"""simple docstring"""





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_     =	'''bert-base-cased'''
lowerCAmelCase_     =	'''google/pegasus-xsum'''
lowerCAmelCase_     =	[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCAmelCase_     =	['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCAmelCase_     =	'''patrickvonplaten/t5-tiny-random'''
lowerCAmelCase_     =	'''sshleifer/bart-tiny-random'''
lowerCAmelCase_     =	'''sshleifer/tiny-mbart'''
lowerCAmelCase_     =	'''sshleifer/tiny-marian-en-de'''


def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    "\n".join(__SCREAMING_SNAKE_CASE  )
							Path(__SCREAMING_SNAKE_CASE  ).open("""w"""  ).writelines(__SCREAMING_SNAKE_CASE  )


def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:






							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(__SCREAMING_SNAKE_CASE     ,	F"""{split}.source"""  )     ,	__SCREAMING_SNAKE_CASE  )
														_dump_articles(os.path.join(__SCREAMING_SNAKE_CASE     ,	F"""{split}.target"""  )     ,	__SCREAMING_SNAKE_CASE  )
							return tmp_dir




class  _snake_case		(    snake_case__ ):







				"""simple docstring"""






				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]      ,    )
				@slow
				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Dict			  =    AutoTokenizer.from_pretrained(lowercase_)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    max(len(tokenizer.encode(lowercase_)) for a in ARTICLES)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    max(len(tokenizer.encode(lowercase_)) for a in SUMMARIES)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    4
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    8
											assert max_len_target > max_src_len  # Will be truncated
											assert max_len_source > max_src_len  # Will be truncated
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
											_SCREAMING_SNAKE_CASE		:						Dict			  =    SeqaSeqDataset(
											    lowercase_      ,    data_dir=lowercase_      ,    type_path="""train"""      ,    max_source_length=lowercase_      ,    max_target_length=lowercase_      ,    src_lang=lowercase_      ,    tgt_lang=lowercase_      ,    )
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    DataLoader(lowercase_      ,    batch_size=2      ,    collate_fn=train_dataset.collate_fn)
											for batch in dataloader:
																		assert isinstance(lowercase_      ,    lowercase_)
																		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
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    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 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoTokenizer.from_pretrained(lowercase_)
											_SCREAMING_SNAKE_CASE		:						int			  =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    max(len(tokenizer.encode(lowercase_)) for a in ARTICLES)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    max(len(tokenizer.encode(lowercase_)) for a in SUMMARIES)
											_SCREAMING_SNAKE_CASE		:						int			  =    4
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    LegacySeqaSeqDataset(
											    lowercase_      ,    data_dir=lowercase_      ,    type_path="""train"""      ,    max_source_length=2_0      ,    max_target_length=lowercase_      ,    )
											_SCREAMING_SNAKE_CASE		:						Dict			  =    DataLoader(lowercase_      ,    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 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25""")

											_SCREAMING_SNAKE_CASE		:						int			  =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
											_SCREAMING_SNAKE_CASE		:						int			  =    tmp_dir.joinpath("""train.source""").open().readlines()
											_SCREAMING_SNAKE_CASE		:						Any			  =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
											pack_data_dir(lowercase_      ,    lowercase_      ,    1_2_8      ,    lowercase_)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {x.name for x in tmp_dir.iterdir()}
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    {x.name for x in save_dir.iterdir()}
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    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(lowercase_) < len(lowercase_)
											assert len(lowercase_) == 1
											assert len(packed_examples[0]) == sum(len(lowercase_) for x in orig_examples)
											assert orig_paths == new_paths





