

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							(    _lowercase):




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




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		int		, _SCREAMING_SNAKE_CASE						:		str = "pytorch"		)->	Optional[int]:
			lowerCAmelCase__       :      Optional[int]   					= self.get_auto_remove_tmp_dir()
			lowerCAmelCase__       :      Optional[Any]   					= os.path.join(_SCREAMING_SNAKE_CASE		, '''output'''		)
			lowerCAmelCase__       :      Optional[int]   					= os.path.join(_SCREAMING_SNAKE_CASE		, '''data'''		)
			self._create_dummy_data(data_dir=_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      int   					= 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'''		)

			lowerCAmelCase__       :      List[Any]   					= [sys.executable, str(Path(finetune_rag.__file__		).resolve()		)] + testargs
			execute_subprocess_async(_SCREAMING_SNAKE_CASE		, env=self.get_env()		)

			lowerCAmelCase__       :      List[Any]   					= os.path.join(_SCREAMING_SNAKE_CASE		, '''metrics.json'''		)
			with open(_SCREAMING_SNAKE_CASE		) as f:
					lowerCAmelCase__       :      int   					= json.load(_SCREAMING_SNAKE_CASE		)
			return result




	@require_torch_gpu
	def 							UpperCAmelCase__( self						:		List[str]		)->	Dict:
			lowerCAmelCase__       :      int   					= self._run_finetune(gpus=1		)
			self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		, 0.2		)




	@require_torch_multi_gpu
	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Dict:
			lowerCAmelCase__       :      Optional[int]   					= self._run_finetune(gpus=2		)
			self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		, 0.2		)




	@require_torch_gpu
	@require_ray
	def 							UpperCAmelCase__( self						:		Optional[Any]		)->	List[Any]:
			lowerCAmelCase__       :      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						:		str		)->	Union[str, Any]:
			lowerCAmelCase__       :      Union[str, Any]   					= self._run_finetune(gpus=1		, distributed_retriever='''ray'''		)
			self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		, 0.2		)








import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


lowerCamelCase						=		logging.get_logger(__name__)

lowerCamelCase						=		{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase						=		[
    '''small''',
    '''small-base''',
    '''medium''',
    '''medium-base''',
    '''intermediate''',
    '''intermediate-base''',
    '''large''',
    '''large-base''',
    '''xlarge''',
    '''xlarge-base''',
]

lowerCamelCase						=		{
    '''vocab_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt''',
        '''funnel-transformer/small-base''': '''https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt''',
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt''',
        '''funnel-transformer/large-base''': '''https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json''',
        '''funnel-transformer/small-base''': (
            '''https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/medium''': '''https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json''',
        '''funnel-transformer/medium-base''': (
            '''https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate''': (
            '''https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/intermediate-base''': (
            '''https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/large''': '''https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json''',
        '''funnel-transformer/large-base''': (
            '''https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'''
        ),
        '''funnel-transformer/xlarge''': '''https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json''',
        '''funnel-transformer/xlarge-base''': (
            '''https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'''
        ),
    },
}
lowerCamelCase						=		{f'''funnel-transformer/{name}''': 512 for name in _model_names}
lowerCamelCase						=		{f'''funnel-transformer/{name}''': {'''do_lower_case''': True} for name in _model_names}





class  _a							(    _lowercase):
	_a  :  Tuple				   =							VOCAB_FILES_NAMES
	_a  :  Dict				   =							PRETRAINED_VOCAB_FILES_MAP
	_a  :  Dict				   =							PRETRAINED_INIT_CONFIGURATION
	_a  :  Union[str, Any]				   =							FunnelTokenizer
	_a  :  int				   =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_a  :  int				   =							2




