






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


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







def       lowercase__	(					A_:						Any  ,      A_:						int		)	->				List[str]:







       """simple docstring"""






       __UpperCAmelCase     				="""\n""".join(A_		)
       Path(A_		).open("""w"""		).writelines(A_		)







def       lowercase__	(					A_:						Any		)	->				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 				_A			(     __lowerCamelCase				):
    """simple docstring"""

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

           __UpperCAmelCase     				=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
           __UpperCAmelCase     				=tmp_dir.joinpath("""train.source"""      ).open().readlines()
           __UpperCAmelCase     				=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
           pack_data_dir(lowerCamelCase__ ,     lowerCamelCase__ ,     128 ,     lowerCamelCase__      )
           __UpperCAmelCase     				={x.name for x in tmp_dir.iterdir()}
           __UpperCAmelCase     				={x.name for x in save_dir.iterdir()}
           __UpperCAmelCase     				=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 _a							(				self      :     Any      )      ->							List[Any]:





           if not FAIRSEQ_AVAILABLE:
                  return
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=self._get_dataset(max_len=64      )
           __UpperCAmelCase     				=64
           __UpperCAmelCase     				=ds.make_dynamic_sampler(lowerCamelCase__ ,     required_batch_size_multiple=lowerCamelCase__      )
           __UpperCAmelCase     				=[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
           __UpperCAmelCase     				=DataLoader(lowerCamelCase__ ,     batch_sampler=lowerCamelCase__ ,     collate_fn=ds.collate_fn ,     num_workers=2      )
           __UpperCAmelCase     				=[]
           __UpperCAmelCase     				=[]
           for batch in data_loader:
                  __UpperCAmelCase     				=batch["""input_ids"""].shape
                  __UpperCAmelCase     				=src_shape[0]
                  assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                  __UpperCAmelCase     				=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 _a							(				self      :     int      )      ->							Dict:
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=self._get_dataset(max_len=512      )
           __UpperCAmelCase     				=2
           __UpperCAmelCase     				=ds.make_sortish_sampler(lowerCamelCase__ ,     shuffle=lowerCamelCase__      )

           __UpperCAmelCase     				=DataLoader(lowerCamelCase__ ,     batch_size=lowerCamelCase__ ,     collate_fn=ds.collate_fn ,     num_workers=2      )
           __UpperCAmelCase     				=DataLoader(lowerCamelCase__ ,     batch_size=lowerCamelCase__ ,     collate_fn=ds.collate_fn ,     num_workers=2 ,     sampler=lowerCamelCase__      )

           __UpperCAmelCase     				=tokenizer.pad_token_id

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





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

           __UpperCAmelCase     				=AutoTokenizer.from_pretrained(lowerCamelCase__      )
           __UpperCAmelCase     				=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 _a							(				self      :     Optional[int]      )      ->							List[Any]:
           __UpperCAmelCase				, __UpperCAmelCase				, __UpperCAmelCase     				=self._get_dataset()
           __UpperCAmelCase     				=set(DistributedSortishSampler(lowerCamelCase__ ,     256 ,     num_replicas=2 ,     rank=0 ,     add_extra_examples=lowerCamelCase__      )      )
           __UpperCAmelCase     				=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 _a							(				self      :     List[Any] ,     __SCREAMING_SNAKE_CASE      :     Tuple      )      ->							Optional[int]:
           __UpperCAmelCase     				=AutoTokenizer.from_pretrained(lowerCamelCase__ ,     use_fast=lowerCamelCase__      )
           if tok_name == MBART_TINY:
                  __UpperCAmelCase     				=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""" ,     )
                  __UpperCAmelCase     				=train_dataset.dataset_kwargs
                  assert "src_lang" in kwargs and "tgt_lang" in kwargs
           else:
                  __UpperCAmelCase     				=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 ,     )
                  __UpperCAmelCase     				=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







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowercase				      =						logging.get_logger(__name__)

