





import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
_snake_case  :   Optional[Any]						   =  logging.getLogger()

def   a_   (			lowerCAmelCase_				:      Path,     lowerCAmelCase_				:      list     ):
   __lowerCAmelCase							     =     '\n'.join(lowerCAmelCase_     )
   Path(lowerCAmelCase_     ).open('w'     ).writelines(lowerCAmelCase_     )


_snake_case  :   Dict						   =  'patrickvonplaten/t5-tiny-random'
_snake_case  :   Dict						   =  'sshleifer/bart-tiny-random'
_snake_case  :   Any						   =  'sshleifer/tiny-mbart'

_snake_case  :   Any						   =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class     _UpperCAmelCase   (   _UpperCamelCase   ):

  """simple docstring"""


  def     lowercase		(    self			:		Dict ,  lowerCAmelCase_			:		Optional[Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     Path(self.get_auto_remove_tmp_dir()					) / 'utest_input.source'
     __lowerCAmelCase							     =     input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __lowerCAmelCase							     =     [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(lowerCAmelCase_ ,  lowerCAmelCase_					)

     __lowerCAmelCase							     =     str(Path(self.get_auto_remove_tmp_dir()					) / 'scores.json'					)
     __lowerCAmelCase							     =     'translation_en_to_de' if model == T5_TINY else 'summarization'
     __lowerCAmelCase							     =     f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

     with patch.object(lowerCAmelCase_ ,  'argv' ,  lowerCAmelCase_					):
        run_generate()
        assert Path(lowerCAmelCase_					).exists()
        # os.remove(Path(output_file_name))






  def     lowercase		(    self			:		Dict					)							->						Optional[int]:
     self.run_eval_tester(lowerCAmelCase_					)






  @parameterized.expand([BART_TINY, MBART_TINY]					)
  @slow
  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Any					)							->						Tuple:
     self.run_eval_tester(lowerCAmelCase_					)






  @parameterized.expand([T5_TINY, MBART_TINY]					)
  @slow
  def     lowercase		(    self			:		Any ,  lowerCAmelCase_			:		Optional[Any]					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     Path(self.get_auto_remove_tmp_dir()					) / 'utest_input.source'
     __lowerCAmelCase							     =     input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __lowerCAmelCase							     =     {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __lowerCAmelCase							     =     Path(self.get_auto_remove_tmp_dir()					)
     __lowerCAmelCase							     =     str(tmp_dir / 'scores.json'					)
     __lowerCAmelCase							     =     str(tmp_dir / 'val.target'					)
     _dump_articles(lowerCAmelCase_ ,  text['en']					)
     _dump_articles(lowerCAmelCase_ ,  text['de']					)
     __lowerCAmelCase							     =     'translation_en_to_de' if model == T5_TINY else 'summarization'
     __lowerCAmelCase							     =     f"""
            run_eval_search.py
            {model}
            {str(lowerCAmelCase_					)}
            {str(lowerCAmelCase_					)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']					)

     with patch.object(lowerCAmelCase_ ,  'argv' ,  lowerCAmelCase_					):
        with CaptureStdout() as cs:
           run_search()
        __lowerCAmelCase							     =     [' num_beams | length_penalty', model, 'Best score args']
        __lowerCAmelCase							     =     ['Info']
        if "translation" in task:
           expected_strings.append('bleu'					)
        else:
           expected_strings.extend(lowerCAmelCase_					)
        for w in expected_strings:
           assert w in cs.out
        for w in un_expected_strings:
           assert w not in cs.out
        assert Path(lowerCAmelCase_					).exists()
        os.remove(Path(lowerCAmelCase_					)					)








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   (   _UpperCamelCase		,   unittest.TestCase   ):

  """simple docstring"""


