





import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCAmelCase_				= getLogger(__name__)




def     A_      (	lowercase_   ,		lowercase_   ,		lowercase_   ,		lowercase_ = 8   ,		lowercase_ = 1024   ,		lowercase_="val"   ,		lowercase_=None   ,		lowercase_=False   ,		lowercase_="summarization"   ,		lowercase_=None   ,		lowercase_=1   ,		lowercase_ = None   ,		lowercase_=""   ,		**lowercase_   ,		)      ->    List[str]:
       _snake_case   :				Optional[int]  					=    str(__A	)
       assert local_rank is not None
       torch.distributed.init_process_group(backend='''nccl'''   ,		rank=__A	)

       _snake_case   :				List[str]  					=    Path(__A	)
       _snake_case   :				Any  					=    save_dir.joinpath(f'''rank_{local_rank}_output.json'''	)
       torch.cuda.set_device(__A	)
       _snake_case   :				int  					=    AutoModelForSeqaSeqLM.from_pretrained(__A	).cuda()
       if fpaa:
              _snake_case   :				Any  					=    model.half()
       # determine if we need to increase num_beams
       use_task_specific_params(__A   ,		__A	)  # update config with task specific params
       _snake_case   :				List[Any]  					=    generate_kwargs.pop('''num_beams'''   ,		model.config.num_beams	)  # AttributeError risk?
       if num_return_sequences > num_beams:
              _snake_case   :				Any  					=    num_return_sequences

       _snake_case   :				List[Any]  					=    AutoTokenizer.from_pretrained(__A	)
       logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''	)  # if this is wrong, check config.model_type.

       if max_source_length is None:
              _snake_case   :				str  					=    tokenizer.model_max_length
       if prefix is None:
              _snake_case   :				List[Any]  					=    prefix or getattr(model.config   ,		'''prefix'''   ,		''''''	) or ''''''
       _snake_case   :				Union[str, Any]  					=    SeqaSeqDataset(
           __A   ,		__A   ,		__A   ,		max_target_length=1024   ,		type_path=__A   ,		n_obs=__A   ,		prefix=__A   ,		**__A   ,		)
       # I set shuffle=True for a more accurate progress bar.
       # If all the longest samples are first, the prog bar estimate is too high at the beginning.
       _snake_case   :				Tuple  					=    ds.make_sortish_sampler(__A   ,		distributed=__A   ,		add_extra_examples=__A   ,		shuffle=__A	)
       _snake_case   :				str  					=    DataLoader(__A   ,		sampler=__A   ,		batch_size=__A   ,		collate_fn=ds.collate_fn	)
       _snake_case   :				Optional[int]  					=    []
       for batch in tqdm(__A	):
              _snake_case   :				Any  					=    model.generate(
                  input_ids=batch['''input_ids'''].to(model.device	)   ,		attention_mask=batch['''attention_mask'''].to(model.device	)   ,		num_return_sequences=__A   ,		num_beams=__A   ,		**__A   ,		)
              _snake_case   :				Optional[int]  					=    tokenizer.batch_decode(__A   ,		skip_special_tokens=__A   ,		clean_up_tokenization_spaces=__A	)
              _snake_case   :				Union[str, Any]  					=    batch['''ids''']
              if num_return_sequences > 1:
                     _snake_case   :				int  					=    chunks(__A   ,		__A	)  # batch size chunks, each of size num_return_seq
              for i, pred in enumerate(__A	):
                     results.append({'''pred''': pred, '''id''': ids[i].item()}	)
       save_json(__A   ,		__A	)
       return results, sampler.num_replicas




def     A_      (	)      ->    Tuple:
       _snake_case   :				Any  					=    argparse.ArgumentParser(
           epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''	)
       parser.add_argument('''--data_dir'''   ,		type=__A   ,		help='''like cnn_dm/test.source'''	)
       parser.add_argument(
           '''--model_name'''   ,		type=__A   ,		help='''like facebook/bart-large-cnn,t5-base, etc.'''   ,		default='''sshleifer/distilbart-xsum-12-3'''   ,		)
       parser.add_argument('''--save_dir'''   ,		type=__A   ,		help='''where to save'''   ,		default='''tmp_gen'''	)
       parser.add_argument('''--max_source_length'''   ,		type=__A   ,		default=__A	)
       parser.add_argument(
           '''--type_path'''   ,		type=__A   ,		default='''test'''   ,		help='''which subset to evaluate typically train/val/test'''	)
       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(
           '''--local_rank'''   ,		type=__A   ,		default=-1   ,		required=__A   ,		help='''should be passed by distributed.launch'''	)

       parser.add_argument(
           '''--n_obs'''   ,		type=__A   ,		default=__A   ,		required=__A   ,		help='''How many observations. Defaults to all.'''	)
       parser.add_argument(
           '''--num_return_sequences'''   ,		type=__A   ,		default=1   ,		required=__A   ,		help='''How many sequences to return'''	)
       parser.add_argument(
           '''--sync_timeout'''   ,		type=__A   ,		default=600   ,		required=__A   ,		help='''How long should master process wait for other processes to finish.'''   ,		)
       parser.add_argument('''--src_lang'''   ,		type=__A   ,		default=__A   ,		required=__A	)
       parser.add_argument('''--tgt_lang'''   ,		type=__A   ,		default=__A   ,		required=__A	)
       parser.add_argument(
           '''--prefix'''   ,		type=__A   ,		required=__A   ,		default=__A   ,		help='''will be added to the begininng of src examples'''	)
       parser.add_argument('''--fp16'''   ,		action='''store_true'''	)
       parser.add_argument('''--debug'''   ,		action='''store_true'''	)
       _snake_case   :				Union[str, Any]  					=    time.time()
       _snake_case   :				Dict  					=    parser.parse_known_args()
       _snake_case   :				Optional[Any]  					=    parse_numeric_n_bool_cl_kwargs(__A	)
       if generate_kwargs and args.local_rank <= 0:
              print(f'''parsed the following generate kwargs: {generate_kwargs}'''	)
       _snake_case   :				Any  					=    Path(args.save_dir + '''_tmp'''	)
       Path(__A	).mkdir(exist_ok=__A	)  # this handles locking.
       _snake_case   :				List[str]  					=    list(json_save_dir.glob('''rank_*.json'''	)	)
       if intermediate_files:
              raise ValueError(f'''Found files at {json_save_dir} please move or remove them.'''	)
              # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
       _snake_case   :				Tuple  					=    {}
       if args.src_lang is not None:
              _snake_case   :				Optional[Any]  					=    args.src_lang
       if args.tgt_lang is not None:
              _snake_case   :				List[str]  					=    args.tgt_lang

