
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
							import transformers







def        A__					(						lowerCamelCase						)  ->	Optional[int]:

				'''simple docstring'''





				random.seed(lowerCamelCase						)
				np.random.seed(lowerCamelCase						)
				torch.manual_seed(lowerCamelCase						)
				torch.cuda.manual_seed_all(lowerCamelCase						)
				# ^^ safe to call this function even if cuda is not available





class        _UpperCamelCase				:






			'''simple docstring'''

			def __init__(       self :       List[str]						,	snake_case_ :       Iterable[torch.nn.Parameter]						,	snake_case_ :       float = 0.9999						,	snake_case_ :       float = 0.0						,	snake_case_ :       int = 0						,	snake_case_ :       bool = False						,	snake_case_ :       Union[float, int] = 1.0						,	snake_case_ :       Union[float, int] = 2 / 3						,	snake_case_ :       Optional[Any] = None						,	snake_case_ :       Dict[str, Any] = None						,	**snake_case_ :       List[str]						,	):







							if isinstance(UpperCamelCase__						,	torch.nn.Module      ):
											UpperCamelCase_:							str	=						(
											    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
											    """Please pass the parameters of the module instead."""
											)
											deprecate(
											    """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""						,	"""1.0.0"""						,	UpperCamelCase__						,	standard_warn=UpperCamelCase__						,	)
											UpperCamelCase_:							int	=						parameters.parameters()

											# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
											UpperCamelCase_:							Any	=						True

							if kwargs.get("""max_value"""						,	UpperCamelCase__      ) is not None:
											UpperCamelCase_:							List[str]	=						"""The `max_value` argument is deprecated. Please use `decay` instead."""
											deprecate("""max_value"""						,	"""1.0.0"""						,	UpperCamelCase__						,	standard_warn=UpperCamelCase__      )
											UpperCamelCase_:							int	=						kwargs["""max_value"""]

							if kwargs.get("""min_value"""						,	UpperCamelCase__      ) is not None:
											UpperCamelCase_:							List[Any]	=						"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
											deprecate("""min_value"""						,	"""1.0.0"""						,	UpperCamelCase__						,	standard_warn=UpperCamelCase__      )
											UpperCamelCase_:							Tuple	=						kwargs["""min_value"""]

							UpperCamelCase_:							str	=						list(UpperCamelCase__      )
							UpperCamelCase_:							Union[str, Any]	=						[p.clone().detach() for p in parameters]

							if kwargs.get("""device"""						,	UpperCamelCase__      ) is not None:
											UpperCamelCase_:							List[str]	=						"""The `device` argument is deprecated. Please use `to` instead."""
											deprecate("""device"""						,	"""1.0.0"""						,	UpperCamelCase__						,	standard_warn=UpperCamelCase__      )
											self.to(device=kwargs["""device"""]      )

							UpperCamelCase_:							str	=						None

							UpperCamelCase_:							List[str]	=						decay
							UpperCamelCase_:							List[str]	=						min_decay
							UpperCamelCase_:							Optional[Any]	=						update_after_step
							UpperCamelCase_:							Any	=						use_ema_warmup
							UpperCamelCase_:							Optional[int]	=						inv_gamma
							UpperCamelCase_:							List[Any]	=						power
							UpperCamelCase_:							Dict	=						0
							UpperCamelCase_:							int	=						None  # set in `step()`

							UpperCamelCase_:							Any	=						model_cls
							UpperCamelCase_:							Union[str, Any]	=						model_config

			@classmethod
			def       lowerCAmelCase__	(       cls :       List[Any]						,	snake_case_ :       List[str]						,	snake_case_ :       Optional[Any]      ):
							UpperCamelCase_,   UpperCamelCase_:							int	=						model_cls.load_config(UpperCamelCase__						,	return_unused_kwargs=UpperCamelCase__      )
							UpperCamelCase_:							List[Any]	=						model_cls.from_pretrained(UpperCamelCase__      )

							UpperCamelCase_:							str	=						cls(model.parameters()						,	model_cls=UpperCamelCase__						,	model_config=model.config      )

							ema_model.load_state_dict(UpperCamelCase__      )
							return ema_model

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Dict      ):







							if self.model_cls is None:
											raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""      )

							if self.model_config is None:
											raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""      )

