

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 						UpperCamelCase					(lowercase_: Tuple    ,      lowercase_: Optional[Any]    ,      lowercase_: Dict    ,      lowercase_: Tuple    ,      lowercase_: Union[str, Any]=True    ,      lowercase_: Optional[int]="pt"					)     ->      Optional[Any]:
	A__							:    Dict        =		{"""add_prefix_space""": True} if isinstance(lowercase_    ,      lowercase_					) and not line.startswith(""" """					) else {}
	A__							:    Any        =		padding_side
	return tokenizer(
	    [line]    ,      max_length=lowercase_    ,      padding="""max_length""" if pad_to_max_length else None    ,      truncation=lowercase_    ,      return_tensors=lowercase_    ,      add_special_tokens=lowercase_    ,      **lowercase_    ,      )

def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: str    ,      lowercase_: Optional[int]=None    ,      )     ->      Optional[Any]:
	A__							:    Optional[int]        =		input_ids.ne(lowercase_					).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						(__magic_name__				):


					'''simple docstring'''






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






					def __len__( self			):
						return len(self.src_lens			)






					def __getitem__( self    ,       A__			):
						A__							:    str        =		index + 1  # linecache starts at 1
						A__							:    Optional[int]        =		self.prefix + linecache.getline(str(self.src_file			)    ,       A__			).rstrip("""\n"""			)
						A__							:    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
						A__							:    Dict        =		(
						    self.tokenizer.question_encoder if isinstance(self.tokenizer    ,       A__			) else self.tokenizer
						)
						A__							:    Tuple        =		self.tokenizer.generator if isinstance(self.tokenizer    ,       A__			) else self.tokenizer

						A__							:    int        =		encode_line(A__    ,       A__    ,       self.max_source_length    ,       """right"""			)
						A__							:    Any        =		encode_line(A__    ,       A__    ,       self.max_target_length    ,       """right"""			)

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






					@staticmethod
					def   __A			( A__			):
						return [len(A__			) for x in Path(A__			).open().readlines()]






					def   __A			( self    ,       A__			):
						A__							:    int        =		torch.stack([x["""input_ids"""] for x in batch]			)
						A__							:    Optional[Any]        =		torch.stack([x["""attention_mask"""] for x in batch]			)
						A__							:    Any        =		torch.stack([x["""decoder_input_ids"""] for x in batch]			)
						A__							:    int        =		(
						    self.tokenizer.generator.pad_token_id
						    if isinstance(self.tokenizer    ,       A__			)
						    else self.tokenizer.pad_token_id
						)
						A__							:    Optional[Any]        =		(
						    self.tokenizer.question_encoder.pad_token_id
						    if isinstance(self.tokenizer    ,       A__			)
						    else self.tokenizer.pad_token_id
						)
						A__							:    Dict        =		trim_batch(A__    ,       A__			)
						A__   ,   A__							:    Optional[Any]        =		trim_batch(A__    ,       A__    ,       attention_mask=A__			)
						A__							:    str        =		{
						    """input_ids""": source_ids,
						    """attention_mask""": source_mask,
						    """decoder_input_ids""": y,
						}
						return batch



A_       : int         =			getLogger(__name__)

def 						UpperCamelCase					(lowercase_: List[List]					)     ->      Optional[int]:
	return list(itertools.chain.from_iterable(lowercase_					)					)

def 						UpperCamelCase					(lowercase_: str					)     ->      None:
	A__							:    int        =		get_git_info()
	save_json(lowercase_    ,      os.path.join(lowercase_    ,      """git_log.json"""					)					)

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: Any    ,      lowercase_: Any=4    ,      **lowercase_: List[Any]					)     ->      int:
	with open(lowercase_    ,      """w"""					) as f:
		json.dump(lowercase_    ,      lowercase_    ,      indent=lowercase_    ,      **lowercase_					)

def 						UpperCamelCase					(lowercase_: Tuple					)     ->      List[str]:
	with open(lowercase_					) as f:
		return json.load(lowercase_					)

def 						UpperCamelCase					()     ->      Tuple:
	A__							:    Any        =		git.Repo(search_parent_directories=lowercase_					)
	A__							:    Dict        =		{
	    """repo_id""": str(lowercase_					),
	    """repo_sha""": str(repo.head.object.hexsha					),
	    """repo_branch""": str(repo.active_branch					),
	    """hostname""": str(socket.gethostname()					),
	}
	return repo_infos

def 						UpperCamelCase					(lowercase_: Callable    ,      lowercase_: Iterable					)     ->      List:
	return list(map(lowercase_    ,      lowercase_					)					)

def 						UpperCamelCase					(lowercase_: List[Any]    ,      lowercase_: Union[str, Any]					)     ->      Dict:
	with open(lowercase_    ,      """wb"""					) as f:
		return pickle.dump(lowercase_    ,      lowercase_					)

def 						UpperCamelCase					(lowercase_: Dict					)     ->      Dict:

	def remove_articles(lowercase_: Dict					):
		return re.sub(r"""\b(a|an|the)\b"""    ,      """ """    ,      lowercase_					)

