

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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase=None   ,)    ->    List[Any]:
	UpperCAmelCase_		:      List[str]       	=					input_ids.ne(UpperCamelCase  ).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 					lowercase						(  a_ ):


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


							def __len__(			self)							->				List[Any]:
								return len(self.src_lens)


							def __getitem__(			self    ,			_snake_case)							->				Dict[str, torch.Tensor]:
								UpperCAmelCase_		:      Tuple       	=					index + 1  # linecache starts at 1
								UpperCAmelCase_		:      Dict       	=					self.prefix + linecache.getline(str(self.src_file)    ,			_snake_case).rstrip('\n')
								UpperCAmelCase_		:      List[Any]       	=					linecache.getline(str(self.tgt_file)    ,			_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    ,			_snake_case):
									source_line += self.tokenizer.eos_token
									tgt_line += self.tokenizer.eos_token

								# Pad source and target to the right
								UpperCAmelCase_		:      List[str]       	=					(
								    self.tokenizer.question_encoder if isinstance(self.tokenizer    ,			_snake_case) else self.tokenizer
								)
								UpperCAmelCase_		:      List[str]       	=					self.tokenizer.generator if isinstance(self.tokenizer    ,			_snake_case) else self.tokenizer

								UpperCAmelCase_		:      Any       	=					encode_line(_snake_case    ,			_snake_case    ,			self.max_source_length    ,			'right')
								UpperCAmelCase_		:      Any       	=					encode_line(_snake_case    ,			_snake_case    ,			self.max_target_length    ,			'right')

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


							@staticmethod
							def 						_snake_case							(			_snake_case)							->				List[str]:
								return [len(_snake_case) for x in Path(_snake_case).open().readlines()]





							def 						_snake_case							(			self    ,			_snake_case)							->				Dict[str, torch.Tensor]:
								UpperCAmelCase_		:      Optional[Any]       	=					torch.stack([x['input_ids'] for x in batch])
								UpperCAmelCase_		:      Optional[Any]       	=					torch.stack([x['attention_mask'] for x in batch])
								UpperCAmelCase_		:      int       	=					torch.stack([x['decoder_input_ids'] for x in batch])
								UpperCAmelCase_		:      Optional[int]       	=					(
								    self.tokenizer.generator.pad_token_id
								    if isinstance(self.tokenizer    ,			_snake_case)
								    else self.tokenizer.pad_token_id
								)
								UpperCAmelCase_		:      int       	=					(
								    self.tokenizer.question_encoder.pad_token_id
								    if isinstance(self.tokenizer    ,			_snake_case)
								    else self.tokenizer.pad_token_id
								)
								UpperCAmelCase_		:      str       	=					trim_batch(_snake_case    ,			_snake_case)
								UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					trim_batch(_snake_case    ,			_snake_case    ,			attention_mask=_snake_case)
								UpperCAmelCase_		:      str       	=					{
								    'input_ids': source_ids,
								    'attention_mask': source_mask,
								    'decoder_input_ids': y,
								}
								return batch


lowerCAmelCase__										= getLogger(__name__)







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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    None:
	UpperCAmelCase_		:      int       	=					get_git_info()
	save_json(UpperCamelCase   ,os.path.join(UpperCamelCase   ,'git_log.json'  )  )







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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	with open(UpperCamelCase  ) as f:
		return json.load(UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  )    ->    str:
	UpperCAmelCase_		:      Tuple       	=					git.Repo(search_parent_directories=UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					{
	    'repo_id': str(UpperCamelCase  ),
	    'repo_sha': str(repo.head.object.hexsha  ),
	    'repo_branch': str(repo.active_branch  ),
	    'hostname': str(socket.gethostname()  ),
	}
	return repo_infos







