





'''simple docstring'''


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput

@dataclass
class       lowerCAmelCase_						(						snake_case__						):
 __lowerCamelCase  :							str				     =    42
 __lowerCamelCase  :							Optional[Any]				     =    42







class       lowerCAmelCase_						(						snake_case__  ,snake_case__						):
 __lowerCamelCase  :							Optional[int]				     =    1




 @register_to_config
 def __init__(		self	,   _lowerCAmelCase = 2000	,   _lowerCAmelCase = 0.15	,   _lowerCAmelCase = 0.01	,   _lowerCAmelCase = 1348.0	,   _lowerCAmelCase = 1E-5	,   _lowerCAmelCase = 1	,   )			->							str:
     _lowerCAmelCase			=					sigma_max

     # setable values
     _lowerCAmelCase			=					None

     self.set_sigmas(_lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase = None						)			->							Union[str, Any]:
     return sample




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None						)			->							str:
     _lowerCAmelCase			=					sampling_eps if sampling_eps is not None else self.config.sampling_eps

     _lowerCAmelCase			=					torch.linspace(1	,   _lowerCAmelCase	,   _lowerCAmelCase	,   device=_lowerCAmelCase						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None	,   _lowerCAmelCase = None						)			->							Optional[int]:
     _lowerCAmelCase			=					sigma_min if sigma_min is not None else self.config.sigma_min
     _lowerCAmelCase			=					sigma_max if sigma_max is not None else self.config.sigma_max
     _lowerCAmelCase			=					sampling_eps if sampling_eps is not None else self.config.sampling_eps
     if self.timesteps is None:
         self.set_timesteps(_lowerCAmelCase	,   _lowerCAmelCase						)

     _lowerCAmelCase			=					sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
     _lowerCAmelCase			=					torch.exp(torch.linspace(math.log(_lowerCAmelCase						)	,   math.log(_lowerCAmelCase						)	,   _lowerCAmelCase						)						)
     _lowerCAmelCase			=					torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase						)			->							Optional[Any]:
     return torch.where(
         timesteps == 0	,   torch.zeros_like(t.to(timesteps.device						)						)	,   self.discrete_sigmas[timesteps - 1].to(timesteps.device						)	,   )




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase = None	,   _lowerCAmelCase = True	,   )			->							Any:







     if self.timesteps is None:
         raise ValueError(
             "`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"						)

     _lowerCAmelCase			=					timestep * torch.ones(
         sample.shape[0]	,   device=sample.device						)  # torch.repeat_interleave(timestep, sample.shape[0])
     _lowerCAmelCase			=					(timestep * (len(self.timesteps						) - 1)).long()

     # mps requires indices to be in the same device, so we use cpu as is the default with cuda
     _lowerCAmelCase			=					timesteps.to(self.discrete_sigmas.device						)

     _lowerCAmelCase			=					self.discrete_sigmas[timesteps].to(sample.device						)
     _lowerCAmelCase			=					self.get_adjacent_sigma(_lowerCAmelCase	,   _lowerCAmelCase						).to(sample.device						)
     _lowerCAmelCase			=					torch.zeros_like(_lowerCAmelCase						)
     _lowerCAmelCase			=					(sigma**2 - adjacent_sigma**2) ** 0.5

     # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
     # also equation 47 shows the analog from SDE models to ancestral sampling methods
     _lowerCAmelCase			=					diffusion.flatten()
     while len(diffusion.shape						) < len(sample.shape						):
         _lowerCAmelCase			=					diffusion.unsqueeze(-1						)
     _lowerCAmelCase			=					drift - diffusion**2 * model_output

     #  equation 6: sample noise for the diffusion term of
     _lowerCAmelCase			=					randn_tensor(
         sample.shape	,   layout=sample.layout	,   generator=_lowerCAmelCase	,   device=sample.device	,   dtype=sample.dtype						)
     _lowerCAmelCase			=					sample - drift  # subtract because `dt` is a small negative timestep
     # TODO is the variable diffusion the correct scaling term for the noise?
     _lowerCAmelCase			=					prev_sample_mean + diffusion * noise  # add impact of diffusion field g

     if not return_dict:
         return (prev_sample, prev_sample_mean)

     return SdeVeOutput(prev_sample=_lowerCAmelCase	,   prev_sample_mean=_lowerCAmelCase						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase = None	,   _lowerCAmelCase = True	,   )			->							Dict:







     if self.timesteps is None:
         raise ValueError(
             "`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler"						)

     # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
     # sample noise for correction
     _lowerCAmelCase			=					randn_tensor(sample.shape	,   layout=sample.layout	,   generator=_lowerCAmelCase						).to(sample.device						)

     # compute step size from the model_output, the noise, and the snr
     _lowerCAmelCase			=					torch.norm(model_output.reshape(model_output.shape[0]	,   -1						)	,   dim=-1						).mean()
     _lowerCAmelCase			=					torch.norm(noise.reshape(noise.shape[0]	,   -1						)	,   dim=-1						).mean()
     _lowerCAmelCase			=					(self.config.snr * noise_norm / grad_norm) ** 2 * 2
     _lowerCAmelCase			=					step_size * torch.ones(sample.shape[0]						).to(sample.device						)
     # self.repeat_scalar(step_size, sample.shape[0])

     # compute corrected sample: model_output term and noise term
     _lowerCAmelCase			=					step_size.flatten()
     while len(step_size.shape						) < len(sample.shape						):
         _lowerCAmelCase			=					step_size.unsqueeze(-1						)
     _lowerCAmelCase			=					sample + step_size * model_output
     _lowerCAmelCase			=					prev_sample_mean + ((step_size * 2) ** 0.5) * noise

     if not return_dict:
         return (prev_sample,)

     return SchedulerOutput(prev_sample=_lowerCAmelCase						)




 def 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   )			->							Dict:
     _lowerCAmelCase			=					timesteps.to(original_samples.device						)
     _lowerCAmelCase			=					self.discrete_sigmas.to(original_samples.device						)[timesteps]
     _lowerCAmelCase			=					(
         noise * sigmas[:, None, None, None]
         if noise is not None
         else torch.randn_like(_lowerCAmelCase						) * sigmas[:, None, None, None]
     )
     _lowerCAmelCase			=					noise + original_samples
     return noisy_samples





 def __len__(		self						)			->							Union[str, Any]:
     return self.config.num_train_timesteps










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)
_a      	=  logging.getLogger()

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





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							os.makedirs(UpperCAmelCase       ,    exist_ok=UpperCAmelCase    )
							_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(UpperCAmelCase       ,    F"""{split}.{field}"""    )       ,    'w'    ) as f:
																									f.write(UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase = "pytorch"    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_auto_remove_tmp_dir()
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'output'    )
							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'data'    )
							self._create_dummy_data(data_dir=UpperCAmelCase    )

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

							_UpperCAmelCase									=			os.path.join(UpperCAmelCase       ,    'metrics.json'    )
							with open(UpperCAmelCase    ) as f:
													_UpperCAmelCase									=			json.load(UpperCAmelCase    )
							return result

	@require_torch_gpu
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@require_torch_multi_gpu
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

	@require_torch_gpu
	@require_ray
	def        UpperCamelCase    (							self    ):
							"""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        UpperCamelCase    (							self    ):
							"""simple docstring"""







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