"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer





def       lowerCAmelCase__			(	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: Optional[int]       ,	_UpperCamelCase				: str       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: int=True       ,	_UpperCamelCase				: Optional[int]="pt"    )    -> int:





						"""simple docstring"""






						snake_case	    =	{"add_prefix_space": True} if isinstance(_lowerCAmelCase       ,	_lowerCAmelCase    ) and not line.startswith(' '    ) else {}
						snake_case	    =	padding_side
						return tokenizer(
						    [line]       ,	max_length=_lowerCAmelCase       ,	padding='max_length' if pad_to_max_length else None       ,	truncation=_lowerCAmelCase       ,	return_tensors=_lowerCAmelCase       ,	add_special_tokens=_lowerCAmelCase       ,	**_lowerCAmelCase       ,	)





def       lowerCAmelCase__			(	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: str       ,	_UpperCamelCase				: Any=None       ,	)    -> str:





						"""simple docstring"""






						snake_case	    =	input_ids.ne(_lowerCAmelCase    ).any(dim=0    )
						if attention_mask is None:
												return input_ids[:, keep_column_mask]
						else:
												return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])



class      lowerCAmelCase_			(	__snake_case    ):



				"""simple docstring"""

				def __init__( self						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase						,			lowerCAmelCase="train"						,			lowerCAmelCase=None						,			lowerCAmelCase=None						,			lowerCAmelCase=None						,			lowerCAmelCase=""						,			):






										"""simple docstring"""

										super().__init__()
										snake_case	    =	Path(A_			).joinpath(type_path + '.source'			)
										snake_case	    =	Path(A_			).joinpath(type_path + '.target'			)
										snake_case	    =	self.get_char_lens(self.src_file			)
										snake_case	    =	max_source_length
										snake_case	    =	max_target_length
										assert min(self.src_lens			) > 0, F"""found empty line in {self.src_file}"""
										snake_case	    =	tokenizer
										snake_case	    =	prefix
										if n_obs is not None:
																snake_case	    =	self.src_lens[:n_obs]
										snake_case	    =	src_lang
										snake_case	    =	tgt_lang

				def __len__( self			):






										"""simple docstring"""

										return len(self.src_lens			)

				def __getitem__( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	index + 1  # linecache starts at 1
										snake_case	    =	self.prefix + linecache.getline(str(self.src_file			)						,			A_			).rstrip('\n'			)
										snake_case	    =	linecache.getline(str(self.tgt_file			)						,			A_			).rstrip('\n'			)
										assert source_line, F"""empty source line for index {index}"""
										assert tgt_line, F"""empty tgt line for index {index}"""

										# Need to add eos token manually for T5
										if isinstance(self.tokenizer						,			A_			):
																source_line += self.tokenizer.eos_token
																tgt_line += self.tokenizer.eos_token

										# Pad source and target to the right
										snake_case	    =	(
										    self.tokenizer.question_encoder if isinstance(self.tokenizer						,			A_			) else self.tokenizer
										)
										snake_case	    =	self.tokenizer.generator if isinstance(self.tokenizer						,			A_			) else self.tokenizer

										snake_case	    =	encode_line(A_						,			A_						,			self.max_source_length						,			'right'			)
										snake_case	    =	encode_line(A_						,			A_						,			self.max_target_length						,			'right'			)

										snake_case	    =	source_inputs["input_ids"].squeeze()
										snake_case	    =	target_inputs["input_ids"].squeeze()
										snake_case	    =	source_inputs["attention_mask"].squeeze()
										return {
										    "input_ids": source_ids,
										    "attention_mask": src_mask,
										    "decoder_input_ids": target_ids,
										}

				@staticmethod
				def 						snake_case       ( lowerCAmelCase			):






										"""simple docstring"""

										return [len(A_			) for x in Path(A_			).open().readlines()]

				def 						snake_case       ( self						,			lowerCAmelCase			):






										"""simple docstring"""

										snake_case	    =	torch.stack([x['input_ids'] for x in batch]			)
										snake_case	    =	torch.stack([x['attention_mask'] for x in batch]			)
										snake_case	    =	torch.stack([x['decoder_input_ids'] for x in batch]			)
										snake_case	    =	(
										    self.tokenizer.generator.pad_token_id
										    if isinstance(self.tokenizer						,			A_			)
										    else self.tokenizer.pad_token_id
										)
										snake_case	    =	(
										    self.tokenizer.question_encoder.pad_token_id
										    if isinstance(self.tokenizer						,			A_			)
										    else self.tokenizer.pad_token_id
										)
										snake_case	    =	trim_batch(A_						,			A_			)
										snake_case	    =	trim_batch(A_						,			A_						,			attention_mask=A_			)
										snake_case	    =	{
										    "input_ids": source_ids,
										    "attention_mask": source_mask,
										    "decoder_input_ids": y,
										}
										return batch





