


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__			(      a__:		Optional[int]      ,  a__:		int      ,  a__:		Tuple      ,  a__:		List[str]      ,  a__:		Dict=True      ,  a__:		Optional[int]="pt"	)  ->		Any:


							'''simple docstring'''





							_UpperCAmelCase					=      {'add_prefix_space': True} if isinstance(a__      ,  a__	) and not line.startswith(' '	) else {}
							_UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		List[Any]      ,  a__:		Dict      ,  a__:		Union[str, Any]=None      ,  )  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      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 		__a				(    UpperCAmelCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE="train"      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=""      ,   )			->	Dict:




									"""simple docstring"""





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




									"""simple docstring"""





									return len(self.src_lens						)
		def __getitem__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict[str, torch.Tensor]:




									"""simple docstring"""





									_UpperCAmelCase					=      index + 1  # linecache starts at 1
									_UpperCAmelCase					=      self.prefix + linecache.getline(str(self.src_file						)      ,   _SCREAMING_SNAKE_CASE						).rstrip('\n'						)
									_UpperCAmelCase					=      linecache.getline(str(self.tgt_file						)      ,   _SCREAMING_SNAKE_CASE						).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      ,   _SCREAMING_SNAKE_CASE						):
																source_line += self.tokenizer.eos_token
																tgt_line += self.tokenizer.eos_token

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

									_UpperCAmelCase					=      encode_line(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   self.max_source_length      ,   'right'						)
									_UpperCAmelCase					=      encode_line(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   self.max_target_length      ,   'right'						)

									_UpperCAmelCase					=      source_inputs['input_ids'].squeeze()
									_UpperCAmelCase					=      target_inputs['input_ids'].squeeze()
									_UpperCAmelCase					=      source_inputs['attention_mask'].squeeze()
									return {
									    "input_ids": source_ids,
									    "attention_mask": src_mask,
									    "decoder_input_ids": target_ids,
									}
		@staticmethod
		def      UpperCAmelCase__    (      _SCREAMING_SNAKE_CASE						)			->	Tuple:




									"""simple docstring"""





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






		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	Dict[str, torch.Tensor]:




									"""simple docstring"""





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



lowerCAmelCase__  :Tuple             =							getLogger(__name__)

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


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		str	)  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      get_git_info()
							save_json(a__      ,  os.path.join(a__      ,  'git_log.json'	)	)

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


							'''simple docstring'''





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

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


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      )  ->		Dict:


							'''simple docstring'''





							_UpperCAmelCase					=      git.Repo(search_parent_directories=a__	)
							_UpperCAmelCase					=      {
							    '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 	lowerCAmelCase__			(      a__:		Callable      ,  a__:		Iterable	)  ->		List:


							'''simple docstring'''





							return list(map(a__      ,  a__	)	)

def 	lowerCAmelCase__			(      a__:		Any      ,  a__:		Dict	)  ->		Optional[int]:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		List[str]	)  ->		Dict:


							'''simple docstring'''





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

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

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

							def lower(a__:		Optional[Any]	):
														return text.lower()

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

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


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		Optional[Any]      ,  a__:		Union[str, Any]	)  ->		List[str]:


							'''simple docstring'''





							return normalize_answer(a__	) == normalize_answer(a__	)

def 	lowerCAmelCase__			(      a__:		List[str]      ,  a__:		List[str]	)  ->		Dict:


							'''simple docstring'''





							assert len(a__	) == len(a__	)
							_UpperCAmelCase					=      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 	lowerCAmelCase__			(      a__:		Union[str, Any]	)  ->		Optional[Any]:


							'''simple docstring'''





							return model_prefix.startswith('rag'	)







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


							'''simple docstring'''





							_UpperCAmelCase					=      {p: p for p in extra_params}
							# T5 models don't have `dropout` param, they have `dropout_rate` instead
							_UpperCAmelCase					=      '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
																					_UpperCAmelCase					=      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_torch_available,
    is_vision_available,
)


lowerCAmelCase__  :Tuple             =							{'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							['''BeitFeatureExtractor''']
		lowerCAmelCase__  :Optional[Any]             =							['''BeitImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :int             =							[
		    '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BeitForImageClassification''',
		    '''BeitForMaskedImageModeling''',
		    '''BeitForSemanticSegmentation''',
		    '''BeitModel''',
		    '''BeitPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Tuple             =							[
		    '''FlaxBeitForImageClassification''',
		    '''FlaxBeitForMaskedImageModeling''',
		    '''FlaxBeitModel''',
		    '''FlaxBeitPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_beit import BeitFeatureExtractor
				from .image_processing_beit import BeitImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_beit import (
				    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				    BeitPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_beit import (
				    FlaxBeitForImageClassification,
				    FlaxBeitForMaskedImageModeling,
				    FlaxBeitModel,
				    FlaxBeitPreTrainedModel,
				)


else:
		import sys

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