





'''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


lowercase__       : List[str]         =       getLogger(__name__)


lowercase__       : List[Any]         =       '''cuda''' if torch.cuda.is_available() else '''cpu'''






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

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

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






def 			_lowerCAmelCase     (    )	->	Optional[int]:
     return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'     )






def 			_lowerCAmelCase     (    __snake_case    :					Tuple=True     )	->	int:
     __A		:					Any						=			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
     __A					,__A		:					Optional[Any]						=			parser.parse_known_args()
     __A		:					List[str]						=			parse_numeric_n_bool_cl_kwargs(__snake_case     )
     if parsed_args and verbose:
          print(f'parsed the following generate kwargs: {parsed_args}'     )
     __A		:					List[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:
          __A		:					Optional[Any]						=			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.'     )

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

     if args.dump_args:
          scores.update(__snake_case     )
     if args.info:
          __A		:					Union[str, Any]						=			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)

"""simple docstring"""




import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
  from transformers import WhisperFeatureExtractor

if is_torch_available():
  import torch

A__ :       List[Any]				= random.Random()




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase=1.0			,					_UpperCamelCase=None			,					_UpperCamelCase=None    ):






  """simple docstring"""



  if rng is None:
    _lowercase:      Any   			=  global_rng

  _lowercase:      List[str]   			=  []
  for batch_idx in range(shape[0]    ):
    values.append([]    )
    for _ in range(shape[1]    ):
      values[-1].append(rng.random() * scale    )

  return values


@require_torch
@require_torchaudio
class       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_=7			,					A_=400			,					A_=2000			,					A_=10			,					A_=160			,					A_=8			,					A_=0.0			,					A_=4000			,					A_=False			,					A_=True			,					)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      str   			=  parent
         _lowercase:      int   			=  batch_size
         _lowercase:      Tuple   			=  min_seq_length
         _lowercase:      Any   			=  max_seq_length
         _lowercase:      List[str]   			=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _lowercase:      Any   			=  padding_value
         _lowercase:      List[str]   			=  sampling_rate
         _lowercase:      Union[str, Any]   			=  return_attention_mask
         _lowercase:      Optional[Any]   			=  do_normalize
         _lowercase:      List[str]   			=  feature_size
         _lowercase:      Optional[Any]   			=  chunk_length
         _lowercase:      str   			=  hop_length
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         return {
             "feature_size": self.feature_size,
             "hop_length": self.hop_length,
             "chunk_length": self.chunk_length,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }



       def 				lowercase_				(  self			,					A_=False			,					A_=False							)  ->						List[Any]:





         """simple docstring"""




         def _flatten(A_							):
           return list(itertools.chain(*A_							)							)

         if equal_length:
           _lowercase:      List[str]   			=  [floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
         else:
           # make sure that inputs increase in size
           _lowercase:      Union[str, Any]   			=  [
               floats_list((x, self.feature_size)							)
               for x in range(self.min_seq_length			,					self.max_seq_length			,					self.seq_length_diff							)
           ]
         if numpify:
           _lowercase:      str   			=  [np.asarray(A_							) for x in speech_inputs]
         return speech_inputs


@require_torch
@require_torchaudio
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		WhisperFeatureExtractor if is_speech_available() else None
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      int   			=  WhisperFeatureExtractionTester(self							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
           _lowercase:      Any   			=  feat_extract_first.save_pretrained(A_							)[0]
           check_json_file_has_correct_format(A_							)
           _lowercase:      str   			=  self.feature_extraction_class.from_pretrained(A_							)

         _lowercase:      Dict   			=  feat_extract_first.to_dict()
         _lowercase:      List[str]   			=  feat_extract_second.to_dict()
         _lowercase:      List[str]   			=  feat_extract_first.mel_filters
         _lowercase:      str   			=  feat_extract_second.mel_filters
         self.assertTrue(np.allclose(A_			,					A_							)							)
         self.assertEqual(A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
           _lowercase:      Union[str, Any]   			=  os.path.join(A_			,					'''feat_extract.json'''							)
           feat_extract_first.to_json_file(A_							)
           _lowercase:      Tuple   			=  self.feature_extraction_class.from_json_file(A_							)

