


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       :      int	=	getLogger(__name__)





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     str							,					UpperCamelCase__:     str							,					UpperCamelCase__:     int = 8							,					UpperCamelCase__:     int = 10_24							,					UpperCamelCase__:     Optional[int]="val"							,					UpperCamelCase__:     Optional[int]=None							,					UpperCamelCase__:     Tuple=False							,					UpperCamelCase__:     Dict="summarization"							,					UpperCamelCase__:     Union[str, Any]=None							,					UpperCamelCase__:     List[str]=1							,					UpperCamelCase__:     Dict = None							,					UpperCamelCase__:     List[str]=""							,					**UpperCamelCase__:     Any							,					) ->   Dict:





	"""simple docstring"""







	A								=				str(UpperCamelCase__						)
	assert local_rank is not None
	torch.distributed.init_process_group(backend="""nccl"""							,					rank=UpperCamelCase__						)

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

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

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





def   _lowerCAmelCase				(   ) ->   List[str]:





	"""simple docstring"""







	A								=				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=UpperCamelCase__							,					help="""like cnn_dm/test.source"""						)
	parser.add_argument(
	    """--model_name"""							,					type=UpperCamelCase__							,					help="""like facebook/bart-large-cnn,t5-base, etc."""							,					default="""sshleifer/distilbart-xsum-12-3"""							,					)
	parser.add_argument("""--save_dir"""							,					type=UpperCamelCase__							,					help="""where to save"""							,					default="""tmp_gen"""						)
	parser.add_argument("""--max_source_length"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__						)
	parser.add_argument(
	    """--type_path"""							,					type=UpperCamelCase__							,					default="""test"""							,					help="""which subset to evaluate typically train/val/test"""						)
	parser.add_argument("""--task"""							,					type=UpperCamelCase__							,					default="""summarization"""							,					help="""used for task_specific_params + metrics"""						)
	parser.add_argument("""--bs"""							,					type=UpperCamelCase__							,					default=8							,					required=UpperCamelCase__							,					help="""batch size"""						)
	parser.add_argument(
	    """--local_rank"""							,					type=UpperCamelCase__							,					default=-1							,					required=UpperCamelCase__							,					help="""should be passed by distributed.launch"""						)

	parser.add_argument(
	    """--n_obs"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					required=UpperCamelCase__							,					help="""How many observations. Defaults to all."""						)
	parser.add_argument(
	    """--num_return_sequences"""							,					type=UpperCamelCase__							,					default=1							,					required=UpperCamelCase__							,					help="""How many sequences to return"""						)
	parser.add_argument(
	    """--sync_timeout"""							,					type=UpperCamelCase__							,					default=6_00							,					required=UpperCamelCase__							,					help="""How long should master process wait for other processes to finish."""							,					)
	parser.add_argument("""--src_lang"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					required=UpperCamelCase__						)
	parser.add_argument("""--tgt_lang"""							,					type=UpperCamelCase__							,					default=UpperCamelCase__							,					required=UpperCamelCase__						)
	parser.add_argument(
	    """--prefix"""							,					type=UpperCamelCase__							,					required=UpperCamelCase__							,					default=UpperCamelCase__							,					help="""will be added to the begininng of src examples"""						)
	parser.add_argument("""--fp16"""							,					action="""store_true"""						)
	parser.add_argument("""--debug"""							,					action="""store_true"""						)
	A								=				time.time()
	A					,			A								=				parser.parse_known_args()
	A								=				parse_numeric_n_bool_cl_kwargs(UpperCamelCase__						)
	if generate_kwargs and args.local_rank <= 0:
		print(f'parsed the following generate kwargs: {generate_kwargs}'						)
	A								=				Path(args.save_dir + """_tmp"""						)
	Path(UpperCamelCase__						).mkdir(exist_ok=UpperCamelCase__						)  # this handles locking.
	A								=				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.
	A								=				{}
	if args.src_lang is not None:
		A								=				args.src_lang
	if args.tgt_lang is not None:
		A								=				args.tgt_lang

	Path(args.save_dir						).mkdir(exist_ok=UpperCamelCase__						)
	A					,			A								=				eval_data_dir(
	    args.data_dir							,					UpperCamelCase__							,					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=UpperCamelCase__							,					**UpperCamelCase__							,					)

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

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





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]						) ->   List:





	"""simple docstring"""







	A								=				[]
	for partial_result in partial_results:
		records.extend(UpperCamelCase__						)
	A								=				sorted(UpperCamelCase__							,					key=lambda UpperCamelCase__						: x["id"]						)
	A								=				[x["""pred"""] for x in records]
	return preds





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]						) ->   List[Dict[str, List]]:





	"""simple docstring"""







	A								=				time.time()
	logger.info("""waiting for all nodes to finish"""						)
	A								=				None
	while (time.time() - start_wait) < timeout:
		A								=				list(save_dir.glob("""rank_*.json"""						)						)
		if len(UpperCamelCase__						) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			A								=				lmap(UpperCamelCase__							,					UpperCamelCase__						)
			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()







import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
				from transformers import SpeechaTextFeatureExtractor

