

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any,lowercase_			:	Any=2,lowercase_			:	int=3,lowercase_			:	Optional[Any]=6_4,lowercase_			:	Any=None		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       np.random.default_rng(lowercase_		)
        A__   				=       length
        A__   				=       rng.normal(size=(length,)		).astype(np.floataa		)
        A__   				=       a * self.x + b + rng.normal(scale=0.1,size=(length,)		).astype(np.floataa		)


    def __len__(				self			:	Dict		)->   Union[str, Any]:





        '''simple docstring'''







        return self.length


    def __getitem__(				self			:	Any,lowercase_			:	Union[str, Any]		)->   Union[str, Any]:





        '''simple docstring'''







        return {"x": self.x[i], "y": self.y[i]}




class 			A						(							torch.nn.Module			):







    """simple docstring"""





    def __init__(				self			:	List[Any],lowercase_			:	Dict=0,lowercase_			:	Any=0,lowercase_			:	List[str]=False		)->   List[Any]:





        '''simple docstring'''







        super().__init__()
        A__   				=       torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
        A__   				=       torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
        A__   				=       True


    def 						snake_case__					(				self			:	Optional[Any],lowercase_			:	Optional[int]=None		)->   Optional[int]:





        '''simple docstring'''







        if self.first_batch:
            print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'		)
            A__   				=       False
        return x * self.a[0] + self.b[0]




class 			A						(							torch.nn.Module			):







    """simple docstring"""





    def __init__(				self			:	Union[str, Any],lowercase_			:	Dict=0,lowercase_			:	Dict=0,lowercase_			:	List[Any]=False		)->   List[Any]:





        '''simple docstring'''







        super().__init__()
        A__   				=       torch.nn.Parameter(torch.tensor(lowercase_		).float()		)
        A__   				=       torch.nn.Parameter(torch.tensor(lowercase_		).float()		)
        A__   				=       True


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	int=None		)->   int:





        '''simple docstring'''







        if self.first_batch:
            print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'		)
            A__   				=       False
        return x * self.a + self.b




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     int = 16				)							->				Optional[Any]:





    '''simple docstring'''






    from datasets import load_dataset
    from transformers import AutoTokenizer

    A__   				=       AutoTokenizer.from_pretrained('bert-base-cased'				)
    A__   				=       {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
    A__   				=       load_dataset('csv'   ,      data_files=SCREAMING_SNAKE_CASE__				)
    A__   				=       datasets['train'].unique('label'				)

    A__   				=       {v: i for i, v in enumerate(SCREAMING_SNAKE_CASE__				)}

    def tokenize_function(SCREAMING_SNAKE_CASE__      :     Tuple				):
        # max_length=None => use the model max length (it's actually the default)
        A__   				=       tokenizer(
            examples['sentence1']   ,      examples['sentence2']   ,      truncation=SCREAMING_SNAKE_CASE__   ,      max_length=SCREAMING_SNAKE_CASE__   ,      padding='max_length'				)
        if "label" in examples:
            A__   				=       [label_to_id[l] for l in examples['label']]
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    A__   				=       datasets.map(
        SCREAMING_SNAKE_CASE__   ,      batched=SCREAMING_SNAKE_CASE__   ,      remove_columns=['sentence1', 'sentence2', 'label']   ,      )

    def collate_fn(SCREAMING_SNAKE_CASE__      :     str				):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(SCREAMING_SNAKE_CASE__   ,      padding='max_length'   ,      max_length=128   ,      return_tensors='pt'				)
        return tokenizer.pad(SCREAMING_SNAKE_CASE__   ,      padding='longest'   ,      return_tensors='pt'				)

    # Instantiate dataloaders.
    A__   				=       DataLoader(tokenized_datasets['train']   ,      shuffle=SCREAMING_SNAKE_CASE__   ,      collate_fn=SCREAMING_SNAKE_CASE__   ,      batch_size=2				)
    A__   				=       DataLoader(tokenized_datasets['validation']   ,      shuffle=SCREAMING_SNAKE_CASE__   ,      collate_fn=SCREAMING_SNAKE_CASE__   ,      batch_size=1				)

    return train_dataloader, eval_dataloader




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

