


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







def        SCREAMING_SNAKE_CASE_						(			__A :			Path  ,    __A :			list				)	->		Tuple:

       """simple docstring"""

       a_					:		List[str]							=  '\n'.join(__A				)
       Path(__A				).open('w'				).writelines(__A				)







def        SCREAMING_SNAKE_CASE_						(			__A :			Dict				)	->		List[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  SCREAMING_SNAKE_CASE__  (				lowercase__			):
      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]     ,							)
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     )		->			Optional[Any]:
             a_					:		Any							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     )
             a_					:		Union[str, Any]							=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
             a_					:		Any							=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__     )     ) for a in ARTICLES     )
             a_					:		Tuple							=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__     )     ) for a in SUMMARIES     )
             a_					:		Optional[Any]							=  4
             a_					:		str							=  8
             assert max_len_target > max_src_len  # Will be truncated
             assert max_len_source > max_src_len  # Will be truncated
             a_							,		a_					:		str							=  'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
             a_					:		str							=  SeqaSeqDataset(
                 SCREAMING_SNAKE_CASE__     ,							data_dir=SCREAMING_SNAKE_CASE__     ,							type_path='train'     ,							max_source_length=SCREAMING_SNAKE_CASE__     ,							max_target_length=SCREAMING_SNAKE_CASE__     ,							src_lang=SCREAMING_SNAKE_CASE__     ,							tgt_lang=SCREAMING_SNAKE_CASE__     ,							)
             a_					:		Tuple							=  DataLoader(SCREAMING_SNAKE_CASE__     ,							batch_size=2     ,							collate_fn=train_dataset.collate_fn     )
             for batch in dataloader:
                    assert isinstance(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
                    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_					:		Tuple							=  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        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[Any]     )		->			List[Any]:
             a_					:		str							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     )
             a_					:		Any							=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
             a_					:		Any							=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__     )     ) for a in ARTICLES     )
             a_					:		int							=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__     )     ) for a in SUMMARIES     )
             a_					:		List[Any]							=  4
             a_					:		int							=  LegacySeqaSeqDataset(
                 SCREAMING_SNAKE_CASE__     ,							data_dir=SCREAMING_SNAKE_CASE__     ,							type_path='train'     ,							max_source_length=2_0     ,							max_target_length=SCREAMING_SNAKE_CASE__     ,							)
             a_					:		Any							=  DataLoader(SCREAMING_SNAKE_CASE__     ,							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        SCREAMING_SNAKE_CASE							(       self				:       int     )		->			Dict:
             a_					:		int							=  AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'     )

             a_					:		Tuple							=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
             a_					:		Union[str, Any]							=  tmp_dir.joinpath('train.source'     ).open().readlines()
             a_					:		int							=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
             pack_data_dir(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     ,							1_2_8     ,							SCREAMING_SNAKE_CASE__     )
             a_					:		List[str]							=  {x.name for x in tmp_dir.iterdir()}
             a_					:		Optional[Any]							=  {x.name for x in save_dir.iterdir()}
             a_					:		Optional[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(SCREAMING_SNAKE_CASE__     ) < len(SCREAMING_SNAKE_CASE__     )
             assert len(SCREAMING_SNAKE_CASE__     ) == 1
             assert len(packed_examples[0]     ) == sum(len(SCREAMING_SNAKE_CASE__     ) for x in orig_examples     )
             assert orig_paths == new_paths
      @pytest.mark.skipif(not FAIRSEQ_AVAILABLE     ,							reason='This test requires fairseq'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			List[str]:
             if not FAIRSEQ_AVAILABLE:
                    return
             a_							,		a_							,		a_					:		Optional[int]							=  self._get_dataset(max_len=6_4     )
             a_					:		Tuple							=  6_4
             a_					:		Any							=  ds.make_dynamic_sampler(SCREAMING_SNAKE_CASE__     ,							required_batch_size_multiple=SCREAMING_SNAKE_CASE__     )
             a_					:		List[Any]							=  [len(SCREAMING_SNAKE_CASE__     ) for x in batch_sampler]
             assert len(set(SCREAMING_SNAKE_CASE__     )     ) > 1  # it's not dynamic batch size if every batch is the same length
             assert sum(SCREAMING_SNAKE_CASE__     ) == len(SCREAMING_SNAKE_CASE__     )  # no dropped or added examples
             a_					:		Dict							=  DataLoader(SCREAMING_SNAKE_CASE__     ,							batch_sampler=SCREAMING_SNAKE_CASE__     ,							collate_fn=ds.collate_fn     ,							num_workers=2     )
             a_					:		Tuple							=  []
             a_					:		Optional[int]							=  []
             for batch in data_loader:
                    a_					:		Any							=  batch['input_ids'].shape
                    a_					:		Any							=  src_shape[0]
                    assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                    a_					:		List[Any]							=  np.product(batch['input_ids'].shape     )
                    num_src_per_batch.append(SCREAMING_SNAKE_CASE__     )
                    if num_src_tokens > (max_tokens * 1.1):
                           failures.append(SCREAMING_SNAKE_CASE__     )
             assert num_src_per_batch[0] == max(SCREAMING_SNAKE_CASE__     )
             if failures:
                    raise AssertionError(F"""too many tokens in {len(SCREAMING_SNAKE_CASE__     )} batches"""     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             a_							,		a_							,		a_					:		Union[str, Any]							=  self._get_dataset(max_len=5_1_2     )
             a_					:		Union[str, Any]							=  2
             a_					:		Tuple							=  ds.make_sortish_sampler(SCREAMING_SNAKE_CASE__     ,							shuffle=SCREAMING_SNAKE_CASE__     )