__lowercase				      =						{
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Any  					=    'table-transformer'
   _lowercase     :       List[Any]  					=    ['past_key_values']
   _lowercase     :       Union[str, Any]  					=    {
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }

   def __init__(				self	:    List[Any]			,						lowerCamelCase__	:    Any=True			,						lowerCamelCase__	:    List[Any]=None			,						lowerCamelCase__	:    Any=3			,						lowerCamelCase__	:    Any=1_0_0			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[Any]=2_0_4_8			,						lowerCamelCase__	:    List[str]=8			,						lowerCamelCase__	:    int=6			,						lowerCamelCase__	:    List[str]=2_0_4_8			,						lowerCamelCase__	:    Optional[int]=8			,						lowerCamelCase__	:    Union[str, Any]=0.0			,						lowerCamelCase__	:    Any=0.0			,						lowerCamelCase__	:    str=True			,						lowerCamelCase__	:    Optional[Any]="relu"			,						lowerCamelCase__	:    Optional[int]=2_5_6			,						lowerCamelCase__	:    List[str]=0.1			,						lowerCamelCase__	:    str=0.0			,						lowerCamelCase__	:    Optional[int]=0.0			,						lowerCamelCase__	:    Dict=0.02			,						lowerCamelCase__	:    Union[str, Any]=1.0			,						lowerCamelCase__	:    Union[str, Any]=False			,						lowerCamelCase__	:    Optional[int]="sine"			,						lowerCamelCase__	:    str="resnet50"			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    Optional[Any]=False			,						lowerCamelCase__	:    Any=1			,						lowerCamelCase__	:    int=5			,						lowerCamelCase__	:    Union[str, Any]=2			,						lowerCamelCase__	:    List[str]=1			,						lowerCamelCase__	:    Union[str, Any]=1			,						lowerCamelCase__	:    Optional[Any]=5			,						lowerCamelCase__	:    Tuple=2			,						lowerCamelCase__	:    List[str]=0.1			,						**lowerCamelCase__	:    List[str]			,						) ->			Dict:
         """simple docstring"""





         if backbone_config is not None and use_timm_backbone:
               raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''       )

         if not use_timm_backbone:
               if backbone_config is None:
                     logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
                     A_							       =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']       )
               elif isinstance(lowerCamelCase__			,						lowerCamelCase__       ):
                     A_							       =		backbone_config.get('''model_type'''       )
                     A_							       =		CONFIG_MAPPING[backbone_model_type]
                     A_							       =		config_class.from_dict(lowerCamelCase__       )
               # set timm attributes to None
               A_       ,A_       ,A_							       =		None, None, None

         A_							       =		use_timm_backbone
         A_							       =		backbone_config
         A_							       =		num_channels
         A_							       =		num_queries
         A_							       =		d_model
         A_							       =		encoder_ffn_dim
         A_							       =		encoder_layers
         A_							       =		encoder_attention_heads
         A_							       =		decoder_ffn_dim
         A_							       =		decoder_layers
         A_							       =		decoder_attention_heads
         A_							       =		dropout
         A_							       =		attention_dropout
         A_							       =		activation_dropout
         A_							       =		activation_function
         A_							       =		init_std
         A_							       =		init_xavier_std
         A_							       =		encoder_layerdrop
         A_							       =		decoder_layerdrop
         A_							       =		encoder_layers
         A_							       =		auxiliary_loss
         A_							       =		position_embedding_type
         A_							       =		backbone
         A_							       =		use_pretrained_backbone
         A_							       =		dilation
         # Hungarian matcher
         A_							       =		class_cost
         A_							       =		bbox_cost
         A_							       =		giou_cost
         # Loss coefficients
         A_							       =		mask_loss_coefficient
         A_							       =		dice_loss_coefficient
         A_							       =		bbox_loss_coefficient
         A_							       =		giou_loss_coefficient
         A_							       =		eos_coefficient
         super().__init__(is_encoder_decoder=lowerCamelCase__			,						**lowerCamelCase__       )

   @property
   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			int:
         """simple docstring"""





         return self.encoder_attention_heads





   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			int:
         """simple docstring"""





         return self.d_model



class    _lowercase				(					__lowerCamelCase			):
   _lowercase     :       Tuple  					=    version.parse('1.11'			)

   @property
   def     UpperCamelCase					(				self	:    Any       ) ->			Mapping[str, Mapping[int, str]]:
         """simple docstring"""





         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ]       )

   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			float:
         """simple docstring"""





         return 1e-5





   @property
   def     UpperCamelCase					(				self	:    List[Any]       ) ->			int:
         """simple docstring"""





         return 1_2





