

"""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     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_		,	A_		,	A_=True		,	A_="pt"					):
	lowerCAmelCase__      :		Tuple    							=      {'''add_prefix_space''': True} if isinstance(A_		,	A_					) and not line.startswith(''' '''					) else {}
	lowerCAmelCase__      :		str    							=      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     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_=None		,	):
	lowerCAmelCase__      :		Union[str, Any]    							=      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 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



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







					def __len__(			self     :							Union[str, Any]						):
						return len(self.src_lens						)







					def __getitem__(			self     :							int  ,lowercase_     :							List[Any]						):
						lowerCAmelCase__      :		List[str]    							=      index + 1  # linecache starts at 1
						lowerCAmelCase__      :		List[Any]    							=      self.prefix + linecache.getline(str(self.src_file						)  ,lowercase_						).rstrip('''\n'''						)
						lowerCAmelCase__      :		Union[str, Any]    							=      linecache.getline(str(self.tgt_file						)  ,lowercase_						).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  ,lowercase_						):
							source_line += self.tokenizer.eos_token
							tgt_line += self.tokenizer.eos_token

						# Pad source and target to the right
						lowerCAmelCase__      :		Optional[int]    							=      (
						    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,lowercase_						) else self.tokenizer
						)
						lowerCAmelCase__      :		Union[str, Any]    							=      self.tokenizer.generator if isinstance(self.tokenizer  ,lowercase_						) else self.tokenizer

						lowerCAmelCase__      :		Optional[int]    							=      encode_line(lowercase_  ,lowercase_  ,self.max_source_length  ,'''right'''						)
						lowerCAmelCase__      :		Optional[Any]    							=      encode_line(lowercase_  ,lowercase_  ,self.max_target_length  ,'''right'''						)

						lowerCAmelCase__      :		int    							=      source_inputs['''input_ids'''].squeeze()
						lowerCAmelCase__      :		str    							=      target_inputs['''input_ids'''].squeeze()
						lowerCAmelCase__      :		List[Any]    							=      source_inputs['''attention_mask'''].squeeze()
						return {
						    "input_ids": source_ids,
						    "attention_mask": src_mask,
						    "decoder_input_ids": target_ids,
						}







					@staticmethod
					def 			__lowerCAmelCase					(			lowercase_     :							Any						):
						return [len(lowercase_						) for x in Path(lowercase_						).open().readlines()]







					def 			__lowerCAmelCase					(			self     :							Optional[Any]  ,lowercase_     :							List[str]						):
						lowerCAmelCase__      :		List[str]    							=      torch.stack([x['''input_ids'''] for x in batch]						)
						lowerCAmelCase__      :		int    							=      torch.stack([x['''attention_mask'''] for x in batch]						)
						lowerCAmelCase__      :		int    							=      torch.stack([x['''decoder_input_ids'''] for x in batch]						)
						lowerCAmelCase__      :		List[Any]    							=      (
						    self.tokenizer.generator.pad_token_id
						    if isinstance(self.tokenizer  ,lowercase_						)
						    else self.tokenizer.pad_token_id
						)
						lowerCAmelCase__      :		Any    							=      (
						    self.tokenizer.question_encoder.pad_token_id
						    if isinstance(self.tokenizer  ,lowercase_						)
						    else self.tokenizer.pad_token_id
						)
						lowerCAmelCase__      :		Tuple    							=      trim_batch(lowercase_  ,lowercase_						)
						lowerCAmelCase__							,lowerCAmelCase__      :		Optional[Any]    							=      trim_batch(lowercase_  ,lowercase_  ,attention_mask=lowercase_						)
						lowerCAmelCase__      :		str    							=      {
						    '''input_ids''': source_ids,
						    '''attention_mask''': source_mask,
						    '''decoder_input_ids''': y,
						}
						return batch

__UpperCamelCase    : Any				 =						getLogger(__name__)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	return list(itertools.chain.from_iterable(A_					)					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		Any    							=      get_git_info()
	save_json(A_		,	os.path.join(A_		,	'''git_log.json'''					)					)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_=4		,	**A_					):
	with open(A_		,	'''w'''					) as f:
		json.dump(A_		,	A_		,	indent=A_		,	**A_					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	with open(A_					) as f:
		return json.load(A_					)

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		int    							=      git.Repo(search_parent_directories=A_					)
	lowerCAmelCase__      :		Any    							=      {
	    '''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     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	return list(map(A_		,	A_					)					)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	with open(A_		,	'''wb'''					) as f:
		return pickle.dump(A_		,	A_					)

def     __SCREAMING_SNAKE_CASE    ( A_					):

	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_					):
		lowerCAmelCase__      :		Optional[Any]    							=      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     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	lowerCAmelCase__      :		Any    							=      normalize_answer(A_					).split()
	lowerCAmelCase__      :		Any    							=      normalize_answer(A_					).split()
	lowerCAmelCase__      :		Union[str, Any]    							=      Counter(A_					) & Counter(A_					)
	lowerCAmelCase__      :		str    							=      sum(common.values()					)
	if num_same == 0:
		return 0
	lowerCAmelCase__      :		Optional[int]    							=      1.0 * num_same / len(A_					)
	lowerCAmelCase__      :		Tuple    							=      1.0 * num_same / len(A_					)
	lowerCAmelCase__      :		List[str]    							=      (2 * precision * recall) / (precision + recall)
	return fa

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	return normalize_answer(A_					) == normalize_answer(A_					)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_					):
	assert len(A_					) == len(A_					)
	lowerCAmelCase__      :		Optional[Any]    							=      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     __SCREAMING_SNAKE_CASE    ( A_					):
	return model_prefix.startswith('''rag'''					)





def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_					):
	lowerCAmelCase__      :		Optional[int]    							=      {p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	lowerCAmelCase__      :		str    							=      '''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
			lowerCAmelCase__      :		List[Any]    							=      p if hasattr(A_		,	A_					) else equivalent_param[p]
			setattr(A_		,	A_		,	getattr(A_		,	A_					)					)
			delattr(A_		,	A_					)
	return hparams, config







"""simple docstring"""





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,
)


__UpperCamelCase    : List[str]				 =						{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : Union[str, Any]				 =						['''XGLMTokenizer''']

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

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


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


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase    : str				 =						[
							    '''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

							__UpperCamelCase    : Optional[int]				 =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)




