



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)
UpperCAmelCase_									=							logging.getLogger()

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





class 		UpperCAmelCase						(						UpperCAmelCase__				):





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





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase       ,     _lowerCAmelCase = "pytorch"			):
         _lowerCAmelCase 			=  self.get_auto_remove_tmp_dir()
         _lowerCAmelCase 			=  os.path.join(_lowerCAmelCase       ,     '''output'''			)
         _lowerCAmelCase 			=  os.path.join(_lowerCAmelCase       ,     '''data'''			)
         self._create_dummy_data(data_dir=_lowerCAmelCase			)

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

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





   @require_torch_gpu
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self._run_finetune(gpus=1			)
         self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,     0.2			)





   @require_torch_multi_gpu
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  self._run_finetune(gpus=2			)
         self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,     0.2			)





   @require_torch_gpu
   @require_ray
   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  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			):
         _lowerCAmelCase 			=  self._run_finetune(gpus=1       ,     distributed_retriever='''ray'''			)
         self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']       ,     0.2			)






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
    if not (is_transformers_available() and is_torch_available()):
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
    from .camera import create_pan_cameras
    from .pipeline_shap_e import ShapEPipeline
    from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
    from .renderer import (
        BoundingBoxVolume,
        ImportanceRaySampler,
        MLPNeRFModelOutput,
        MLPNeRSTFModel,
        ShapEParamsProjModel,
        ShapERenderer,
        StratifiedRaySampler,
        VoidNeRFModel,
    )