

"""simple docstring"""






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 	__UpperCamelCase    (    a__						):

  def __init__(		self    ,_A=-1      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    label_idx

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      if isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   Any  =    mode.value
      _lowerCAmelCase       :   Optional[Any]  =    os.path.join(_A    ,F"""{mode}.txt"""      )
      _lowerCAmelCase       :   Optional[Any]  =    1
      _lowerCAmelCase       :   Tuple  =    []
      with open(_A    ,encoding='utf-8'      ) as f:
          _lowerCAmelCase       :   Any  =    []
          _lowerCAmelCase       :   Any  =    []
          for line in f:
              if line.startswith('-DOCSTART-'      ) or line == "" or line == "\n":
                  if words:
                      examples.append(InputExample(guid=F"""{mode}-{guid_index}"""    ,words=_A    ,labels=_A      )      )
                      guid_index += 1
                      _lowerCAmelCase       :   str  =    []
                      _lowerCAmelCase       :   int  =    []
              else:
                  _lowerCAmelCase       :   int  =    line.split(' '      )
                  words.append(splits[0]      )
                  if len(_A      ) > 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=_A    ,labels=_A      )      )
      return examples

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

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







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

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







class 	__UpperCamelCase    (    a__						):

  def __init__(		self      ):
      '''simple docstring'''

      super().__init__(label_idx=-2      )







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if path:
          with open(_A    ,'r'      ) as f:
              _lowerCAmelCase       :   Union[str, Any]  =    f.read().splitlines()
          if "O" not in labels:
              _lowerCAmelCase       :   Any  =    ['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 	__UpperCamelCase    (    a__						):

  def __lowerCamelCase		(		self    ,_A    ,_A      ):
      '''simple docstring'''

      if isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   Dict  =    mode.value
      _lowerCAmelCase       :   Dict  =    os.path.join(_A    ,F"""{mode}.txt"""      )
      _lowerCAmelCase       :   List[str]  =    1
      _lowerCAmelCase       :   str  =    []

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

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    0
      for sentence in parse_incr(_A      ):
          _lowerCAmelCase       :   str  =    preds_list[example_id]
          _lowerCAmelCase       :   Tuple  =    ''
          for token in sentence:
              out += F"""{token['form']} ({token['upos']}|{s_p.pop(0      )}) """
          out += "\n"
          writer.write(_A      )
          example_id += 1







  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if path:
          with open(_A    ,'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",
          ]








"""simple docstring"""






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 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		DanceDiffusionPipeline
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
  _UpperCAmelCase          =		PipelineTesterMixin.required_optional_params - {
      "callback",
      "latents",
      "callback_steps",
      "output_type",
      "num_images_per_prompt",
  }
  _UpperCAmelCase          =		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
  _UpperCAmelCase          =		False
  _UpperCAmelCase          =		False

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      torch.manual_seed(0      )
      _lowerCAmelCase       :   List[Any]  =    UNetaDModel(
          block_out_channels=(32, 32, 64)    ,extra_in_channels=16    ,sample_size=512    ,sample_rate=1_6000    ,in_channels=2    ,out_channels=2    ,flip_sin_to_cos=_A    ,use_timestep_embedding=_A    ,time_embedding_type='fourier'    ,mid_block_type='UNetMidBlock1D'    ,down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D')    ,up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip')    ,)
      _lowerCAmelCase       :   int  =    IPNDMScheduler()

      _lowerCAmelCase       :   Union[str, Any]  =    {
          'unet': unet,
          'scheduler': scheduler,
      }
      return components

  def __lowerCamelCase		(		self    ,_A    ,_A=0      ):
      '''simple docstring'''

      if str(_A      ).startswith('mps'      ):
          _lowerCAmelCase       :   str  =    torch.manual_seed(_A      )
      else:
          _lowerCAmelCase       :   Optional[Any]  =    torch.Generator(device=_A      ).manual_seed(_A      )
      _lowerCAmelCase       :   int  =    {
          'batch_size': 1,
          'generator': generator,
          'num_inference_steps': 4,
      }
      return inputs

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[str]  =    'cpu'  # ensure determinism for the device-dependent torch.Generator
      _lowerCAmelCase       :   int  =    self.get_dummy_components()
      _lowerCAmelCase       :   Optional[Any]  =    DanceDiffusionPipeline(**_A      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.get_dummy_inputs(_A      )
      _lowerCAmelCase       :   List[str]  =    pipe(**_A      )
      _lowerCAmelCase       :   List[Any]  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, components["unet"].sample_size)
      _lowerCAmelCase       :   Optional[Any]  =    np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0]      )
      assert np.abs(audio_slice.flatten() - expected_slice      ).max() < 1E-2

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_save_load_local()

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_save_load_optional_components()

  @skip_mps
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return super().test_attention_slicing_forward_pass()







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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







@slow
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase						):

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

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

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    torch_device

      _lowerCAmelCase       :   int  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'      )
      _lowerCAmelCase       :   int  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Optional[int]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   str  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   str  =    output.audios

      _lowerCAmelCase       :   List[str]  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   Union[str, Any]  =    np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0]      )

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







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    torch_device

      _lowerCAmelCase       :   Tuple  =    DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'    ,torch_dtype=torch.floataa      )
      _lowerCAmelCase       :   Optional[int]  =    pipe.to(_A      )
      pipe.set_progress_bar_config(disable=_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    torch.manual_seed(0      )
      _lowerCAmelCase       :   Optional[int]  =    pipe(generator=_A    ,num_inference_steps=100    ,audio_length_in_s=4.0_9_6      )
      _lowerCAmelCase       :   Union[str, Any]  =    output.audios

      _lowerCAmelCase       :   int  =    audio[0, -3:, -3:]

      assert audio.shape == (1, 2, pipe.unet.sample_size)
      _lowerCAmelCase       :   List[str]  =    np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1]      )

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