							UpperCamelCase_:							Optional[Any]	=						self.model_cls.from_config(self.model_config      )
							UpperCamelCase_:							List[Any]	=						self.state_dict()
							state_dict.pop("""shadow_params"""						,	UpperCamelCase__      )

							model.register_to_config(**UpperCamelCase__      )
							self.copy_to(model.parameters()      )
							model.save_pretrained(UpperCamelCase__      )

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       int      ):
							UpperCamelCase_:							Any	=						max(0						,	optimization_step - self.update_after_step - 1      )

							if step <= 0:
											return 0.0

							if self.use_ema_warmup:
											UpperCamelCase_:							Tuple	=						1 - (1 + step / self.inv_gamma) ** -self.power
							else:
											UpperCamelCase_:							List[Any]	=						(1 + step) / (10 + step)

							UpperCamelCase_:							List[str]	=						min(UpperCamelCase__						,	self.decay      )
							# make sure decay is not smaller than min_decay
							UpperCamelCase_:							Any	=						max(UpperCamelCase__						,	self.min_decay      )
							return cur_decay_value

			@torch.no_grad()
			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Iterable[torch.nn.Parameter]      ):







							if isinstance(UpperCamelCase__						,	torch.nn.Module      ):
											UpperCamelCase_:							Any	=						(
											    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
											    """Please pass the parameters of the module instead."""
											)
											deprecate(
											    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""						,	"""1.0.0"""						,	UpperCamelCase__						,	standard_warn=UpperCamelCase__						,	)
											UpperCamelCase_:							int	=						parameters.parameters()

							UpperCamelCase_:							Optional[Any]	=						list(UpperCamelCase__      )

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							UpperCamelCase_:							int	=						self.get_decay(self.optimization_step      )
							UpperCamelCase_:							int	=						decay
							UpperCamelCase_:							int	=						1 - decay

							UpperCamelCase_:							Dict	=						contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
											import deepspeed

							for s_param, param in zip(self.shadow_params						,	UpperCamelCase__      ):
											if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
															UpperCamelCase_:							Optional[int]	=						deepspeed.zero.GatheredParameters(UpperCamelCase__						,	modifier_rank=UpperCamelCase__      )

											with context_manager():
															if param.requires_grad:
																			s_param.sub_(one_minus_decay * (s_param - param)      )
															else:
																			s_param.copy_(UpperCamelCase__      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Iterable[torch.nn.Parameter]      ):
							UpperCamelCase_:							Optional[Any]	=						list(UpperCamelCase__      )
							for s_param, param in zip(self.shadow_params						,	UpperCamelCase__      ):
											param.data.copy_(s_param.to(param.device      ).data      )

			def       lowerCAmelCase__	(       self :       List[str]						,	snake_case_ :       Tuple=None						,	snake_case_ :       Optional[int]=None      ):
							UpperCamelCase_:							Union[str, Any]	=						[
							    p.to(device=UpperCamelCase__						,	dtype=UpperCamelCase__      ) if p.is_floating_point() else p.to(device=UpperCamelCase__      )
							    for p in self.shadow_params
							]

			def       lowerCAmelCase__	(       self :       List[Any]      ):
							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Iterable[torch.nn.Parameter]      ):
							UpperCamelCase_:							List[Any]	=						[param.detach().cpu().clone() for param in parameters]

			def       lowerCAmelCase__	(       self :       str						,	snake_case_ :       Iterable[torch.nn.Parameter]      ):







							if self.temp_stored_params is None:
											raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""      )
							for c_param, param in zip(self.temp_stored_params						,	UpperCamelCase__      ):
											param.data.copy_(c_param.data      )

							# Better memory-wise.
							UpperCamelCase_:							str	=						None




			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       dict      ):
							UpperCamelCase_:							Union[str, Any]	=						copy.deepcopy(UpperCamelCase__      )

							UpperCamelCase_:							Dict	=						state_dict.get("""decay"""						,	self.decay      )
							if self.decay < 0.0 or self.decay > 1.0:
											raise ValueError("""Decay must be between 0 and 1"""      )

