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


lowerCamelCase_    :		int							=	"""bert-base-cased"""
lowerCamelCase_    :		List[str]							=	"""google/pegasus-xsum"""
lowerCamelCase_    :		Any							=	[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
lowerCamelCase_    :		Optional[int]							=	["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
lowerCamelCase_    :		List[str]							=	"""patrickvonplaten/t5-tiny-random"""
lowerCamelCase_    :		Optional[Any]							=	"""sshleifer/bart-tiny-random"""
lowerCamelCase_    :		Optional[Any]							=	"""sshleifer/tiny-mbart"""
lowerCamelCase_    :		Dict							=	"""sshleifer/tiny-marian-en-de"""
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       '\n'.join(__lowerCamelCase						)
					Path(__lowerCamelCase						).open('w'						).writelines(__lowerCamelCase						)
def 							lowerCAmelCase(     __lowerCamelCase						):
					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(__lowerCamelCase				,    f'''{split}.source'''						)				,    __lowerCamelCase						)
										_dump_articles(os.path.join(__lowerCamelCase				,    f'''{split}.target'''						)				,    __lowerCamelCase						)
					return tmp_dir



class 						a__							(       __snake_case		):



			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]				,						)
			@slow
			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   int:
								__a		 =       AutoTokenizer.from_pretrained(UpperCAmelCase   )
								__a		 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
								__a		 =       max(len(tokenizer.encode(UpperCAmelCase   )   ) for a in ARTICLES   )
								__a		 =       max(len(tokenizer.encode(UpperCAmelCase   )   ) for a in SUMMARIES   )
								__a		 =       4
								__a		 =       8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								__a ,				__a		 =       'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
								__a		 =       SeqaSeqDataset(
								    UpperCAmelCase				,						data_dir=UpperCAmelCase				,						type_path='train'				,						max_source_length=UpperCAmelCase				,						max_target_length=UpperCAmelCase				,						src_lang=UpperCAmelCase				,						tgt_lang=UpperCAmelCase				,						)
								__a		 =       DataLoader(UpperCAmelCase				,						batch_size=2				,						collate_fn=train_dataset.collate_fn   )
								for batch in dataloader:
													assert isinstance(UpperCAmelCase				,						UpperCAmelCase   )
													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
													__a		 =       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 __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Union[str, Any]:
								__a		 =       AutoTokenizer.from_pretrained(UpperCAmelCase   )
								__a		 =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
								__a		 =       max(len(tokenizer.encode(UpperCAmelCase   )   ) for a in ARTICLES   )
								__a		 =       max(len(tokenizer.encode(UpperCAmelCase   )   ) for a in SUMMARIES   )
								__a		 =       4
								__a		 =       LegacySeqaSeqDataset(
								    UpperCAmelCase				,						data_dir=UpperCAmelCase				,						type_path='train'				,						max_source_length=2_0				,						max_target_length=UpperCAmelCase				,						)
								__a		 =       DataLoader(UpperCAmelCase				,						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 2_0 >= 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 __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a		 =       AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'   )

								__a		 =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
								__a		 =       tmp_dir.joinpath('train.source'   ).open().readlines()
								__a		 =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
								pack_data_dir(UpperCAmelCase				,						UpperCAmelCase				,						1_2_8				,						UpperCAmelCase   )
								__a		 =       {x.name for x in tmp_dir.iterdir()}
								__a		 =       {x.name for x in save_dir.iterdir()}
								__a		 =       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(UpperCAmelCase   ) < len(UpperCAmelCase   )
								assert len(UpperCAmelCase   ) == 1
								assert len(packed_examples[0]   ) == sum(len(UpperCAmelCase   ) for x in orig_examples   )
								assert orig_paths == new_paths



