

import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


lowercase_			=  logging.get_logger(__name__)


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def __init__(				self			:	List[str],lowercase_			:	Union[List[ControlNetModel], Tuple[ControlNetModel]]		)->   Any:





        '''simple docstring'''







        super().__init__()
        A__   				=       nn.ModuleList(lowercase_		)


    def 						snake_case__					(				self			:	List[str],lowercase_			:	torch.FloatTensor,lowercase_			:	Union[torch.Tensor, float, int],lowercase_			:	torch.Tensor,lowercase_			:	List[torch.tensor],lowercase_			:	List[float],lowercase_			:	Optional[torch.Tensor] = None,lowercase_			:	Optional[torch.Tensor] = None,lowercase_			:	Optional[torch.Tensor] = None,lowercase_			:	Optional[Dict[str, Any]] = None,lowercase_			:	bool = False,lowercase_			:	bool = True,)->   Union[ControlNetOutput, Tuple]:





        '''simple docstring'''







        for i, (image, scale, controlnet) in enumerate(zip(lowercase_,lowercase_,self.nets		)		):
            A__				,						A__   				=       controlnet(
                lowercase_,lowercase_,lowercase_,lowercase_,lowercase_,lowercase_,lowercase_,lowercase_,lowercase_,lowercase_,lowercase_,)

            # merge samples
            if i == 0:
                A__				,						A__   				=       down_samples, mid_sample
            else:
                A__   				=       [
                    samples_prev + samples_curr
                    for samples_prev, samples_curr in zip(lowercase_,lowercase_		)
                ]
                mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Union[str, os.PathLike],lowercase_			:	bool = True,lowercase_			:	Callable = None,lowercase_			:	bool = False,lowercase_			:	Optional[str] = None,)->   List[Any]:





        '''simple docstring'''







        A__   				=       0
        A__   				=       save_directory
        for controlnet in self.nets:
            controlnet.save_pretrained(
                lowercase_,is_main_process=lowercase_,save_function=lowercase_,safe_serialization=lowercase_,variant=lowercase_,)

            idx += 1
            A__   				=       model_path_to_save + F'_{idx}'


    @classmethod
    def 						snake_case__					(				cls			:	List[str],lowercase_			:	Optional[Union[str, os.PathLike]],**lowercase_			:	int		)->   Dict:





        '''simple docstring'''







        A__   				=       0
        A__   				=       []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        A__   				=       pretrained_model_path
        while os.path.isdir(lowercase_		):
            A__   				=       ControlNetModel.from_pretrained(lowercase_,**lowercase_		)
            controlnets.append(lowercase_		)

            idx += 1
            A__   				=       pretrained_model_path + F'_{idx}'

        logger.info(F'{len(lowercase_		)} controlnets loaded from {pretrained_model_path}.'		)

        if len(lowercase_		) == 0:
            raise ValueError(
                F'No ControlNets found under {os.path.dirname(lowercase_		)}. Expected at least {pretrained_model_path + "_0"}.'		)

        return cls(lowercase_		)




import datasets

from .evaluate import evaluate


lowercase_			=  "\\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"

lowercase_			=  "\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering 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"

lowercase_			=  "\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - 'id': id of the question-answer pair as given in the references (see below)\n        - 'prediction_text': the text of the 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 Dict in the SQuAD dataset format\n            {\n                'text': list of possible texts for the answer, as a list of strings\n                'answer_start': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    'exact_match': Exact match (the normalized answer exactly match the gold answer)\n    'f1': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]\n    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]\n    >>> squad_metric = datasets.load_metric(\"squad\")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'exact_match': 100.0, 'f1': 100.0}\n"


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







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any]		)->   Tuple:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'predictions': {'id': datasets.Value('string'		), 'prediction_text': datasets.Value('string'		)},
                    '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 						snake_case__					(				self			:	str,lowercase_			:	List[Any],lowercase_			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       {prediction['id']: prediction['prediction_text'] for prediction in predictions}
        A__   				=       [
            {
                'paragraphs': [
                    {
                        'qas': [
                            {
                                'answers': [{'text': answer_text} for answer_text in ref['answers']['text']],
                                'id': ref['id'],
                            }
                            for ref in references
                        ]
                    }
                ]
            }
        ]
        A__   				=       evaluate(dataset=lowercase_,predictions=lowercase_		)
        return score