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List:
	return list(map(UpperCamelCase   ,UpperCamelCase  )  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	with open(UpperCamelCase   ,'wb'  ) as f:
		return pickle.dump(UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Union[str, Any]:

	def remove_articles(UpperCamelCase  ):
		return re.sub(r'\b(a|an|the)\b'   ,' '   ,UpperCamelCase  )

	def white_space_fix(UpperCamelCase  ):
		return " ".join(text.split()  )

	def remove_punc(UpperCamelCase  ):
		UpperCAmelCase_		:      Optional[int]       	=					set(string.punctuation  )
		return "".join(ch for ch in text if ch not in exclude  )

	def lower(UpperCamelCase  ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase  )  )  )  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Tuple:
	UpperCAmelCase_		:      Optional[int]       	=					normalize_answer(UpperCamelCase  ).split()
	UpperCAmelCase_		:      Optional[Any]       	=					normalize_answer(UpperCamelCase  ).split()
	UpperCAmelCase_		:      int       	=					Counter(UpperCamelCase  ) & Counter(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					sum(common.values()  )
	if num_same == 0:
		return 0
	UpperCAmelCase_		:      Any       	=					1.0 * num_same / len(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					1.0 * num_same / len(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					(2 * precision * recall) / (precision + recall)
	return fa







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    List[Any]:
	return normalize_answer(UpperCamelCase  ) == normalize_answer(UpperCamelCase  )







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







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    str:
	return model_prefix.startswith('rag'  )







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









'''simple docstring'''


import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[Any]:
	if k == "embeddings.weight":
		return "shared.weight"

	for parlai_name, hf_name in PATTERNS:
		UpperCAmelCase_		:      Optional[int]       	=					k.replace(UpperCamelCase   ,UpperCamelCase  )

	if k.startswith('encoder'  ):
		UpperCAmelCase_		:      Dict       	=					k.replace('.attn'   ,'.self_attn'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'final_layer_norm'  )
	elif k.startswith('decoder'  ):
		UpperCAmelCase_		:      Union[str, Any]       	=					k.replace('norm1'   ,'self_attn_layer_norm'  )
		UpperCAmelCase_		:      int       	=					k.replace('norm2'   ,'encoder_attn_layer_norm'  )
		UpperCAmelCase_		:      List[str]       	=					k.replace('norm3'   ,'final_layer_norm'  )
	return k







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Optional[Any]:
	UpperCAmelCase_		:      Union[str, Any]       	=					[
	    'model.encoder.layernorm_embedding.weight',
	    'model.encoder.layernorm_embedding.bias',
	    'model.decoder.layernorm_embedding.weight',
	    'model.decoder.layernorm_embedding.bias',
	]
	for k in keys:
		UpperCAmelCase_		:      Optional[int]       	=					sd.pop(UpperCamelCase  )
		UpperCAmelCase_		:      List[Any]       	=					k.replace('layernorm_embedding'   ,'layer_norm'  )
		assert new_k not in sd
		UpperCAmelCase_		:      Any       	=					v


lowerCAmelCase__										= ["START"]







@torch.no_grad()
def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Dict:
	UpperCAmelCase_		:      str       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )
	UpperCAmelCase_		:      Tuple       	=					model['model']
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotConfig.from_json_file(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					BlenderbotForConditionalGeneration(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					m.model.state_dict().keys()
	UpperCAmelCase_		:      Optional[Any]       	=					[]
	UpperCAmelCase_		:      Dict       	=					{}
	for k, v in sd.items():
		if k in IGNORE_KEYS:
			continue

		UpperCAmelCase_		:      Union[str, Any]       	=					rename_state_dict_key(UpperCamelCase  )
		if new_k not in valid_keys:
			failures.append([k, new_k]  )
		else:
			UpperCAmelCase_		:      List[str]       	=					v
	if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
		rename_layernorm_keys(UpperCamelCase  )
	m.model.load_state_dict(UpperCamelCase   ,strict=UpperCamelCase  )
	m.half()
	m.save_pretrained(UpperCamelCase  )


if __name__ == "__main__":
						lowerCAmelCase__										= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
						parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
						parser.add_argument(
						    "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
						)
						lowerCAmelCase__										= parser.parse_args()
						convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






