





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,
)


_lowercase		:  str			       =getLogger(__name__)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__ = 8							,lowerCAmelCase__ = 10_24							,lowerCAmelCase__="val"							,lowerCAmelCase__=None							,lowerCAmelCase__=False							,lowerCAmelCase__="summarization"							,lowerCAmelCase__=None							,lowerCAmelCase__=1							,lowerCAmelCase__ = None							,lowerCAmelCase__=""							,**lowerCAmelCase__							,):
		lowerCamelCase_	:     int 						=      str(__snake_case						)
		assert local_rank is not None
		torch.distributed.init_process_group(backend='nccl'							,rank=__snake_case						)

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

		lowerCamelCase_	:     Optional[Any] 						=      AutoTokenizer.from_pretrained(__snake_case						)
		logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"						)  # if this is wrong, check config.model_type.

		if max_source_length is None:
				lowerCamelCase_	:     int 						=      tokenizer.model_max_length
		if prefix is None:
				lowerCamelCase_	:     Union[str, Any] 						=      prefix or getattr(model.config							,'prefix'							,''						) or ''
		lowerCamelCase_	:     int 						=      SeqaSeqDataset(
		    __snake_case							,__snake_case							,__snake_case							,max_target_length=10_24							,type_path=__snake_case							,n_obs=__snake_case							,prefix=__snake_case							,**__snake_case							,)
		# 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.
		lowerCamelCase_	:     List[str] 						=      ds.make_sortish_sampler(__snake_case							,distributed=__snake_case							,add_extra_examples=__snake_case							,shuffle=__snake_case						)
		lowerCamelCase_	:     Tuple 						=      DataLoader(__snake_case							,sampler=__snake_case							,batch_size=__snake_case							,collate_fn=ds.collate_fn						)
		lowerCamelCase_	:     List[str] 						=      []
		for batch in tqdm(__snake_case						):
				lowerCamelCase_	:     str 						=      model.generate(
				    input_ids=batch['input_ids'].to(model.device						)							,attention_mask=batch['attention_mask'].to(model.device						)							,num_return_sequences=__snake_case							,num_beams=__snake_case							,**__snake_case							,)
				lowerCamelCase_	:     List[str] 						=      tokenizer.batch_decode(__snake_case							,skip_special_tokens=__snake_case							,clean_up_tokenization_spaces=__snake_case						)
				lowerCamelCase_	:     Optional[int] 						=      batch['ids']
				if num_return_sequences > 1:
						lowerCamelCase_	:     Dict 						=      chunks(__snake_case							,__snake_case						)  # batch size chunks, each of size num_return_seq
				for i, pred in enumerate(__snake_case						):
						results.append({'pred': pred, 'id': ids[i].item()}						)
		save_json(__snake_case							,__snake_case						)
		return results, sampler.num_replicas


def 		_SCREAMING_SNAKE_CASE     ( ):
		lowerCamelCase_	:     List[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=__snake_case							,help='like cnn_dm/test.source'						)
		parser.add_argument(
		    '--model_name'							,type=__snake_case							,help='like facebook/bart-large-cnn,t5-base, etc.'							,default='sshleifer/distilbart-xsum-12-3'							,)
		parser.add_argument('--save_dir'							,type=__snake_case							,help='where to save'							,default='tmp_gen'						)
		parser.add_argument('--max_source_length'							,type=__snake_case							,default=__snake_case						)
		parser.add_argument(
		    '--type_path'							,type=__snake_case							,default='test'							,help='which subset to evaluate typically train/val/test'						)
		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(
		    '--local_rank'							,type=__snake_case							,default=-1							,required=__snake_case							,help='should be passed by distributed.launch'						)

