

'''simple docstring'''

import gc
import unittest

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    ClapTextConfig,
    ClapTextModelWithProjection,
    RobertaTokenizer,
    SpeechTaHifiGan,
    SpeechTaHifiGanConfig,
)

from diffusers import (
    AudioLDMPipeline,
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 							_snake_case (       __UpperCAmelCase	,  unittest.TestCase						):
   _A       :     Any       =					AudioLDMPipeline
   _A       :     Tuple       =					TEXT_TO_AUDIO_PARAMS
   _A       :     str       =					TEXT_TO_AUDIO_BATCH_PARAMS
   _A       :     Any       =					frozenset(
       [
           '''num_inference_steps''',
           '''num_waveforms_per_prompt''',
           '''generator''',
           '''latents''',
           '''output_type''',
           '''return_dict''',
           '''callback''',
           '''callback_steps''',
       ]						)
   def     __UpperCamelCase						( self			:      Any							):
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Tuple						      =					UNetaDConditionModel(
         block_out_channels=(32, 64)     ,layers_per_block=2     ,sample_size=32     ,in_channels=4     ,out_channels=4     ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")     ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")     ,cross_attention_dim=(32, 64)     ,class_embed_type="simple_projection"     ,projection_class_embeddings_input_dim=32     ,class_embeddings_concat=__SCREAMING_SNAKE_CASE     ,)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					DDIMScheduler(
         beta_start=0.00_085     ,beta_end=0.012     ,beta_schedule="scaled_linear"     ,clip_sample=__SCREAMING_SNAKE_CASE     ,set_alpha_to_one=__SCREAMING_SNAKE_CASE     ,)
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					AutoencoderKL(
         block_out_channels=[32, 64]     ,in_channels=1     ,out_channels=1     ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]     ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]     ,latent_channels=4     ,)
     torch.manual_seed(0							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					ClapTextConfig(
         bos_token_id=0     ,eos_token_id=2     ,hidden_size=32     ,intermediate_size=37     ,layer_norm_eps=1e-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1_000     ,projection_dim=32     ,)
     SCREAMING_SNAKE_CASE:List[Any]						      =					ClapTextModelWithProjection(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					RobertaTokenizer.from_pretrained("hf-internal-testing/tiny-random-roberta"     ,model_max_length=77							)

     SCREAMING_SNAKE_CASE:int						      =					SpeechTaHifiGanConfig(
         model_in_dim=8     ,sampling_rate=16_000     ,upsample_initial_channel=16     ,upsample_rates=[2, 2]     ,upsample_kernel_sizes=[4, 4]     ,resblock_kernel_sizes=[3, 7]     ,resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]]     ,normalize_before=__SCREAMING_SNAKE_CASE     ,)

     SCREAMING_SNAKE_CASE:Dict						      =					SpeechTaHifiGan(__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:Tuple						      =					{
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "vocoder": vocoder,
     }
     return components
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]     ,SCREAMING_SNAKE_CASE__			:      str=0							):

     if str(__SCREAMING_SNAKE_CASE							).startswith("mps"							):
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.manual_seed(__SCREAMING_SNAKE_CASE							)
     else:
       SCREAMING_SNAKE_CASE:Dict						      =					torch.Generator(device=__SCREAMING_SNAKE_CASE							).manual_seed(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:List[str]						      =					{
         "prompt": "A hammer hitting a wooden surface",
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 6.0,
     }
     return inputs
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					"cpu"  # ensure determinism for the device-dependent torch.Generator

     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.get_dummy_components()
     SCREAMING_SNAKE_CASE:Tuple						      =					AudioLDMPipeline(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:int						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					audioldm_pipe(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Any						      =					output.audios[0]

     assert audio.ndim == 1
     assert len(__SCREAMING_SNAKE_CASE							) == 256

     SCREAMING_SNAKE_CASE:Dict						      =					audio[:10]
     SCREAMING_SNAKE_CASE:Tuple						      =					np.array(
         [-0.0_050, 0.0_050, -0.0_060, 0.0_033, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_033]							)

     assert np.abs(audio_slice - expected_slice							).max() < 1e-2
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Dict						      =					self.get_dummy_components()
     SCREAMING_SNAKE_CASE:List[Any]						      =					AudioLDMPipeline(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:List[str]						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					3 * [inputs["prompt"]]

