
'''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


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


def A (__lowerCamelCase :Path					,      __lowerCamelCase :list						):
						_lowerCAmelCase    		=					"""\n""".join(__lowerCamelCase						)
						Path(__lowerCamelCase						).open("""w"""						).writelines(__lowerCamelCase						)


def A (__lowerCamelCase :Union[str, 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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]       ,  )
						@slow
						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					AutoTokenizer.from_pretrained(_lowercase )
												_lowerCAmelCase    		=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
												_lowerCAmelCase    		=					max(len(tokenizer.encode(_lowercase ) ) for a in ARTICLES )
												_lowerCAmelCase    		=					max(len(tokenizer.encode(_lowercase ) ) 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		,       _lowerCAmelCase    		=					"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
												_lowerCAmelCase    		=					SeqaSeqDataset(
												    _lowercase       ,  data_dir=_lowercase       ,  type_path="""train"""       ,  max_source_length=_lowercase       ,  max_target_length=_lowercase       ,  src_lang=_lowercase       ,  tgt_lang=_lowercase       ,  )
												_lowerCAmelCase    		=					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
																		_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    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					AutoTokenizer.from_pretrained(_lowercase )
												_lowerCAmelCase    		=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
												_lowerCAmelCase    		=					max(len(tokenizer.encode(_lowercase ) ) for a in ARTICLES )
												_lowerCAmelCase    		=					max(len(tokenizer.encode(_lowercase ) ) for a in SUMMARIES )
												_lowerCAmelCase    		=					4
												_lowerCAmelCase    		=					LegacySeqaSeqDataset(
												    _lowercase       ,  data_dir=_lowercase       ,  type_path="""train"""       ,  max_source_length=20       ,  max_target_length=_lowercase       ,  )
												_lowerCAmelCase    		=					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 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    _lowercase      (   self ):

												"""simple docstring"""



												_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(_lowercase       ,  _lowercase       ,  128       ,  _lowercase )
												_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(_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    _lowercase      (   self ):

												"""simple docstring"""



												if not FAIRSEQ_AVAILABLE:
																		return
												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					self._get_dataset(max_len=64 )
												_lowerCAmelCase    		=					64
												_lowerCAmelCase    		=					ds.make_dynamic_sampler(_lowercase       ,  required_batch_size_multiple=_lowercase )
												_lowerCAmelCase    		=					[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
												_lowerCAmelCase    		=					DataLoader(_lowercase       ,  batch_sampler=_lowercase       ,  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(_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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					self._get_dataset(max_len=512 )
												_lowerCAmelCase    		=					2
												_lowerCAmelCase    		=					ds.make_sortish_sampler(_lowercase       ,  shuffle=_lowercase )

												_lowerCAmelCase    		=					DataLoader(_lowercase       ,  batch_size=_lowercase       ,  collate_fn=ds.collate_fn       ,  num_workers=2 )
												_lowerCAmelCase    		=					DataLoader(_lowercase       ,  batch_size=_lowercase       ,  collate_fn=ds.collate_fn       ,  num_workers=2       ,  sampler=_lowercase )

												_lowerCAmelCase    		=					tokenizer.pad_token_id

												def count_pad_tokens(_lowercase       ,  _lowercase="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    _lowercase      (   self       ,  _lowercase=1_000       ,  _lowercase=128 ):

												"""simple docstring"""



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

												_lowerCAmelCase    		=					AutoTokenizer.from_pretrained(_lowercase )
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase		,       _lowerCAmelCase		,       _lowerCAmelCase    		=					self._get_dataset()
												_lowerCAmelCase    		=					set(DistributedSortishSampler(_lowercase       ,  256       ,  num_replicas=2       ,  rank=0       ,  add_extra_examples=_lowercase ) )
												_lowerCAmelCase    		=					set(DistributedSortishSampler(_lowercase       ,  256       ,  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    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					AutoTokenizer.from_pretrained(_lowercase       ,  use_fast=_lowercase )
												if tok_name == MBART_TINY:
																		_lowerCAmelCase    		=					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"""       ,  )
																		_lowerCAmelCase    		=					train_dataset.dataset_kwargs
																		assert "src_lang" in kwargs and "tgt_lang" in kwargs
												else:
																		_lowerCAmelCase    		=					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       ,  )
																		_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(_lowercase ) == 1 if tok_name == BART_TINY else len(_lowercase ) == 0

'''simple docstring'''


from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch






@require_torch
@require_tf
class     A ( UpperCAmelCase ):





							def    snake_case__    (   self					:				str						)				->						Any:
								__UpperCAmelCase      					=      SMALL_MODEL_IDENTIFIER
								__UpperCAmelCase      					=      '''pt'''
								__UpperCAmelCase      					=      '''tf'''





							def    snake_case__    (   self					:				int  ,		__a					:				int						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      AutoModel.from_pretrained(self.test_model						)
								model_pt.save_pretrained(__a						)





							def    snake_case__    (   self					:				Any  ,		__a					:				Optional[int]						)				->						Tuple:
								__UpperCAmelCase      					=      TFAutoModel.from_pretrained(self.test_model  ,		from_pt=__a						)
								model_tf.save_pretrained(__a						)





							def    snake_case__    (   self					:				Dict						)				->						Dict:
								__UpperCAmelCase      					=      '''mock_framework'''

								# Framework provided - return whatever the user provides
								__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model  ,		__a						)
								self.assertEqual(__a  ,		__a						)

								# Local checkpoint and framework provided - return provided framework
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
									self._setup_pt_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a  ,		__a						)
									self.assertEqual(__a  ,		__a						)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
									self._setup_tf_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a  ,		__a						)
									self.assertEqual(__a  ,		__a						)





							def    snake_case__    (   self					:				Dict						)				->						List[str]:
								# PyTorch checkpoint
								with TemporaryDirectory() as local_pt_ckpt:
									self._setup_pt_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# TensorFlow checkpoint
								with TemporaryDirectory() as local_tf_ckpt:
									self._setup_tf_ckpt(__a						)
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)
									self.assertEqual(__a  ,		self.framework_tf						)

								# Invalid local checkpoint
								with TemporaryDirectory() as local_invalid_ckpt:
									with self.assertRaises(__a						):
										__UpperCAmelCase      					=      FeaturesManager.determine_framework(__a						)






							def    snake_case__    (   self					:				str						)				->						Union[str, Any]:
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# PyTorch not in environment -> use TensorFlow
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_torch_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_tf						)

								# Both in environment -> use PyTorch
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						), patch(
								    '''transformers.onnx.features.is_torch_available'''  ,		__a						):
									__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
									self.assertEqual(__a  ,		self.framework_pt						)

								# Both not in environment -> raise error
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								__UpperCAmelCase      					=      MagicMock(return_value=__a						)
								with patch('''transformers.onnx.features.is_tf_available'''  ,		__a						), patch(
								    '''transformers.onnx.features.is_torch_available'''  ,		__a						):
									with self.assertRaises(__a						):
										__UpperCAmelCase      					=      FeaturesManager.determine_framework(self.test_model						)
