

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     a__  (    __SCREAMING_SNAKE_CASE	):
			"""simple docstring"""







			def    UpperCamelCase						(			self					,					lowercase				)     ->  Tuple:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = "pytorch"				)     ->  int:
							'''simple docstring'''






							A__						=	self.get_auto_remove_tmp_dir()
							A__						=	os.path.join(_a					,					"output"				)
							A__						=	os.path.join(_a					,					"data"				)
							self._create_dummy_data(data_dir=_a				)

							A__						=	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"				)

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

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


			@require_torch_gpu
			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






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


			@require_torch_multi_gpu
			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






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


			@require_torch_gpu
			@require_ray
			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	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				)     ->  List[str]:
							'''simple docstring'''






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





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_			     =  {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XGLMConfig"]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizer"]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  ["XGLMTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "XGLMForCausalLM",
				    "XGLMModel",
				    "XGLMPreTrainedModel",
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "FlaxXGLMForCausalLM",
				    "FlaxXGLMModel",
				    "FlaxXGLMPreTrainedModel",
				]


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_			     =  [
				    "TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFXGLMForCausalLM",
				    "TFXGLMModel",
				    "TFXGLMPreTrainedModel",
				]


if TYPE_CHECKING:
				from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm import XGLMTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_xglm_fast import XGLMTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_xglm import (
								    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFXGLMForCausalLM,
								    TFXGLMModel,
								    TFXGLMPreTrainedModel,
								)


else:
				import sys

				lowercase_			     =  _LazyModule(__name__, globals()["__file__"], _import_structure)