     # forward
     SCREAMING_SNAKE_CASE:str						      =					audioldm_pipe(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:str						      =					output.audios[0]

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					3 * [inputs.pop("prompt"							)]

     SCREAMING_SNAKE_CASE:Any						      =					audioldm_pipe.tokenizer(
         __SCREAMING_SNAKE_CASE     ,padding="max_length"     ,max_length=audioldm_pipe.tokenizer.model_max_length     ,truncation=__SCREAMING_SNAKE_CASE     ,return_tensors="pt"     ,)
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					text_inputs["input_ids"].to(__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:List[str]						      =					audioldm_pipe.text_encoder(
         __SCREAMING_SNAKE_CASE     ,)
     SCREAMING_SNAKE_CASE:int						      =					prompt_embeds.text_embeds
     # additional L_2 normalization over each hidden-state
     SCREAMING_SNAKE_CASE:int						      =					F.normalize(__SCREAMING_SNAKE_CASE     ,dim=-1							)

     SCREAMING_SNAKE_CASE:List[str]						      =					prompt_embeds

     # forward
     SCREAMING_SNAKE_CASE:Any						      =					audioldm_pipe(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:int						      =					output.audios[0]

     assert np.abs(audio_a - audio_a							).max() < 1e-2
   def     __UpperCamelCase						( self			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Any						      =					self.get_dummy_components()
     SCREAMING_SNAKE_CASE:Dict						      =					AudioLDMPipeline(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:List[str]						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:Any						      =					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Optional[int]						      =					3 * ["this is a negative prompt"]
     SCREAMING_SNAKE_CASE:List[Any]						      =					negative_prompt
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					3 * [inputs["prompt"]]

     # forward
     SCREAMING_SNAKE_CASE:str						      =					audioldm_pipe(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:str						      =					output.audios[0]

     SCREAMING_SNAKE_CASE:int						      =					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					3 * [inputs.pop("prompt"							)]

     SCREAMING_SNAKE_CASE:Dict						      =					[]
     for p in [prompt, negative_prompt]:
       SCREAMING_SNAKE_CASE:List[Any]						      =					audioldm_pipe.tokenizer(
           __SCREAMING_SNAKE_CASE     ,padding="max_length"     ,max_length=audioldm_pipe.tokenizer.model_max_length     ,truncation=__SCREAMING_SNAKE_CASE     ,return_tensors="pt"     ,)
       SCREAMING_SNAKE_CASE:str						      =					text_inputs["input_ids"].to(__SCREAMING_SNAKE_CASE							)

       SCREAMING_SNAKE_CASE:Any						      =					audioldm_pipe.text_encoder(
           __SCREAMING_SNAKE_CASE     ,)
       SCREAMING_SNAKE_CASE:Optional[int]						      =					text_embeds.text_embeds
       # additional L_2 normalization over each hidden-state
       SCREAMING_SNAKE_CASE:Dict						      =					F.normalize(__SCREAMING_SNAKE_CASE     ,dim=-1							)

       embeds.append(__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					embeds

     # forward
     SCREAMING_SNAKE_CASE:List[str]						      =					audioldm_pipe(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:str						      =					output.audios[0]

     assert np.abs(audio_a - audio_a							).max() < 1e-2
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					"cpu"  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE:str						      =					self.get_dummy_components()
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:List[str]						      =					AudioLDMPipeline(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Any						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:int						      =					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Tuple						      =					"egg cracking"
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					audioldm_pipe(**__SCREAMING_SNAKE_CASE     ,negative_prompt=__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					output.audios[0]

     assert audio.ndim == 1
     assert len(__SCREAMING_SNAKE_CASE							) == 256

     SCREAMING_SNAKE_CASE:List[str]						      =					audio[:10]
     SCREAMING_SNAKE_CASE:Tuple						      =					np.array(
         [-0.0_051, 0.0_050, -0.0_060, 0.0_034, -0.0_026, 0.0_033, -0.0_027, 0.0_033, -0.0_028, 0.0_032]							)

     assert np.abs(audio_slice - expected_slice							).max() < 1e-2
   def     __UpperCamelCase						( self			:      List[str]							):
     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					"cpu"  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE:Optional[int]						      =					self.get_dummy_components()
     SCREAMING_SNAKE_CASE:Optional[int]						      =					PNDMScheduler(skip_prk_steps=__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Dict						      =					AudioLDMPipeline(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:Any						      =					"A hammer hitting a wooden surface"