SCREAMING_SNAKE_CASE__					  =     getLogger(__name__)





def       lowerCAmelCase__			(	_UpperCamelCase				: List[Any]    )    -> List[Any]:





						"""simple docstring"""






						return list(itertools.chain.from_iterable(_lowerCAmelCase    )    )





def       lowerCAmelCase__			(	_UpperCamelCase				: Any    )    -> None:





						"""simple docstring"""






						snake_case	    =	get_git_info()
						save_json(_lowerCAmelCase       ,	os.path.join(_lowerCAmelCase       ,	'git_log.json'    )    )





def       lowerCAmelCase__			(	_UpperCamelCase				: List[Any]       ,	_UpperCamelCase				: Optional[int]       ,	_UpperCamelCase				: str=4       ,	**_UpperCamelCase				: Any    )    -> List[str]:





						"""simple docstring"""






						with open(_lowerCAmelCase       ,	'w'    ) as f:
												json.dump(_lowerCAmelCase       ,	_lowerCAmelCase       ,	indent=_lowerCAmelCase       ,	**_lowerCAmelCase    )





def       lowerCAmelCase__			(	_UpperCamelCase				: str    )    -> Any:





						"""simple docstring"""






						with open(_lowerCAmelCase    ) as f:
												return json.load(_lowerCAmelCase    )





def       lowerCAmelCase__			(	)    -> Any:





						"""simple docstring"""






						snake_case	    =	git.Repo(search_parent_directories=_lowerCAmelCase    )
						snake_case	    =	{
						    "repo_id": str(_lowerCAmelCase    ),
						    "repo_sha": str(repo.head.object.hexsha    ),
						    "repo_branch": str(repo.active_branch    ),
						    "hostname": str(socket.gethostname()    ),
						}
						return repo_infos





def       lowerCAmelCase__			(	_UpperCamelCase				: Optional[int]       ,	_UpperCamelCase				: Optional[Any]    )    -> List:





						"""simple docstring"""






						return list(map(_lowerCAmelCase       ,	_lowerCAmelCase    )    )





def       lowerCAmelCase__			(	_UpperCamelCase				: List[str]       ,	_UpperCamelCase				: Any    )    -> List[Any]:





						"""simple docstring"""






						with open(_lowerCAmelCase       ,	'wb'    ) as f:
												return pickle.dump(_lowerCAmelCase       ,	_lowerCAmelCase    )





def       lowerCAmelCase__			(	_UpperCamelCase				: int    )    -> Union[str, Any]:





						"""simple docstring"""






						def remove_articles(_UpperCamelCase				: Any    ):
												return re.sub(r'\b(a|an|the)\b'       ,	' '       ,	_lowerCAmelCase    )

						def white_space_fix(_UpperCamelCase				: List[str]    ):
												return " ".join(text.split()    )

						def remove_punc(_UpperCamelCase				: str    ):
												snake_case	    =	set(string.punctuation    )
												return "".join(ch for ch in text if ch not in exclude    )

						def lower(_UpperCamelCase				: int    ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase    )    )    )    )





def       lowerCAmelCase__			(	_UpperCamelCase				: Any       ,	_UpperCamelCase				: Any    )    -> List[Any]:





						"""simple docstring"""






						snake_case	    =	normalize_answer(_lowerCAmelCase    ).split()
						snake_case	    =	normalize_answer(_lowerCAmelCase    ).split()
						snake_case	    =	Counter(_lowerCAmelCase    ) & Counter(_lowerCAmelCase    )
						snake_case	    =	sum(common.values()    )
						if num_same == 0:
												return 0
						snake_case	    =	1.0 * num_same / len(_lowerCAmelCase    )
						snake_case	    =	1.0 * num_same / len(_lowerCAmelCase    )
						snake_case	    =	(2 * precision * recall) / (precision + recall)
						return fa





def       lowerCAmelCase__			(	_UpperCamelCase				: int       ,	_UpperCamelCase				: List[Any]    )    -> Union[str, Any]:





						"""simple docstring"""






						return normalize_answer(_lowerCAmelCase    ) == normalize_answer(_lowerCAmelCase    )





def       lowerCAmelCase__			(	_UpperCamelCase				: Any       ,	_UpperCamelCase				: Union[str, Any]    )    -> Dict:





						"""simple docstring"""






						assert len(_lowerCAmelCase    ) == len(_lowerCAmelCase    )
						snake_case	    =	0
						for hypo, pred in zip(_lowerCAmelCase       ,	_lowerCAmelCase    ):
												em += exact_match_score(_lowerCAmelCase       ,	_lowerCAmelCase    )
						if len(_lowerCAmelCase    ) > 0:
												em /= len(_lowerCAmelCase    )
						return {"em": em}





def       lowerCAmelCase__			(	_UpperCamelCase				: str    )    -> str:





						"""simple docstring"""






						return model_prefix.startswith('rag'    )





def       lowerCAmelCase__			(	_UpperCamelCase				: Tuple       ,	_UpperCamelCase				: int       ,	_UpperCamelCase				: Dict    )    -> int:





						"""simple docstring"""






						snake_case	    =	{p: p for p in extra_params}
						# T5 models don't have `dropout` param, they have `dropout_rate` instead
						snake_case	    =	"dropout_rate"
						for p in extra_params:
												if getattr(_lowerCAmelCase       ,	_lowerCAmelCase       ,	_lowerCAmelCase    ):
																		if not hasattr(_lowerCAmelCase       ,	_lowerCAmelCase    ) and not hasattr(_lowerCAmelCase       ,	equivalent_param[p]    ):
																								logger.info('config doesn\'t have a `{}` attribute'.format(_lowerCAmelCase    )    )
																								delattr(_lowerCAmelCase       ,	_lowerCAmelCase    )
																								continue
																		snake_case	    =	p if hasattr(_lowerCAmelCase       ,	_lowerCAmelCase    ) else equivalent_param[p]
																		setattr(_lowerCAmelCase       ,	_lowerCAmelCase       ,	getattr(_lowerCAmelCase       ,	_lowerCAmelCase    )    )
																		delattr(_lowerCAmelCase       ,	_lowerCAmelCase    )
						return hparams, config









from typing import TYPE_CHECKING

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


__lowerCamelCase       :     Any   =     {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Dict   =     ["""ElectraTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     Tuple   =     [
        """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ElectraForCausalLM""",
        """ElectraForMaskedLM""",
        """ElectraForMultipleChoice""",
        """ElectraForPreTraining""",
        """ElectraForQuestionAnswering""",
        """ElectraForSequenceClassification""",
        """ElectraForTokenClassification""",
        """ElectraModel""",
        """ElectraPreTrainedModel""",
        """load_tf_weights_in_electra""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[Any]   =     [
        """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFElectraForMaskedLM""",
        """TFElectraForMultipleChoice""",
        """TFElectraForPreTraining""",
        """TFElectraForQuestionAnswering""",
        """TFElectraForSequenceClassification""",
        """TFElectraForTokenClassification""",
        """TFElectraModel""",
        """TFElectraPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase       :     List[str]   =     [
        """FlaxElectraForCausalLM""",
        """FlaxElectraForMaskedLM""",
        """FlaxElectraForMultipleChoice""",
        """FlaxElectraForPreTraining""",
        """FlaxElectraForQuestionAnswering""",
        """FlaxElectraForSequenceClassification""",
        """FlaxElectraForTokenClassification""",
        """FlaxElectraModel""",
        """FlaxElectraPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
    from .tokenization_electra import ElectraTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_electra_fast import ElectraTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_electra import (
            ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            ElectraForCausalLM,
            ElectraForMaskedLM,
            ElectraForMultipleChoice,
            ElectraForPreTraining,
            ElectraForQuestionAnswering,
            ElectraForSequenceClassification,
            ElectraForTokenClassification,
            ElectraModel,
            ElectraPreTrainedModel,
            load_tf_weights_in_electra,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_electra import (
            TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFElectraForMaskedLM,
            TFElectraForMultipleChoice,
            TFElectraForPreTraining,
            TFElectraForQuestionAnswering,
            TFElectraForSequenceClassification,
            TFElectraForTokenClassification,
            TFElectraModel,
            TFElectraPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_electra import (
            FlaxElectraForCausalLM,
            FlaxElectraForMaskedLM,
            FlaxElectraForMultipleChoice,
            FlaxElectraForPreTraining,
            FlaxElectraForQuestionAnswering,
            FlaxElectraForSequenceClassification,
            FlaxElectraForTokenClassification,
            FlaxElectraModel,
            FlaxElectraPreTrainedModel,
        )

else:
    import sys

    __lowerCamelCase       :     Any   =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