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

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

	def lower(lowercase_: Optional[int]					):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(lowercase_					)					)					)					)

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: List[Any]					)     ->      Any:
	A__							:    List[Any]        =		normalize_answer(lowercase_					).split()
	A__							:    Optional[int]        =		normalize_answer(lowercase_					).split()
	A__							:    Union[str, Any]        =		Counter(lowercase_					) & Counter(lowercase_					)
	A__							:    str        =		sum(common.values()					)
	if num_same == 0:
		return 0
	A__							:    int        =		1.0 * num_same / len(lowercase_					)
	A__							:    Dict        =		1.0 * num_same / len(lowercase_					)
	A__							:    Any        =		(2 * precision * recall) / (precision + recall)
	return fa

def 						UpperCamelCase					(lowercase_: Optional[int]    ,      lowercase_: List[Any]					)     ->      Optional[int]:
	return normalize_answer(lowercase_					) == normalize_answer(lowercase_					)

def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: List[str]					)     ->      Dict:
	assert len(lowercase_					) == len(lowercase_					)
	A__							:    Optional[Any]        =		0
	for hypo, pred in zip(lowercase_    ,      lowercase_					):
		em += exact_match_score(lowercase_    ,      lowercase_					)
	if len(lowercase_					) > 0:
		em /= len(lowercase_					)
	return {"em": em}

def 						UpperCamelCase					(lowercase_: Dict					)     ->      str:
	return model_prefix.startswith("""rag"""					)


def 						UpperCamelCase					(lowercase_: Dict    ,      lowercase_: Union[str, Any]    ,      lowercase_: Dict					)     ->      Dict:
	A__							:    Optional[int]        =		{p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	A__							:    List[Any]        =		"""dropout_rate"""
	for p in extra_params:
		if getattr(lowercase_    ,      lowercase_    ,      lowercase_					):
			if not hasattr(lowercase_    ,      lowercase_					) and not hasattr(lowercase_    ,      equivalent_param[p]					):
				logger.info("""config doesn't have a `{}` attribute""".format(lowercase_					)					)
				delattr(lowercase_    ,      lowercase_					)
				continue
			A__							:    int        =		p if hasattr(lowercase_    ,      lowercase_					) else equivalent_param[p]
			setattr(lowercase_    ,      lowercase_    ,      getattr(lowercase_    ,      lowercase_					)					)
			delattr(lowercase_    ,      lowercase_					)
	return hparams, config






import re
import time
from typing import Optional

import IPython.display as disp

from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length

def 						UpperCamelCase					(lowercase_: str					)     ->      Dict:
	A__							:    int        =		int(lowercase_					)
	A__   ,   A__   ,   A__							:    Tuple        =		t // 3600, (t // 60) % 60, t % 60
	return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""

def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: Optional[Any]    ,      lowercase_: Union[str, Any]    ,      lowercase_: Tuple    ,      lowercase_: Any=300					)     ->      Optional[int]:
	# docstyle-ignore
	return f"""
    <div>
      {prefix}
      <progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
      {label}
    </div>
    """

def 						UpperCamelCase					(lowercase_: Tuple					)     ->      Optional[int]:
	A__							:    Tuple        =		"""<table border=\"1\" class=\"dataframe\">\n"""
	html_code += """  <thead>\n <tr style="text-align: left;">\n"""
	for i in items[0]:
		html_code += f"""      <th>{i}</th>\n"""
	html_code += "    </tr>\n  </thead>\n  <tbody>\n"
	for line in items[1:]:
		html_code += "    <tr>\n"
		for elt in line:
			A__							:    str        =		f"""{elt:.6f}""" if isinstance(lowercase_    ,      lowercase_					) else str(lowercase_					)
			html_code += f"""      <td>{elt}</td>\n"""
		html_code += "    </tr>\n"
	html_code += "  </tbody>\n</table><p>"
	return html_code



class     _a						:


					'''simple docstring'''






					UpperCAmelCase__:   str  							=       5
					UpperCAmelCase__:   int  							=       0.2






					def __init__( self    ,       A__    ,       A__ = None    ,       A__ = True    ,       A__ = None    ,       A__ = 300    ,       ):
						A__							:    Optional[int]        =		total
						A__							:    Tuple        =		"""""" if prefix is None else prefix
						A__							:    str        =		leave
						A__							:    str        =		parent
						A__							:    int        =		width
						A__							:    Dict        =		None
						A__							:    List[str]        =		None
						A__							:    Optional[int]        =		None






					def   __A			( self    ,       A__    ,       A__ = False    ,       A__ = None			):
						A__							:    Tuple        =		value
						if comment is not None:
							A__							:    Any        =		comment
						if self.last_value is None:
							A__							:    int        =		time.time()
							A__							:    Dict        =		value
							A__							:    int        =		None
							A__							:    int        =		self.warmup
							A__							:    str        =		1
							self.update_bar(A__			)
						elif value <= self.last_value and not force_update:
							return
						elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for    ,       self.total			):
							if self.first_calls > 0:
								self.first_calls -= 1
							A__							:    Any        =		time.time()
							A__							:    str        =		current_time - self.start_time
							# We could have value = self.start_value if the update is called twixe with the same start value.
							if value > self.start_value:
								A__							:    Dict        =		self.elapsed_time / (value - self.start_value)
							else:
								A__							:    List[str]        =		None
							if value >= self.total:
								A__							:    Optional[Any]        =		self.total
								A__							:    List[Any]        =		None
								if not self.leave:
									self.close()
							elif self.average_time_per_item is not None:
								A__							:    List[Any]        =		self.average_time_per_item * (self.total - value)
							self.update_bar(A__			)
							A__							:    Any        =		value
							A__							:    List[str]        =		current_time
							if self.average_time_per_item is None:
								A__							:    str        =		1
							else:
								A__							:    Optional[Any]        =		max(int(self.update_every / self.average_time_per_item			)    ,       1			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Tuple        =		""" """ * (len(str(self.total			)			) - len(str(A__			)			)) + str(A__			)
						if self.elapsed_time is None:
							A__							:    Union[str, Any]        =		F"""[{spaced_value}/{self.total} : < :"""
						elif self.predicted_remaining is None:
							A__							:    Tuple        =		F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time			)}"""
						else:
							A__							:    Optional[int]        =		(
							    F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time			)} <"""
							    F""" {format_time(self.predicted_remaining			)}"""
							)
							self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
						self.label += "]" if self.comment is None or len(self.comment			) == 0 else F""", {self.comment}]"""
						self.display()






					def   __A			( self			):
						A__							:    str        =		html_progress_bar(self.value    ,       self.total    ,       self.prefix    ,       self.label    ,       self.width			)
						if self.parent is not None:
							# If this is a child bar, the parent will take care of the display.
							self.parent.display()
							return
						if self.output is None:
							A__							:    str        =		disp.display(disp.HTML(self.html_code			)    ,       display_id=A__			)
						else:
							self.output.update(disp.HTML(self.html_code			)			)






					def   __A			( self			):
						if self.parent is None and self.output is not None:
							self.output.update(disp.HTML(""""""			)			)



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       A__    ,       A__=None			):
						super().__init__(A__			)
						A__							:    Optional[Any]        =		None if column_names is None else [column_names]
						A__							:    Optional[Any]        =		None






					def   __A			( self			):
						A__							:    List[str]        =		html_progress_bar(self.value    ,       self.total    ,       self.prefix    ,       self.label    ,       self.width			)
						if self.inner_table is not None:
							self.html_code += text_to_html_table(self.inner_table			)
						if self.child_bar is not None:
							self.html_code += self.child_bar.html_code
						if self.output is None:
							A__							:    Optional[int]        =		disp.display(disp.HTML(self.html_code			)    ,       display_id=A__			)
						else:
							self.output.update(disp.HTML(self.html_code			)			)






					def   __A			( self    ,       A__			):
						if self.inner_table is None:
							A__							:    List[str]        =		[list(values.keys()			), list(values.values()			)]
						else:
							A__							:    Optional[Any]        =		self.inner_table[0]
							if len(self.inner_table			) == 1:
								# We give a chance to update the column names at the first iteration
								for key in values.keys():
									if key not in columns:
										columns.append(A__			)
								A__							:    Any        =		columns
							self.inner_table.append([values[c] for c in columns]			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=300			):
						A__							:    Optional[Any]        =		NotebookProgressBar(A__    ,       prefix=A__    ,       parent=self    ,       width=A__			)
						return self.child_bar






					def   __A			( self			):
						A__							:    List[str]        =		None
						self.display()



class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self			):
						A__							:    int        =		None
						A__							:    List[str]        =		None
						A__							:    Union[str, Any]        =		False