     # test num_waveforms_per_prompt=1 (default)
     SCREAMING_SNAKE_CASE:List[Any]						      =					audioldm_pipe(__SCREAMING_SNAKE_CASE     ,num_inference_steps=2							).audios

     assert audios.shape == (1, 256)

     # test num_waveforms_per_prompt=1 (default) for batch of prompts
     SCREAMING_SNAKE_CASE:List[Any]						      =					2
     SCREAMING_SNAKE_CASE:List[str]						      =					audioldm_pipe([prompt] * batch_size     ,num_inference_steps=2							).audios

     assert audios.shape == (batch_size, 256)

     # test num_waveforms_per_prompt for single prompt
     SCREAMING_SNAKE_CASE:str						      =					2
     SCREAMING_SNAKE_CASE:List[str]						      =					audioldm_pipe(__SCREAMING_SNAKE_CASE     ,num_inference_steps=2     ,num_waveforms_per_prompt=__SCREAMING_SNAKE_CASE							).audios

     assert audios.shape == (num_waveforms_per_prompt, 256)

     # test num_waveforms_per_prompt for batch of prompts
     SCREAMING_SNAKE_CASE:Optional[int]						      =					2
     SCREAMING_SNAKE_CASE:List[str]						      =					audioldm_pipe(
         [prompt] * batch_size     ,num_inference_steps=2     ,num_waveforms_per_prompt=__SCREAMING_SNAKE_CASE							).audios

     assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
   def     __UpperCamelCase						( self			:      Tuple							):
     SCREAMING_SNAKE_CASE:int						      =					"cpu"  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE:List[str]						      =					self.get_dummy_components()
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					AudioLDMPipeline(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Any						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Dict						      =					audioldm_pipe.vocoder.config.sampling_rate

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.get_dummy_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					audioldm_pipe(audio_length_in_s=0.016     ,**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Tuple						      =					output.audios[0]

     assert audio.ndim == 1
     assert len(__SCREAMING_SNAKE_CASE							) / vocoder_sampling_rate == 0.016

     SCREAMING_SNAKE_CASE:Optional[int]						      =					audioldm_pipe(audio_length_in_s=0.032     ,**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Tuple						      =					output.audios[0]

     assert audio.ndim == 1
     assert len(__SCREAMING_SNAKE_CASE							) / vocoder_sampling_rate == 0.032
   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:List[str]						      =					self.get_dummy_components()
     SCREAMING_SNAKE_CASE:int						      =					AudioLDMPipeline(**__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:int						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					["hey"]

     SCREAMING_SNAKE_CASE:Optional[Any]						      =					audioldm_pipe(__SCREAMING_SNAKE_CASE     ,num_inference_steps=1							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					output.audios.shape
     assert audio_shape == (1, 256)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					audioldm_pipe.vocoder.config
     config.model_in_dim *= 2
     SCREAMING_SNAKE_CASE:int						      =					SpeechTaHifiGan(__SCREAMING_SNAKE_CASE							).to(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Dict						      =					audioldm_pipe(__SCREAMING_SNAKE_CASE     ,num_inference_steps=1							)
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					output.audios.shape
     # waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
     assert audio_shape == (1, 256)
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     self._test_attention_slicing_forward_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE							)
   def     __UpperCamelCase						( self			:      str							):
     self._test_inference_batch_single_identical(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE							)







   @unittest.skipIf(
       torch_device != "cuda" or not is_xformers_available()     ,reason="XFormers attention is only available with CUDA and `xformers` installed"     ,)
   def     __UpperCamelCase						( self			:      Any							):
     self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=__SCREAMING_SNAKE_CASE							)