       Path(args.save_dir	).mkdir(exist_ok=__A	)
       _snake_case   :				Union[str, Any]  					=    eval_data_dir(
           args.data_dir   ,		__A   ,		args.model_name   ,		type_path=args.type_path   ,		bs=args.bs   ,		fpaa=args.fpaa   ,		task=args.task   ,		local_rank=args.local_rank   ,		n_obs=args.n_obs   ,		max_source_length=args.max_source_length   ,		num_return_sequences=args.num_return_sequences   ,		prefix=args.prefix   ,		dataset_kwargs=__A   ,		**__A   ,		)

       if args.local_rank <= 0:
              _snake_case   :				Union[str, Any]  					=    Path(args.save_dir	)
              save_dir.mkdir(exist_ok=__A	)
              _snake_case   :				Tuple  					=    gather_results_from_each_node(__A   ,		__A   ,		args.sync_timeout	)
              _snake_case   :				int  					=    combine_partial_results(__A	)
              if args.num_return_sequences > 1:
                     _snake_case   :				int  					=    save_dir.joinpath('''pseudolabel_results.json'''	)
                     print(f'''Saving aggregated results at {save_path}, intermediate in {json_save_dir}/'''	)
                     save_json(__A   ,		__A	)
                     return
              _snake_case   :				Dict  					=    Path(args.data_dir	).joinpath(args.type_path + '''.target'''	)
              with open(__A	) as f:
                     _snake_case   :				Union[str, Any]  					=    [x.rstrip() for x in f.readlines()][: len(__A	)]

              # Calculate metrics, save metrics,  and save _generations.txt
              _snake_case   :				Dict  					=    '''translation''' in args.task
              _snake_case   :				Tuple  					=    calculate_bleu if calc_bleu else calculate_rouge
              _snake_case   :				Tuple  					=    '''bleu''' if calc_bleu else '''rouge'''
              _snake_case   :				Dict  					=    score_fn(__A   ,		__A	)
              _snake_case   :				List[Any]  					=    len(__A	)
              _snake_case   :				Optional[Any]  					=    time.time() - start_time
              _snake_case   :				Any  					=    round(runtime / metrics['''n_obs''']   ,		4	)
              _snake_case   :				Tuple  					=    num_replicas
              # TODO(@stas00): add whatever metadata to metrics
              _snake_case   :				Any  					=    save_dir.joinpath(f'''{args.type_path}_{metric_name}.json'''	)
              save_json(__A   ,		__A   ,		indent=__A	)
              print(__A	)
              write_txt_file(__A   ,		save_dir.joinpath(f'''{args.type_path}_generations.txt'''	)	)
              if args.debug:
                     write_txt_file(__A   ,		save_dir.joinpath(f'''{args.type_path}.target'''	)	)
              else:
                     shutil.rmtree(__A	)




def     A_      (	lowercase_	)      ->    str:
       _snake_case   :				Dict  					=    []
       for partial_result in partial_results:
              records.extend(__A	)
       _snake_case   :				List[Any]  					=    sorted(__A   ,		key=lambda lowercase_	: x["id"]	)
       _snake_case   :				Tuple  					=    [x['''pred'''] for x in records]
       return preds




def     A_      (	lowercase_   ,		lowercase_   ,		lowercase_	)      ->    List[str]:
       # WAIT FOR lots of .json files
       _snake_case   :				List[str]  					=    time.time()
       logger.info('''waiting for all nodes to finish'''	)
       _snake_case   :				Dict  					=    None
       while (time.time() - start_wait) < timeout:
              _snake_case   :				str  					=    list(save_dir.glob('''rank_*.json'''	)	)
              if len(__A	) < num_replicas:
                     continue
              try:
                     # make sure all json files are fully saved
                     _snake_case   :				Tuple  					=    lmap(__A   ,		__A	)
                     return json_data
              except JSONDecodeError:
                     continue
       else:
              raise TimeoutError('''Rank 0 gave up on waiting for other processes'''	)
       # Unreachable


if __name__ == "__main__":
   # Usage for MT:
   run_generate()














'''simple docstring'''







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):
			snake_case__	=					"openai/whisper-base"
			snake_case__	=					(
			    "This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the "
			    "transcribed text."
			)
			snake_case__	=					"transcriber"
			snake_case__	=					WhisperProcessor
			snake_case__	=					WhisperForConditionalGeneration

			snake_case__	=					["audio"]
			snake_case__	=					["text"]






			def       SCREAMING_SNAKE_CASE   (			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Tuple       )      ->      str:
							return self.pre_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''pt'''       ).input_features






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       )      ->      List[str]:
							return self.model.generate(inputs=__SCREAMING_SNAKE_CASE       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      Optional[int]       )      ->      Optional[Any]:
							return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE	,    skip_special_tokens=__SCREAMING_SNAKE_CASE       )[0]




