
"""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


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 					_UpperCAmelCase      ( __lowerCamelCase   :       Path ,					__lowerCamelCase   :       list		)     ->					str:
  _snake_case					     =       '''\n'''.join(__lowerCamelCase		)
  Path(__lowerCamelCase		).open('''w'''		).writelines(__lowerCamelCase		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       int		)     ->					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       lowerCAmelCase__				(						A_	):







     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,      )
     @slow
     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			Union[str, Any]			):
       _snake_case					     =       AutoTokenizer.from_pretrained(_lowerCamelCase			)
       _snake_case					     =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
       _snake_case					     =       max(len(tokenizer.encode(_lowerCamelCase			)			) for a in ARTICLES			)
       _snake_case					     =       max(len(tokenizer.encode(_lowerCamelCase			)			) for a in SUMMARIES			)
       _snake_case					     =       4
       _snake_case					     =       8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       _snake_case     ,					_snake_case					     =       '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
       _snake_case					     =       SeqaSeqDataset(
           _lowerCamelCase    ,      data_dir=_lowerCamelCase    ,      type_path='''train'''    ,      max_source_length=_lowerCamelCase    ,      max_target_length=_lowerCamelCase    ,      src_lang=_lowerCamelCase    ,      tgt_lang=_lowerCamelCase    ,      )
       _snake_case					     =       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
         _snake_case					     =       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			:			Union[str, Any]    ,      _lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       AutoTokenizer.from_pretrained(_lowerCamelCase			)
       _snake_case					     =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
       _snake_case					     =       max(len(tokenizer.encode(_lowerCamelCase			)			) for a in ARTICLES			)
       _snake_case					     =       max(len(tokenizer.encode(_lowerCamelCase			)			) for a in SUMMARIES			)
       _snake_case					     =       4
       _snake_case					     =       LegacySeqaSeqDataset(
           _lowerCamelCase    ,      data_dir=_lowerCamelCase    ,      type_path='''train'''    ,      max_source_length=20    ,      max_target_length=_lowerCamelCase    ,      )
       _snake_case					     =       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      lowercase	(							self			:			str			):
       _snake_case					     =       AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''			)

       _snake_case					     =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
       _snake_case					     =       tmp_dir.joinpath('''train.source'''			).open().readlines()
       _snake_case					     =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
       pack_data_dir(_lowerCamelCase    ,      _lowerCamelCase    ,      128    ,      _lowerCamelCase			)
       _snake_case					     =       {x.name for x in tmp_dir.iterdir()}
       _snake_case					     =       {x.name for x in save_dir.iterdir()}
       _snake_case					     =       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      lowercase	(							self			:			str			):
       if not FAIRSEQ_AVAILABLE:
         return
       _snake_case     ,					_snake_case     ,					_snake_case					     =       self._get_dataset(max_len=64			)
       _snake_case					     =       64
       _snake_case					     =       ds.make_dynamic_sampler(_lowerCamelCase    ,      required_batch_size_multiple=_lowerCamelCase			)
       _snake_case					     =       [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
       _snake_case					     =       DataLoader(_lowerCamelCase    ,      batch_sampler=_lowerCamelCase    ,      collate_fn=ds.collate_fn    ,      num_workers=2			)
       _snake_case					     =       []
       _snake_case					     =       []
       for batch in data_loader:
         _snake_case					     =       batch['''input_ids'''].shape
         _snake_case					     =       src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         _snake_case					     =       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      lowercase	(							self			:			Optional[Any]			):
       _snake_case     ,					_snake_case     ,					_snake_case					     =       self._get_dataset(max_len=512			)
       _snake_case					     =       2
       _snake_case					     =       ds.make_sortish_sampler(_lowerCamelCase    ,      shuffle=_lowerCamelCase			)

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

       _snake_case					     =       tokenizer.pad_token_id

       def count_pad_tokens(_lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			Optional[int]="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      lowercase	(							self			:			List[Any]    ,      _lowerCamelCase			:			Any=1000    ,      _lowerCamelCase			:			Optional[Any]=128			):
       if os.getenv('''USE_REAL_DATA'''    ,      _lowerCamelCase			):
         _snake_case					     =       '''examples/seq2seq/wmt_en_ro'''
         _snake_case					     =       max_len * 2 * 64
         if not Path(_lowerCamelCase			).joinpath('''train.len'''			).exists():
           save_len_file(_lowerCamelCase    ,      _lowerCamelCase			)
       else:
         _snake_case					     =       '''examples/seq2seq/test_data/wmt_en_ro'''
         _snake_case					     =       max_len * 4
         save_len_file(_lowerCamelCase    ,      _lowerCamelCase			)

       _snake_case					     =       AutoTokenizer.from_pretrained(_lowerCamelCase			)
       _snake_case					     =       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      lowercase	(							self			:			Optional[Any]			):
       _snake_case     ,					_snake_case     ,					_snake_case					     =       self._get_dataset()
       _snake_case					     =       set(DistributedSortishSampler(_lowerCamelCase    ,      256    ,      num_replicas=2    ,      rank=0    ,      add_extra_examples=_lowerCamelCase			)			)
       _snake_case					     =       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      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			Any			):
       _snake_case					     =       AutoTokenizer.from_pretrained(_lowerCamelCase    ,      use_fast=_lowerCamelCase			)
       if tok_name == MBART_TINY:
         _snake_case					     =       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'''    ,      )
         _snake_case					     =       train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
         _snake_case					     =       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    ,      )
         _snake_case					     =       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"""



import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase__          = '1'
UpperCAmelCase__          = '0'
UpperCAmelCase__          = '1'

UpperCAmelCase__          = ort.SessionOptions()
UpperCAmelCase__          = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
UpperCAmelCase__          = ['TensorrtExecutionProvider', 'CUDAExecutionProvider']
UpperCAmelCase__          = ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase__          = ort.RunOptions()

UpperCAmelCase__          = 128
UpperCAmelCase__          = 1
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
UpperCAmelCase__          = time.time()
UpperCAmelCase__          = 2000
UpperCAmelCase__          = {}
for iter in range(max_iters):
 UpperCAmelCase__          = sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))