@slow
class 							_snake_case (       unittest.TestCase						):
   def     __UpperCamelCase						( self			:      List[str]							):
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]="cpu"     ,SCREAMING_SNAKE_CASE__			:      List[Any]=torch.floataa     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=0							):
     SCREAMING_SNAKE_CASE:Dict						      =					torch.Generator(device=__SCREAMING_SNAKE_CASE							).manual_seed(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Any						      =					np.random.RandomState(__SCREAMING_SNAKE_CASE							).standard_normal((1, 8, 128, 16)							)
     SCREAMING_SNAKE_CASE:Tuple						      =					torch.from_numpy(__SCREAMING_SNAKE_CASE							).to(device=__SCREAMING_SNAKE_CASE     ,dtype=__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Dict						      =					{
         "prompt": "A hammer hitting a wooden surface",
         "latents": latents,
         "generator": generator,
         "num_inference_steps": 3,
         "guidance_scale": 2.5,
     }
     return inputs
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					AudioLDMPipeline.from_pretrained("cvssp/audioldm"							)
     SCREAMING_SNAKE_CASE:str						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:List[str]						      =					self.get_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Tuple						      =					25
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					audioldm_pipe(**__SCREAMING_SNAKE_CASE							).audios[0]

     assert audio.ndim == 1
     assert len(__SCREAMING_SNAKE_CASE							) == 81_920

     SCREAMING_SNAKE_CASE:str						      =					audio[77_230:77_240]
     SCREAMING_SNAKE_CASE:Tuple						      =					np.array(
         [-0.4_884, -0.4_607, 0.0_023, 0.5_007, 0.5_896, 0.5_151, 0.3_813, -0.0_208, -0.3_687, -0.4_315]							)
     SCREAMING_SNAKE_CASE:Tuple						      =					np.abs(expected_slice - audio_slice							).max()
     assert max_diff < 1e-2







   def     __UpperCamelCase						( self			:      Union[str, Any]							):
     SCREAMING_SNAKE_CASE:Tuple						      =					AudioLDMPipeline.from_pretrained("cvssp/audioldm"							)
     SCREAMING_SNAKE_CASE:Tuple						      =					LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config							)
     SCREAMING_SNAKE_CASE:List[Any]						      =					audioldm_pipe.to(__SCREAMING_SNAKE_CASE							)
     audioldm_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE							)

     SCREAMING_SNAKE_CASE:Union[str, Any]						      =					self.get_inputs(__SCREAMING_SNAKE_CASE							)
     SCREAMING_SNAKE_CASE:Any						      =					audioldm_pipe(**__SCREAMING_SNAKE_CASE							).audios[0]

     assert audio.ndim == 1
     assert len(__SCREAMING_SNAKE_CASE							) == 81_920

     SCREAMING_SNAKE_CASE:Dict						      =					audio[27_780:27_790]
     SCREAMING_SNAKE_CASE:int						      =					np.array([-0.2_131, -0.0_873, -0.0_124, -0.0_189, 0.0_569, 0.1_373, 0.1_883, 0.2_886, 0.3_297, 0.2_212]							)
     SCREAMING_SNAKE_CASE:List[str]						      =					np.abs(expected_slice - audio_slice							).max()
     assert max_diff < 3e-2









'''simple docstring'''

import coval  # From: git+https://github.com/ns-moosavi/coval.git # noqa: F401
from coval.conll import reader, util
from coval.eval import evaluator

import datasets


A_       						=							datasets.logging.get_logger(__name__)


A_       						=							"\\n@InProceedings{moosavi2019minimum,\n    author = { Nafise Sadat Moosavi, Leo Born, Massimo Poesio and Michael Strube},\n    title = {Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection},\n    year = {2019},\n    booktitle = {Proceedings of the 57th Annual Meeting of\n        the Association for Computational Linguistics (Volume 1: Long Papers)},\n    publisher = {Association for Computational Linguistics},\n    address = {Florence, Italy},\n}\n\n@inproceedings{10.3115/1072399.1072405,\nauthor = {Vilain, Marc and Burger, John and Aberdeen, John and Connolly, Dennis and Hirschman, Lynette},\ntitle = {A Model-Theoretic Coreference Scoring Scheme},\nyear = {1995},\nisbn = {1558604022},\npublisher = {Association for Computational Linguistics},\naddress = {USA},\nurl = {https://doi.org/10.3115/1072399.1072405},\ndoi = {10.3115/1072399.1072405},\nbooktitle = {Proceedings of the 6th Conference on Message Understanding},\npages = {45–52},\nnumpages = {8},\nlocation = {Columbia, Maryland},\nseries = {MUC6 ’95}\n}\n\n@INPROCEEDINGS{Bagga98algorithmsfor,\n    author = {Amit Bagga and Breck Baldwin},\n    title = {Algorithms for Scoring Coreference Chains},\n    booktitle = {In The First International Conference on Language Resources and Evaluation Workshop on Linguistics Coreference},\n    year = {1998},\n    pages = {563--566}\n}\n\n@INPROCEEDINGS{Luo05oncoreference,\n    author = {Xiaoqiang Luo},\n    title = {On coreference resolution performance metrics},\n    booktitle = {In Proc. of HLT/EMNLP},\n    year = {2005},\n    pages = {25--32},\n    publisher = {URL}\n}\n\n@inproceedings{moosavi-strube-2016-coreference,\n    title = \"Which Coreference Evaluation Metric Do You Trust? A Proposal for a Link-based Entity Aware Metric\",\n    author = \"Moosavi, Nafise Sadat  and\n      Strube, Michael\",\n    booktitle = \"Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)\",\n    month = aug,\n    year = \"2016\",\n    address = \"Berlin, Germany\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/P16-1060\",\n    doi = \"10.18653/v1/P16-1060\",\n    pages = \"632--642\",\n}\n\n"

