






'''simple docstring'''

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)
UpperCAmelCase__						:Dict          =			logging.getLogger()
def     __lowercase  (_lowercase,						_lowercase     )   ->     List[Any]:


     """simple docstring"""
     __lowerCamelCase						:       Dict				   =				"""\n""".join(_lowercase     )
     Path(_lowercase     ).open("""w"""     ).writelines(_lowercase     )


UpperCAmelCase__						:int          =			"""patrickvonplaten/t5-tiny-random"""
UpperCAmelCase__						:List[str]          =			"""sshleifer/bart-tiny-random"""
UpperCAmelCase__						:str          =			"""sshleifer/tiny-mbart"""

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







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def 					a_					(					self      : Optional[Any]	,    A__      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				Path(self.get_auto_remove_tmp_dir()					) / """utest_input.source"""
            __lowerCamelCase						:       int				   =				input_file_name.parent / """utest_output.txt"""
            assert not output_file_name.exists()
            __lowerCamelCase						:       List[str]				   =				[""" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."""]
            _dump_articles(A__	,    A__					)

            __lowerCamelCase						:       Optional[Any]				   =				str(Path(self.get_auto_remove_tmp_dir()					) / """scores.json"""					)
            __lowerCamelCase						:       Tuple				   =				"""translation_en_to_de""" if model == T5_TINY else """summarization"""
            __lowerCamelCase						:       Optional[int]				   =				f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

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




       def 					a_					(					self      : str					):


            """simple docstring"""







            self.run_eval_tester(A__					)




       @parameterized.expand([BART_TINY, MBART_TINY]					)
       @slow
       def 					a_					(					self      : List[Any]	,    A__      : Dict					):


            """simple docstring"""







            self.run_eval_tester(A__					)




       @parameterized.expand([T5_TINY, MBART_TINY]					)
       @slow
       def 					a_					(					self      : Union[str, Any]	,    A__      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				Path(self.get_auto_remove_tmp_dir()					) / """utest_input.source"""
            __lowerCamelCase						:       List[Any]				   =				input_file_name.parent / """utest_output.txt"""
            assert not output_file_name.exists()

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

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











'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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


UpperCAmelCase__						:Dict          =			logging.get_logger(__name__)  # pylint: disable=invalid-name







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : int	,    A__      : List[Any]	,    A__      : Tuple					):


            """simple docstring"""







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




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


            """simple docstring"""







            if audio_length_in_s is None:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.unet.config.sample_size / self.unet.config.sample_rate

            __lowerCamelCase						:       int				   =				audio_length_in_s * self.unet.config.sample_rate

            __lowerCamelCase						:       List[Any]				   =				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}."					)

            __lowerCamelCase						:       Any				   =				int(A__					)
            if sample_size % down_scale_factor != 0:
                 __lowerCamelCase						:       Optional[Any]				   =				(
                     (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."""					)
            __lowerCamelCase						:       Dict				   =				int(A__					)

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

            __lowerCamelCase						:       int				   =				randn_tensor(A__	,    generator=A__	,    device=self.device	,    dtype=A__					)

            # set step values
            self.scheduler.set_timesteps(A__	,    device=audio.device					)
            __lowerCamelCase						:       List[Any]				   =				self.scheduler.timesteps.to(A__					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                 # 1. predict noise model_output
                 __lowerCamelCase						:       List[str]				   =				self.unet(A__	,    A__					).sample

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

            __lowerCamelCase						:       Optional[int]				   =				audio.clamp(-1	,    1					).float().cpu().numpy()

            __lowerCamelCase						:       int				   =				audio[:, :, :original_sample_size]

            if not return_dict:
                 return (audio,)

            return AudioPipelineOutput(audios=A__					)