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



											"""simple docstring"""







											if not FAIRSEQ_AVAILABLE:
																		return
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self._get_dataset(max_len=6_4)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    6_4
											_SCREAMING_SNAKE_CASE		:						str			  =    ds.make_dynamic_sampler(lowercase_      ,    required_batch_size_multiple=lowercase_)
											_SCREAMING_SNAKE_CASE		:						int			  =    [len(lowercase_) for x in batch_sampler]
											assert len(set(lowercase_)) > 1  # it's not dynamic batch size if every batch is the same length
											assert sum(lowercase_) == len(lowercase_)  # no dropped or added examples
											_SCREAMING_SNAKE_CASE		:						int			  =    DataLoader(lowercase_      ,    batch_sampler=lowercase_      ,    collate_fn=ds.collate_fn      ,    num_workers=2)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    []
											_SCREAMING_SNAKE_CASE		:						Dict			  =    []
											for batch in data_loader:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    batch["input_ids"].shape
																		_SCREAMING_SNAKE_CASE		:						Any			  =    src_shape[0]
																		assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    np.product(batch["""input_ids"""].shape)
																		num_src_per_batch.append(lowercase_)
																		if num_src_tokens > (max_tokens * 1.1):
																									failures.append(lowercase_)
											assert num_src_per_batch[0] == max(lowercase_)
											if failures:
																		raise AssertionError(f"""too many tokens in {len(lowercase_)} batches""")





				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self._get_dataset(max_len=5_1_2)
											_SCREAMING_SNAKE_CASE		:						str			  =    2
											_SCREAMING_SNAKE_CASE		:						Dict			  =    ds.make_sortish_sampler(lowercase_      ,    shuffle=lowercase_)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    DataLoader(lowercase_      ,    batch_size=lowercase_      ,    collate_fn=ds.collate_fn      ,    num_workers=2)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    DataLoader(lowercase_      ,    batch_size=lowercase_      ,    collate_fn=ds.collate_fn      ,    num_workers=2      ,    sampler=lowercase_)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tokenizer.pad_token_id

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

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





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							str=1_0_0_0      ,    _A							:							List[Any]=1_2_8):



											"""simple docstring"""







											if os.getenv("""USE_REAL_DATA"""      ,    lowercase_):
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    "examples/seq2seq/wmt_en_ro"
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    max_len * 2 * 6_4
																		if not Path(lowercase_).joinpath("""train.len""").exists():
																									save_len_file(lowercase_      ,    lowercase_)
											else:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    "examples/seq2seq/test_data/wmt_en_ro"
																		_SCREAMING_SNAKE_CASE		:						str			  =    max_len * 4
																		save_len_file(lowercase_      ,    lowercase_)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    AutoTokenizer.from_pretrained(lowercase_)
											_SCREAMING_SNAKE_CASE		:						Any			  =    SeqaSeqDataset(
											    lowercase_      ,    data_dir=lowercase_      ,    type_path="""train"""      ,    max_source_length=lowercase_      ,    max_target_length=lowercase_      ,    n_obs=lowercase_      ,    )
											return ds, max_tokens, tokenizer





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self._get_dataset()
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    set(DistributedSortishSampler(lowercase_      ,    2_5_6      ,    num_replicas=2      ,    rank=0      ,    add_extra_examples=lowercase_))
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    set(DistributedSortishSampler(lowercase_      ,    2_5_6      ,    num_replicas=2      ,    rank=1      ,    add_extra_examples=lowercase_))
											assert idsa.intersection(lowercase_) == set()





				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]      ,    )
				def 		_lowerCAmelCase		(      self							:							List[str]      ,    _A							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    AutoTokenizer.from_pretrained(lowercase_      ,    use_fast=lowercase_)
											if tok_name == MBART_TINY:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    SeqaSeqDataset(
																		    lowercase_      ,    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"""      ,    )
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    train_dataset.dataset_kwargs
																		assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
																		_SCREAMING_SNAKE_CASE		:						Any			  =    SeqaSeqDataset(
																		    lowercase_      ,    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      ,    )
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    train_dataset.dataset_kwargs
																		assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																		assert len(lowercase_) == 1 if tok_name == BART_TINY else len(lowercase_) == 0



"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     =	{'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''UniSpeechConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase_     =	[
			    '''UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''UniSpeechForCTC''',
			    '''UniSpeechForPreTraining''',
			    '''UniSpeechForSequenceClassification''',
			    '''UniSpeechModel''',
			    '''UniSpeechPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_unispeech import (
						    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
						    UniSpeechForCTC,
						    UniSpeechForPreTraining,
						    UniSpeechForSequenceClassification,
						    UniSpeechModel,
						    UniSpeechPreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase_     =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


