maps qid to True/False
												UpperCAmelCase_     							=     [k for k, v in qid_to_has_ans.items() if v]
												UpperCAmelCase_     							=     [k for k, v in qid_to_has_ans.items() if not v]

												UpperCAmelCase_							,      UpperCAmelCase_     							=     get_raw_scores(_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     apply_no_ans_threshold(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase)

												if has_ans_qids:
																		UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase   ,						qid_list=_lowercase)
																		merge_eval(_lowercase   ,						_lowercase   ,						'''HasAns''')
												if no_ans_qids:
																		UpperCAmelCase_     							=     make_eval_dict(_lowercase   ,						_lowercase   ,						qid_list=_lowercase)
																		merge_eval(_lowercase   ,						_lowercase   ,						'''NoAns''')
												find_all_best_thresh(_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase   ,						_lowercase)
												return dict(_lowercase)