		parser.add_argument(
		    '--n_obs'							,type=__snake_case							,default=__snake_case							,required=__snake_case							,help='How many observations. Defaults to all.'						)
		parser.add_argument(
		    '--num_return_sequences'							,type=__snake_case							,default=1							,required=__snake_case							,help='How many sequences to return'						)
		parser.add_argument(
		    '--sync_timeout'							,type=__snake_case							,default=6_00							,required=__snake_case							,help='How long should master process wait for other processes to finish.'							,)
		parser.add_argument('--src_lang'							,type=__snake_case							,default=__snake_case							,required=__snake_case						)
		parser.add_argument('--tgt_lang'							,type=__snake_case							,default=__snake_case							,required=__snake_case						)
		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('--fp16'							,action='store_true'						)
		parser.add_argument('--debug'							,action='store_true'						)
		lowerCamelCase_	:     int 						=      time.time()
		lowerCamelCase_						,      lowerCamelCase_	:     Union[str, Any] 						=      parser.parse_known_args()
		lowerCamelCase_	:     int 						=      parse_numeric_n_bool_cl_kwargs(__snake_case						)
		if generate_kwargs and args.local_rank <= 0:
				print(F"parsed the following generate kwargs: {generate_kwargs}"						)
		lowerCamelCase_	:     str 						=      Path(args.save_dir + '_tmp'						)
		Path(__snake_case						).mkdir(exist_ok=__snake_case						)  # this handles locking.
		lowerCamelCase_	:     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.
		lowerCamelCase_	:     Tuple 						=      {}
		if args.src_lang is not None:
				lowerCamelCase_	:     Optional[Any] 						=      args.src_lang
		if args.tgt_lang is not None:
				lowerCamelCase_	:     Optional[Any] 						=      args.tgt_lang

		Path(args.save_dir						).mkdir(exist_ok=__snake_case						)
		lowerCamelCase_						,      lowerCamelCase_	:     str 						=      eval_data_dir(
		    args.data_dir							,__snake_case							,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=__snake_case							,**__snake_case							,)

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

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


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		lowerCamelCase_	:     int 						=      []
		for partial_result in partial_results:
				records.extend(__snake_case						)
		lowerCamelCase_	:     Dict 						=      sorted(__snake_case							,key=lambda lowerCAmelCase__						: x["id"]						)
		lowerCamelCase_	:     Union[str, Any] 						=      [x['pred'] for x in records]
		return preds


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		# WAIT FOR lots of .json files
		lowerCamelCase_	:     int 						=      time.time()
		logger.info('waiting for all nodes to finish'						)
		lowerCamelCase_	:     List[str] 						=      None
		while (time.time() - start_wait) < timeout:
				lowerCamelCase_	:     Any 						=      list(save_dir.glob('rank_*.json'						)						)
				if len(__snake_case						) < num_replicas:
						continue
				try:
						# make sure all json files are fully saved
						lowerCamelCase_	:     Optional[Any] 						=      lmap(__snake_case							,__snake_case						)
						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'''






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

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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			Dict=1.0   ,		__snake_case							:			List[Any]=None   ,		__snake_case							:			List[str]=None				):
     if rng is None:
          _A    		=  global_rng

     _A    		=  []
     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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def __init__(      self      :			Tuple, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Union[str, Any]=7, UpperCamelCase__      :			List[str]=4_00, UpperCamelCase__      :			Any=20_00, UpperCamelCase__      :			int=10, UpperCamelCase__      :			Tuple=1_60, UpperCamelCase__      :			Tuple=8, UpperCamelCase__      :			List[str]=0.0, UpperCamelCase__      :			Optional[Any]=40_00, UpperCamelCase__      :			List[str]=False, UpperCamelCase__      :			List[Any]=True, )  ->			Dict:
        _A    		=  parent
        _A    		=  batch_size
        _A    		=  min_seq_length
        _A    		=  max_seq_length
        _A    		=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _A    		=  padding_value
        _A    		=  sampling_rate
        _A    		=  return_attention_mask
        _A    		=  do_normalize
        _A    		=  feature_size
        _A    		=  chunk_length
        _A    		=  hop_length
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[Any]:
        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 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			List[Any]=False, UpperCamelCase__      :			str=False     )  ->			Any:
        def _flatten(UpperCamelCase__      :			List[str]     ):
             return list(itertools.chain(*UpperCamelCase__     )     )

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







@require_torch
@require_torchaudio
class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   WhisperFeatureExtractor if is_speech_available() else None
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Optional[Any]:
        _A    		=  WhisperFeatureExtractionTester(self     )
   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			int:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

        with tempfile.TemporaryDirectory() as tmpdirname:
             _A    		=  feat_extract_first.save_pretrained(UpperCamelCase__     )[0]
             check_json_file_has_correct_format(UpperCamelCase__     )
             _A    		=  self.feature_extraction_class.from_pretrained(UpperCamelCase__     )

