





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


UpperCAmelCase__  :     Union[str, Any]   			='''bert-base-cased'''
UpperCAmelCase__  :     int   			='''google/pegasus-xsum'''
UpperCAmelCase__  :     List[str]   			=[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
UpperCAmelCase__  :     List[str]   			=['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
UpperCAmelCase__  :     Optional[int]   			='''patrickvonplaten/t5-tiny-random'''
UpperCAmelCase__  :     Any   			='''sshleifer/bart-tiny-random'''
UpperCAmelCase__  :     Optional[int]   			='''sshleifer/tiny-mbart'''
UpperCAmelCase__  :     Dict   			='''sshleifer/tiny-marian-en-de'''





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase			)  ->					Any:
				lowerCamelCase			='\n'.join(__a			)
				Path(__a			).open("""w"""			).writelines(__a			)





def        _lowercase   (  _UpperCAmelCase			)  ->					int:
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(__a     , F"""{split}.source"""			)     , __a			)
								_dump_articles(os.path.join(__a     , F"""{split}.target"""			)     , __a			)
				return tmp_dir




class  __A       (  snake_case_      ):


				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]				,	)
				@slow
				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=AutoTokenizer.from_pretrained(_A							)
								lowerCamelCase			=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
								lowerCamelCase			=max(len(tokenizer.encode(_A							)							) for a in ARTICLES							)
								lowerCamelCase			=max(len(tokenizer.encode(_A							)							) for a in SUMMARIES							)
								lowerCamelCase			=4
								lowerCamelCase			=8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								lowerCamelCase			='ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
								lowerCamelCase			=SeqaSeqDataset(
								    _A				,	data_dir=_A				,	type_path="""train"""				,	max_source_length=_A				,	max_target_length=_A				,	src_lang=_A				,	tgt_lang=_A				,	)
								lowerCamelCase			=DataLoader(_A				,	batch_size=2				,	collate_fn=train_dataset.collate_fn							)
								for batch in dataloader:
												assert isinstance(_A				,	_A							)
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_src_len
												# show that targets are the same len
												assert batch["labels"].shape[1] == max_tgt_len
												if tok_name != MBART_TINY:
																continue
												# check language codes in correct place
												lowerCamelCase			=shift_tokens_right(batch["""labels"""]				,	tokenizer.pad_token_id							)
												assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
												assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

												break  # No need to test every batch


				@parameterized.expand([BART_TINY, BERT_BASE_CASED]							)
				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=AutoTokenizer.from_pretrained(_A							)
								lowerCamelCase			=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
								lowerCamelCase			=max(len(tokenizer.encode(_A							)							) for a in ARTICLES							)
								lowerCamelCase			=max(len(tokenizer.encode(_A							)							) for a in SUMMARIES							)
								lowerCamelCase			=4
								lowerCamelCase			=LegacySeqaSeqDataset(
								    _A				,	data_dir=_A				,	type_path="""train"""				,	max_source_length=20				,	max_target_length=_A				,	)
								lowerCamelCase			=DataLoader(_A				,	batch_size=2				,	collate_fn=train_dataset.collate_fn							)
								for batch in dataloader:
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_len_source
												assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
												# show that targets were truncated
												assert batch["labels"].shape[1] == trunc_target  # Truncated
												assert max_len_target > trunc_target  # Truncated
												break  # No need to test every batch


				def _snake_case    (						self							):
								lowerCamelCase			=AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""							)

								lowerCamelCase			=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
								lowerCamelCase			=tmp_dir.joinpath("""train.source"""							).open().readlines()
								lowerCamelCase			=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
								pack_data_dir(_A				,	_A				,	128				,	_A							)
								lowerCamelCase			={x.name for x in tmp_dir.iterdir()}
								lowerCamelCase			={x.name for x in save_dir.iterdir()}
								lowerCamelCase			=save_dir.joinpath("""train.source"""							).open().readlines()
								# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
								# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
								assert len(_A							) < len(_A							)
								assert len(_A							) == 1
								assert len(packed_examples[0]							) == sum(len(_A							) for x in orig_examples							)
								assert orig_paths == new_paths


