


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


_snake_case					:		Optional[Any]      =    getLogger(__name__)


_snake_case					:		Optional[int]      =    "cuda" if torch.cuda.is_available() else "cpu"







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase = 8							,     __lowerCamelCase = DEFAULT_DEVICE							,     __lowerCamelCase=False							,     __lowerCamelCase="summarization"							,     __lowerCamelCase=None							,     **__lowerCamelCase							,     ):
							__snake_case		:      Dict										=       Path(__lowerCamelCase     ).open("w"							,     encoding="utf-8"     )
							__snake_case		:      Any										=       str(__lowerCamelCase     )
							__snake_case		:      Any										=       AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase     ).to(__lowerCamelCase     )
							if fpaa:
														__snake_case		:      List[str]										=       model.half()

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

							__snake_case		:      Dict										=       time.time()
							# update config with task specific params
							use_task_specific_params(__lowerCamelCase							,     __lowerCamelCase     )
							if prefix is None:
														__snake_case		:      Optional[int]										=       prefix or getattr(model.config							,     "prefix"							,     ""     ) or ""
							for examples_chunk in tqdm(list(chunks(__lowerCamelCase							,     __lowerCamelCase     )     )     ):
														__snake_case		:      Tuple										=       [prefix + text for text in examples_chunk]
														__snake_case		:      str										=       tokenizer(__lowerCamelCase							,     return_tensors="pt"							,     truncation=__lowerCamelCase							,     padding="longest"     ).to(__lowerCamelCase     )
														__snake_case		:      List[str]										=       model.generate(
														    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **__lowerCamelCase							,     )
														__snake_case		:      str										=       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()
							__snake_case		:      Tuple										=       int(time.time() - start_time     )  # seconds
							__snake_case		:      Optional[Any]										=       len(__lowerCamelCase     )
							return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4     )}







def     lowerCAmelCase_					( ):
							return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"     )







def     lowerCAmelCase_					( __lowerCamelCase=True     ):
							__snake_case		:      int										=       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
							__snake_case			,      __snake_case		:      int										=       parser.parse_known_args()
							__snake_case		:      Optional[Any]										=       parse_numeric_n_bool_cl_kwargs(__lowerCamelCase     )
							if parsed_args and verbose:
														print(F'parsed the following generate kwargs: {parsed_args}'     )
							__snake_case		:      List[Any]										=       [" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in open(args.input_path     ).readlines()]
							if args.n_obs > 0:
														__snake_case		:      Any										=       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.'     )

							if args.device == "cpu" and args.fpaa:
														# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
														raise ValueError("Can't mix --fp16 and --device cpu"     )

							__snake_case		:      Optional[int]										=       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
							__snake_case		:      Tuple										=       calculate_bleu if "translation" in args.task else calculate_rouge
							__snake_case		:      Any										=       [x.rstrip() for x in open(args.save_path     ).readlines()]
							__snake_case		:      Optional[Any]										=       [x.rstrip() for x in open(args.reference_path     ).readlines()][: len(__lowerCamelCase     )]
							__snake_case		:      dict										=       score_fn(__lowerCamelCase							,     __lowerCamelCase     )
							scores.update(__lowerCamelCase     )

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





from typing import Dict

import numpy as np
import torch

from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Any										=       []
							__snake_case		:      Optional[Any]										=       []
							__snake_case		:      List[Any]										=       []

							for rt in rc.restypes:
														__snake_case		:      Any										=       rc.restype_name_to_atomaa_names[rc.restype_atoa[rt]]
														restype_atomaa_to_atomaa_list.append([(rc.atom_order[name] if name else 0) for name in atom_names]     )
														__snake_case		:      Tuple										=       {name: i for i, name in enumerate(__lowerCamelCase     )}
														restype_atomaa_to_atomaa_list.append(
														    [(atom_name_to_idxaa[name] if name in atom_name_to_idxaa else 0) for name in rc.atom_types]     )

														restype_atomaa_mask_list.append([(1.0 if name else 0.0) for name in atom_names]     )

							# Add dummy mapping for restype 'UNK'
							restype_atomaa_to_atomaa_list.append([0] * 1_4     )
							restype_atomaa_to_atomaa_list.append([0] * 3_7     )
							restype_atomaa_mask_list.append([0.0] * 1_4     )

							__snake_case		:      int										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      List[str]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.intaa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[Any]										=       torch.tensor(
							    __lowerCamelCase							,     dtype=torch.floataa							,     device=protein["aatype"].device							,     )
							__snake_case		:      Optional[int]										=       protein["aatype"].to(torch.long     )

							# create the mapping for (residx, atom14) --> atom37, i.e. an array
							# with shape (num_res, 14) containing the atom37 indices for this protein
							__snake_case		:      Optional[Any]										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Tuple										=       restype_atomaa_mask[protein_aatype]

							__snake_case		:      Optional[Any]										=       residx_atomaa_mask
							__snake_case		:      Union[str, Any]										=       residx_atomaa_to_atomaa.long()

							# create the gather indices for mapping back
							__snake_case		:      Dict										=       restype_atomaa_to_atomaa[protein_aatype]
							__snake_case		:      Dict										=       residx_atomaa_to_atomaa.long()

							# create the corresponding mask
							__snake_case		:      List[str]										=       torch.zeros([2_1, 3_7]							,     dtype=torch.floataa							,     device=protein["aatype"].device     )
							for restype, restype_letter in enumerate(rc.restypes     ):
														__snake_case		:      List[str]										=       rc.restype_atoa[restype_letter]
														__snake_case		:      List[Any]										=       rc.residue_atoms[restype_name]
														for atom_name in atom_names:
																					__snake_case		:      Union[str, Any]										=       rc.atom_order[atom_name]
																					__snake_case		:      str										=       1

							__snake_case		:      List[str]										=       restype_atomaa_mask[protein_aatype]
							__snake_case		:      List[str]										=       residx_atomaa_mask

							return protein







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      str										=       tree_map(lambda __lowerCamelCase     : torch.tensor(__lowerCamelCase							,     device=batch["aatype"].device     )							,     __lowerCamelCase							,     np.ndarray     )
							__snake_case		:      str										=       tensor_tree_map(lambda __lowerCamelCase     : np.array(__lowerCamelCase     )							,     make_atomaa_masks(__lowerCamelCase     )     )
							return out

