
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowerCamelCase	:    List[str]          =      logging.get_logger(__name__)  # pylint: disable=invalid-name




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def __init__(  self     :   Union[str, Any]      ,  lowercase     :   Optional[Any]      ,  lowercase     :   str     ):


				'''simple docstring'''


				super().__init__()
				self.register_modules(unet=lowercase      ,  scheduler=lowercase     )





	@torch.no_grad()
	def __call__(  self     :   Union[str, Any]      ,  lowercase     :   int = 1      ,  lowercase     :   int = 100      ,  lowercase     :   Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,  lowercase     :   Optional[float] = None      ,  lowercase     :   bool = True      ,  ):


				'''simple docstring'''


				if audio_length_in_s is None:
							_snake_case          =      self.unet.config.sample_size / self.unet.config.sample_rate

				_snake_case          =      audio_length_in_s * self.unet.config.sample_rate

				_snake_case          =      2 ** len(self.unet.up_blocks     )
				if sample_size < 3 * down_scale_factor:
							raise ValueError(
							    f'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
							    f''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

				_snake_case          =      int(lowercase     )
				if sample_size % down_scale_factor != 0:
							_snake_case          =      (
							    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
							) * down_scale_factor
							logger.info(
							    f'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
							    f''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
							    ' process.'     )
				_snake_case          =      int(lowercase     )

				_snake_case          =      next(iter(self.unet.parameters()     )     ).dtype
				_snake_case          =      (batch_size, self.unet.config.in_channels, sample_size)
				if isinstance(lowercase      ,  lowercase     ) and len(lowercase     ) != batch_size:
							raise ValueError(
							    f'''You have passed a list of generators of length {len(lowercase     )}, but requested an effective batch'''
							    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

				_snake_case          =      randn_tensor(lowercase      ,  generator=lowercase      ,  device=self.device      ,  dtype=lowercase     )

				# set step values
				self.scheduler.set_timesteps(lowercase      ,  device=audio.device     )
				_snake_case          =      self.scheduler.timesteps.to(lowercase     )

				for t in self.progress_bar(self.scheduler.timesteps     ):
							# 1. predict noise model_output
							_snake_case          =      self.unet(lowercase      ,  lowercase     ).sample

							# 2. compute previous image: x_t -> t_t-1
							_snake_case          =      self.scheduler.step(lowercase      ,  lowercase      ,  lowercase     ).prev_sample

				_snake_case          =      audio.clamp(-1      ,  1     ).float().cpu().numpy()

				_snake_case          =      audio[:, :, :original_sample_size]

				if not return_dict:
							return (audio,)

				return AudioPipelineOutput(audios=lowercase     )

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)
_lowerCamelCase	:    Union[str, Any]          =      logging.getLogger()






def 	a_		(	__lowercase       :     Path	, __lowercase       :     list			)			->  Tuple:
			_snake_case          =      '\n'.join(__lowercase			)
			Path(__lowercase			).open('w'			).writelines(__lowercase			)


_lowerCamelCase	:    Any          =      '''patrickvonplaten/t5-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase	:    List[Any]          =      '''sshleifer/tiny-mbart'''

_lowerCamelCase	:    Union[str, Any]          =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()
				_snake_case          =      [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
				_dump_articles(lowercase      ,  lowercase     )

				_snake_case          =      str(Path(self.get_auto_remove_tmp_dir()     ) / 'scores.json'     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      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(lowercase      ,  'argv'      ,  lowercase     ):
							run_generate()
							assert Path(lowercase     ).exists()
							# os.remove(Path(output_file_name))



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )



	@parameterized.expand([BART_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   int     ):


				'''simple docstring'''


				self.run_eval_tester(lowercase     )





	@parameterized.expand([T5_TINY, MBART_TINY]     )
	@slow
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     ) / 'utest_input.source'
				_snake_case          =      input_file_name.parent / 'utest_output.txt'
				assert not output_file_name.exists()

				_snake_case          =      {
				    '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!',
				    ],
				}

				_snake_case          =      Path(self.get_auto_remove_tmp_dir()     )
				_snake_case          =      str(tmp_dir / 'scores.json'     )
				_snake_case          =      str(tmp_dir / 'val.target'     )
				_dump_articles(lowercase      ,  text['en']     )
				_dump_articles(lowercase      ,  text['de']     )
				_snake_case          =      'translation_en_to_de' if model == T5_TINY else 'summarization'
				_snake_case          =      f'''
            run_eval_search.py
            {model}
            {str(lowercase     )}
            {str(lowercase     )}
            --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(lowercase      ,  'argv'      ,  lowercase     ):
							with CaptureStdout() as cs:
										run_search()
							_snake_case          =      [' num_beams | length_penalty', model, 'Best score args']
							_snake_case          =      ['Info']
							if "translation" in task:
										expected_strings.append('bleu'     )
							else:
										expected_strings.extend(lowercase     )
							for w in expected_strings:
										assert w in cs.out
							for w in un_expected_strings:
										assert w not in cs.out
							assert Path(lowercase     ).exists()
							os.remove(Path(lowercase     )     )