"""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 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Any   , _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : int   , _lowerCAmelCase  : List[Any]=True   , _lowerCAmelCase  : Optional[Any]="pt"     ):





       '''simple docstring'''

       lowercase__			:		Optional[int]       =	{'add_prefix_space': True} if isinstance(_lowerCAmelCase   , _lowerCAmelCase     ) and not line.startswith(' '     ) else {}
       lowercase__			:		str       =	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 				a_     (      _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : str=None   , ):





       '''simple docstring'''

       lowercase__			:		List[Any]       =	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      UpperCAmelCase_	(    _a):


      def __init__(   self		,			a		,			a		,			a		,			a		,			a="train"		,			a=None		,			a=None		,			a=None		,			a=""		,			)       ->					Any:
             super().__init__()
             lowercase__			:		Any       =	Path(a							).joinpath(type_path + '.source'							)
             lowercase__			:		Union[str, Any]       =	Path(a							).joinpath(type_path + '.target'							)
             lowercase__			:		List[str]       =	self.get_char_lens(self.src_file							)
             lowercase__			:		Any       =	max_source_length
             lowercase__			:		str       =	max_target_length
             assert min(self.src_lens							) > 0, f"""found empty line in {self.src_file}"""
             lowercase__			:		List[Any]       =	tokenizer
             lowercase__			:		Optional[Any]       =	prefix
             if n_obs is not None:
                    lowercase__			:		str       =	self.src_lens[:n_obs]
             lowercase__			:		Optional[Any]       =	src_lang
             lowercase__			:		Tuple       =	tgt_lang


      def __len__(   self							)       ->					List[Any]:
             return len(self.src_lens							)


      def __getitem__(   self		,			a							)       ->					Dict[str, torch.Tensor]:
             lowercase__			:		Optional[Any]       =	index + 1  # linecache starts at 1
             lowercase__			:		Tuple       =	self.prefix + linecache.getline(str(self.src_file							)		,			a							).rstrip('\n'							)
             lowercase__			:		List[Any]       =	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
             lowercase__			:		str       =	(
                 self.tokenizer.question_encoder if isinstance(self.tokenizer		,			a							) else self.tokenizer
             )
             lowercase__			:		List[Any]       =	self.tokenizer.generator if isinstance(self.tokenizer		,			a							) else self.tokenizer

             lowercase__			:		Dict       =	encode_line(a		,			a		,			self.max_source_length		,			'right'							)
             lowercase__			:		Optional[int]       =	encode_line(a		,			a		,			self.max_target_length		,			'right'							)

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


      @staticmethod
      def 		_UpperCAmelCase       (   a							)       ->					Optional[Any]:
             return [len(a							) for x in Path(a							).open().readlines()]


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Dict[str, torch.Tensor]:
             lowercase__			:		Union[str, Any]       =	torch.stack([x['input_ids'] for x in batch]							)
             lowercase__			:		Dict       =	torch.stack([x['attention_mask'] for x in batch]							)
             lowercase__			:		List[str]       =	torch.stack([x['decoder_input_ids'] for x in batch]							)
             lowercase__			:		Optional[Any]       =	(
                 self.tokenizer.generator.pad_token_id
                 if isinstance(self.tokenizer		,			a							)
                 else self.tokenizer.pad_token_id
             )
             lowercase__			:		Optional[int]       =	(
                 self.tokenizer.question_encoder.pad_token_id
                 if isinstance(self.tokenizer		,			a							)
                 else self.tokenizer.pad_token_id
             )
             lowercase__			:		Optional[Any]       =	trim_batch(a		,			a							)
             lowercase__    ,      lowercase__			:		Optional[int]       =	trim_batch(a		,			a		,			attention_mask=a							)
             lowercase__			:		str       =	{
                 'input_ids': source_ids,
                 'attention_mask': source_mask,
                 'decoder_input_ids': y,
             }
             return batch





_UpperCamelCase   :						Any      =    getLogger(__name__)





def 				a_     (      _lowerCAmelCase  : List[List]     ):





       '''simple docstring'''

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





def 				a_     (      _lowerCAmelCase  : str     ):





       '''simple docstring'''

       lowercase__			:		int       =	get_git_info()
       save_json(_lowerCAmelCase   , os.path.join(_lowerCAmelCase   , 'git_log.json'     )     )





def 				a_     (      _lowerCAmelCase  : Any   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : List[Any]=4   , **_lowerCAmelCase  : Dict     ):





       '''simple docstring'''

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





def 				a_     (      _lowerCAmelCase  : List[str]     ):





       '''simple docstring'''

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





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		int       =	git.Repo(search_parent_directories=_lowerCAmelCase     )
       lowercase__			:		Union[str, Any]       =	{
           '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 				a_     (      _lowerCAmelCase  : Callable   , _lowerCAmelCase  : Iterable     ):





       '''simple docstring'''

       return list(map(_lowerCAmelCase   , _lowerCAmelCase     )     )





def 				a_     (      _lowerCAmelCase  : int   , _lowerCAmelCase  : Optional[int]     ):





       '''simple docstring'''

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





def 				a_     (      _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       def remove_articles(_lowerCAmelCase  : Union[str, Any]     ):
              return re.sub(R'\b(a|an|the)\b'   , ' '   , _lowerCAmelCase     )

       def white_space_fix(_lowerCAmelCase  : Optional[Any]     ):
              return " ".join(text.split()     )

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

       def lower(_lowerCAmelCase  : Dict     ):
              return text.lower()

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





def 				a_     (      _lowerCAmelCase  : Dict   , _lowerCAmelCase  : Optional[Any]     ):





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	normalize_answer(_lowerCAmelCase     ).split()
       lowercase__			:		Dict       =	normalize_answer(_lowerCAmelCase     ).split()
       lowercase__			:		Tuple       =	Counter(_lowerCAmelCase     ) & Counter(_lowerCAmelCase     )
       lowercase__			:		Tuple       =	sum(common.values()     )
       if num_same == 0:
              return 0
       lowercase__			:		List[str]       =	1.0 * num_same / len(_lowerCAmelCase     )
       lowercase__			:		List[Any]       =	1.0 * num_same / len(_lowerCAmelCase     )
       lowercase__			:		List[Any]       =	(2 * precision * recall) / (precision + recall)
       return fa





def 				a_     (      _lowerCAmelCase  : Any   , _lowerCAmelCase  : Tuple     ):





       '''simple docstring'''

       return normalize_answer(_lowerCAmelCase     ) == normalize_answer(_lowerCAmelCase     )





def 				a_     (      _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : List[str]     ):





       '''simple docstring'''

       assert len(_lowerCAmelCase     ) == len(_lowerCAmelCase     )
       lowercase__			:		Dict       =	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 				a_     (      _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       return model_prefix.startswith('rag'     )






def 				a_     (      _lowerCAmelCase  : int   , _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Optional[int]     ):





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	{p: p for p in extra_params}
       # T5 models don't have `dropout` param, they have `dropout_rate` instead
       lowercase__			:		int       =	'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
                     lowercase__			:		Any       =	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_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A				={'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=['''XGLMTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''XGLMForCausalLM''',
			    '''XGLMModel''',
			    '''XGLMPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''FlaxXGLMForCausalLM''',
			    '''FlaxXGLMModel''',
			    '''FlaxXGLMPreTrainedModel''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFXGLMForCausalLM''',
			    '''TFXGLMModel''',
			    '''TFXGLMPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm import XGLMTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xglm_fast import XGLMTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xglm import (
						    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXGLMForCausalLM,
						    TFXGLMModel,
						    TFXGLMPreTrainedModel,
						)


else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure)


