


'''simple docstring'''

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


__lowercase:		List[Any]    						=  logging.get_logger(__name__)

__lowercase:		Dict    						=  {
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       str            =       'musicgen_decoder'
 _lowerCamelCase			:       str            =       ['past_key_values']

 def __init__(			self			:						Dict,       a_			:						Union[str, Any]=2048,       a_			:						str=2048,       a_			:						Optional[Any]=24,       a_			:						Optional[int]=4096,       a_			:						Optional[Any]=16,       a_			:						str=0.0,       a_			:						Optional[int]=True,       a_			:						Tuple="gelu",       a_			:						Dict=1024,       a_			:						Optional[int]=0.1,       a_			:						Union[str, Any]=0.0,       a_			:						Dict=0.0,       a_			:						int=0.02,       a_			:						Any=False,       a_			:						Any=4,       a_			:						int=2048,       a_			:						Tuple=2048,       a_			:						Optional[Any]=None,       a_			:						Tuple=False,       **a_			:						List[str],       ):





    """simple docstring"""



    UpperCamelCase__			    =    vocab_size
    UpperCamelCase__			    =    max_position_embeddings
    UpperCamelCase__			    =    hidden_size
    UpperCamelCase__			    =    ffn_dim
    UpperCamelCase__			    =    num_hidden_layers
    UpperCamelCase__			    =    num_attention_heads
    UpperCamelCase__			    =    dropout
    UpperCamelCase__			    =    attention_dropout
    UpperCamelCase__			    =    activation_dropout
    UpperCamelCase__			    =    activation_function
    UpperCamelCase__			    =    initializer_factor
    UpperCamelCase__			    =    layerdrop
    UpperCamelCase__			    =    use_cache
    UpperCamelCase__			    =    scale_embedding  # scale factor will be sqrt(d_model) if True
    UpperCamelCase__			    =    num_codebooks
    super().__init__(
        pad_token_id=a_,       bos_token_id=a_,       eos_token_id=a_,       tie_word_embeddings=a_,       **a_,       )







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Optional[Any]            =       'musicgen'
 _lowerCamelCase			:       Optional[Any]            =       True

 def __init__(			self			:						Any,       **a_			:						Any       ):





    """simple docstring"""



    super().__init__(**a_       )
    if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
       raise ValueError("Config has to be initialized with text_encoder, audio_encoder and decoder config"       )

    UpperCamelCase__			    =    kwargs.pop("text_encoder"       )
    UpperCamelCase__			    =    text_encoder_config.pop("model_type"       )

    UpperCamelCase__			    =    kwargs.pop("audio_encoder"       )
    UpperCamelCase__			    =    audio_encoder_config.pop("model_type"       )

    UpperCamelCase__			    =    kwargs.pop("decoder"       )

    UpperCamelCase__			    =    AutoConfig.for_model(a_,       **a_       )
    UpperCamelCase__			    =    AutoConfig.for_model(a_,       **a_       )
    UpperCamelCase__			    =    MusicgenDecoderConfig(**a_       )
    UpperCamelCase__			    =    True

 @classmethod
 def 		lowercase_    (			cls			:						Optional[Any],       a_			:						PretrainedConfig,       a_			:						PretrainedConfig,       a_			:						MusicgenDecoderConfig,       **a_			:						Tuple,       ):





    """simple docstring"""



    return cls(
        text_encoder=text_encoder_config.to_dict(),       audio_encoder=audio_encoder_config.to_dict(),       decoder=decoder_config.to_dict(),       **a_,       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    copy.deepcopy(self.__dict__       )
    UpperCamelCase__			    =    self.text_encoder.to_dict()
    UpperCamelCase__			    =    self.audio_encoder.to_dict()
    UpperCamelCase__			    =    self.decoder.to_dict()
    UpperCamelCase__			    =    self.__class__.model_type
    return output

