






"""simple docstring"""

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)
lowercase__          = logging.getLogger()

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


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






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





		def    lowerCamelCase       (   self							:	str						,			a_							:	int						,			a_							:	str = "pytorch"	):
				lowerCAmelCase_   :							Any									=			self.get_auto_remove_tmp_dir()
				lowerCAmelCase_   :							str									=			os.path.join(a_						,			"output"	)
				lowerCAmelCase_   :							Optional[int]									=			os.path.join(a_						,			"data"	)
				self._create_dummy_data(data_dir=a_	)

				lowerCAmelCase_   :							List[str]									=			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"	)

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

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





		@require_torch_gpu
		def    lowerCamelCase       (   self							:	Any	):
				lowerCAmelCase_   :							int									=			self._run_finetune(gpus=1	)
				self.assertGreaterEqual(result["test"][0]["test_avg_em"]						,			0.2	)





		@require_torch_multi_gpu
		def    lowerCamelCase       (   self							:	List[str]	):
				lowerCAmelCase_   :							Optional[int]									=			self._run_finetune(gpus=2	)
				self.assertGreaterEqual(result["test"][0]["test_avg_em"]						,			0.2	)





		@require_torch_gpu
		@require_ray
		def    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_   :							Tuple									=			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							:	Tuple	):
				lowerCAmelCase_   :							Dict									=			self._run_finetune(gpus=1						,			distributed_retriever="ray"	)
				self.assertGreaterEqual(result["test"][0]["test_avg_em"]						,			0.2	)








"""simple docstring"""

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    Dict:

		"""simple docstring"""







		return EnvironmentCommand()

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    Optional[int]:

		"""simple docstring"""







		return EnvironmentCommand(args.accelerate_config_file       )



class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		@staticmethod
		def    lowerCamelCase       (   a_							:	ArgumentParser	):
				lowerCAmelCase_   :							str									=			parser.add_parser("env"	)
				download_parser.set_defaults(func=a_	)
				download_parser.add_argument(
				    "--accelerate-config_file"						,			default=a_						,			help="The accelerate config file to use for the default values in the launching script."						,			)
				download_parser.set_defaults(func=a_	)





		def __init__(   self							:	Dict						,			a_							:	Dict						,			*a_							:	str	):
				lowerCAmelCase_   :							Union[str, Any]									=			accelerate_config_file





		def    lowerCamelCase       (   self							:	Any	):
				lowerCAmelCase_   :							Optional[int]									=			"not installed"
				if is_safetensors_available():
						import safetensors

						lowerCAmelCase_   :							int									=			safetensors.__version__
				elif importlib.util.find_spec("safetensors"	) is not None:
						import safetensors

						lowerCAmelCase_   :							Optional[Any]									=			f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

				lowerCAmelCase_   :							List[Any]									=			"not installed"
				lowerCAmelCase_   :							Dict									=			"not found"
				if is_accelerate_available():
						import accelerate
						from accelerate.commands.config import default_config_file, load_config_from_file

						lowerCAmelCase_   :							int									=			accelerate.__version__
						# Get the default from the config file.
						if self._accelerate_config_file is not None or os.path.isfile(a_	):
								lowerCAmelCase_   :							int									=			load_config_from_file(self._accelerate_config_file	).to_dict()

						lowerCAmelCase_   :							Any									=			(
						    "\n".join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]	)
						    if isinstance(a_						,			a_	)
						    else f'''\t{accelerate_config}'''
						)

				lowerCAmelCase_   :							Union[str, Any]									=			"not installed"
				lowerCAmelCase_   :							Dict									=			"NA"
				if is_torch_available():
						import torch

						lowerCAmelCase_   :							Tuple									=			torch.__version__
						lowerCAmelCase_   :							Union[str, Any]									=			torch.cuda.is_available()

				lowerCAmelCase_   :							List[str]									=			"not installed"
				lowerCAmelCase_   :							Tuple									=			"NA"
				if is_tf_available():
						import tensorflow as tf

						lowerCAmelCase_   :							Union[str, Any]									=			tf.__version__
						try:
								# deprecated in v2.1
								lowerCAmelCase_   :							Tuple									=			tf.test.is_gpu_available()
						except AttributeError:
								# returns list of devices, convert to bool
								lowerCAmelCase_   :							List[str]									=			bool(tf.config.list_physical_devices("GPU"	)	)

				lowerCAmelCase_   :							Optional[Any]									=			"not installed"
				lowerCAmelCase_   :							Optional[int]									=			"not installed"
				lowerCAmelCase_   :							Tuple									=			"not installed"
				lowerCAmelCase_   :							Tuple									=			"NA"
				if is_flax_available():
						import flax
						import jax
						import jaxlib

						lowerCAmelCase_   :							List[Any]									=			flax.__version__
						lowerCAmelCase_   :							Tuple									=			jax.__version__
						lowerCAmelCase_   :							List[Any]									=			jaxlib.__version__
						lowerCAmelCase_   :							str									=			jax.lib.xla_bridge.get_backend().platform

				lowerCAmelCase_   :							Dict									=			{
				    "`transformers` version": version,
				    "Platform": platform.platform(),
				    "Python version": platform.python_version(),
				    "Huggingface_hub version": huggingface_hub.__version__,
				    "Safetensors version": f'''{safetensors_version}''',
				    "Accelerate version": f'''{accelerate_version}''',
				    "Accelerate config": f'''{accelerate_config_str}''',
				    "PyTorch version (GPU?)": f'''{pt_version} ({pt_cuda_available})''',
				    "Tensorflow version (GPU?)": f'''{tf_version} ({tf_cuda_available})''',
				    "Flax version (CPU?/GPU?/TPU?)": f'''{flax_version} ({jax_backend})''',
				    "Jax version": f'''{jax_version}''',
				    "JaxLib version": f'''{jaxlib_version}''',
				    "Using GPU in script?": "<fill in>",
				    "Using distributed or parallel set-up in script?": "<fill in>",
				}

				print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"	)
				print(self.format_dict(a_	)	)

				return info





		@staticmethod
		def    lowerCamelCase       (   a_							:	Tuple	):
				return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()]	) + "\n"
