


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


a_ :Tuple  							=			getLogger(__name__)


a_ :Optional[Any]  							=			'cuda' if torch.cuda.is_available() else 'cpu'




def    a   (							A__      ,    A__      ,    A__      ,    A__ = 8      ,    A__ = DEFAULT_DEVICE      ,    A__=False      ,    A__="summarization"      ,    A__=None      ,    **A__      ,    )	-> Dict:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		Path(A__							).open('''w'''      ,    encoding='''utf-8'''							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		str(A__							)
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		AutoModelForSeqaSeqLM.from_pretrained(A__							).to(A__							)
      if fpaa:
            SCREAMING_SNAKE_CASE__       :  Any						     =		model.half()

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		AutoTokenizer.from_pretrained(A__							)
      logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""							)  # if this is wrong, check config.model_type.

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




def    a   (							)	-> Tuple:




      '''simple docstring'''

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




def    a   (							A__=True							)	-> str:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__       :  List[str]						     =		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
      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		parser.parse_known_args()
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		parse_numeric_n_bool_cl_kwargs(A__							)
      if parsed_args and verbose:
            print(f"""parsed the following generate kwargs: {parsed_args}"""							)
      SCREAMING_SNAKE_CASE__       :  Union[str, 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:
            SCREAMING_SNAKE_CASE__       :  str						     =		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'''							)

      SCREAMING_SNAKE_CASE__       :  List[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
      SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		calculate_bleu if '''translation''' in args.task else calculate_rouge
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		[x.rstrip() for x in open(args.save_path							).readlines()]
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		[x.rstrip() for x in open(args.reference_path							).readlines()][: len(A__							)]
      SCREAMING_SNAKE_CASE__       :  dict						     =		score_fn(A__      ,    A__							)
      scores.update(A__							)

      if args.dump_args:
            scores.update(A__							)
      if args.info:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		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)













import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
		from .features import FeatureType


SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Any			=   False, False, False

@dataclass
class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		None
	__UpperCamelCase   =		True
	__UpperCamelCase   =		True
	__UpperCamelCase   =		None
	# Automatically constructed
	__UpperCamelCase   =		"dict"
	__UpperCamelCase   =		pa.struct({'''bytes''': pa.binary(), '''path''': pa.string()}						)
	__UpperCamelCase   =		field(default='''Audio'''     ,     init=UpperCAmelCase_     ,     repr=UpperCAmelCase_						)




	def __call__(						self							:     Optional[Any]   )       -> List[str]:

			'''simple docstring'''




			return self.pa_type




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     Union[str, bytes, dict]   )       -> dict:

			'''simple docstring'''




			try:
					import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
			except ImportError as err:
					raise ImportError('To support encoding audio data, please install \'soundfile\'.'   ) from err
			if isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					return {"bytes": None, "path": value}
			elif isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					return {"bytes": value, "path": None}
			elif "array" in value:
					# convert the audio array to wav bytes
					__lowercase											=     BytesIO()
					sf.write(__lowerCamelCase					,      value['array']					,      value['sampling_rate']					,      format='wav'   )
					return {"bytes": buffer.getvalue(), "path": None}
			elif value.get('path'   ) is not None and os.path.isfile(value['path']   ):
					# we set "bytes": None to not duplicate the data if they're already available locally
					if value["path"].endswith('pcm'   ):
							# "PCM" only has raw audio bytes
							if value.get('sampling_rate'   ) is None:
									# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
									raise KeyError('To use PCM files, please specify a \'sampling_rate\' in Audio object'   )
							if value.get('bytes'   ):
									# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
									__lowercase											=     np.frombuffer(value['bytes']					,      dtype=np.intaa   ).astype(np.floataa   ) / 32_767
							else:
									__lowercase											=     np.memmap(value['path']					,      dtype='h'					,      mode='r'   ).astype(np.floataa   ) / 32_767

							__lowercase											=     BytesIO(bytes()   )
							sf.write(__lowerCamelCase					,      __lowerCamelCase					,      value['sampling_rate']					,      format='wav'   )
							return {"bytes": buffer.getvalue(), "path": None}
					else:
							return {"bytes": None, "path": value.get('path'   )}
			elif value.get('bytes'   ) is not None or value.get('path'   ) is not None:
					# store the audio bytes, and path is used to infer the audio format using the file extension
					return {"bytes": value.get('bytes'   ), "path": value.get('path'   )}
			else:
					raise ValueError(
					    F"An audio sample should have one of 'path' or 'bytes' but they are missing or None in {value}."   )




	def    UpperCAmelCase			(						self							:     Any					,      __lowerCamelCase							:     dict					,      __lowerCamelCase							:     Optional[Dict[str, Union[str, bool, None]]] = None   )       -> dict:

			'''simple docstring'''




			if not self.decode:
					raise RuntimeError('Decoding is disabled for this feature. Please use Audio(decode=True) instead.'   )

