


"""simple docstring"""



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


UpperCAmelCase      		=			getLogger(__name__)


UpperCAmelCase      		=			'''cuda''' if torch.cuda.is_available() else '''cpu'''






def       lowerCamelCase					(a_						:List[str]				,				a_						:str				,				a_						:str				,				a_						:int = 8				,				a_						:str = DEFAULT_DEVICE				,				a_						:int=False				,				a_						:Union[str, Any]="summarization"				,				a_						:Dict=None				,				**a_						:Tuple				,				)     ->							Dict:
       lowercase		:str       							=				Path(a_).open('''w'''				,				encoding='''utf-8''')
       lowercase		:List[str]       							=				str(a_)
       lowercase		:int       							=				AutoModelForSeqaSeqLM.from_pretrained(a_).to(a_)
       if fpaa:
              lowercase		:Any       							=				model.half()

       lowercase		:Union[str, Any]       							=				AutoTokenizer.from_pretrained(a_)
       logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}""")  # if this is wrong, check config.model_type.

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






def       lowerCamelCase					()     ->							Union[str, Any]:
       return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S''')






def       lowerCamelCase					(a_						:int=True)     ->							List[str]:
       lowercase		:Optional[int]       							=				argparse.ArgumentParser()
       parser.add_argument('''model_name'''				,				type=a_				,				help='''like facebook/bart-large-cnn,t5-base, etc.''')
       parser.add_argument('''input_path'''				,				type=a_				,				help='''like cnn_dm/test.source''')
       parser.add_argument('''save_path'''				,				type=a_				,				help='''where to save summaries''')
       parser.add_argument('''--reference_path'''				,				type=a_				,				required=a_				,				help='''like cnn_dm/test.target''')
       parser.add_argument('''--score_path'''				,				type=a_				,				required=a_				,				default='''metrics.json'''				,				help='''where to save metrics''')
       parser.add_argument('''--device'''				,				type=a_				,				required=a_				,				default=a_				,				help='''cuda, cuda:1, cpu etc.''')
       parser.add_argument(
           '''--prefix'''				,				type=a_				,				required=a_				,				default=a_				,				help='''will be added to the begininng of src examples''')
       parser.add_argument('''--task'''				,				type=a_				,				default='''summarization'''				,				help='''used for task_specific_params + metrics''')
       parser.add_argument('''--bs'''				,				type=a_				,				default=8				,				required=a_				,				help='''batch size''')
       parser.add_argument(
           '''--n_obs'''				,				type=a_				,				default=-1				,				required=a_				,				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=a_				,				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
       lowercase							,  lowercase		:Union[str, Any]       							=				parser.parse_known_args()
       lowercase		:str       							=				parse_numeric_n_bool_cl_kwargs(a_)
       if parsed_args and verbose:
              print(F"""parsed the following generate kwargs: {parsed_args}""")
       lowercase		:Dict       							=				[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path).readlines()]
       if args.n_obs > 0:
              lowercase		:List[Any]       							=				examples[: args.n_obs]
       Path(args.save_path).parent.mkdir(exist_ok=a_)

       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''')

       lowercase		:Union[str, Any]       							=				generate_summaries_or_translations(
           a_				,				args.save_path				,				args.model_name				,				batch_size=args.bs				,				device=args.device				,				fpaa=args.fpaa				,				task=args.task				,				prefix=args.prefix				,				**a_				,				)

       if args.reference_path is None:
              return {}

       # Compute scores
       lowercase		:List[str]       							=				calculate_bleu if '''translation''' in args.task else calculate_rouge
       lowercase		:str       							=				[x.rstrip() for x in open(args.save_path).readlines()]
       lowercase		:List[str]       							=				[x.rstrip() for x in open(args.reference_path).readlines()][: len(a_)]
       lowercase		:dict       							=				score_fn(a_				,				a_)
       scores.update(a_)

       if args.dump_args:
              scores.update(a_)
       if args.info:
              lowercase		:List[str]       							=				args.info

       if verbose:
              print(a_)

       if args.score_path is not None:
              json.dump(a_				,				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)





"""simple docstring"""



import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


UpperCAmelCase      		=			[
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]






def       lowerCamelCase					(a_						:Union[str, Any])     ->							Optional[Any]:
       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






def       lowerCamelCase					(a_						:dict				,				a_						:dict)     ->							PegasusForConditionalGeneration:
       lowercase		:Union[str, Any]       							=				DEFAULTS.copy()
       cfg_kwargs.update(a_)
       lowercase		:Optional[Any]       							=				PegasusConfig(**a_)
       lowercase		:int       							=				PegasusForConditionalGeneration(a_)
       lowercase		:Optional[int]       							=				torch_model.model.state_dict()
       lowercase		:Tuple       							=				{}
       for k, v in tf_weights.items():
              lowercase		:Dict       							=				rename_state_dict_key(a_)
              if new_k not in sd:
                     raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""")

              if "dense" in k or "proj" in new_k:
                     lowercase		:Union[str, Any]       							=				v.T
              lowercase		:List[str]       							=				torch.tensor(a_				,				dtype=sd[new_k].dtype)
              assert v.shape == sd[new_k].shape, F"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
       # make sure embedding.padding_idx is respected
       lowercase		:Optional[int]       							=				torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1])
       lowercase		:Union[str, Any]       							=				mapping['''shared.weight''']
       lowercase		:Dict       							=				mapping['''shared.weight''']
       lowercase		:int       							=				{k: torch.zeros_like(a_) for k, v in sd.items() if k.endswith('''bias''') and k not in mapping}
       mapping.update(**a_)
       lowercase							,  lowercase		:Tuple       							=				torch_model.model.load_state_dict(a_				,				strict=a_)
       lowercase		:Dict       							=				[
           k for k in missing if k not in ['''encoder.embed_positions.weight''', '''decoder.embed_positions.weight''']
       ]
       assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
       assert extra == [], F"""no matches found for the following tf keys {extra}"""
       return torch_model






def       lowerCamelCase					(a_						:Union[str, Any]="./ckpt/aeslc/model.ckpt-32000")     ->							Dict:
       lowercase		:Any       							=				tf.train.list_variables(a_)
       lowercase		:str       							=				{}
       lowercase		:Optional[Any]       							=				['''Adafactor''', '''global_step''']
       for name, shape in tqdm(a_				,				desc='''converting tf checkpoint to dict'''):
              lowercase		:str       							=				any(pat in name for pat in ignore_name)
              if skip_key:
                     continue
              lowercase		:str       							=				tf.train.load_variable(a_				,				a_)
              lowercase		:Dict       							=				array
       return tf_weights






def       lowerCamelCase					(a_						:str				,				a_						:str)     ->							List[str]:
       # save tokenizer first
       lowercase		:List[str]       							=				Path(a_).parent.name
       lowercase		:List[Any]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:List[Any]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Any       							=				get_tf_weights_as_numpy(a_)
       lowercase		:str       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:int       							=				task_specific_params
       lowercase		:List[Any]       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:Union[str, Any]       							=				torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight''')
       sd.pop('''model.encoder.embed_positions.weight''')
       torch.save(a_				,				Path(a_) / '''pytorch_model.bin''')


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
 parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
 UpperCAmelCase      		=			parser.parse_args()
 if args.save_dir is None:
  UpperCAmelCase      		=			Path(args.tf_ckpt_path).parent.name
  UpperCAmelCase      		=			os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)

