



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      __lowercase       (			a__							,       a__							,       a__							,       a__							,       a__=True							,       a__="pt"       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= {'add_prefix_space': True} if isinstance(a__							,       a__       ) and not line.startswith(' '       ) else {}
							__SCREAMING_SNAKE_CASE									= padding_side
							return tokenizer(
							    [line]							,       max_length=a__							,       padding='max_length' if pad_to_max_length else None							,       truncation=a__							,       return_tensors=a__							,       add_special_tokens=a__							,       **a__							,       )






def      __lowercase       (			a__							,       a__							,       a__=None							,       )    ->			Any:
							__SCREAMING_SNAKE_CASE									= input_ids.ne(a__       ).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_  (       UpperCamelCase_	):

						'''simple docstring'''




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







													'''simple docstring'''

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






						def __len__(   self		):







													'''simple docstring'''

													return len(self.src_lens		)






						def __getitem__(   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= index + 1  # linecache starts at 1
													__SCREAMING_SNAKE_CASE									= self.prefix + linecache.getline(str(self.src_file		)				,							_A		).rstrip('\n'		)
													__SCREAMING_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
													__SCREAMING_SNAKE_CASE									= (
													    self.tokenizer.question_encoder if isinstance(self.tokenizer				,							_A		) else self.tokenizer
													)
													__SCREAMING_SNAKE_CASE									= self.tokenizer.generator if isinstance(self.tokenizer				,							_A		) else self.tokenizer

													__SCREAMING_SNAKE_CASE									= encode_line(_A				,							_A				,							self.max_source_length				,							'right'		)
													__SCREAMING_SNAKE_CASE									= encode_line(_A				,							_A				,							self.max_target_length				,							'right'		)

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






						@staticmethod
						def 	_A     (   _A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

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







lowerCAmelCase__		:					str							=getLogger(__name__)






def      __lowercase       (			a__       )    ->			List[str]:
							return list(itertools.chain.from_iterable(a__       )       )






def      __lowercase       (			a__       )    ->			None:
							__SCREAMING_SNAKE_CASE									= get_git_info()
							save_json(a__							,       os.path.join(a__							,       'git_log.json'       )       )






def      __lowercase       (			a__							,       a__							,       a__=4							,       **a__       )    ->			str:
							with open(a__							,       'w'       ) as f:
														json.dump(a__							,       a__							,       indent=a__							,       **a__       )






def      __lowercase       (			a__       )    ->			List[Any]:
							with open(a__       ) as f:
														return json.load(a__       )






def      __lowercase       (			)    ->			Dict:
							__SCREAMING_SNAKE_CASE									= git.Repo(search_parent_directories=a__       )
							__SCREAMING_SNAKE_CASE									= {
							    'repo_id': str(a__       ),
							    'repo_sha': str(repo.head.object.hexsha       ),
							    'repo_branch': str(repo.active_branch       ),
							    'hostname': str(socket.gethostname()       ),
							}
							return repo_infos






def      __lowercase       (			a__							,       a__       )    ->			List:
							return list(map(a__							,       a__       )       )






def      __lowercase       (			a__							,       a__       )    ->			List[str]:
							with open(a__							,       'wb'       ) as f:
														return pickle.dump(a__							,       a__       )






def      __lowercase       (			a__       )    ->			List[str]:

							def remove_articles(a__       ):
														return re.sub(R'\b(a|an|the)\b'							,       ' '							,       a__       )

							def white_space_fix(a__       ):
														return " ".join(text.split()       )

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

							def lower(a__       ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(a__       )       )       )       )






def      __lowercase       (			a__							,       a__       )    ->			Union[str, Any]:
							__SCREAMING_SNAKE_CASE									= normalize_answer(a__       ).split()
							__SCREAMING_SNAKE_CASE									= normalize_answer(a__       ).split()
							__SCREAMING_SNAKE_CASE									= Counter(a__       ) & Counter(a__       )
							__SCREAMING_SNAKE_CASE									= sum(common.values()       )
							if num_same == 0:
														return 0
							__SCREAMING_SNAKE_CASE									= 1.0 * num_same / len(a__       )
							__SCREAMING_SNAKE_CASE									= 1.0 * num_same / len(a__       )
							__SCREAMING_SNAKE_CASE									= (2 * precision * recall) / (precision + recall)
							return fa






def      __lowercase       (			a__							,       a__       )    ->			int:
							return normalize_answer(a__       ) == normalize_answer(a__       )






def      __lowercase       (			a__							,       a__       )    ->			Dict:
							assert len(a__       ) == len(a__       )
							__SCREAMING_SNAKE_CASE									= 0
							for hypo, pred in zip(a__							,       a__       ):
														em += exact_match_score(a__							,       a__       )
							if len(a__       ) > 0:
														em /= len(a__       )
							return {"em": em}






def      __lowercase       (			a__       )    ->			Union[str, Any]:
							return model_prefix.startswith('rag'       )






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











from typing import TYPE_CHECKING

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


lowerCAmelCase__		:					str							={'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=[
			    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ViTMAEForPreTraining''',
			    '''ViTMAELayer''',
			    '''ViTMAEModel''',
			    '''ViTMAEPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TFViTMAEForPreTraining''',
			    '''TFViTMAEModel''',
			    '''TFViTMAEPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit_mae import (
						    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTMAEForPreTraining,
						    ViTMAELayer,
						    ViTMAEModel,
						    ViTMAEPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
			import sys

			lowerCAmelCase__		:					Optional[int]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