A_       						=							"\\nCoVal is a coreference evaluation tool for the CoNLL and ARRAU datasets which\nimplements of the common evaluation metrics including MUC [Vilain et al, 1995],\nB-cubed [Bagga and Baldwin, 1998], CEAFe [Luo et al., 2005],\nLEA [Moosavi and Strube, 2016] and the averaged CoNLL score\n(the average of the F1 values of MUC, B-cubed and CEAFe)\n[Denis and Baldridge, 2009a; Pradhan et al., 2011].\n\nThis wrapper of CoVal currently only work with CoNLL line format:\nThe CoNLL format has one word per line with all the annotation for this word in column separated by spaces:\nColumn	Type	Description\n1	Document ID	This is a variation on the document filename\n2	Part number	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.\n3	Word number\n4	Word itself	This is the token as segmented/tokenized in the Treebank. Initially the *_skel file contain the placeholder [WORD] which gets replaced by the actual token from the Treebank which is part of the OntoNotes release.\n5	Part-of-Speech\n6	Parse bit	This is the bracketed structure broken before the first open parenthesis in the parse, and the word/part-of-speech leaf replaced with a *. The full parse can be created by substituting the asterix with the \"([pos] [word])\" string (or leaf) and concatenating the items in the rows of that column.\n7	Predicate lemma	The predicate lemma is mentioned for the rows for which we have semantic role information. All other rows are marked with a \"-\"\n8	Predicate Frameset ID	This is the PropBank frameset ID of the predicate in Column 7.\n9	Word sense	This is the word sense of the word in Column 3.\n10	Speaker/Author	This is the speaker or author name where available. Mostly in Broadcast Conversation and Web Log data.\n11	Named Entities	These columns identifies the spans representing various named entities.\n12:N	Predicate Arguments	There is one column each of predicate argument structure information for the predicate mentioned in Column 7.\nN	Coreference	Coreference chain information encoded in a parenthesis structure.\nMore informations on the format can be found here (section \"*_conll File Format\"): http://www.conll.cemantix.org/2012/data.html\n\nDetails on the evaluation on CoNLL can be found here: https://github.com/ns-moosavi/coval/blob/master/conll/README.md\n\nCoVal code was written by @ns-moosavi.\nSome parts are borrowed from https://github.com/clarkkev/deep-coref/blob/master/evaluation.py\nThe test suite is taken from https://github.com/conll/reference-coreference-scorers/\nMention evaluation and the test suite are added by @andreasvc.\nParsing CoNLL files is developed by Leo Born.\n"

