
"""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							     =			getLogger(__name__)


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






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

	A  				=				AutoTokenizer.from_pretrained(snake_case__       )
	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(snake_case__		, snake_case__       )
	if prefix is None:
		A  				=				prefix or getattr(model.config		, 'prefix'		, ''       ) or ''
	for examples_chunk in tqdm(list(chunks(snake_case__		, snake_case__       )       )       ):
		A  				=				[prefix + text for text in examples_chunk]
		A  				=				tokenizer(snake_case__		, return_tensors='pt'		, truncation=snake_case__		, padding='longest'       ).to(snake_case__       )
		A  				=				model.generate(
		    input_ids=batch.input_ids		, attention_mask=batch.attention_mask		, **snake_case__		, )
		A  				=				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  				=				int(time.time() - start_time       )  # seconds
	A  				=				len(snake_case__       )
	return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs		, 4       )}






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






def  _snake_case			(						snake_case__				:       Dict=True       ):
	A  				=				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  				=				parser.parse_known_args()
	A  				=				parse_numeric_n_bool_cl_kwargs(snake_case__       )
	if parsed_args and verbose:
		print(F'parsed the following generate kwargs: {parsed_args}'       )
	with open(args.input_path       ) as f:
		A  				=				[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
	if args.n_obs > 0:
		A  				=				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.'       )
	A  				=				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  				=				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(snake_case__       )]
	A  				=				score_fn(snake_case__		, snake_case__       )
	scores.update(snake_case__       )

	if args.dump_args:
		scores.update(snake_case__       )
	if args.info:
		A  				=				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 random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
		import torch


_lowercase											=			random.Random()






def  _snake_case			(						snake_case__				:       List[str]		, snake_case__				:       Union[str, Any]=1.0		, snake_case__				:       int=None		, snake_case__				:       Dict=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
class 			lowerCAmelCase_			( unittest.TestCase      ):



						'''simple docstring'''


						def __init__(	self       : int					,A_       : str					,A_       : List[str]=7					,A_       : Tuple=400					,A_       : Union[str, Any]=2000					,A_       : Optional[Any]=1					,A_       : Optional[int]=0.0					,A_       : str=2_4000					,A_       : Optional[int]=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  				=				feature_size
							A  				=				padding_value
							A  				=				sampling_rate
							A  				=				return_attention_mask


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[Any] )       ->				Optional[int]:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							}




						def   _SCREAMING_SNAKE_CASE (	self       : Dict					,A_       : List[str]=False					,A_       : str=False )       ->				Optional[Any]:
							def _flatten(A_       : int ):
								return list(itertools.chain(*A_ ) )

							if equal_length:
								A  				=				floats_list((self.batch_size, self.max_seq_length) )
							else:
								# make sure that inputs increase in size
								A  				=				[
								    _flatten(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 audio_inputs]

							return audio_inputs







@require_torch
class 			lowerCAmelCase_			( _lowercase						,   unittest.TestCase      ):



						'''simple docstring'''


						_lowerCamelCase:      Union[str, Any] =      EncodecFeatureExtractor


						def   _SCREAMING_SNAKE_CASE (	self       : List[str] )       ->				List[Any]:
							A  				=				EnCodecFeatureExtractionTester(self )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				str:
							# 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 audio_input in audio_inputs]

							# Test not batched input
							A  				=				feat_extract(audio_inputs[0]					,return_tensors='np' ).input_values
							A  				=				feat_extract(np_audio_inputs[0]					,return_tensors='np' ).input_values
							self.assertTrue(np.allclose(A_					,A_					,atol=1e-3 ) )

							# Test batched
							A  				=				feat_extract(A_					,padding=A_					,return_tensors='np' ).input_values
							A  				=				feat_extract(A_					,padding=A_					,return_tensors='np' ).input_values
							for enc_seq_a, enc_seq_a in zip(A_					,A_ ):
								self.assertTrue(np.allclose(A_					,A_					,atol=1e-3 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Tuple )       ->				Dict:
							A  				=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
							A  				=				np.random.rand(100 ).astype(np.floataa )
							A  				=				np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
								A  				=				feature_extractor.pad([{'input_values': inputs}]					,return_tensors='np' )
								self.assertTrue(np_processed.input_values.dtype == np.floataa )
								A  				=				feature_extractor.pad([{'input_values': inputs}]					,return_tensors='pt' )
								self.assertTrue(pt_processed.input_values.dtype == torch.floataa )


						def   _SCREAMING_SNAKE_CASE (	self       : Optional[int]					,A_       : int )       ->				List[Any]:
							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 audio_samples]


						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				Optional[int]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				EncodecFeatureExtractor()
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 1, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )


						def   _SCREAMING_SNAKE_CASE (	self       : Union[str, Any] )       ->				List[Any]:
							# fmt: off
							A  				=				torch.tensor(
							    [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
							     3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
							     2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
							     4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
							     7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
							     4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03] )
							# fmt: on
							A  				=				self._load_datasamples(1 )
							A  				=				[np.tile(input_audio[0][None]					,reps=(2, 1) )]
							input_audio[0][1] *= 0.5
							A  				=				EncodecFeatureExtractor(feature_size=2 )
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(input_values.shape					,(1, 2, 9_3680) )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]					,A_					,atol=1e-6 ) )
							self.assertTrue(torch.allclose(input_values[0, 1, :30]					,EXPECTED_INPUT_VALUES * 0.5					,atol=1e-6 ) )




						def   _SCREAMING_SNAKE_CASE (	self       : Dict )       ->				List[str]:
							A  				=				self._load_datasamples(2 )
							# would be easier if the stride was like
							A  				=				EncodecFeatureExtractor(feature_size=1					,chunk_length_s=1					,overlap=0.01 )

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    A_					,'^Both padding and truncation were set. Make sure you only set one.$'					,):
								A  				=				feature_extractor(
								    A_					,padding='max_length'					,truncation=A_					,return_tensors='pt' ).input_values

							# truncate to chunk
							A  				=				feature_extractor(A_					,truncation=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 7_1520) )  # 2 chunks

							# force truncate to max_length
							A  				=				feature_extractor(
							    A_					,truncation=A_					,max_length=4_8000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 4_8000) )

							# pad to chunk
							A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(padded_outputs.shape					,(2, 1, 9_5280) )

							# pad to chunk
							A  				=				feature_extractor(A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 9_5280) )

							# force pad to max length
							A  				=				feature_extractor(
							    A_					,padding='max_length'					,max_length=10_0000					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(2, 1, 10_0000) )

							# force no pad
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

							# no pad if no chunk_length_s
							A  				=				None
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )

							# no pad if no overlap
							A  				=				2
							A  				=				None
							with self.assertRaisesRegex(
							    A_					,'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'					,):
								A  				=				feature_extractor(A_					,padding=A_					,return_tensors='pt' ).input_values

							A  				=				feature_extractor(input_audio[0]					,padding=A_					,return_tensors='pt' ).input_values
							self.assertEquals(truncated_outputs.shape					,(1, 1, 9_3680) )