





'''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


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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					Path						,    lowerCamelCase_     :					list			):
			__lowercase			  =							'''\n'''.join(lowerCamelCase_			)
			Path(lowerCamelCase_			).open('''w'''			).writelines(lowerCamelCase_			)




def 				_lowerCAmelCase  (			lowerCamelCase_     :					List[Any]			):
			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 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]       ,)
	@slow
	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							List[Any]:







				'''simple docstring'''





				__lowercase			  =							AutoTokenizer.from_pretrained(_lowerCamelCase	)
				__lowercase			  =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
				__lowercase			  =							max(len(tokenizer.encode(_lowerCamelCase	)	) for a in ARTICLES	)
				__lowercase			  =							max(len(tokenizer.encode(_lowerCamelCase	)	) for a in SUMMARIES	)
				__lowercase			  =							4
				__lowercase			  =							8
				assert max_len_target > max_src_len  # Will be truncated
				assert max_len_source > max_src_len  # Will be truncated
				__lowercase  ,						__lowercase			  =							'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
				__lowercase			  =							SeqaSeqDataset(
				    _lowerCamelCase       ,data_dir=_lowerCamelCase       ,type_path='''train'''       ,max_source_length=_lowerCamelCase       ,max_target_length=_lowerCamelCase       ,src_lang=_lowerCamelCase       ,tgt_lang=_lowerCamelCase       ,)
				__lowercase			  =							DataLoader(_lowerCamelCase       ,batch_size=2       ,collate_fn=train_dataset.collate_fn	)
				for batch in dataloader:
							assert isinstance(_lowerCamelCase       ,_lowerCamelCase	)
							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
							__lowercase			  =							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   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Tuple:







				'''simple docstring'''





				__lowercase			  =							AutoTokenizer.from_pretrained(_lowerCamelCase	)
				__lowercase			  =							make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
				__lowercase			  =							max(len(tokenizer.encode(_lowerCamelCase	)	) for a in ARTICLES	)
				__lowercase			  =							max(len(tokenizer.encode(_lowerCamelCase	)	) for a in SUMMARIES	)
				__lowercase			  =							4
				__lowercase			  =							LegacySeqaSeqDataset(
				    _lowerCamelCase       ,data_dir=_lowerCamelCase       ,type_path='''train'''       ,max_source_length=20       ,max_target_length=_lowerCamelCase       ,)
				__lowercase			  =							DataLoader(_lowerCamelCase       ,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   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase			  =							AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''	)

				__lowercase			  =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
				__lowercase			  =							tmp_dir.joinpath('''train.source'''	).open().readlines()
				__lowercase			  =							Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
				pack_data_dir(_lowerCamelCase       ,_lowerCamelCase       ,128       ,_lowerCamelCase	)
				__lowercase			  =							{x.name for x in tmp_dir.iterdir()}
				__lowercase			  =							{x.name for x in save_dir.iterdir()}
				__lowercase			  =							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(_lowerCamelCase	) < len(_lowerCamelCase	)
				assert len(_lowerCamelCase	) == 1
				assert len(packed_examples[0]	) == sum(len(_lowerCamelCase	) for x in orig_examples	)
				assert orig_paths == new_paths


	@pytest.mark.skipif(not FAIRSEQ_AVAILABLE       ,reason='''This test requires fairseq'''	)
	def   _UpperCAmelCase       (self	)							->							Union[str, Any]:







				'''simple docstring'''





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


	def   _UpperCAmelCase       (self	)							->							Any:







				'''simple docstring'''





				__lowercase  ,						__lowercase  ,						__lowercase			  =							self._get_dataset(max_len=512	)
				__lowercase			  =							2
				__lowercase			  =							ds.make_sortish_sampler(_lowerCamelCase       ,shuffle=_lowerCamelCase	)

				__lowercase			  =							DataLoader(_lowerCamelCase       ,batch_size=_lowerCamelCase       ,collate_fn=ds.collate_fn       ,num_workers=2	)
				__lowercase			  =							DataLoader(_lowerCamelCase       ,batch_size=_lowerCamelCase       ,collate_fn=ds.collate_fn       ,num_workers=2       ,sampler=_lowerCamelCase	)

				__lowercase			  =							tokenizer.pad_token_id

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

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


	def   _UpperCAmelCase       (self       ,_lowerCamelCase=1000       ,_lowerCamelCase=128	)							->							int:







				'''simple docstring'''





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

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


	def   _UpperCAmelCase       (self	)							->							List[str]:







				'''simple docstring'''





				__lowercase  ,						__lowercase  ,						__lowercase			  =							self._get_dataset()
				__lowercase			  =							set(DistributedSortishSampler(_lowerCamelCase       ,256       ,num_replicas=2       ,rank=0       ,add_extra_examples=_lowerCamelCase	)	)
				__lowercase			  =							set(DistributedSortishSampler(_lowerCamelCase       ,256       ,num_replicas=2       ,rank=1       ,add_extra_examples=_lowerCamelCase	)	)
				assert idsa.intersection(_lowerCamelCase	) == set()


	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]       ,)
	def   _UpperCAmelCase       (self       ,_lowerCamelCase	)							->							Optional[Any]:







				'''simple docstring'''





				__lowercase			  =							AutoTokenizer.from_pretrained(_lowerCamelCase       ,use_fast=_lowerCamelCase	)
				if tok_name == MBART_TINY:
							__lowercase			  =							SeqaSeqDataset(
							    _lowerCamelCase       ,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'''       ,)
							__lowercase			  =							train_dataset.dataset_kwargs
							assert "src_lang" in kwargs and "tgt_lang" in kwargs
				else:
							__lowercase			  =							SeqaSeqDataset(
							    _lowerCamelCase       ,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       ,)
							__lowercase			  =							train_dataset.dataset_kwargs
							assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
							assert len(_lowerCamelCase	) == 1 if tok_name == BART_TINY else len(_lowerCamelCase	) == 0












'''simple docstring'''



from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE   =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      ['''BlenderbotTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlenderbotForCausalLM''',
	    '''BlenderbotForConditionalGeneration''',
	    '''BlenderbotModel''',
	    '''BlenderbotPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''TFBlenderbotForConditionalGeneration''',
	    '''TFBlenderbotModel''',
	    '''TFBlenderbotPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_SCREAMING_SNAKE_CASE   =      [
	    '''FlaxBlenderbotForConditionalGeneration''',
	    '''FlaxBlenderbotModel''',
	    '''FlaxBlenderbotPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

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