							UpperCamelCase_:							Optional[int]	=						state_dict.get("""min_decay"""						,	self.min_decay      )
							if not isinstance(self.min_decay						,	UpperCamelCase__      ):
											raise ValueError("""Invalid min_decay"""      )

							UpperCamelCase_:							Optional[int]	=						state_dict.get("""optimization_step"""						,	self.optimization_step      )
							if not isinstance(self.optimization_step						,	UpperCamelCase__      ):
											raise ValueError("""Invalid optimization_step"""      )

							UpperCamelCase_:							str	=						state_dict.get("""update_after_step"""						,	self.update_after_step      )
							if not isinstance(self.update_after_step						,	UpperCamelCase__      ):
											raise ValueError("""Invalid update_after_step"""      )

							UpperCamelCase_:							List[str]	=						state_dict.get("""use_ema_warmup"""						,	self.use_ema_warmup      )
							if not isinstance(self.use_ema_warmup						,	UpperCamelCase__      ):
											raise ValueError("""Invalid use_ema_warmup"""      )

							UpperCamelCase_:							Tuple	=						state_dict.get("""inv_gamma"""						,	self.inv_gamma      )
							if not isinstance(self.inv_gamma						,	(float, int)      ):
											raise ValueError("""Invalid inv_gamma"""      )

							UpperCamelCase_:							Tuple	=						state_dict.get("""power"""						,	self.power      )
							if not isinstance(self.power						,	(float, int)      ):
											raise ValueError("""Invalid power"""      )

							UpperCamelCase_:							str	=						state_dict.get("""shadow_params"""						,	UpperCamelCase__      )
							if shadow_params is not None:
											UpperCamelCase_:							int	=						shadow_params
											if not isinstance(self.shadow_params						,	UpperCamelCase__      ):
															raise ValueError("""shadow_params must be a list"""      )
											if not all(isinstance(UpperCamelCase__						,	torch.Tensor      ) for p in self.shadow_params      ):
															raise ValueError("""shadow_params must all be Tensors"""      )








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

lowerCamelCase_						: List[str]         =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       List[Any]						,	snake_case_ :       Dict      ):
							os.makedirs(snake_case_						,	exist_ok=snake_case_      )
							UpperCamelCase_:							int	=						{"""source""": """What is love ?""", """target""": """life"""}
							UpperCamelCase_:							Tuple	=						{"""train""": 12, """val""": 2, """test""": 2}
							for split in ["train", "test", "val"]:
											for field in ["source", "target"]:
															UpperCamelCase_:							Tuple	=						"""\n""".join([contents[field]] * n_lines[split]      )
															with open(os.path.join(snake_case_						,	f'''{split}.{field}'''      )						,	"""w"""      ) as f:
																			f.write(snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       int						,	snake_case_ :       str = "pytorch"      ):
							UpperCamelCase_:							Optional[Any]	=						self.get_auto_remove_tmp_dir()
							UpperCamelCase_:							Dict	=						os.path.join(snake_case_						,	"""output"""      )
							UpperCamelCase_:							Any	=						os.path.join(snake_case_						,	"""data"""      )
							self._create_dummy_data(data_dir=snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						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_:							Optional[Any]	=						[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
							execute_subprocess_async(snake_case_						,	env=self.get_env()      )

							UpperCamelCase_:							Optional[int]	=						os.path.join(snake_case_						,	"""metrics.json"""      )
							with open(snake_case_      ) as f:
											UpperCamelCase_:							Any	=						json.load(snake_case_      )
							return result

			@require_torch_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							List[str]	=						self._run_finetune(gpus=1      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_multi_gpu
			def       lowerCAmelCase__	(       self :       Optional[Any]      ):
							UpperCamelCase_:							Any	=						self._run_finetune(gpus=2      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )

			@require_torch_gpu
			@require_ray
			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						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       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							List[Any]	=						self._run_finetune(gpus=1						,	distributed_retriever="""ray"""      )
							self.assertGreaterEqual(result["""test"""][0]["""test_avg_em"""]						,	0.2      )






