'''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       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :List[Any]=True    ,	_SCREAMING_SNAKE_CASE      :Optional[int]="pt"       ):
				SCREAMING_SNAKE_CASE  :						List[Any]      					= {"add_prefix_space": True} if isinstance(__snake_case    ,	__snake_case       ) and not line.startswith(''' '''       ) else {}
				SCREAMING_SNAKE_CASE  :						Tuple      					= padding_side
				return tokenizer(
				    [line]    ,	max_length=__snake_case    ,	padding='''max_length''' if pad_to_max_length else None    ,	truncation=__snake_case    ,	return_tensors=__snake_case    ,	add_special_tokens=__snake_case    ,	**__snake_case    ,	)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]=None    ,	):
				SCREAMING_SNAKE_CASE  :						Dict      					= input_ids.ne(__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 	a__     (				_A	):


	def __init__(self : Union[str, Any],					__UpperCAmelCase : str,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any,					__UpperCAmelCase : str="train",					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : Optional[int]=None,					__UpperCAmelCase : str=None,					__UpperCAmelCase : int="",					)     ->			str:

					"""simple docstring"""

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


	def __len__(self : List[Any]      )     ->			int:

					"""simple docstring"""

					return len(self.src_lens      )


	def __getitem__(self : Any,					__UpperCAmelCase : Tuple      )     ->			Dict[str, torch.Tensor]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= index + 1  # linecache starts at 1
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.prefix + linecache.getline(str(self.src_file      ),					__UpperCAmelCase      ).rstrip('''\n'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= linecache.getline(str(self.tgt_file      ),					__UpperCAmelCase      ).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,					__UpperCAmelCase      ):
									source_line += self.tokenizer.eos_token
									tgt_line += self.tokenizer.eos_token

					# Pad source and target to the right
					SCREAMING_SNAKE_CASE  :						Dict      					= (
					    self.tokenizer.question_encoder if isinstance(self.tokenizer,					__UpperCAmelCase      ) else self.tokenizer
					)
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.tokenizer.generator if isinstance(self.tokenizer,					__UpperCAmelCase      ) else self.tokenizer

					SCREAMING_SNAKE_CASE  :						List[Any]      					= encode_line(__UpperCAmelCase,					__UpperCAmelCase,					self.max_source_length,					'''right'''      )
					SCREAMING_SNAKE_CASE  :						str      					= encode_line(__UpperCAmelCase,					__UpperCAmelCase,					self.max_target_length,					'''right'''      )

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


	@staticmethod
	def 				lowercase__  (__UpperCAmelCase : Optional[Any]      )     ->			int:

					"""simple docstring"""

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






	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Tuple      )     ->			Dict[str, torch.Tensor]:

					"""simple docstring"""

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



snake_case_			=     getLogger(__name__)






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[List]       ):
				return list(itertools.chain.from_iterable(__snake_case       )       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= get_git_info()
				save_json(__snake_case    ,	os.path.join(__snake_case    ,	'''git_log.json'''       )       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Tuple    ,	_SCREAMING_SNAKE_CASE      :Tuple=4    ,	**_SCREAMING_SNAKE_CASE      :Optional[Any]       ):


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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Dict       ):


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






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Tuple      					= git.Repo(search_parent_directories=__snake_case       )
				SCREAMING_SNAKE_CASE  :						Any      					= {
				    "repo_id": str(__snake_case       ),
				    "repo_sha": str(repo.head.object.hexsha       ),
				    "repo_branch": str(repo.active_branch       ),
				    "hostname": str(socket.gethostname()       ),
				}
				return repo_infos






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Callable    ,	_SCREAMING_SNAKE_CASE      :Iterable       ):
				return list(map(__snake_case    ,	__snake_case       )       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :List[Any]       ):


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






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str       ):


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

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

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

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

				return white_space_fix(remove_articles(remove_punc(lower(__snake_case       )       )       )       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= normalize_answer(__snake_case       ).split()
				SCREAMING_SNAKE_CASE  :						Any      					= normalize_answer(__snake_case       ).split()
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= Counter(__snake_case       ) & Counter(__snake_case       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= sum(common.values()       )
				if num_same == 0:
								return 0
				SCREAMING_SNAKE_CASE  :						Tuple      					= 1.0 * num_same / len(__snake_case       )
				SCREAMING_SNAKE_CASE  :						Any      					= 1.0 * num_same / len(__snake_case       )
				SCREAMING_SNAKE_CASE  :						Any      					= (2 * precision * recall) / (precision + recall)
				return fa