	def __init__( self						:		List[Any]		, _SCREAMING_SNAKE_CASE						:		str=None		, _SCREAMING_SNAKE_CASE						:		str=None		, _SCREAMING_SNAKE_CASE						:		Any=True		, _SCREAMING_SNAKE_CASE						:		Any="<unk>"		, _SCREAMING_SNAKE_CASE						:		Dict="<sep>"		, _SCREAMING_SNAKE_CASE						:		Optional[int]="<pad>"		, _SCREAMING_SNAKE_CASE						:		str="<cls>"		, _SCREAMING_SNAKE_CASE						:		List[str]="<mask>"		, _SCREAMING_SNAKE_CASE						:		Optional[int]="<s>"		, _SCREAMING_SNAKE_CASE						:		Dict="</s>"		, _SCREAMING_SNAKE_CASE						:		Any=True		, _SCREAMING_SNAKE_CASE						:		Dict=True		, _SCREAMING_SNAKE_CASE						:		Tuple=None		, _SCREAMING_SNAKE_CASE						:		str="##"		, **_SCREAMING_SNAKE_CASE						:		List[str]		, )->	List[str]:
			super().__init__(
			    _SCREAMING_SNAKE_CASE		, tokenizer_file=_SCREAMING_SNAKE_CASE		, do_lower_case=_SCREAMING_SNAKE_CASE		, unk_token=_SCREAMING_SNAKE_CASE		, sep_token=_SCREAMING_SNAKE_CASE		, pad_token=_SCREAMING_SNAKE_CASE		, cls_token=_SCREAMING_SNAKE_CASE		, mask_token=_SCREAMING_SNAKE_CASE		, bos_token=_SCREAMING_SNAKE_CASE		, eos_token=_SCREAMING_SNAKE_CASE		, clean_text=_SCREAMING_SNAKE_CASE		, tokenize_chinese_chars=_SCREAMING_SNAKE_CASE		, strip_accents=_SCREAMING_SNAKE_CASE		, wordpieces_prefix=_SCREAMING_SNAKE_CASE		, **_SCREAMING_SNAKE_CASE		, )

			lowerCAmelCase__       :      Any   					= json.loads(self.backend_tokenizer.normalizer.__getstate__()		)
			if (
			    normalizer_state.get('''lowercase'''		, _SCREAMING_SNAKE_CASE		) != do_lower_case
			    or normalizer_state.get('''strip_accents'''		, _SCREAMING_SNAKE_CASE		) != strip_accents
			    or normalizer_state.get('''handle_chinese_chars'''		, _SCREAMING_SNAKE_CASE		) != tokenize_chinese_chars
			):
					lowerCAmelCase__       :      int   					= getattr(_SCREAMING_SNAKE_CASE		, normalizer_state.pop('''type'''		)		)
					lowerCAmelCase__       :      Dict   					= do_lower_case
					lowerCAmelCase__       :      str   					= strip_accents
					lowerCAmelCase__       :      Dict   					= tokenize_chinese_chars
					lowerCAmelCase__       :      str   					= normalizer_class(**_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__       :      List[Any]   					= do_lower_case




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]		, _SCREAMING_SNAKE_CASE						:		Union[str, Any]=None		)->	Optional[int]:
			lowerCAmelCase__       :      Tuple   					= [self.cls_token_id] + token_ids_a + [self.sep_token_id]

			if token_ids_a:
					output += token_ids_a + [self.sep_token_id]

			return output




	def 							UpperCAmelCase__( self						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		List[int]		, _SCREAMING_SNAKE_CASE						:		Optional[List[int]] = None		)->	List[int]:
			lowerCAmelCase__       :      str   					= [self.sep_token_id]
			lowerCAmelCase__       :      Optional[int]   					= [self.cls_token_id]
			if token_ids_a is None:
					return len(cls		) * [self.cls_token_type_id] + len(token_ids_a + sep		) * [0]
			return len(cls		) * [self.cls_token_type_id] + len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]




	def 							UpperCAmelCase__( self						:		str		, _SCREAMING_SNAKE_CASE						:		str		, _SCREAMING_SNAKE_CASE						:		Optional[str] = None		)->	Tuple[str]:
			lowerCAmelCase__       :      Any   					= self._tokenizer.model.save(_SCREAMING_SNAKE_CASE		, name=_SCREAMING_SNAKE_CASE		)
			return tuple(_SCREAMING_SNAKE_CASE		)