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       **A__			):
						A__							:    List[str]        =		"""Epoch""" if args.evaluation_strategy == IntervalStrategy.EPOCH else """Step"""
						A__							:    Dict        =		0
						A__							:    Tuple        =		0
						A__							:    Optional[int]        =		[self.first_column] + ["""Training Loss"""]
						if args.evaluation_strategy != IntervalStrategy.NO:
							column_names.append("""Validation Loss"""			)
						A__							:    Union[str, Any]        =		NotebookTrainingTracker(state.max_steps    ,       A__			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       **A__			):
						A__							:    Any        =		int(state.epoch			) if int(state.epoch			) == state.epoch else F"""{state.epoch:.2f}"""
						self.training_tracker.update(
						    state.global_step + 1    ,       comment=F"""Epoch {epoch}/{state.num_train_epochs}"""    ,       force_update=self._force_next_update    ,       )
						A__							:    str        =		False






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__=None    ,       **A__			):
						if not has_length(A__			):
							return
						if self.prediction_bar is None:
							if self.training_tracker is not None:
								A__							:    Union[str, Any]        =		self.training_tracker.add_child(len(A__			)			)
							else:
								A__							:    Tuple        =		NotebookProgressBar(len(A__			)			)
							self.prediction_bar.update(1			)
						else:
							self.prediction_bar.update(self.prediction_bar.value + 1			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       **A__			):
						if self.prediction_bar is not None:
							self.prediction_bar.close()
						A__							:    List[str]        =		None






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__=None    ,       **A__			):
						# Only for when there is no evaluation
						if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
							A__							:    Dict        =		{"""Training Loss""": logs["""loss"""]}
							# First column is necessarily Step sine we're not in epoch eval strategy
							A__							:    List[Any]        =		state.global_step
							self.training_tracker.write_line(A__			)






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       A__=None    ,       **A__			):
						if self.training_tracker is not None:
							A__							:    Tuple        =		{"""Training Loss""": """No log""", """Validation Loss""": """No log"""}
							for log in reversed(state.log_history			):
								if "loss" in log:
									A__							:    Dict        =		log["""loss"""]
									break

							if self.first_column == "Epoch":
								A__							:    List[Any]        =		int(state.epoch			)
							else:
								A__							:    Optional[Any]        =		state.global_step
							A__							:    Optional[Any]        =		"""eval"""
							for k in metrics:
								if k.endswith("""_loss"""			):
									A__							:    Optional[int]        =		re.sub(r"""\_loss$"""    ,       """"""    ,       A__			)
							A__							:    int        =		metrics.pop("""total_flos"""    ,       A__			)
							A__							:    int        =		metrics.pop("""epoch"""    ,       A__			)
							A__							:    Optional[int]        =		metrics.pop(F"""{metric_key_prefix}_runtime"""    ,       A__			)
							A__							:    Any        =		metrics.pop(F"""{metric_key_prefix}_samples_per_second"""    ,       A__			)
							A__							:    List[Any]        =		metrics.pop(F"""{metric_key_prefix}_steps_per_second"""    ,       A__			)
							A__							:    Optional[Any]        =		metrics.pop(F"""{metric_key_prefix}_jit_compilation_time"""    ,       A__			)
							for k, v in metrics.items():
								if k == F"""{metric_key_prefix}_loss""":
									A__							:    Any        =		v
								else:
									A__							:    Optional[Any]        =		k.split("""_"""			)
									A__							:    Any        =		""" """.join([part.capitalize() for part in splits[1:]]			)
									A__							:    List[str]        =		v
							self.training_tracker.write_line(A__			)
							self.training_tracker.remove_child()
							A__							:    Dict        =		None
							# Evaluation takes a long time so we should force the next update.
							A__							:    Union[str, Any]        =		True






					def   __A			( self    ,       A__    ,       A__    ,       A__    ,       **A__			):
						self.training_tracker.update(
						    state.global_step    ,       comment=F"""Epoch {int(state.epoch			)}/{state.num_train_epochs}"""    ,       force_update=A__			)
						A__							:    Optional[int]        =		None