_lowercase       :      Tuple	=	random.Random()





def   _lowerCAmelCase				(   UpperCamelCase__:     List[str]							,					UpperCamelCase__:     str=1.0							,					UpperCamelCase__:     int=None							,					UpperCamelCase__:     Optional[Any]=None						) ->   str:





	"""simple docstring"""







	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       _UpperCamelCase    (					unittest.TestCase				):

				"""simple docstring"""







				def __init__(						self				,   a__				,   a__=7				,   a__=400				,   a__=2000				,   a__=24				,   a__=24				,   a__=0.0				,   a__=1_6000				,   a__=True				,   a__=True				,   )	->				Any:
					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								=				feature_size
					A								=				num_mel_bins
					A								=				padding_value
					A								=				sampling_rate
					A								=				return_attention_mask
					A								=				do_normalize

				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					return {
					    "feature_size": self.feature_size,
					    "num_mel_bins": self.num_mel_bins,
					    "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				,   a__=False				,   a__=False   )	->				Tuple:
					def _flatten(a__   ):
						return list(itertools.chain(*a__   )   )

					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(a__   ) for x in speech_inputs]
					return speech_inputs






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

				"""simple docstring"""







				lowerCAmelCase 		= SpeechaTextFeatureExtractor if is_speech_available() else None

				def      _UpperCAmelCase					(						self   )	->				Dict:
					A								=				SpeechaTextFeatureExtractionTester(self   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				Any:
					self.assertTrue(np.all(np.mean(a__				,   axis=0   ) < 1e-3   )   )
					self.assertTrue(np.all(np.abs(np.var(a__				,   axis=0   ) - 1   ) < 1e-3   )   )

				def      _UpperCAmelCase					(						self   )	->				Dict:
					# 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(800				,   1400				,   200   )]
					A								=				[np.asarray(a__   ) for speech_input in speech_inputs]

					# Test feature size
					A								=				feature_extractor(a__				,   padding=a__				,   return_tensors="""np"""   ).input_features
					self.assertTrue(input_features.ndim == 3   )
					self.assertTrue(input_features.shape[-1] == 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(a__				,   a__				,   atol=1e-3   )   )

					# Test batched
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					A								=				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.
					A								=				[floats_list((1, x)   )[0] for x in (800, 800, 800)]
					A								=				np.asarray(a__   )
					A								=				feature_extractor(a__				,   return_tensors="""np"""   ).input_features
					A								=				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      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]

					A								=				["""longest""", """max_length""", """do_not_pad"""]
					A								=				[None, 16, None]
					for max_length, padding in zip(a__				,   a__   ):
						A								=				feature_extractor(
						    a__				,   padding=a__				,   max_length=a__				,   return_attention_mask=a__   )
						A								=				inputs.input_features
						A								=				inputs.attention_mask
						A								=				[np.sum(a__   ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]

					A								=				["""longest""", """max_length""", """do_not_pad"""]
					A								=				[None, 16, None]
					for max_length, padding in zip(a__				,   a__   ):
						A								=				feature_extractor(
						    a__				,   max_length=a__				,   padding=a__				,   return_tensors="""np"""				,   return_attention_mask=a__   )
						A								=				inputs.input_features
						A								=				inputs.attention_mask
						A								=				[np.sum(a__   ) for x in attention_mask]

						self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]   )
						self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6   )
						self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]   )
						self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6   )
						self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]   )

				def      _UpperCAmelCase					(						self   )	->				str:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""max_length"""				,   max_length=4				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""longest"""				,   max_length=4				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape				,   (3, 4, 24)   )

					A								=				[floats_list((1, x)   )[0] for x in range(800				,   1400				,   200   )]
					A								=				feature_extractor(
					    a__				,   padding="""longest"""				,   max_length=16				,   truncation=a__				,   return_tensors="""np"""				,   return_attention_mask=a__				,   )
					A								=				inputs.input_features
					A								=				inputs.attention_mask
					A								=				np.sum(attention_mask == 1				,   axis=1   )

					self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]   )
					self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]   )
					self._check_zero_mean_unit_variance(input_features[2]   )

					# make sure that if max_length < longest -> then pad to max_length
					self.assertEqual(input_features.shape				,   (3, 6, 24)   )

				def      _UpperCAmelCase					(						self   )	->				Any:
					import torch

					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				np.random.rand(100				,   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				,   a__   )	->				Optional[int]:
					from datasets import load_dataset

					A								=				load_dataset("""hf-internal-testing/librispeech_asr_dummy"""				,   """clean"""				,   split="""validation"""   )
					# automatic decoding with librispeech
					A								=				ds.sort("""id"""   ).select(range(a__   )   )[:num_samples]["""audio"""]

					return [x["array"] for x in speech_samples]

				def      _UpperCAmelCase					(						self   )	->				Any:
					# fmt: off
					A								=				np.array([
					    -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
					    -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
					    -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
					]   )
					# fmt: on

					A								=				self._load_datasamples(1   )
					A								=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
					A								=				feature_extractor(a__				,   return_tensors="""pt"""   ).input_features
					self.assertEquals(input_features.shape				,   (1, 584, 24)   )
					self.assertTrue(np.allclose(input_features[0, 0, :30]				,   a__				,   atol=1e-4   )   )