				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,	reason="""This test requires fairseq"""							)
				def _snake_case    (						self							):


								if not FAIRSEQ_AVAILABLE:
												return
								lowerCamelCase			=self._get_dataset(max_len=64							)
								lowerCamelCase			=64
								lowerCamelCase			=ds.make_dynamic_sampler(_A				,	required_batch_size_multiple=_A							)
								lowerCamelCase			=[len(_A							) for x in batch_sampler]
								assert len(set(_A							)							) > 1  # it's not dynamic batch size if every batch is the same length
								assert sum(_A							) == len(_A							)  # no dropped or added examples
								lowerCamelCase			=DataLoader(_A				,	batch_sampler=_A				,	collate_fn=ds.collate_fn				,	num_workers=2							)
								lowerCamelCase			=[]
								lowerCamelCase			=[]
								for batch in data_loader:
												lowerCamelCase			=batch['input_ids'].shape
												lowerCamelCase			=src_shape[0]
												assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
												lowerCamelCase			=np.product(batch["""input_ids"""].shape							)
												num_src_per_batch.append(_A							)
												if num_src_tokens > (max_tokens * 1.1):
																failures.append(_A							)
								assert num_src_per_batch[0] == max(_A							)
								if failures:
												raise AssertionError(f"""too many tokens in {len(_A							)} batches"""							)


				def _snake_case    (						self							):
								lowerCamelCase			=self._get_dataset(max_len=512							)
								lowerCamelCase			=2
								lowerCamelCase			=ds.make_sortish_sampler(_A				,	shuffle=_A							)

								lowerCamelCase			=DataLoader(_A				,	batch_size=_A				,	collate_fn=ds.collate_fn				,	num_workers=2							)
								lowerCamelCase			=DataLoader(_A				,	batch_size=_A				,	collate_fn=ds.collate_fn				,	num_workers=2				,	sampler=_A							)

								lowerCamelCase			=tokenizer.pad_token_id

								def count_pad_tokens(UpperCAmelCase_				,	UpperCAmelCase_="input_ids"							):
												return [batch[k].eq(_A							).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(_A				,	k="""labels"""							)							) < sum(count_pad_tokens(_A				,	k="""labels"""							)							)
								assert sum(count_pad_tokens(_A							)							) < sum(count_pad_tokens(_A							)							)
								assert len(_A							) == len(_A							)


				def _snake_case    (						self				,	UpperCAmelCase_=1000				,	UpperCAmelCase_=128							):


								if os.getenv("""USE_REAL_DATA"""				,	_A							):
												lowerCamelCase			='examples/seq2seq/wmt_en_ro'
												lowerCamelCase			=max_len * 2 * 64
												if not Path(_A							).joinpath("""train.len"""							).exists():
																save_len_file(_A				,	_A							)
								else:
												lowerCamelCase			='examples/seq2seq/test_data/wmt_en_ro'
												lowerCamelCase			=max_len * 4
												save_len_file(_A				,	_A							)

								lowerCamelCase			=AutoTokenizer.from_pretrained(_A							)
								lowerCamelCase			=SeqaSeqDataset(
								    _A				,	data_dir=_A				,	type_path="""train"""				,	max_source_length=_A				,	max_target_length=_A				,	n_obs=_A				,	)
								return ds, max_tokens, tokenizer


				def _snake_case    (						self							):
								lowerCamelCase			=self._get_dataset()
								lowerCamelCase			=set(DistributedSortishSampler(_A				,	256				,	num_replicas=2				,	rank=0				,	add_extra_examples=_A							)							)
								lowerCamelCase			=set(DistributedSortishSampler(_A				,	256				,	num_replicas=2				,	rank=1				,	add_extra_examples=_A							)							)
								assert idsa.intersection(_A							) == set()