             a_					:		Tuple							=  DataLoader(SCREAMING_SNAKE_CASE__     ,							batch_size=SCREAMING_SNAKE_CASE__     ,							collate_fn=ds.collate_fn     ,							num_workers=2     )
             a_					:		Optional[Any]							=  DataLoader(SCREAMING_SNAKE_CASE__     ,							batch_size=SCREAMING_SNAKE_CASE__     ,							collate_fn=ds.collate_fn     ,							num_workers=2     ,							sampler=SCREAMING_SNAKE_CASE__     )

             a_					:		Optional[Any]							=  tokenizer.pad_token_id

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

             assert sum(count_pad_tokens(SCREAMING_SNAKE_CASE__     ,							k='labels'     )     ) < sum(count_pad_tokens(SCREAMING_SNAKE_CASE__     ,							k='labels'     )     )
             assert sum(count_pad_tokens(SCREAMING_SNAKE_CASE__     )     ) < sum(count_pad_tokens(SCREAMING_SNAKE_CASE__     )     )
             assert len(SCREAMING_SNAKE_CASE__     ) == len(SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[Any]     ,							SCREAMING_SNAKE_CASE__				:       Any=1_0_0_0     ,							SCREAMING_SNAKE_CASE__				:       Any=1_2_8     )		->			Dict:
             if os.getenv('USE_REAL_DATA'     ,							SCREAMING_SNAKE_CASE__     ):
                    a_					:		Optional[Any]							=  'examples/seq2seq/wmt_en_ro'
                    a_					:		Dict							=  max_len * 2 * 6_4
                    if not Path(SCREAMING_SNAKE_CASE__     ).joinpath('train.len'     ).exists():
                           save_len_file(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )
             else:
                    a_					:		Any							=  'examples/seq2seq/test_data/wmt_en_ro'
                    a_					:		Tuple							=  max_len * 4
                    save_len_file(SCREAMING_SNAKE_CASE__     ,							SCREAMING_SNAKE_CASE__     )

             a_					:		Any							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     )
             a_					:		Optional[Any]							=  SeqaSeqDataset(
                 SCREAMING_SNAKE_CASE__     ,							data_dir=SCREAMING_SNAKE_CASE__     ,							type_path='train'     ,							max_source_length=SCREAMING_SNAKE_CASE__     ,							max_target_length=SCREAMING_SNAKE_CASE__     ,							n_obs=SCREAMING_SNAKE_CASE__     ,							)
             return ds, max_tokens, tokenizer
      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Tuple:
             a_							,		a_							,		a_					:		Optional[int]							=  self._get_dataset()
             a_					:		Optional[Any]							=  set(DistributedSortishSampler(SCREAMING_SNAKE_CASE__     ,							2_5_6     ,							num_replicas=2     ,							rank=0     ,							add_extra_examples=SCREAMING_SNAKE_CASE__     )     )
             a_					:		List[Any]							=  set(DistributedSortishSampler(SCREAMING_SNAKE_CASE__     ,							2_5_6     ,							num_replicas=2     ,							rank=1     ,							add_extra_examples=SCREAMING_SNAKE_CASE__     )     )
             assert idsa.intersection(SCREAMING_SNAKE_CASE__     ) == set()

      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]     ,							)
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			str:
             a_					:		str							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__     ,							use_fast=SCREAMING_SNAKE_CASE__     )
             if tok_name == MBART_TINY:
                    a_					:		Dict							=  SeqaSeqDataset(
                        SCREAMING_SNAKE_CASE__     ,							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_					:		Dict							=  train_dataset.dataset_kwargs
                    assert "src_lang" in kwargs and "tgt_lang" in kwargs
             else:
                    a_					:		Any							=  SeqaSeqDataset(
                        SCREAMING_SNAKE_CASE__     ,							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_					:		List[Any]							=  train_dataset.dataset_kwargs
                    assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
                    assert len(SCREAMING_SNAKE_CASE__     ) == 1 if tok_name == BART_TINY else len(SCREAMING_SNAKE_CASE__     ) == 0










"""simple docstring"""




import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

   super().__init__(
       pad_token_id=UpperCAmelCase__							,						bos_token_id=UpperCAmelCase__							,						eos_token_id=UpperCAmelCase__							,						is_encoder_decoder=UpperCAmelCase__							,						decoder_start_token_id=UpperCAmelCase__							,						forced_eos_token_id=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	self.bos_token_id
     warnings.warn(
         f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
         """The config can simply be saved and uploaded again to be fixed."""							)

