

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42


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






							super().__init__()
							self.register_modules(unet=lowercase      ,      scheduler=lowercase       )


			@torch.no_grad()
			def __call__(    self      ,      lowercase = 1      ,      lowercase = 50      ,      lowercase = None      ,      lowercase = "pil"      ,      lowercase = True      ,      **lowercase      ,      )  ->						Union[Tuple, ImagePipelineOutput]:
							'''simple docstring'''






							A__        =			self.unet.config.sample_size
							A__        =			(batch_size, 3, img_size, img_size)

							A__        =			self.unet

							# sample x_0 ~ N(0, sigma_0^2 * I)
							A__        =			randn_tensor(lowercase      ,      generator=lowercase      ,      device=self.device       ) * self.scheduler.init_noise_sigma

							self.scheduler.set_timesteps(lowercase       )

							for t in self.progress_bar(self.scheduler.timesteps       ):
											# here sigma_t == t_i from the paper
											A__        =			self.scheduler.schedule[t]
											A__        =			self.scheduler.schedule[t - 1] if t > 0 else 0

											# 1. Select temporarily increased noise level sigma_hat
											# 2. Add new noise to move from sample_i to sample_hat
											A__    ,				A__        =			self.scheduler.add_noise_to_input(lowercase      ,      lowercase      ,      generator=lowercase       )

											# 3. Predict the noise residual given the noise magnitude `sigma_hat`
											# The model inputs and output are adjusted by following eq. (213) in [1].
											A__        =			(sigma_hat / 2) * model((sample_hat + 1) / 2      ,      sigma_hat / 2       ).sample

											# 4. Evaluate dx/dt at sigma_hat
											# 5. Take Euler step from sigma to sigma_prev
											A__        =			self.scheduler.step(lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )

											if sigma_prev != 0:
															# 6. Apply 2nd order correction
															# The model inputs and output are adjusted by following eq. (213) in [1].
															A__        =			(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2      ,      sigma_prev / 2       ).sample
															A__        =			self.scheduler.step_correct(
															    lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      step_output.prev_sample      ,      step_output["derivative"]      ,      )
											A__        =			step_output.prev_sample

							A__        =			(sample / 2 + 0.5).clamp(0      ,      1       )
							A__        =			sample.cpu().permute(0      ,      2      ,      3      ,      1       ).numpy()
							if output_type == "pil":
											A__        =			self.numpy_to_pil(lowercase       )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=lowercase       )




import datasets

from .evaluate import evaluate


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

lowerCAmelCase__	   = """
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
"""

lowerCAmelCase__	   = """
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the SQuAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
    >>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
    >>> squad_metric = datasets.load_metric(\"squad\")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,	_KWARGS_DESCRIPTION			)
class 					a__						(					datasets.Metric			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''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      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						Union[str, Any]:
							'''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