				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]				,	)
				def _snake_case    (						self				,	UpperCAmelCase_							):
								lowerCamelCase			=AutoTokenizer.from_pretrained(_A				,	use_fast=_A							)
								if tok_name == MBART_TINY:
												lowerCamelCase			=SeqaSeqDataset(
												    _A				,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)				,	type_path="""train"""				,	max_source_length=4				,	max_target_length=8				,	src_lang="""EN"""				,	tgt_lang="""FR"""				,	)
												lowerCamelCase			=train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												lowerCamelCase			=SeqaSeqDataset(
												    _A				,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)				,	type_path="""train"""				,	max_source_length=4				,	max_target_length=8				,	)
												lowerCamelCase			=train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(_A							) == 1 if tok_name == BART_TINY else len(_A							) == 0












from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)			)  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)

				lowerCamelCase			=(1 - _cos) / 2
				lowerCamelCase			=1 - _cos

				lowerCamelCase			=1 + alpha
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)			)  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)

				lowerCamelCase			=(1 + _cos) / 2
				lowerCamelCase			=-1 - _cos

				lowerCamelCase			=1 + alpha
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)			)  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)

				lowerCamelCase			=_sin / 2
				lowerCamelCase			=0
				lowerCamelCase			=-ba

				lowerCamelCase			=1 + alpha
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)			)  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)

				lowerCamelCase			=1 - alpha
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 + alpha

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([ba, ba, ba]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)     , )  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)
				lowerCamelCase			=10 ** (gain_db / 40)

				lowerCamelCase			=1 + alpha * big_a
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha * big_a
				lowerCamelCase			=1 + alpha / big_a
				lowerCamelCase			=-2 * _cos
				lowerCamelCase			=1 - alpha / big_a

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)     , )  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)
				lowerCamelCase			=10 ** (gain_db / 40)
				lowerCamelCase			=(big_a + 1) - (big_a - 1) * _cos
				lowerCamelCase			=(big_a + 1) + (big_a - 1) * _cos
				lowerCamelCase			=(big_a - 1) - (big_a + 1) * _cos
				lowerCamelCase			=(big_a - 1) + (big_a + 1) * _cos
				lowerCamelCase			=2 * sqrt(_UpperCAmelCase			) * alpha

				lowerCamelCase			=big_a * (pmc + aaa)
				lowerCamelCase			=2 * big_a * mpc
				lowerCamelCase			=big_a * (pmc - aaa)
				lowerCamelCase			=ppmc + aaa
				lowerCamelCase			=-2 * pmpc
				lowerCamelCase			=ppmc - aaa

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = 1 / sqrt(2			)     , )  ->					IIRFilter:
				lowerCamelCase			=tau * frequency / samplerate
				lowerCamelCase			=sin(_UpperCAmelCase			)
				lowerCamelCase			=cos(_UpperCAmelCase			)
				lowerCamelCase			=_sin / (2 * q_factor)
				lowerCamelCase			=10 ** (gain_db / 40)
				lowerCamelCase			=(big_a + 1) - (big_a - 1) * _cos
				lowerCamelCase			=(big_a + 1) + (big_a - 1) * _cos
				lowerCamelCase			=(big_a - 1) - (big_a + 1) * _cos
				lowerCamelCase			=(big_a - 1) + (big_a + 1) * _cos
				lowerCamelCase			=2 * sqrt(_UpperCAmelCase			) * alpha

				lowerCamelCase			=big_a * (ppmc + aaa)
				lowerCamelCase			=-2 * big_a * pmpc
				lowerCamelCase			=big_a * (ppmc - aaa)
				lowerCamelCase			=pmc + aaa
				lowerCamelCase			=2 * mpc
				lowerCamelCase			=pmc - aaa

				lowerCamelCase			=IIRFilter(2			)
				filt.set_coefficients([aa, aa, aa]     , [ba, ba, ba]			)
				return filt