			__lowercase						,      __lowercase											=     (value['path'], BytesIO(value['bytes']   )) if value['bytes'] is not None else (value['path'], None)
			if path is None and file is None:
					raise ValueError(F"An audio sample should have one of 'path' or 'bytes' but both are None in {value}."   )

			try:
					import librosa
					import soundfile as sf
			except ImportError as err:
					raise ImportError('To support decoding audio files, please install \'librosa\' and \'soundfile\'.'   ) from err

			__lowercase											=     xsplitext(__lowerCamelCase   )[1][1:].lower() if path is not None else None
			if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
					raise RuntimeError(
					    'Decoding \'opus\' files requires system library \'libsndfile\'>=1.0.31, '
					    'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '   )
			elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
					raise RuntimeError(
					    'Decoding \'mp3\' files requires system library \'libsndfile\'>=1.1.0, '
					    'You can try to update `soundfile` python library: `pip install "soundfile>=0.12.1"`. '   )

			if file is None:
					__lowercase											=     token_per_repo_id or {}
					__lowercase											=     path.split('::'   )[-1]
					try:
							__lowercase											=     string_to_dict(__lowerCamelCase					,      config.HUB_DATASETS_URL   )['repo_id']
							__lowercase											=     token_per_repo_id[repo_id]
					except (ValueError, KeyError):
							__lowercase											=     None

					with xopen(__lowerCamelCase					,      'rb'					,      use_auth_token=__lowerCamelCase   ) as f:
							__lowercase						,      __lowercase											=     sf.read(__lowerCamelCase   )

			else:
					__lowercase						,      __lowercase											=     sf.read(__lowerCamelCase   )

			__lowercase											=     array.T
			if self.mono:
					__lowercase											=     librosa.to_mono(__lowerCamelCase   )
			if self.sampling_rate and self.sampling_rate != sampling_rate:
					__lowercase											=     librosa.resample(__lowerCamelCase					,      orig_sr=__lowerCamelCase					,      target_sr=self.sampling_rate   )
					__lowercase											=     self.sampling_rate

			return {"path": path, "array": array, "sampling_rate": sampling_rate}




	def    UpperCAmelCase			(						self							:     int   )       -> Union["FeatureType", Dict[str, "FeatureType"]]:

			'''simple docstring'''




			from .features import Value

			if self.decode:
					raise ValueError('Cannot flatten a decoded Audio feature.'   )
			return {
			    "bytes": Value('binary'   ),
			    "path": Value('string'   ),
			}




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     Union[pa.StringArray, pa.StructArray]   )       -> pa.StructArray:

			'''simple docstring'''




			if pa.types.is_string(storage.type   ):
					__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.binary()   )
					__lowercase											=     pa.StructArray.from_arrays([bytes_array, storage]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			elif pa.types.is_binary(storage.type   ):
					__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.string()   )
					__lowercase											=     pa.StructArray.from_arrays([storage, path_array]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			elif pa.types.is_struct(storage.type   ) and storage.type.get_all_field_indices('array'   ):
					__lowercase											=     pa.array([Audio().encode_example(__lowerCamelCase   ) if x is not None else None for x in storage.to_pylist()]   )
			elif pa.types.is_struct(storage.type   ):
					if storage.type.get_field_index('bytes'   ) >= 0:
							__lowercase											=     storage.field('bytes'   )
					else:
							__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.binary()   )
					if storage.type.get_field_index('path'   ) >= 0:
							__lowercase											=     storage.field('path'   )
					else:
							__lowercase											=     pa.array([None] * len(__lowerCamelCase   )					,      type=pa.string()   )
					__lowercase											=     pa.StructArray.from_arrays([bytes_array, path_array]					,      ['bytes', 'path']					,      mask=storage.is_null()   )
			return array_cast(__lowerCamelCase					,      self.pa_type   )






	def    UpperCAmelCase			(						self							:     Union[str, Any]					,      __lowerCamelCase							:     pa.StructArray   )       -> pa.StructArray:

			'''simple docstring'''




			@no_op_if_value_is_null
			def path_to_bytes(__lowerCamelCase							:     Any   ):
					with xopen(__lowerCamelCase					,      'rb'   ) as f:
							__lowercase											=     f.read()
					return bytes_

			__lowercase											=     pa.array(
			    [
			        (path_to_bytes(x['path']   ) if x['bytes'] is None else x['bytes']) if x is not None else None
			        for x in storage.to_pylist()
			    ]					,      type=pa.binary()					,      )
			__lowercase											=     pa.array(
			    [os.path.basename(__lowerCamelCase   ) if path is not None else None for path in storage.field('path'   ).to_pylist()]					,      type=pa.string()					,      )
			__lowercase											=     pa.StructArray.from_arrays([bytes_array, path_array]					,      ['bytes', 'path']					,      mask=bytes_array.is_null()   )
			return array_cast(__lowerCamelCase					,      self.pa_type   )




