

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin

@flax.struct.dataclass
class 					a__						:
			"""simple docstring"""







			__lowerCamelCase        =       None
			__lowerCamelCase        =       None
			__lowerCamelCase        =       None  # sigma(t_i)


			@classmethod
			def      UpperCamelCase		(    cls       )  ->						Optional[Any]:
							'''simple docstring'''






							return cls()

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







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42
			__lowerCamelCase        =       42

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







			@property
			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							return True


			@register_to_config
			def __init__(    self      ,      lowercase = 0.02      ,      lowercase = 100      ,      lowercase = 1.007      ,      lowercase = 80      ,      lowercase = 0.05      ,      lowercase = 50      ,      )  ->						Any:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							return KarrasVeSchedulerState.create()


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase = ()       )  ->						KarrasVeSchedulerState:
							'''simple docstring'''






							A__        =			jnp.arange(0      ,      lowercase       )[::-1].copy()
							A__        =			[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in timesteps
							]

							return state.replace(
							    num_inference_steps=lowercase      ,      schedule=jnp.array(lowercase      ,      dtype=jnp.floataa       )      ,      timesteps=lowercase      ,      )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      )  ->						Tuple[jnp.ndarray, float]:
							'''simple docstring'''






							if self.config.s_min <= sigma <= self.config.s_max:
											A__        =			min(self.config.s_churn / state.num_inference_steps      ,      2**0.5 - 1       )
							else:
											A__        =			0

							# sample eps ~ N(0, S_noise^2 * I)
							A__        =			random.split(lowercase      ,      num=1       )
							A__        =			self.config.s_noise * random.normal(key=lowercase      ,      shape=sample.shape       )
							A__        =			sigma + gamma * sigma
							A__        =			sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = True      ,      )  ->						Union[FlaxKarrasVeOutput, Tuple]:
							'''simple docstring'''






							A__        =			sample_hat + sigma_hat * model_output
							A__        =			(sample_hat - pred_original_sample) / sigma_hat
							A__        =			sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
											return (sample_prev, derivative, state)

							return FlaxKarrasVeOutput(prev_sample=lowercase      ,      derivative=lowercase      ,      state=lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase = True      ,      )  ->						Union[FlaxKarrasVeOutput, Tuple]:
							'''simple docstring'''






							A__        =			sample_prev + sigma_prev * model_output
							A__        =			(sample_prev - pred_original_sample) / sigma_prev
							A__        =			sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
											return (sample_prev, derivative, state)

							return FlaxKarrasVeOutput(prev_sample=lowercase      ,      derivative=lowercase      ,      state=lowercase       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Optional[Any]:
							'''simple docstring'''






							raise NotImplementedError()




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

