

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowerCAmelCase__	   = logging.getLogger(__name__)

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







			def __init__(    self      ,      lowercase=-1       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			label_idx


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						List[InputExample]:
							'''simple docstring'''






							if isinstance(lowercase      ,      lowercase       ):
											A__        =			mode.value
							A__        =			os.path.join(lowercase      ,      F'{mode}.txt'       )
							A__        =			1
							A__        =			[]
							with open(lowercase      ,      encoding="utf-8"       ) as f:
											A__        =			[]
											A__        =			[]
											for line in f:
															if line.startswith("-DOCSTART-"       ) or line == "" or line == "\n":
																			if words:
																							examples.append(InputExample(guid=F'{mode}-{guid_index}'      ,      words=lowercase      ,      labels=lowercase       )       )
																							guid_index += 1
																							A__        =			[]
																							A__        =			[]
															else:
																			A__        =			line.split(" "       )
																			words.append(splits[0]       )
																			if len(lowercase       ) > 1:
																							labels.append(splits[self.label_idx].replace("\n"      ,      ""       )       )
																			else:
																							# Examples could have no label for mode = "test"
																							labels.append("O"       )
											if words:
															examples.append(InputExample(guid=F'{mode}-{guid_index}'      ,      words=lowercase      ,      labels=lowercase       )       )
							return examples


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






							A__        =			0
							for line in test_input_reader:
											if line.startswith("-DOCSTART-"       ) or line == "" or line == "\n":
															writer.write(lowercase       )
															if not preds_list[example_id]:
																			example_id += 1
											elif preds_list[example_id]:
															A__        =			line.split()[0] + " " + preds_list[example_id].pop(0       ) + "\n"
															writer.write(lowercase       )
											else:
															logger.warning("Maximum sequence length exceeded: No prediction for '%s'."      ,      line.split()[0]       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






							if path:
											with open(lowercase      ,      "r"       ) as f:
															A__        =			f.read().splitlines()
											if "O" not in labels:
															A__        =			["O"] + labels
											return labels
							else:
											return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]

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







			def __init__(    self       )  ->						Dict:
							'''simple docstring'''






							super().__init__(label_idx=-2       )


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






							if path:
											with open(lowercase      ,      "r"       ) as f:
															A__        =			f.read().splitlines()
											if "O" not in labels:
															A__        =			["O"] + labels
											return labels
							else:
											return [
											    "O",
											    "B-ADVP",
											    "B-INTJ",
											    "B-LST",
											    "B-PRT",
											    "B-NP",
											    "B-SBAR",
											    "B-VP",
											    "B-ADJP",
											    "B-CONJP",
											    "B-PP",
											    "I-ADVP",
											    "I-INTJ",
											    "I-LST",
											    "I-PRT",
											    "I-NP",
											    "I-SBAR",
											    "I-VP",
											    "I-ADJP",
											    "I-CONJP",
											    "I-PP",
											]

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







			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase       )  ->						List[InputExample]:
							'''simple docstring'''






							if isinstance(lowercase      ,      lowercase       ):
											A__        =			mode.value
							A__        =			os.path.join(lowercase      ,      F'{mode}.txt'       )
							A__        =			1
							A__        =			[]

							with open(lowercase      ,      encoding="utf-8"       ) as f:
											for sentence in parse_incr(lowercase       ):
															A__        =			[]
															A__        =			[]
															for token in sentence:
																			words.append(token["form"]       )
																			labels.append(token["upos"]       )
															assert len(lowercase       ) == len(lowercase       )
															if words:
																			examples.append(InputExample(guid=F'{mode}-{guid_index}'      ,      words=lowercase      ,      labels=lowercase       )       )
																			guid_index += 1
							return examples


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






							A__        =			0
							for sentence in parse_incr(lowercase       ):
											A__        =			preds_list[example_id]
											A__        =			""
											for token in sentence:
															out += F'{token["form"]} ({token["upos"]}|{s_p.pop(0       )}) '
											out += "\n"
											writer.write(lowercase       )
											example_id += 1


			def      UpperCamelCase		(    self      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






							if path:
											with open(lowercase      ,      "r"       ) as f:
															return f.read().splitlines()
							else:
											return [
											    "ADJ",
											    "ADP",
											    "ADV",
											    "AUX",
											    "CCONJ",
											    "DET",
											    "INTJ",
											    "NOUN",
											    "NUM",
											    "PART",
											    "PRON",
											    "PROPN",
											    "PUNCT",
											    "SCONJ",
											    "SYM",
											    "VERB",
											    "X",
											]




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

