
import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
lowerCamelCase__	=							logging.getLogger()

lowerCamelCase__	=							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        lowerCAmelCase__      (   __lowercase	):

						def  A_				(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								os.makedirs(a       ,							exist_ok=a				)
								_UpperCamelCase						    =	{"""source""": """What is love ?""", """target""": """life"""}
								_UpperCamelCase						    =	{"""train""": 12, """val""": 2, """test""": 2}
								for split in ["train", "test", "val"]:
										for field in ["source", "target"]:
												_UpperCamelCase						    =	"""\n""".join([contents[field]] * n_lines[split]				)
												with open(os.path.join(a       ,							F'{split}.{field}'				)       ,							"""w"""				) as f:
														f.write(a				)

						def  A_				(			self       ,							a       ,							a = "pytorch"				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.get_auto_remove_tmp_dir()
								_UpperCamelCase						    =	os.path.join(a       ,							"""output"""				)
								_UpperCamelCase						    =	os.path.join(a       ,							"""data"""				)
								self._create_dummy_data(data_dir=a				)

								_UpperCamelCase						    =	F'\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            '.split()

								if gpus > 0:
										testargs.append(F'--gpus={gpus}'				)
										if is_apex_available():
												testargs.append("""--fp16"""				)
								else:
										testargs.append("""--gpus=0"""				)
										testargs.append("""--distributed_backend=ddp_cpu"""				)
										testargs.append("""--num_processes=2"""				)

								_UpperCamelCase						    =	[sys.executable, str(Path(finetune_rag.__file__				).resolve()				)] + testargs
								execute_subprocess_async(a       ,							env=self.get_env()				)

								_UpperCamelCase						    =	os.path.join(a       ,							"""metrics.json"""				)
								with open(a				) as f:
										_UpperCamelCase						    =	json.load(a				)
								return result

						@require_torch_gpu
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self._run_finetune(gpus=1				)
								self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,							0.2				)

						@require_torch_multi_gpu
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self._run_finetune(gpus=2				)
								self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,							0.2				)

						@require_torch_gpu
						@require_ray
						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._run_finetune(gpus=1       ,							distributed_retriever="""ray"""				)
								self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,							0.2				)







						@require_torch_multi_gpu
						@require_ray
						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._run_finetune(gpus=1       ,							distributed_retriever="""ray"""				)
								self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]       ,							0.2				)

from typing import List, Optional, Tuple, Union

import torch

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


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







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

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







						@torch.no_grad()
						def __call__(			self       ,							a = 1       ,							a = 1_00       ,							a = None       ,							a = None       ,							a = True       ,							)					->      Union[AudioPipelineOutput, Tuple]:


								'''simple docstring'''

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

								_UpperCamelCase						    =	audio_length_in_s * self.unet.config.sample_rate

								_UpperCamelCase						    =	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}.'				)

								_UpperCamelCase						    =	int(a				)
								if sample_size % down_scale_factor != 0:
										_UpperCamelCase						    =	(
										    (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."""				)
								_UpperCamelCase						    =	int(a				)

								_UpperCamelCase						    =	next(iter(self.unet.parameters()				)				).dtype
								_UpperCamelCase						    =	(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.'				)

								_UpperCamelCase						    =	randn_tensor(a       ,							generator=a       ,							device=self.device       ,							dtype=a				)

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

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

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

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

								_UpperCamelCase						    =	audio[:, :, :original_sample_size]

								if not return_dict:
										return (audio,)

								return AudioPipelineOutput(audios=a				)