A_       						=							"\nCalculates coreference evaluation metrics.\nArgs:\n    predictions: list of sentences. Each sentence is a list of word predictions to score in the CoNLL format.\n        Each prediction is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    references: list of sentences. Each sentence is a list of word reference to score in the CoNLL format.\n        Each reference is a word with its annotations as a string made of columns joined with spaces.\n        Only columns 4, 5, 6 and the last column are used (word, POS, Pars and coreference annotation)\n        See the details on the format in the description of the metric.\n    keep_singletons: After extracting all mentions of key or system files,\n        mentions whose corresponding coreference chain is of size one,\n        are considered as singletons. The default evaluation mode will include\n        singletons in evaluations if they are included in the key or the system files.\n        By setting 'keep_singletons=False', all singletons in the key and system files\n        will be excluded from the evaluation.\n    NP_only: Most of the recent coreference resolvers only resolve NP mentions and\n        leave out the resolution of VPs. By setting the 'NP_only' option, the scorer will only evaluate the resolution of NPs.\n    min_span: By setting 'min_span', the scorer reports the results based on automatically detected minimum spans.\n        Minimum spans are determined using the MINA algorithm.\n\nReturns:\n    'mentions': mentions\n    'muc': MUC metric [Vilain et al, 1995]\n    'bcub': B-cubed [Bagga and Baldwin, 1998]\n    'ceafe': CEAFe [Luo et al., 2005]\n    'lea': LEA [Moosavi and Strube, 2016]\n    'conll_score': averaged CoNLL score (the average of the F1 values of MUC, B-cubed and CEAFe)\n\nExamples:\n\n    >>> coval = datasets.load_metric('coval')\n    >>> words = ['bc/cctv/00/cctv_0005   0   0       Thank   VBP  (TOP(S(VP*    thank  01   1    Xu_li  *           (V*)        *       -',\n    ... 'bc/cctv/00/cctv_0005   0   1         you   PRP        (NP*)      -    -   -    Xu_li  *        (ARG1*)   (ARG0*)   (116)',\n    ... 'bc/cctv/00/cctv_0005   0   2    everyone    NN        (NP*)      -    -   -    Xu_li  *    (ARGM-DIS*)        *    (116)',\n    ... 'bc/cctv/00/cctv_0005   0   3         for    IN        (PP*       -    -   -    Xu_li  *        (ARG2*         *       -',\n    ... 'bc/cctv/00/cctv_0005   0   4    watching   VBG   (S(VP*))))   watch  01   1    Xu_li  *             *)      (V*)      -',\n    ... 'bc/cctv/00/cctv_0005   0   5           .     .          *))      -    -   -    Xu_li  *             *         *       -']\n    >>> references = [words]\n    >>> predictions = [words]\n    >>> results = coval.compute(predictions=predictions, references=references)\n    >>> print(results) # doctest:+ELLIPSIS\n    {'mentions/recall': 1.0,[...] 'conll_score': 100.0}\n"





def    A_  ( snake_case      ,       snake_case      ,       snake_case=False      ,       snake_case=False      ,       snake_case=True      ,       snake_case=False      ,       snake_case="dummy_doc" ):
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{doc: key_lines}
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					{doc: sys_lines}

  SCREAMING_SNAKE_CASE:Dict						      =					{}

  SCREAMING_SNAKE_CASE:Optional[int]						      =					0
  SCREAMING_SNAKE_CASE:Dict						      =					0
  SCREAMING_SNAKE_CASE:int						      =					0
  SCREAMING_SNAKE_CASE:int						      =					0
  SCREAMING_SNAKE_CASE:Dict						      =					0
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					0

  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					reader.get_doc_mentions(snake_case      ,       key_doc_lines[doc]      ,       snake_case )
  key_singletons_num += singletons_num

  if NP_only or min_span:
    SCREAMING_SNAKE_CASE:Dict						      =					reader.set_annotated_parse_trees(snake_case      ,       key_doc_lines[doc]      ,       snake_case      ,       snake_case )

  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					reader.get_doc_mentions(snake_case      ,       sys_doc_lines[doc]      ,       snake_case )
  sys_singletons_num += singletons_num

  if NP_only or min_span:
    SCREAMING_SNAKE_CASE:str						      =					reader.set_annotated_parse_trees(snake_case      ,       key_doc_lines[doc]      ,       snake_case      ,       snake_case )

  if remove_nested:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					reader.remove_nested_coref_mentions(snake_case      ,       snake_case )
    key_nested_coref_num += nested_mentions
    key_removed_nested_clusters += removed_clusters

    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					reader.remove_nested_coref_mentions(snake_case      ,       snake_case )
    sys_nested_coref_num += nested_mentions
    sys_removed_nested_clusters += removed_clusters

  SCREAMING_SNAKE_CASE:Optional[Any]						      =					reader.get_mention_assignments(snake_case      ,       snake_case )
  SCREAMING_SNAKE_CASE:Dict						      =					reader.get_mention_assignments(snake_case      ,       snake_case )

