


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

SCREAMING_SNAKE_CASE			: str				   =					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class _lowerCamelCase(		_a  ):
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Optional[Any]:




           """simple docstring"""

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




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						self.get_auto_remove_tmp_dir()
           _lowercase			:				Optional[Any] 						=						os.path.join(lowerCamelCase, 'output')
           _lowercase			:				Optional[Any] 						=						os.path.join(lowerCamelCase, 'data')
           self._create_dummy_data(data_dir=lowerCamelCase)

           _lowercase			:				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')

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

           _lowercase			:				Dict 						=						os.path.join(lowerCamelCase, 'metrics.json')
           with open(lowerCamelCase) as f:
               _lowercase			:				Dict 						=						json.load(lowerCamelCase)
           return result
       @require_torch_gpu
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						self._run_finetune(gpus=1)
           self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)
       @require_torch_multi_gpu
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				int 						=						self._run_finetune(gpus=2)
           self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)
       @require_torch_gpu
       @require_ray
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           _lowercase			:				Union[str, 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      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				Tuple 						=						self._run_finetune(gpus=1, distributed_retriever='ray')
           self.assertGreaterEqual(result['test'][0]['test_avg_em'], 0.2)









import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
_A					:				Optional[int]				=			object()

# For specifying empty leaf dict `{}`
_A					:				Tuple				=			object()


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    SCREAMING_SNAKE_CASE__											=			tuple((re.compile(x + '''$'''	) for x in qs)	)
    for i in range(len(lowerCAmelCase_	) - len(lowerCAmelCase_	) + 1	):
        SCREAMING_SNAKE_CASE__											=			[x.match(lowerCAmelCase_	) for x, y in zip(lowerCAmelCase_ ,							ks[i:]	)]
        if matches and all(lowerCAmelCase_	):
            return True
    return False


def 							__snake_case							(    lowerCAmelCase_	)				->						int:
    def replace(lowerCAmelCase_ ,							lowerCAmelCase_	):
        for rule, replacement in rules:
            if _match(lowerCAmelCase_ ,							lowerCAmelCase_	):
                return replacement
        return val

    return replace


def 							__snake_case							(    )				->						Optional[int]:
    return [
        # embeddings
        (("transformer", "wpe", "embedding"), P('''mp''' ,							lowerCAmelCase_	)),
        (("transformer", "wte", "embedding"), P('''mp''' ,							lowerCAmelCase_	)),
        # atention
        (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowerCAmelCase_ ,							'''mp'''	)),
        (("attention", "out_proj", "kernel"), P('''mp''' ,							lowerCAmelCase_	)),
        (("attention", "out_proj", "bias"), None),
        # mlp
        (("mlp", "c_fc", "kernel"), P(lowerCAmelCase_ ,							'''mp'''	)),
        (("mlp", "c_fc", "bias"), P('''mp'''	)),
        (("mlp", "c_proj", "kernel"), P('''mp''' ,							lowerCAmelCase_	)),
        (("mlp", "c_proj", "bias"), None),
        # layer norms
        ((r"ln_\d+", "bias"), None),
        ((r"\d+", r"ln_\d+", "scale"), None),
        (("ln_f", "bias"), None),
        (("ln_f", "scale"), None),
    ]



def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			_get_partition_rules()
    SCREAMING_SNAKE_CASE__											=			_replacement_rules(lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			{k: _unmatched for k in flatten_dict(lowerCAmelCase_	)}
    SCREAMING_SNAKE_CASE__											=			{k: replace(lowerCAmelCase_ ,							lowerCAmelCase_	) for k, v in initd.items()}
    assert _unmatched not in result.values(), "Incomplete partition spec."
    return freeze(unflatten_dict(lowerCAmelCase_	)	)
