

'''simple docstring'''





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__snake_case	: str       				=							logging.get_logger(__name__)
class        A  (	__A ):
						__UpperCAmelCase				:	str   =   ["""audio_values""", """audio_mask"""]





						def __init__(	self	,    snake_case_=2_0_4_8	,    snake_case_=1	,    snake_case_=[1_6, 1_6]	,    snake_case_=1_2_8	,    snake_case_=4_4_1_0_0	,    snake_case_=8_6	,    snake_case_=2_0_4_8	,    snake_case_=0.0	,    **snake_case_	,    )			->							str:
										super().__init__(
										    feature_size=__A	,    sampling_rate=__A	,    padding_value=__A	,    **__A	,    )

										_a        =   spectrogram_length
										_a        =   num_channels
										_a        =   patch_size
										_a        =   feature_size // self.patch_size[1]
										_a        =   n_fft
										_a        =   sampling_rate // hop_length_to_sampling_rate
										_a        =   sampling_rate
										_a        =   padding_value
										_a        =   mel_filter_bank(
										    num_frequency_bins=1 + n_fft // 2	,    num_mel_filters=__A	,    min_frequency=0.0	,    max_frequency=22_050.0	,    sampling_rate=__A	,    norm="slaney"	,    mel_scale="slaney"	,    ).T





						def __lowerCAmelCase   (	self	,    snake_case_			)			->							Any:
										_a        =   spectrogram(
										    __A	,    window_function(self.n_fft	,    "hann"			)	,    frame_length=self.n_fft	,    hop_length=self.hop_length	,    power=2.0	,    mel_filters=self.mel_filters.T	,    log_mel="dB"	,    db_range=80.0	,    )
										_a        =   log_spec[:, :-1]
										_a        =   log_spec - 20.0
										_a        =   np.clip(log_spec / 40.0	,    -2.0	,    0.0			) + 1.0
										return log_spec





						def __call__(	self	,    snake_case_	,    snake_case_ = None	,    snake_case_ = True	,    snake_case_ = None	,    snake_case_ = False	,    snake_case_ = False	,    **snake_case_	,    )			->							str:

										if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																		raise ValueError(
																		    "This feature extractor is set to support sampling rate"
																		    F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
																		    F''' with {self.sampling_rate} and not {sampling_rate}.'''			)
										else:
														logger.warning(
														    "It is strongly recommended to pass the `sampling_rate` argument to this function. "
														    "Failing to do so can result in silent errors that might be hard to debug."			)

										_a        =   isinstance(__A	,    np.ndarray			) and len(raw_speech.shape			) > 1
										if is_batched_numpy and len(raw_speech.shape			) > 2:
														raise ValueError(F'''Only mono-channel audio is supported for input to {self}'''			)
										_a        =   is_batched_numpy or (
										    isinstance(__A	,    (list, tuple)			) and (isinstance(raw_speech[0]	,    (np.ndarray, tuple, list)			))
										)
										if is_batched:
														_a        =   [np.asarray([speech]	,    dtype=np.floataa			).T for speech in raw_speech]
										elif not is_batched and not isinstance(__A	,    np.ndarray			):
														_a        =   np.asarray(__A	,    dtype=np.floataa			)
										elif isinstance(__A	,    np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
														_a        =   raw_speech.astype(np.floataa			)
										# always return batch
										if not is_batched:
														_a        =   [np.asarray([raw_speech]			).T]

										# Convert audio signals to log mel spectrograms, truncate by time axis
										_a        =   [
										    self._np_extract_fbank_features(waveform.squeeze()			).T[: self.spectrogram_length] for waveform in raw_speech
										]
										if isinstance(audio_features[0]	,    __A			):
														_a        =   [np.asarray(__A	,    dtype=np.floataa			) for feature in audio_features]

										# Create audio attention mask
										_a        =   max(
										    [ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len for feature in audio_features]			)  # The maximum number of audio patches in a batch
										if return_attention_mask:
														_a        =   [
														    (ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len) * [1]
														    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len) * [0]
														    for feature in audio_features
														]
														_a        =   np.array(__A			).astype(np.floataa			)

										# convert into correct format for padding
										_a        =   max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
										_a        =   np.ones([len(__A			), 1, max_time_len, self.feature_size]			).astype(np.floataa			)
										_a        =   padded_audio_features * self.padding_value
										for i in range(len(__A			)			):
														_a        =   audio_features[i]
														_a        =   feature

										# return as BatchFeature
										if return_attention_mask:
														_a        =   {"audio_values": padded_audio_features, "audio_mask": audio_mask}
										else:
														_a        =   {"audio_values": padded_audio_features}

										_a        =   BatchFeature(data=__A	,    tensor_type=__A			)
										return encoded_inputs









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


SCREAMING_SNAKE_CASE							    =							getLogger(__name__)


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




def  a   (lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = 8		,				lowerCAmelCase__ = DEFAULT_DEVICE		,				lowerCAmelCase__=False		,				lowerCAmelCase__="summarization"		,				lowerCAmelCase__=None		,				**lowerCAmelCase__		,				):
   __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  a   ():
   return datetime.datetime.now().strftime("""%Y-%m-%d %H:%M:%S"""   )




def  a   (lowerCAmelCase__=True   ):
   __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	,    __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}'''   )
   __a					=  [""" """ + x.rstrip() if """t5""" in args.model_name else x.rstrip() for x in open(args.input_path   ).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.'''   )

   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"""   )

   __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)



