

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			):


			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




import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       DanceDiffusionPipeline
			__lowerCamelCase        =       UNCONDITIONAL_AUDIO_GENERATION_PARAMS
			__lowerCamelCase        =       PipelineTesterMixin.required_optional_params - {
			    'callback',
			    'latents',
			    'callback_steps',
			    'output_type',
			    'num_images_per_prompt',
			}
			__lowerCamelCase        =       UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							torch.manual_seed(0       )
							A__        =			UNetaDModel(
							    block_out_channels=(32, 32, 64)      ,      extra_in_channels=16      ,      sample_size=512      ,      sample_rate=16000      ,      in_channels=2      ,      out_channels=2      ,      flip_sin_to_cos=lowercase      ,      use_timestep_embedding=lowercase      ,      time_embedding_type="fourier"      ,      mid_block_type="UNetMidBlock1D"      ,      down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,      up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,      )
							A__        =			IPNDMScheduler()

							A__        =			{
							    "unet": unet,
							    "scheduler": scheduler,
							}
							return components


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=0       )  ->						Union[str, Any]:
							'''simple docstring'''






							if str(lowercase       ).startswith("mps"       ):
											A__        =			torch.manual_seed(lowercase       )
							else:
											A__        =			torch.Generator(device=lowercase       ).manual_seed(lowercase       )
							A__        =			{
							    "batch_size": 1,
							    "generator": generator,
							    "num_inference_steps": 4,
							}
							return inputs


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






							A__        =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							A__        =			self.get_dummy_components()
							A__        =			DanceDiffusionPipeline(**lowercase       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			self.get_dummy_inputs(lowercase       )
							A__        =			pipe(**lowercase       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, components["unet"].sample_size)
							A__        =			np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]       )
							assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							return super().test_save_load_local()


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							return super().test_save_load_optional_components()


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return super().test_attention_slicing_forward_pass()


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






							super().test_inference_batch_single_identical(expected_max_diff=3e-3       )

@slow
@require_torch_gpu
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







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






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()


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






							A__        =			torch_device

							A__        =			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			torch.manual_seed(0       )
							A__        =			pipe(generator=lowercase      ,      num_inference_steps=100      ,      audio_length_in_s=4.096       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							A__        =			np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]       )

							assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			torch_device

							A__        =			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"      ,      torch_dtype=torch.floataa       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			torch.manual_seed(0       )
							A__        =			pipe(generator=lowercase      ,      num_inference_steps=100      ,      audio_length_in_s=4.096       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							A__        =			np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]       )

							assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2