  SCREAMING_SNAKE_CASE:List[Any]						      =					(key_clusters, sys_clusters, key_mention_sys_cluster, sys_mention_key_cluster)

  if remove_nested:
    logger.info(
        "Number of removed nested coreferring mentions in the key "
        F'''annotation: {key_nested_coref_num}; and system annotation: {sys_nested_coref_num}''' )
    logger.info(
        "Number of resulting singleton clusters in the key "
        F'''annotation: {key_removed_nested_clusters}; and system annotation: {sys_removed_nested_clusters}''' )

  if not keep_singletons:
    logger.info(
        F'''{key_singletons_num:d} and {sys_singletons_num:d} singletons are removed from the key and system '''
        "files, respectively" )

  return doc_coref_infos





def    A_  ( snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Tuple						      =					get_coref_infos(snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case      ,       snake_case )

  SCREAMING_SNAKE_CASE:Optional[int]						      =					{}
  SCREAMING_SNAKE_CASE:int						      =					0
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					0

  for name, metric in metrics:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Any						      =					evaluator.evaluate_documents(snake_case      ,       snake_case      ,       beta=1 )
    if name in ["muc", "bcub", "ceafe"]:
      conll += fa
      conll_subparts_num += 1
    output_scores.update({F'''{name}/recall''': recall, F'''{name}/precision''': precision, F'''{name}/f1''': fa} )

    logger.info(
        name.ljust(10 )      ,       F'''Recall: {recall * 100:.2f}'''      ,       F''' Precision: {precision * 100:.2f}'''      ,       F''' F1: {fa * 100:.2f}'''      ,       )

  if conll_subparts_num == 3:
    SCREAMING_SNAKE_CASE:Any						      =					(conll / 3) * 100
    logger.info(F'''CoNLL score: {conll:.2f}''' )
    output_scores.update({"conll_score": conll} )

  return output_scores





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					False
  for line in key_lines:
    if not line.startswith("#" ):
      if len(line.split() ) > 6:
        SCREAMING_SNAKE_CASE:Tuple						      =					line.split()[5]
        if not parse_col == "-":
          SCREAMING_SNAKE_CASE:List[str]						      =					True
          break
        else:
          break
  return has_gold_parse





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION						)
class 							_snake_case (       datasets.Metric						):
   def     __UpperCamelCase						( self			:      Optional[Any]							):
     return datasets.MetricInfo(
         description=_DESCRIPTION     ,citation=_CITATION     ,inputs_description=_KWARGS_DESCRIPTION     ,features=datasets.Features(
             {
                 "predictions": datasets.Sequence(datasets.Value("string"							)							),
                 "references": datasets.Sequence(datasets.Value("string"							)							),
             }							)     ,codebase_urls=["https://github.com/ns-moosavi/coval"]     ,reference_urls=[
             "https://github.com/ns-moosavi/coval",
             "https://www.aclweb.org/anthology/P16-1060",
             "http://www.conll.cemantix.org/2012/data.html",
         ]     ,)







   def     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=True     ,SCREAMING_SNAKE_CASE__			:      int=False     ,SCREAMING_SNAKE_CASE__			:      List[str]=False     ,SCREAMING_SNAKE_CASE__			:      str=False							):
     SCREAMING_SNAKE_CASE:Optional[int]						      =					[
         ("mentions", evaluator.mentions),
         ("muc", evaluator.muc),
         ("bcub", evaluator.b_cubed),
         ("ceafe", evaluator.ceafe),
         ("lea", evaluator.lea),
     ]

     if min_span:
       SCREAMING_SNAKE_CASE:Optional[int]						      =					util.check_gold_parse_annotation(SCREAMING_SNAKE_CASE__							)
       if not has_gold_parse:
         raise NotImplementedError("References should have gold parse annotation to use 'min_span'."							)
         # util.parse_key_file(key_file)
         # key_file = key_file + ".parsed"

     SCREAMING_SNAKE_CASE:List[Any]						      =					evaluate(
         key_lines=SCREAMING_SNAKE_CASE__     ,sys_lines=SCREAMING_SNAKE_CASE__     ,metrics=SCREAMING_SNAKE_CASE__     ,NP_only=SCREAMING_SNAKE_CASE__     ,remove_nested=SCREAMING_SNAKE_CASE__     ,keep_singletons=SCREAMING_SNAKE_CASE__     ,min_span=SCREAMING_SNAKE_CASE__     ,)

     return score