			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE				,						reason='This test requires fairseq'   )
			def __SCREAMING_SNAKE_CASE							(						self   )			->   Tuple:
								if not FAIRSEQ_AVAILABLE:
													return
								__a ,				__a ,				__a		 =       self._get_dataset(max_len=6_4   )
								__a		 =       6_4
								__a		 =       ds.make_dynamic_sampler(UpperCAmelCase				,						required_batch_size_multiple=UpperCAmelCase   )
								__a		 =       [len(UpperCAmelCase   ) for x in batch_sampler]
								assert len(set(UpperCAmelCase   )   ) > 1  # it's not dynamic batch size if every batch is the same length
								assert sum(UpperCAmelCase   ) == len(UpperCAmelCase   )  # no dropped or added examples
								__a		 =       DataLoader(UpperCAmelCase				,						batch_sampler=UpperCAmelCase				,						collate_fn=ds.collate_fn				,						num_workers=2   )
								__a		 =       []
								__a		 =       []
								for batch in data_loader:
													__a		 =       batch['input_ids'].shape
													__a		 =       src_shape[0]
													assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
													__a		 =       np.product(batch['input_ids'].shape   )
													num_src_per_batch.append(UpperCAmelCase   )
													if num_src_tokens > (max_tokens * 1.1):
																		failures.append(UpperCAmelCase   )
								assert num_src_per_batch[0] == max(UpperCAmelCase   )
								if failures:
													raise AssertionError(f'''too many tokens in {len(UpperCAmelCase   )} batches'''   )



			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[str]:
								__a ,				__a ,				__a		 =       self._get_dataset(max_len=5_1_2   )
								__a		 =       2
								__a		 =       ds.make_sortish_sampler(UpperCAmelCase				,						shuffle=UpperCAmelCase   )

								__a		 =       DataLoader(UpperCAmelCase				,						batch_size=UpperCAmelCase				,						collate_fn=ds.collate_fn				,						num_workers=2   )
								__a		 =       DataLoader(UpperCAmelCase				,						batch_size=UpperCAmelCase				,						collate_fn=ds.collate_fn				,						num_workers=2				,						sampler=UpperCAmelCase   )

								__a		 =       tokenizer.pad_token_id

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

								assert sum(count_pad_tokens(UpperCAmelCase				,						k='labels'   )   ) < sum(count_pad_tokens(UpperCAmelCase				,						k='labels'   )   )
								assert sum(count_pad_tokens(UpperCAmelCase   )   ) < sum(count_pad_tokens(UpperCAmelCase   )   )
								assert len(UpperCAmelCase   ) == len(UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase=1_0_0_0				,						UpperCAmelCase=1_2_8   )			->   int:
								if os.getenv('USE_REAL_DATA'				,						UpperCAmelCase   ):
													__a		 =       'examples/seq2seq/wmt_en_ro'
													__a		 =       max_len * 2 * 6_4
													if not Path(UpperCAmelCase   ).joinpath('train.len'   ).exists():
																		save_len_file(UpperCAmelCase				,						UpperCAmelCase   )
								else:
													__a		 =       'examples/seq2seq/test_data/wmt_en_ro'
													__a		 =       max_len * 4
													save_len_file(UpperCAmelCase				,						UpperCAmelCase   )

								__a		 =       AutoTokenizer.from_pretrained(UpperCAmelCase   )
								__a		 =       SeqaSeqDataset(
								    UpperCAmelCase				,						data_dir=UpperCAmelCase				,						type_path='train'				,						max_source_length=UpperCAmelCase				,						max_target_length=UpperCAmelCase				,						n_obs=UpperCAmelCase				,						)
								return ds, max_tokens, tokenizer



			def __SCREAMING_SNAKE_CASE							(						self   )			->   Optional[int]:
								__a ,				__a ,				__a		 =       self._get_dataset()
								__a		 =       set(DistributedSortishSampler(UpperCAmelCase				,						2_5_6				,						num_replicas=2				,						rank=0				,						add_extra_examples=UpperCAmelCase   )   )
								__a		 =       set(DistributedSortishSampler(UpperCAmelCase				,						2_5_6				,						num_replicas=2				,						rank=1				,						add_extra_examples=UpperCAmelCase   )   )
								assert idsa.intersection(UpperCAmelCase   ) == set()







