
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


A  :			Any     				=				getLogger(__name__)


A  :			Tuple     				=				"""cuda""" if torch.cuda.is_available() else """cpu"""


def 					SCREAMING_SNAKE_CASE    (    a__		,					a__		,					a__		,					a__ = 8		,					a__ = DEFAULT_DEVICE		,					a__=False		,					a__="summarization"		,					a__=None		,					**a__		,					) ->						Dict:
				__a    							=					Path(__lowerCamelCase						).open('''w'''		,					encoding='''utf-8'''						)
				__a    							=					str(__lowerCamelCase						)
				__a    							=					AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase						).to(__lowerCamelCase						)
				if fpaa:
								__a    							=					model.half()

				__a    							=					AutoTokenizer.from_pretrained(__lowerCamelCase						)
				logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""						)  # if this is wrong, check config.model_type.

				__a    							=					time.time()
				# update config with task specific params
				use_task_specific_params(__lowerCamelCase		,					__lowerCamelCase						)
				if prefix is None:
								__a    							=					prefix or getattr(model.config		,					'''prefix'''		,					''''''						) or """"""
				for examples_chunk in tqdm(list(chunks(__lowerCamelCase		,					__lowerCamelCase						)						)						):
								__a    							=					[prefix + text for text in examples_chunk]
								__a    							=					tokenizer(__lowerCamelCase		,					return_tensors='''pt'''		,					truncation=__lowerCamelCase		,					padding='''longest'''						).to(__lowerCamelCase						)
								__a    							=					model.generate(
								    input_ids=batch.input_ids		,					attention_mask=batch.attention_mask		,					**__lowerCamelCase		,					)
								__a    							=					tokenizer.batch_decode(__lowerCamelCase		,					skip_special_tokens=__lowerCamelCase		,					clean_up_tokenization_spaces=__lowerCamelCase						)
								for hypothesis in dec:
												fout.write(hypothesis + '''\n'''						)
												fout.flush()
				fout.close()
				__a    							=					int(time.time() - start_time						)  # seconds
				__a    							=					len(__lowerCamelCase						)
				return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		,					4						)}


def 					SCREAMING_SNAKE_CASE    (    ) ->						str:
				return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''						)


def 					SCREAMING_SNAKE_CASE    (    a__=True						) ->						Dict:
				__a    							=					argparse.ArgumentParser()
				parser.add_argument('''model_name'''		,					type=__lowerCamelCase		,					help='''like facebook/bart-large-cnn,t5-base, etc.'''						)
				parser.add_argument('''input_path'''		,					type=__lowerCamelCase		,					help='''like cnn_dm/test.source'''						)
				parser.add_argument('''save_path'''		,					type=__lowerCamelCase		,					help='''where to save summaries'''						)
				parser.add_argument('''--reference_path'''		,					type=__lowerCamelCase		,					required=__lowerCamelCase		,					help='''like cnn_dm/test.target'''						)
				parser.add_argument('''--score_path'''		,					type=__lowerCamelCase		,					required=__lowerCamelCase		,					default='''metrics.json'''		,					help='''where to save metrics'''						)
				parser.add_argument('''--device'''		,					type=__lowerCamelCase		,					required=__lowerCamelCase		,					default=__lowerCamelCase		,					help='''cuda, cuda:1, cpu etc.'''						)
				parser.add_argument(
				    '''--prefix'''		,					type=__lowerCamelCase		,					required=__lowerCamelCase		,					default=__lowerCamelCase		,					help='''will be added to the begininng of src examples'''						)
				parser.add_argument('''--task'''		,					type=__lowerCamelCase		,					default='''summarization'''		,					help='''used for task_specific_params + metrics'''						)
				parser.add_argument('''--bs'''		,					type=__lowerCamelCase		,					default=8		,					required=__lowerCamelCase		,					help='''batch size'''						)
				parser.add_argument(
				    '''--n_obs'''		,					type=__lowerCamelCase		,					default=-1		,					required=__lowerCamelCase		,					help='''How many observations. Defaults to all.'''						)
				parser.add_argument('''--fp16'''		,					action='''store_true'''						)
				parser.add_argument('''--dump-args'''		,					action='''store_true'''		,					help='''print the custom hparams with the results'''						)
				parser.add_argument(
				    '''--info'''		,					nargs='''?'''		,					type=__lowerCamelCase		,					const=datetime_now()		,					help=(
				        '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
				        ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
				    )		,					)
				# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
				__a    							=					parser.parse_known_args()
				__a    							=					parse_numeric_n_bool_cl_kwargs(__lowerCamelCase						)
				if parsed_args and verbose:
								print(F"""parsed the following generate kwargs: {parsed_args}"""						)
				with open(args.input_path						) as f:
								__a    							=					[""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in f.readlines()]
				if args.n_obs > 0:
								__a    							=					examples[: args.n_obs]
				Path(args.save_path						).parent.mkdir(exist_ok=__lowerCamelCase						)
				if args.reference_path is None and Path(args.score_path						).exists():
								warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""						)
				__a    							=					generate_summaries_or_translations(
				    __lowerCamelCase		,					args.save_path		,					args.model_name		,					batch_size=args.bs		,					device=args.device		,					fpaa=args.fpaa		,					task=args.task		,					prefix=args.prefix		,					**__lowerCamelCase		,					)