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        _A    		=  self.feature_extraction_class(**self.feat_extract_dict     )

        with tempfile.TemporaryDirectory() as tmpdirname:
             _A    		=  os.path.join(UpperCamelCase__, 'feat_extract.json'     )
             feat_extract_first.to_json_file(UpperCamelCase__     )
             _A    		=  self.feature_extraction_class.from_json_file(UpperCamelCase__     )

        _A    		=  feat_extract_first.to_dict()
        _A    		=  feat_extract_second.to_dict()
        _A    		=  feat_extract_first.mel_filters
        _A    		=  feat_extract_second.mel_filters
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__     )     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        # Tests that all call wrap to encode_plus and batch_encode_plus
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        # create three inputs of length 800, 1000, and 1200
        _A    		=  [floats_list((1, x)     )[0] for x in range(8_00, 14_00, 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        # Test feature size
        _A    		=  feature_extractor(UpperCamelCase__, 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
        _A    		=  feature_extractor(speech_inputs[0], return_tensors='np'     ).input_features
        _A    		=  feature_extractor(np_speech_inputs[0], return_tensors='np'     ).input_features
        self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test batched
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test 2-D numpy arrays are batched.
        _A    		=  [floats_list((1, x)     )[0] for x in (8_00, 8_00, 8_00)]
        _A    		=  np.asarray(UpperCamelCase__     )
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )

        # Test truncation required
        _A    		=  [floats_list((1, x)     )[0] for x in range(2_00, (feature_extractor.n_samples + 5_00), 2_00     )]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs]

        _A    		=  [x[: feature_extractor.n_samples] for x in speech_inputs]
        _A    		=  [np.asarray(UpperCamelCase__     ) for speech_input in speech_inputs_truncated]

        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='np'     ).input_features
        for enc_seq_a, enc_seq_a in zip(UpperCamelCase__, UpperCamelCase__     ):
             self.assertTrue(np.allclose(UpperCamelCase__, UpperCamelCase__, atol=1e-3     )     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Any:
        import torch

        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  np.random.rand(1_00, 32     ).astype(np.floataa     )
        _A    		=  np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
             _A    		=  feature_extractor.pad([{'input_features': inputs}], return_tensors='np'     )
             self.assertTrue(np_processed.input_features.dtype == np.floataa     )
             _A    		=  feature_extractor.pad([{'input_features': inputs}], return_tensors='pt'     )
             self.assertTrue(pt_processed.input_features.dtype == torch.floataa     )
   def 	__UpperCAmelCase   (      self      :			int, UpperCamelCase__      :			int     )  ->			Optional[Any]:
        _A    		=  load_dataset('hf-internal-testing/librispeech_asr_dummy', 'clean', split='validation'     )
        # automatic decoding with librispeech
        _A    		=  ds.sort('id'     ).select(range(UpperCamelCase__     )     )[:num_samples]['audio']

        return [x["array"] for x in speech_samples]
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[Any]:
        # fmt: off
        _A    		=  torch.tensor(
            [
                0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
                0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
                0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
                -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
            ]     )
        # fmt: on

        _A    		=  self._load_datasamples(1     )
        _A    		=  WhisperFeatureExtractor()
        _A    		=  feature_extractor(UpperCamelCase__, return_tensors='pt'     ).input_features
        self.assertEqual(input_features.shape, (1, 80, 30_00)     )
        self.assertTrue(torch.allclose(input_features[0, 0, :30], UpperCamelCase__, atol=1e-4     )     )

   def 	__UpperCAmelCase   (      self      :			int     )  ->			Any:
        _A    		=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
        _A    		=  self._load_datasamples(1     )[0]
        _A    		=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_55_35  # Rescale to [0, 65535] to show issue
        _A    		=  feat_extract.zero_mean_unit_var_norm([audio], attention_mask=UpperCamelCase__     )[0]

        self.assertTrue(np.all(np.mean(UpperCamelCase__     ) < 1e-3     )     )
        self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__     ) - 1     ) < 1e-3     )     )