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				return normalize_answer(__snake_case       ) == normalize_answer(__snake_case       )






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :List[str]       ):
				assert len(__snake_case       ) == len(__snake_case       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 0
				for hypo, pred in zip(__snake_case    ,	__snake_case       ):
								em += exact_match_score(__snake_case    ,	__snake_case       )
				if len(__snake_case       ) > 0:
								em /= len(__snake_case       )
				return {"em": em}






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int       ):
				return model_prefix.startswith('''rag'''       )






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









'''simple docstring'''

from manim import *


class 	a__     (				_lowercase	):


	def 				lowercase__  (self : Optional[Any]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= Rectangle(height=0.5,					width=0.5      )
					SCREAMING_SNAKE_CASE  :						str      					= Rectangle(height=0.46,					width=0.46      ).set_stroke(width=0      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= VGroup(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= Text('''CPU''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					cpu.move_to([-2.5, -0.5, 0]      )
					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Any      					= [mem.copy() for i in range(1      )]
					SCREAMING_SNAKE_CASE  :						Dict      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= Text('''GPU''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					gpu.align_to(__UpperCAmelCase,					__UpperCAmelCase      )
					gpu.set_x(gpu.get_x() - 1      )

					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[Any]      					= [mem.copy() for i in range(6      )]
					SCREAMING_SNAKE_CASE  :						Any      					= VGroup(*__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Text('''Model''',					font_size=24      )
					SCREAMING_SNAKE_CASE  :						Dict      					= Group(__UpperCAmelCase,					__UpperCAmelCase      ).arrange(__UpperCAmelCase,					buff=0.5,					aligned_edge=__UpperCAmelCase      )
					model.move_to([3, -1.0, 0]      )

					self.play(
					    Create(__UpperCAmelCase,					run_time=1      ),					Create(__UpperCAmelCase,					run_time=1      ),					Create(__UpperCAmelCase,					run_time=1      ),					)

					SCREAMING_SNAKE_CASE  :						Tuple      					= MarkupText(
					    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.''',					font_size=24,					)

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= Square(side_length=2.2      )
					key.move_to([-5, 2, 0]      )

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= MarkupText(
					    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''',					font_size=18,					)

					key_text.move_to([-5, 2.4, 0]      )

					step_a.move_to([2, 2, 0]      )
					self.play(Write(__UpperCAmelCase,					run_time=2.5      ),					Write(__UpperCAmelCase      ),					Write(__UpperCAmelCase      )      )

					self.add(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					SCREAMING_SNAKE_CASE  :						Dict      					= []
					for i, rect in enumerate(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= Rectangle(height=0.46,					width=0.46      ).set_stroke(width=0.0      ).set_fill(__UpperCAmelCase,					opacity=0.7      )
									cpu_target.move_to(__UpperCAmelCase      )
									cpu_target.generate_target()
									SCREAMING_SNAKE_CASE  :						List[str]      					= 0.46 / 4
									SCREAMING_SNAKE_CASE  :						Any      					= 0.46 / 3

									if i == 0:
													cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT      ),					buff=0.02,					direction=__UpperCAmelCase      )
													cpu_target.target.set_x(cpu_target.target.get_x() + 0.1      )
									elif i == 3:
													cpu_target.target.next_to(cpu_targs[0].target,					direction=__UpperCAmelCase,					buff=0.0      )
									else:
													cpu_target.target.next_to(cpu_targs[i - 1].target,					direction=__UpperCAmelCase,					buff=0.0      )
									cpu_targs.append(__UpperCAmelCase      )

									first_animations.append(rect.animate(run_time=0.5      ).set_stroke(__UpperCAmelCase      )      )
									second_animations.append(MoveToTarget(__UpperCAmelCase,					run_time=1.5      )      )

					self.play(*__UpperCAmelCase      )
					self.play(*__UpperCAmelCase      )

					self.wait()