				if args.reference_path is None:
								return {}

				# Compute scores
				__a    							=					calculate_bleu if """translation""" in args.task else calculate_rouge
				__a    							=					[x.rstrip() for x in open(args.save_path						).readlines()]
				__a    							=					[x.rstrip() for x in open(args.reference_path						).readlines()][: len(__lowerCamelCase						)]
				__a    							=					score_fn(__lowerCamelCase		,					__lowerCamelCase						)
				scores.update(__lowerCamelCase						)

				if args.dump_args:
								scores.update(__lowerCamelCase						)
				if args.info:
								__a    							=					args.info

				if verbose:
								print(__lowerCamelCase						)

				if args.score_path is not None:
								json.dump(__lowerCamelCase		,					open(args.score_path		,					'''w'''						)						)

				return scores


if __name__ == "__main__":
					# Usage for MT:
					# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
					run_generate(verbose=True)

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


def 					__lowerCAmelCase    (    a__						) ->						Any:
				if "model" in orig_key:
								__a    							=					orig_key.replace('''model.'''		,					''''''						)
				if "norm1" in orig_key:
								__a    							=					orig_key.replace('''norm1'''		,					'''attention.output.LayerNorm'''						)
				if "norm2" in orig_key:
								__a    							=					orig_key.replace('''norm2'''		,					'''output.LayerNorm'''						)
				if "norm" in orig_key:
								__a    							=					orig_key.replace('''norm'''		,					'''LayerNorm'''						)
				if "transformer" in orig_key:
								__a    							=					orig_key.split('''.'''						)[0].split('''_'''						)[-1]
								__a    							=					orig_key.replace(F"""transformer_{layer_num}"""		,					F"""encoder.layer.{layer_num}"""						)
				if "mha.attn" in orig_key:
								__a    							=					orig_key.replace('''mha.attn'''		,					'''attention.self'''						)
				if "mha" in orig_key:
								__a    							=					orig_key.replace('''mha'''		,					'''attention'''						)
				if "W_q" in orig_key:
								__a    							=					orig_key.replace('''W_q'''		,					'''self.query'''						)
				if "W_k" in orig_key:
								__a    							=					orig_key.replace('''W_k'''		,					'''self.key'''						)
				if "W_v" in orig_key:
								__a    							=					orig_key.replace('''W_v'''		,					'''self.value'''						)
				if "ff.0" in orig_key:
								__a    							=					orig_key.replace('''ff.0'''		,					'''intermediate.dense'''						)
				if "ff.2" in orig_key:
								__a    							=					orig_key.replace('''ff.2'''		,					'''output.dense'''						)
				if "ff" in orig_key:
								__a    							=					orig_key.replace('''ff'''		,					'''output.dense'''						)
				if "mlm_class" in orig_key:
								__a    							=					orig_key.replace('''mlm.mlm_class'''		,					'''cls.predictions.decoder'''						)
				if "mlm" in orig_key:
								__a    							=					orig_key.replace('''mlm'''		,					'''cls.predictions.transform'''						)
				if "backbone.backbone.encoders" in orig_key:
								__a    							=					orig_key.replace('''backbone.backbone.encoders'''		,					'''encoder.layer'''						)
				if "cls" not in orig_key:
								__a    							=					'''mra.''' + orig_key

				return orig_key


def 					__lowerCAmelCase    (    a__		,					a__						) ->						Any:
				for key in orig_state_dict.copy().keys():
								__a    							=					orig_state_dict.pop(a__						)

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												__a    							=					val

				__a    							=					orig_state_dict['''cls.predictions.decoder.bias''']
				__a    							=					torch.arange(a__						).expand((1, -1)						) + 2

				return orig_state_dict


def 					__lowerCAmelCase    (    a__		,					a__		,					a__						) ->						List[Any]:
				__a    							=					torch.load(a__		,					map_location='''cpu'''						)['''model_state_dict''']
				__a    							=					MraConfig.from_json_file(a__						)
				__a    							=					MraForMaskedLM(a__						)

				__a    							=					convert_checkpoint_helper(config.max_position_embeddings		,					a__						)

				print(model.load_state_dict(a__						)						)
				model.eval()
				model.save_pretrained(a__						)

				print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""						)


if __name__ == "__main__":
					A  :			Any     				=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to Mra pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for Mra model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					A  :			Tuple     				=				parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)