



'''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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]=True							,		_SCREAMING_SNAKE_CASE					:						Tuple="pt"							)      ->						Any:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Any							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]=None							,		)      ->						int:







	"""simple docstring"""
	lowerCAmelCase   =     input_ids.ne(_SCREAMING_SNAKE_CASE							).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 __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



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


						def __len__( self				)     ->     Tuple:
							return len(self.src_lens				)


						def __getitem__( self     ,	A_				)     ->     Dict[str, torch.Tensor]:
							lowerCAmelCase   =     index + 1  # linecache starts at 1
							lowerCAmelCase   =     self.prefix + linecache.getline(str(self.src_file				)     ,	A_				).rstrip("""\n"""				)
							lowerCAmelCase   =     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
							lowerCAmelCase   =     (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer     ,	A_				) else self.tokenizer
							)
							lowerCAmelCase   =     self.tokenizer.generator if isinstance(self.tokenizer     ,	A_				) else self.tokenizer

							lowerCAmelCase   =     encode_line(A_     ,	A_     ,	self.max_source_length     ,	"""right"""				)
							lowerCAmelCase   =     encode_line(A_     ,	A_     ,	self.max_target_length     ,	"""right"""				)

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


						@staticmethod
						def     __snake_case   ( A_				)     ->     Optional[int]:
							return [len(A_				) for x in Path(A_				).open().readlines()]




						def     __snake_case   ( self     ,	A_				)     ->     Dict[str, torch.Tensor]:
							lowerCAmelCase   =     torch.stack([x["""input_ids"""] for x in batch]				)
							lowerCAmelCase   =     torch.stack([x["""attention_mask"""] for x in batch]				)
							lowerCAmelCase   =     torch.stack([x["""decoder_input_ids"""] for x in batch]				)
							lowerCAmelCase   =     (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer     ,	A_				)
							    else self.tokenizer.pad_token_id
							)
							lowerCAmelCase   =     (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer     ,	A_				)
							    else self.tokenizer.pad_token_id
							)
							lowerCAmelCase   =     trim_batch(A_     ,	A_				)
							lowerCAmelCase,       lowerCAmelCase   =     trim_batch(A_     ,	A_     ,	attention_mask=A_				)
							lowerCAmelCase   =     {
							    """input_ids""": source_ids,
							    """attention_mask""": source_mask,
							    """decoder_input_ids""": y,
							}
							return batch







UpperCAmelCase     		=   getLogger(__name__)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[List]							)      ->						List[Any]:







	"""simple docstring"""
	return list(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE							)							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							)      ->						None:







	"""simple docstring"""
	lowerCAmelCase   =     get_git_info()
	save_json(_SCREAMING_SNAKE_CASE							,		os.path.join(_SCREAMING_SNAKE_CASE							,		"""git_log.json"""							)							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Dict							,		_SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						List[Any]=4							,		**_SCREAMING_SNAKE_CASE					:						int							)      ->						List[str]:







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							,		"""w"""							) as f:
		json.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		indent=_SCREAMING_SNAKE_CASE							,		**_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							)      ->						List[Any]:







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							) as f:
		return json.load(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( )      ->						Dict:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Callable							,		_SCREAMING_SNAKE_CASE					:						Iterable							)      ->						List:







	"""simple docstring"""
	return list(map(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Tuple							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						List[Any]:







	"""simple docstring"""
	with open(_SCREAMING_SNAKE_CASE							,		"""wb"""							) as f:
		return pickle.dump(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Union[str, Any]							)      ->						List[Any]:







	"""simple docstring"""

	def remove_articles(_SCREAMING_SNAKE_CASE					:						Optional[Any]							):
		return re.sub(R"""\b(a|an|the)\b"""							,		""" """							,		_SCREAMING_SNAKE_CASE							)

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

	def remove_punc(_SCREAMING_SNAKE_CASE					:						List[Any]							):
		lowerCAmelCase   =     set(string.punctuation							)
		return "".join(ch for ch in text if ch not in exclude							)

	def lower(_SCREAMING_SNAKE_CASE					:						List[str]							):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(_SCREAMING_SNAKE_CASE							)							)							)							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						str							)      ->						List[str]:







	"""simple docstring"""
	lowerCAmelCase   =     normalize_answer(_SCREAMING_SNAKE_CASE							).split()
	lowerCAmelCase   =     normalize_answer(_SCREAMING_SNAKE_CASE							).split()
	lowerCAmelCase   =     Counter(_SCREAMING_SNAKE_CASE							) & Counter(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     sum(common.values()							)
	if num_same == 0:
		return 0
	lowerCAmelCase   =     1.0 * num_same / len(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     1.0 * num_same / len(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     (2 * precision * recall) / (precision + recall)
	return fa






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						int:







	"""simple docstring"""
	return normalize_answer(_SCREAMING_SNAKE_CASE							) == normalize_answer(_SCREAMING_SNAKE_CASE							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						List[str]							)      ->						Dict:







	"""simple docstring"""
	assert len(_SCREAMING_SNAKE_CASE							) == len(_SCREAMING_SNAKE_CASE							)
	lowerCAmelCase   =     0
	for hypo, pred in zip(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							):
		em += exact_match_score(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
	if len(_SCREAMING_SNAKE_CASE							) > 0:
		em /= len(_SCREAMING_SNAKE_CASE							)
	return {"em": em}






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							)      ->						Optional[Any]:







	"""simple docstring"""
	return model_prefix.startswith("""rag"""							)






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						str							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						List[str]							)      ->						List[str]:







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




'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Union[str, Any]									=		["image_processor", "tokenizer"]
						UpperCAmelCase							:   Tuple									=		"BlipImageProcessor"
						UpperCAmelCase							:   Union[str, Any]									=		("BertTokenizer", "BertTokenizerFast")


						def __init__( self     ,	A_     ,	A_				)     ->     Dict:
							lowerCAmelCase   =     False
							super().__init__(A_     ,	A_				)
							lowerCAmelCase   =     self.image_processor


						def __call__( self     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     BatchEncoding:
							if images is None and text is None:
								raise ValueError("""You have to specify either images or text."""				)

							# Get only text
							if images is None:
								lowerCAmelCase   =     self.tokenizer
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
								return text_encoding

							# add pixel_values
							lowerCAmelCase   =     self.image_processor(A_     ,	return_tensors=A_				)

							if text is not None:
								lowerCAmelCase   =     self.tokenizer(
								    text=A_     ,	add_special_tokens=A_     ,	padding=A_     ,	truncation=A_     ,	max_length=A_     ,	stride=A_     ,	pad_to_multiple_of=A_     ,	return_attention_mask=A_     ,	return_overflowing_tokens=A_     ,	return_special_tokens_mask=A_     ,	return_offsets_mapping=A_     ,	return_token_type_ids=A_     ,	return_length=A_     ,	verbose=A_     ,	return_tensors=A_     ,	**A_     ,	)
							else:
								lowerCAmelCase   =     None

							if text_encoding is not None:
								encoding_image_processor.update(A_				)

							return encoding_image_processor


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Union[str, Any]:
							return self.tokenizer.batch_decode(*A_     ,	**A_				)


						def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
							return self.tokenizer.decode(*A_     ,	**A_				)




						@property
						def     __snake_case   ( self				)     ->     str:
							lowerCAmelCase   =     self.tokenizer.model_input_names
							lowerCAmelCase   =     self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)