






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







def     a_   (__A				,       __A      )				->						Optional[int]:



					"""simple docstring"""
					__a					:						Dict      =					"\n".join(__A      )
					Path(__A      ).open("w"      ).writelines(__A      )







def     a_   (__A      )				->						Union[str, Any]:



					"""simple docstring"""
					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 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ] ,					)
						@slow
						def        UpperCAmelCase__					(self:					Optional[Any] ,					__UpperCAmelCase:					List[str]   )    ->     Union[str, Any]:




											'''simple docstring'''



											__a					:						Tuple      =					AutoTokenizer.from_pretrained(__UpperCAmelCase   )
											__a					:						int      =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
											__a					:						Any      =					max(len(tokenizer.encode(__UpperCAmelCase   )   ) for a in ARTICLES   )
											__a					:						Optional[int]      =					max(len(tokenizer.encode(__UpperCAmelCase   )   ) for a in SUMMARIES   )
											__a					:						int      =					4
											__a					:						Dict      =					8
											assert max_len_target > max_src_len  # Will be truncated
											assert max_len_source > max_src_len  # Will be truncated
											__a      ,			__a					:						List[str]      =					"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
											__a					:						Tuple      =					SeqaSeqDataset(
											    __UpperCAmelCase ,					data_dir=__UpperCAmelCase ,					type_path="train" ,					max_source_length=__UpperCAmelCase ,					max_target_length=__UpperCAmelCase ,					src_lang=__UpperCAmelCase ,					tgt_lang=__UpperCAmelCase ,					)
											__a					:						Any      =					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					:						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        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					Optional[Any]   )    ->     List[str]:




											'''simple docstring'''



											__a					:						int      =					AutoTokenizer.from_pretrained(__UpperCAmelCase   )
											__a					:						Dict      =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )
											__a					:						List[str]      =					max(len(tokenizer.encode(__UpperCAmelCase   )   ) for a in ARTICLES   )
											__a					:						Tuple      =					max(len(tokenizer.encode(__UpperCAmelCase   )   ) for a in SUMMARIES   )
											__a					:						Optional[int]      =					4
											__a					:						int      =					LegacySeqaSeqDataset(
											    __UpperCAmelCase ,					data_dir=__UpperCAmelCase ,					type_path="train" ,					max_source_length=20 ,					max_target_length=__UpperCAmelCase ,					)
											__a					:						Union[str, Any]      =					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 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:					Tuple   )    ->     Optional[int]:




											'''simple docstring'''



											__a					:						Dict      =					AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"   )

											__a					:						Optional[int]      =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
											__a					:						Optional[Any]      =					tmp_dir.joinpath("train.source"   ).open().readlines()
											__a					:						Union[str, Any]      =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
											pack_data_dir(__UpperCAmelCase ,					__UpperCAmelCase ,					128 ,					__UpperCAmelCase   )
											__a					:						Dict      =					{x.name for x in tmp_dir.iterdir()}
											__a					:						Optional[Any]      =					{x.name for x in save_dir.iterdir()}
											__a					:						List[Any]      =					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        UpperCAmelCase__					(self:					int   )    ->     List[str]:




											'''simple docstring'''



											if not FAIRSEQ_AVAILABLE:
																return
											__a      ,			__a      ,			__a					:						Tuple      =					self._get_dataset(max_len=64   )
											__a					:						Tuple      =					64
											__a					:						Union[str, Any]      =					ds.make_dynamic_sampler(__UpperCAmelCase ,					required_batch_size_multiple=__UpperCAmelCase   )
											__a					:						Union[str, Any]      =					[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					:						str      =					DataLoader(__UpperCAmelCase ,					batch_sampler=__UpperCAmelCase ,					collate_fn=ds.collate_fn ,					num_workers=2   )
											__a					:						Optional[int]      =					[]
											__a					:						Optional[Any]      =					[]
											for batch in data_loader:
																__a					:						Optional[Any]      =					batch["input_ids"].shape
																__a					:						Optional[int]      =					src_shape[0]
																assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																__a					:						Any      =					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        UpperCAmelCase__					(self:					List[str]   )    ->     Tuple:




											'''simple docstring'''



											__a      ,			__a      ,			__a					:						Dict      =					self._get_dataset(max_len=512   )
											__a					:						Dict      =					2
											__a					:						Dict      =					ds.make_sortish_sampler(__UpperCAmelCase ,					shuffle=__UpperCAmelCase   )

											__a					:						str      =					DataLoader(__UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					collate_fn=ds.collate_fn ,					num_workers=2   )
											__a					:						Optional[Any]      =					DataLoader(__UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					collate_fn=ds.collate_fn ,					num_workers=2 ,					sampler=__UpperCAmelCase   )

											__a					:						Optional[Any]      =					tokenizer.pad_token_id

											def count_pad_tokens(__UpperCAmelCase:					List[Any] ,					__UpperCAmelCase:					Any="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        UpperCAmelCase__					(self:					Dict ,					__UpperCAmelCase:					str=1000 ,					__UpperCAmelCase:					Optional[int]=128   )    ->     Optional[Any]:




											'''simple docstring'''



											if os.getenv("USE_REAL_DATA" ,					__UpperCAmelCase   ):
																__a					:						List[Any]      =					"examples/seq2seq/wmt_en_ro"
																__a					:						str      =					max_len * 2 * 64
																if not Path(__UpperCAmelCase   ).joinpath("train.len"   ).exists():
																					save_len_file(__UpperCAmelCase ,					__UpperCAmelCase   )
											else:
																__a					:						Any      =					"examples/seq2seq/test_data/wmt_en_ro"
																__a					:						Optional[int]      =					max_len * 4
																save_len_file(__UpperCAmelCase ,					__UpperCAmelCase   )

											__a					:						Optional[Any]      =					AutoTokenizer.from_pretrained(__UpperCAmelCase   )
											__a					:						Optional[Any]      =					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        UpperCAmelCase__					(self:					List[Any]   )    ->     str:




											'''simple docstring'''



											__a      ,			__a      ,			__a					:						List[str]      =					self._get_dataset()
											__a					:						List[Any]      =					set(DistributedSortishSampler(__UpperCAmelCase ,					256 ,					num_replicas=2 ,					rank=0 ,					add_extra_examples=__UpperCAmelCase   )   )
											__a					:						Any      =					set(DistributedSortishSampler(__UpperCAmelCase ,					256 ,					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        UpperCAmelCase__					(self:					List[str] ,					__UpperCAmelCase:					Any   )    ->     Optional[int]:




											'''simple docstring'''



											__a					:						int      =					AutoTokenizer.from_pretrained(__UpperCAmelCase ,					use_fast=__UpperCAmelCase   )
											if tok_name == MBART_TINY:
																__a					:						Union[str, Any]      =					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					:						Optional[int]      =					train_dataset.dataset_kwargs
																assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
																__a					:						int      =					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					:						Optional[int]      =					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













# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import sys

import transformers


UpperCAmelCase__      			=						'''3'''

print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)

try:
			import torch

			print('''Torch version:''', torch.__version__)
			print('''Cuda available:''', torch.cuda.is_available())
			print('''Cuda version:''', torch.version.cuda)
			print('''CuDNN version:''', torch.backends.cudnn.version())
			print('''Number of GPUs available:''', torch.cuda.device_count())
			print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
			print('''Torch version:''', None)

try:
			import deepspeed

			print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
			print('''DeepSpeed version:''', None)

try:
			import tensorflow as tf

			print('''TensorFlow version:''', tf.__version__)
			print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
			print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
			print('''TensorFlow version:''', None)