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






  def     lowercase		(    self			:		List[Any]					)							->						Dict:
     torch.manual_seed(0					)
     __lowerCAmelCase							     =     UNetaDModel(
         block_out_channels=(3_2, 3_2, 6_4) ,  extra_in_channels=1_6 ,  sample_size=5_1_2 ,  sample_rate=1_6_0_0_0 ,  in_channels=2 ,  out_channels=2 ,  flip_sin_to_cos=lowerCAmelCase_ ,  use_timestep_embedding=lowerCAmelCase_ ,  time_embedding_type='fourier' ,  mid_block_type='UNetMidBlock1D' ,  down_block_types=('DownBlock1DNoSkip', 'DownBlock1D', 'AttnDownBlock1D') ,  up_block_types=('AttnUpBlock1D', 'UpBlock1D', 'UpBlock1DNoSkip') ,  )
     __lowerCAmelCase							     =     IPNDMScheduler()

     __lowerCAmelCase							     =     {
         'unet': unet,
         'scheduler': scheduler,
     }
     return components






  def     lowercase		(    self			:		Tuple ,  lowerCAmelCase_			:		Tuple ,  lowerCAmelCase_			:		List[str]=0					)							->						Any:
     if str(lowerCAmelCase_					).startswith('mps'					):
        __lowerCAmelCase							     =     torch.manual_seed(lowerCAmelCase_					)
     else:
        __lowerCAmelCase							     =     torch.Generator(device=lowerCAmelCase_					).manual_seed(lowerCAmelCase_					)
     __lowerCAmelCase							     =     {
         'batch_size': 1,
         'generator': generator,
         'num_inference_steps': 4,
     }
     return inputs






  def     lowercase		(    self			:		Union[str, Any]					)							->						int:
     __lowerCAmelCase							     =     'cpu'  # ensure determinism for the device-dependent torch.Generator
     __lowerCAmelCase							     =     self.get_dummy_components()
     __lowerCAmelCase							     =     DanceDiffusionPipeline(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     self.get_dummy_inputs(lowerCAmelCase_					)
     __lowerCAmelCase							     =     pipe(**lowerCAmelCase_					)
     __lowerCAmelCase							     =     output.audios

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

     assert audio.shape == (1, 2, components["unet"].sample_size)
     __lowerCAmelCase							     =     np.array([-0.72_65, 1.00_00, -0.83_88, 0.11_75, 0.94_98, -1.00_00]					)
     assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2






  @skip_mps
  def     lowercase		(    self			:		Union[str, Any]					)							->						Tuple:
     return super().test_save_load_local()






  @skip_mps
  def     lowercase		(    self			:		List[str]					)							->						Dict:
     return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)






  @skip_mps
  def     lowercase		(    self			:		str					)							->						List[str]:
     return super().test_save_load_optional_components()






  @skip_mps
  def     lowercase		(    self			:		List[Any]					)							->						List[str]:
     return super().test_attention_slicing_forward_pass()






  def     lowercase		(    self			:		str					)							->						int:
     super().test_inference_batch_single_identical(expected_max_diff=3e-3					)





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

  """simple docstring"""


  def     lowercase		(    self			:		Any					)							->						Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






  def     lowercase		(    self			:		List[str]					)							->						List[str]:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k'					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

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

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.01_92, -0.02_31, -0.03_18, -0.00_59, 0.00_02, -0.00_20]					)

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






  def     lowercase		(    self			:		Tuple					)							->						Dict:
     __lowerCAmelCase							     =     torch_device

     __lowerCAmelCase							     =     DanceDiffusionPipeline.from_pretrained('harmonai/maestro-150k' ,  torch_dtype=torch.floataa					)
     __lowerCAmelCase							     =     pipe.to(lowerCAmelCase_					)
     pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

     __lowerCAmelCase							     =     torch.manual_seed(0					)
     __lowerCAmelCase							     =     pipe(generator=lowerCAmelCase_ ,  num_inference_steps=1_0_0 ,  audio_length_in_s=4.0_96					)
     __lowerCAmelCase							     =     output.audios

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

     assert audio.shape == (1, 2, pipe.unet.sample_size)
     __lowerCAmelCase							     =     np.array([-0.03_67, -0.04_88, -0.07_71, -0.05_25, -0.04_44, -0.03_41]					)

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