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]				,						)
			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Union[str, Any]:
								__a		 =       AutoTokenizer.from_pretrained(UpperCAmelCase				,						use_fast=UpperCAmelCase   )
								if tok_name == MBART_TINY:
													__a		 =       SeqaSeqDataset(
													    UpperCAmelCase				,						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'				,						)
													__a		 =       train_dataset.dataset_kwargs
													assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
													__a		 =       SeqaSeqDataset(
													    UpperCAmelCase				,						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				,						)
													__a		 =       train_dataset.dataset_kwargs
													assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
													assert len(UpperCAmelCase   ) == 1 if tok_name == BART_TINY else len(UpperCAmelCase   ) == 0





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer


class 						a__							(       __snake_case		):
			A__    :       Any        =      'Wav2Vec2FeatureExtractor'
			A__    :       str        =      'AutoTokenizer'



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Union[str, Any]:
								super().__init__(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       self.feature_extractor
								__a		 =       False



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								try:
													return super().from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
								except OSError:
													warnings.warn(
													    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
													    ' include a `tokenizer_class` attribute is deprecated and will be '
													    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
													    ' attribute to either your `config.json` or `tokenizer_config.json` '
													    'file to suppress this warning: '				,						UpperCAmelCase				,						)

													__a		 =       WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
													__a		 =       WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )

													return cls(feature_extractor=UpperCAmelCase				,						tokenizer=UpperCAmelCase   )



			def __call__(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[Any]:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor(*UpperCAmelCase				,						**UpperCAmelCase   )

								if "raw_speech" in kwargs:
													warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'   )
													__a		 =       kwargs.pop('raw_speech'   )
								else:
													__a		 =       kwargs.pop('audio'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('sampling_rate'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('text'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if audio is None and text is None:
													raise ValueError('You need to specify either an `audio` or `text` input to process.'   )

								if audio is not None:
													__a		 =       self.feature_extractor(UpperCAmelCase				,						*UpperCAmelCase				,						sampling_rate=UpperCAmelCase				,						**UpperCAmelCase   )
								if text is not None:
													__a		 =       self.tokenizer(UpperCAmelCase				,						**UpperCAmelCase   )

								if text is None:
													return inputs
								elif audio is None:
													return encodings
								else:
													__a		 =       encodings['input_ids']
													return inputs



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								# For backward compatibility
								if self._in_target_context_manager:
													return self.current_processor.pad(*UpperCAmelCase				,						**UpperCAmelCase   )

								__a		 =       kwargs.pop('input_features'				,						UpperCAmelCase   )
								__a		 =       kwargs.pop('labels'				,						UpperCAmelCase   )
								if len(UpperCAmelCase   ) > 0:
													__a		 =       args[0]
													__a		 =       args[1:]

								if input_features is not None:
													__a		 =       self.feature_extractor.pad(UpperCAmelCase				,						*UpperCAmelCase				,						**UpperCAmelCase   )
								if labels is not None:
													__a		 =       self.tokenizer.pad(UpperCAmelCase				,						**UpperCAmelCase   )

								if labels is None:
													return input_features
								elif input_features is None:
													return labels
								else:
													__a		 =       labels['input_ids']
													return input_features



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								return self.tokenizer.batch_decode(*UpperCAmelCase				,						**UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Union[str, Any]:
								return self.tokenizer.decode(*UpperCAmelCase				,						**UpperCAmelCase   )







			@contextmanager
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[Any]:
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your audio inputs, or in a separate call.'   )
								__a		 =       True
								__a		 =       self.tokenizer
								yield
								__a		 =       self.feature_extractor
								__a		 =       False




