'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:     List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Dict  ={
    'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}




class        __A      ( UpperCamelCase__	):
    a__       :						Union[str, Any]          =  """nllb-moe"""
    a__       :						Optional[Any]          =  ["""past_key_values"""]
    a__       :						Dict          =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



    def __init__(self    :    Optional[Any]   ,			__a    :    Tuple=128112   ,			__a    :    Optional[int]=1024   ,			__a    :    List[str]=12   ,			__a    :    int=4096   ,			__a    :    str=16   ,			__a    :    Any=12   ,			__a    :    List[str]=4096   ,			__a    :    str=16   ,			__a    :    Any=0.05   ,			__a    :    str=0.05   ,			__a    :    Dict=True   ,			__a    :    Dict=True   ,			__a    :    Optional[Any]="relu"   ,			__a    :    List[str]=1024   ,			__a    :    Any=0.1   ,			__a    :    Union[str, Any]=0.1   ,			__a    :    List[str]=0.0   ,			__a    :    int=0.02   ,			__a    :    Optional[int]=2   ,			__a    :    List[Any]=True   ,			__a    :    Any=False   ,			__a    :    List[str]="float32"   ,			__a    :    List[str]=False   ,			__a    :    Optional[int]=128   ,			__a    :    Any=64   ,			__a    :    Any=4   ,			__a    :    List[str]=4   ,			__a    :    Union[str, Any]=0.0_01   ,			__a    :    Optional[Any]=0.0_01   ,			__a    :    List[Any]="all"   ,			__a    :    Tuple=False   ,			__a    :    Any=False   ,			__a    :    Dict=1.0   ,			__a    :    int=0.2   ,			__a    :    Dict=1   ,			__a    :    Dict=0   ,			__a    :    Optional[int]=2   ,			__a    :    Any=False   ,			**__a    :    Optional[int]   ,			):
         UpperCAmelCase_           =   vocab_size
         UpperCAmelCase_           =   max_position_embeddings
         UpperCAmelCase_           =   d_model
         UpperCAmelCase_           =   encoder_ffn_dim
         UpperCAmelCase_           =   encoder_layers
         UpperCAmelCase_           =   encoder_attention_heads
         UpperCAmelCase_           =   decoder_ffn_dim
         UpperCAmelCase_           =   decoder_layers
         UpperCAmelCase_           =   decoder_attention_heads
         UpperCAmelCase_           =   dropout
         UpperCAmelCase_           =   attention_dropout
         UpperCAmelCase_           =   activation_dropout
         UpperCAmelCase_           =   activation_function
         UpperCAmelCase_           =   init_std
         UpperCAmelCase_           =   encoder_layerdrop
         UpperCAmelCase_           =   decoder_layerdrop
         UpperCAmelCase_           =   use_cache
         UpperCAmelCase_           =   encoder_layers
         UpperCAmelCase_           =   scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase_           =   router_z_loss_coef
         UpperCAmelCase_           =   router_aux_loss_coef
         UpperCAmelCase_           =   decoder_sparse_step
         UpperCAmelCase_           =   encoder_sparse_step
         UpperCAmelCase_           =   num_experts
         UpperCAmelCase_           =   expert_capacity
         UpperCAmelCase_           =   router_bias
         if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""" )
         UpperCAmelCase_           =   router_dtype

         UpperCAmelCase_           =   router_ignore_padding_tokens
         UpperCAmelCase_           =   batch_prioritized_routing
         UpperCAmelCase_           =   second_expert_policy
         UpperCAmelCase_           =   normalize_router_prob_before_dropping
         UpperCAmelCase_           =   moe_eval_capacity_token_fraction
         UpperCAmelCase_           =   moe_token_dropout
         UpperCAmelCase_           =   output_router_logits
         super().__init__(
             pad_token_id=__a   ,			bos_token_id=__a   ,			eos_token_id=__a   ,			is_encoder_decoder=__a   ,			decoder_start_token_id=__a   ,			**__a   ,			)





'''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)
SCREAMING_SNAKE_CASE_:     Optional[int]  =logging.getLogger()

SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class        __A      ( UpperCamelCase__	):



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    str ):
         os.makedirs(__a   ,			exist_ok=__a )
         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(__a   ,			f"""{split}.{field}""" )   ,			"w" ) as f:
                        f.write(__a )



    def 					_lowercase						(self    :    Optional[int]   ,			__a    :    int   ,			__a    :    str = "pytorch" ):
         UpperCAmelCase_           =   self.get_auto_remove_tmp_dir()
         UpperCAmelCase_           =   os.path.join(__a   ,			"output" )
         UpperCAmelCase_           =   os.path.join(__a   ,			"data" )
         self._create_dummy_data(data_dir=__a )

         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(__a   ,			env=self.get_env() )

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



    @require_torch_gpu
    def 					_lowercase						(self    :    Optional[int] ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_multi_gpu
    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self._run_finetune(gpus=2 )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )



    @require_torch_gpu
    @require_ray
    def 					_lowercase						(self    :    Optional[int] ):
         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 					_lowercase						(self    :    Any ):
         UpperCAmelCase_           =   self._run_finetune(gpus=1   ,			distributed_retriever="ray" )
         self.assertGreaterEqual(result["test"][0]["test_avg_em"]   ,			0.2 )