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


lowerCamelCase_				:							List[str]       =    getLogger(__name__)


lowerCamelCase_				:							Dict       =    "cuda" if torch.cuda.is_available() else "cpu"
def        __lowercase(							__snake_case				:		List[str]    ,__snake_case				:		str    ,__snake_case				:		str    ,__snake_case				:		int = 8    ,__snake_case				:		str = DEFAULT_DEVICE    ,__snake_case				:		str=False    ,__snake_case				:		Tuple="summarization"    ,__snake_case				:		List[str]=None    ,**__snake_case				:		Dict    ,)       ->   Dict:
       __snake_case						       =   Path(__snake_case					).open('w'    ,encoding='utf-8'					)
       __snake_case						       =   str(__snake_case					)
       __snake_case						       =   AutoModelForSeqaSeqLM.from_pretrained(__snake_case					).to(__snake_case					)
       if fpaa:
              __snake_case						       =   model.half()

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

       __snake_case						       =   time.time()
       # update config with task specific params
       use_task_specific_params(__snake_case    ,__snake_case					)
       if prefix is None:
              __snake_case						       =   prefix or getattr(model.config    ,'prefix'    ,''					) or ''
       for examples_chunk in tqdm(list(chunks(__snake_case    ,__snake_case					)					)					):
              __snake_case						       =   [prefix + text for text in examples_chunk]
              __snake_case						       =   tokenizer(__snake_case    ,return_tensors='pt'    ,truncation=__snake_case    ,padding='longest'					).to(__snake_case					)
              __snake_case						       =   model.generate(
                  input_ids=batch.input_ids    ,attention_mask=batch.attention_mask    ,**__snake_case    ,)
              __snake_case						       =   tokenizer.batch_decode(__snake_case    ,skip_special_tokens=__snake_case    ,clean_up_tokenization_spaces=__snake_case					)
              for hypothesis in dec:
                     fout.write(hypothesis + '\n'					)
                     fout.flush()
       fout.close()
       __snake_case						       =   int(time.time() - start_time					)  # seconds
       __snake_case						       =   len(__snake_case					)
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs    ,4					)}
def        __lowercase(							)       ->   Optional[int]:
       return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'					)
def        __lowercase(							__snake_case				:		Optional[int]=True					)       ->   Dict:
       __snake_case						       =   argparse.ArgumentParser()
       parser.add_argument('model_name'    ,type=__snake_case    ,help='like facebook/bart-large-cnn,t5-base, etc.'					)
       parser.add_argument('input_path'    ,type=__snake_case    ,help='like cnn_dm/test.source'					)
       parser.add_argument('save_path'    ,type=__snake_case    ,help='where to save summaries'					)
       parser.add_argument('--reference_path'    ,type=__snake_case    ,required=__snake_case    ,help='like cnn_dm/test.target'					)
       parser.add_argument('--score_path'    ,type=__snake_case    ,required=__snake_case    ,default='metrics.json'    ,help='where to save metrics'					)
       parser.add_argument('--device'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='cuda, cuda:1, cpu etc.'					)
       parser.add_argument(
           '--prefix'    ,type=__snake_case    ,required=__snake_case    ,default=__snake_case    ,help='will be added to the begininng of src examples'					)
       parser.add_argument('--task'    ,type=__snake_case    ,default='summarization'    ,help='used for task_specific_params + metrics'					)
       parser.add_argument('--bs'    ,type=__snake_case    ,default=8    ,required=__snake_case    ,help='batch size'					)
       parser.add_argument(
           '--n_obs'    ,type=__snake_case    ,default=-1    ,required=__snake_case    ,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=__snake_case    ,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						       =   parser.parse_known_args()
       __snake_case						       =   parse_numeric_n_bool_cl_kwargs(__snake_case					)
       if parsed_args and verbose:
              print(f'''parsed the following generate kwargs: {parsed_args}'''					)
       with open(args.input_path					) as f:
              __snake_case						       =   [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
       if args.n_obs > 0:
              __snake_case						       =   examples[: args.n_obs]
       Path(args.save_path					).parent.mkdir(exist_ok=__snake_case					)
       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.'''					)
       __snake_case						       =   generate_summaries_or_translations(
           __snake_case    ,args.save_path    ,args.model_name    ,batch_size=args.bs    ,device=args.device    ,fpaa=args.fpaa    ,task=args.task    ,prefix=args.prefix    ,**__snake_case    ,)

       if args.reference_path is None:
              return {}

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

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

       if verbose:
              print(__snake_case					)

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