         _lowercase:      int   			=  feat_extract_first.to_dict()
         _lowercase:      Optional[int]   			=  feat_extract_second.to_dict()
         _lowercase:      Tuple   			=  feat_extract_first.mel_filters
         _lowercase:      Optional[int]   			=  feat_extract_second.mel_filters
         self.assertTrue(np.allclose(A_			,					A_							)							)
         self.assertEqual(A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Any   			=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         # create three inputs of length 800, 1000, and 1200
         _lowercase:      Tuple   			=  [floats_list((1, x)							)[0] for x in range(800			,					1400			,					200							)]
         _lowercase:      List[str]   			=  [np.asarray(A_							) for speech_input in speech_inputs]

         # Test feature size
         _lowercase:      Dict   			=  feature_extractor(A_			,					padding='''max_length'''			,					return_tensors='''np'''							).input_features
         self.assertTrue(input_features.ndim == 3							)
         self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames							)
         self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size							)

         # Test not batched input
         _lowercase:      Dict   			=  feature_extractor(speech_inputs[0]			,					return_tensors='''np'''							).input_features
         _lowercase:      Dict   			=  feature_extractor(np_speech_inputs[0]			,					return_tensors='''np'''							).input_features
         self.assertTrue(np.allclose(A_			,					A_			,					atol=1E-3							)							)

         # Test batched
         _lowercase:      Union[str, Any]   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         _lowercase:      List[Any]   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         for enc_seq_a, enc_seq_a in zip(A_			,					A_							):
           self.assertTrue(np.allclose(A_			,					A_			,					atol=1E-3							)							)

         # Test 2-D numpy arrays are batched.
         _lowercase:      Optional[Any]   			=  [floats_list((1, x)							)[0] for x in (800, 800, 800)]
         _lowercase:      Any   			=  np.asarray(A_							)
         _lowercase:      Tuple   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         _lowercase:      Dict   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         for enc_seq_a, enc_seq_a in zip(A_			,					A_							):
           self.assertTrue(np.allclose(A_			,					A_			,					atol=1E-3							)							)

         # Test truncation required
         _lowercase:      List[str]   			=  [floats_list((1, x)							)[0] for x in range(200			,					(feature_extractor.n_samples + 500)			,					200							)]
         _lowercase:      List[Any]   			=  [np.asarray(A_							) for speech_input in speech_inputs]

         _lowercase:      Any   			=  [x[: feature_extractor.n_samples] for x in speech_inputs]
         _lowercase:      str   			=  [np.asarray(A_							) for speech_input in speech_inputs_truncated]

         _lowercase:      Optional[Any]   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         _lowercase:      List[str]   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         for enc_seq_a, enc_seq_a in zip(A_			,					A_							):
           self.assertTrue(np.allclose(A_			,					A_			,					atol=1E-3							)							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         import torch

         _lowercase:      List[str]   			=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _lowercase:      List[Any]   			=  np.random.rand(100			,					32							).astype(np.floataa							)
         _lowercase:      Tuple   			=  np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
           _lowercase:      Optional[Any]   			=  feature_extractor.pad([{'''input_features''': inputs}]			,					return_tensors='''np'''							)
           self.assertTrue(np_processed.input_features.dtype == np.floataa							)
           _lowercase:      int   			=  feature_extractor.pad([{'''input_features''': inputs}]			,					return_tensors='''pt'''							)
           self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)
       def 				lowercase_				(  self			,					A_							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,					'''clean'''			,					split='''validation'''							)
         # automatic decoding with librispeech
         _lowercase:      List[str]   			=  ds.sort('''id'''							).select(range(A_							)							)[:num_samples]['''audio''']

         return [x["array"] for x in speech_samples]
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[str]   			=  torch.tensor(
             [
                 0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
                 0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
                 0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
                 -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
             ]							)
         # fmt: on

         _lowercase:      Optional[Any]   			=  self._load_datasamples(1							)
         _lowercase:      Optional[int]   			=  WhisperFeatureExtractor()
         _lowercase:      Dict   			=  feature_extractor(A_			,					return_tensors='''pt'''							).input_features
         self.assertEqual(input_features.shape			,					(1, 80, 3000)							)
         self.assertTrue(torch.allclose(input_features[0, 0, :30]			,					A_			,					atol=1E-4							)							)



       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _lowercase:      List[str]   			=  self._load_datasamples(1							)[0]
         _lowercase:      Union[str, Any]   			=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
         _lowercase:      Any   			=  feat_extract.zero_mean_unit_var_norm([audio]			,					attention_mask=A_							)[0]

         self.assertTrue(np.all(np.mean(A_							) < 1E-3							)							)
         self.assertTrue(np.all(np.abs(np.var(A_							) - 1							) < 1E-3							)							)
