
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


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





def     _a					(						lowerCamelCase,	lowerCamelCase							):
 lowerCamelCase		:	Union[str, Any]							=      """\n""".join(lowerCamelCase							)
 Path(lowerCamelCase							).open("""w"""							).writelines(lowerCamelCase							)





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

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

       lowerCamelCase		:	Optional[int]							=      DataLoader(__magic_name__    ,  batch_size=__magic_name__    ,  collate_fn=ds.collate_fn    ,  num_workers=2      )
       lowerCamelCase		:	Any							=      DataLoader(__magic_name__    ,  batch_size=__magic_name__    ,  collate_fn=ds.collate_fn    ,  num_workers=2    ,  sampler=__magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      tokenizer.pad_token_id

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

       assert sum(count_pad_tokens(__magic_name__    ,  k="""labels"""      )      ) < sum(count_pad_tokens(__magic_name__    ,  k="""labels"""      )      )
       assert sum(count_pad_tokens(__magic_name__      )      ) < sum(count_pad_tokens(__magic_name__      )      )
       assert len(__magic_name__      ) == len(__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__=1_0_0_0    ,  __magic_name__=1_2_8      ):
       if os.getenv("""USE_REAL_DATA"""    ,  __magic_name__      ):
        lowerCamelCase		:	List[str]							=      """examples/seq2seq/wmt_en_ro"""
        lowerCamelCase		:	List[str]							=      max_len * 2 * 6_4
        if not Path(__magic_name__      ).joinpath("""train.len"""      ).exists():
         save_len_file(__magic_name__    ,  __magic_name__      )
       else:
        lowerCamelCase		:	List[Any]							=      """examples/seq2seq/test_data/wmt_en_ro"""
        lowerCamelCase		:	List[Any]							=      max_len * 4
        save_len_file(__magic_name__    ,  __magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      AutoTokenizer.from_pretrained(__magic_name__      )
       lowerCamelCase		:	Tuple							=      SeqaSeqDataset(
           __magic_name__    ,  data_dir=__magic_name__    ,  type_path="""train"""    ,  max_source_length=__magic_name__    ,  max_target_length=__magic_name__    ,  n_obs=__magic_name__    ,  )
       return ds, max_tokens, tokenizer
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	List[Any]							=      self._get_dataset()
       lowerCamelCase		:	str							=      set(DistributedSortishSampler(__magic_name__    ,  2_5_6    ,  num_replicas=2    ,  rank=0    ,  add_extra_examples=__magic_name__      )      )
       lowerCamelCase		:	Optional[Any]							=      set(DistributedSortishSampler(__magic_name__    ,  2_5_6    ,  num_replicas=2    ,  rank=1    ,  add_extra_examples=__magic_name__      )      )
       assert idsa.intersection(__magic_name__      ) == set()






      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]    ,  )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       lowerCamelCase		:	int							=      AutoTokenizer.from_pretrained(__magic_name__    ,  use_fast=__magic_name__      )
       if tok_name == MBART_TINY:
        lowerCamelCase		:	Optional[Any]							=      SeqaSeqDataset(
            __magic_name__    ,  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		:	int							=      train_dataset.dataset_kwargs
        assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
        lowerCamelCase		:	Dict							=      SeqaSeqDataset(
            __magic_name__    ,  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		:	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(__magic_name__      ) == 1 if tok_name == BART_TINY else len(__magic_name__      ) == 0






import copy
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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """microsoft/conditional-detr-resnet-50""": (
        """https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			"""conditional_detr"""
      _UpperCAmelCase				:    Optional[int]           =			["""past_key_values"""]
      _UpperCAmelCase				:    Optional[Any]           =			{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }
      def __init__(      self    ,  __magic_name__=True    ,  __magic_name__=None    ,  __magic_name__=3    ,  __magic_name__=3_0_0    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=6    ,  __magic_name__=2_0_4_8    ,  __magic_name__=8    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=True    ,  __magic_name__="relu"    ,  __magic_name__=2_5_6    ,  __magic_name__=0.1    ,  __magic_name__=0.0    ,  __magic_name__=0.0    ,  __magic_name__=0.02    ,  __magic_name__=1.0    ,  __magic_name__=False    ,  __magic_name__="sine"    ,  __magic_name__="resnet50"    ,  __magic_name__=True    ,  __magic_name__=False    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=1    ,  __magic_name__=1    ,  __magic_name__=2    ,  __magic_name__=5    ,  __magic_name__=2    ,  __magic_name__=0.25    ,  **__magic_name__    ,  ):
       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."""      )
         lowerCamelCase		:	Optional[int]							=      CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]      )
        elif isinstance(__magic_name__    ,  __magic_name__      ):
         lowerCamelCase		:	List[Any]							=      backbone_config.get("""model_type"""      )
         lowerCamelCase		:	Dict							=      CONFIG_MAPPING[backbone_model_type]
         lowerCamelCase		:	str							=      config_class.from_dict(__magic_name__      )

       lowerCamelCase		:	Dict							=      use_timm_backbone
       lowerCamelCase		:	str							=      backbone_config
       lowerCamelCase		:	Tuple							=      num_channels
       lowerCamelCase		:	Dict							=      num_queries
       lowerCamelCase		:	Any							=      d_model
       lowerCamelCase		:	Optional[Any]							=      encoder_ffn_dim
       lowerCamelCase		:	List[str]							=      encoder_layers
       lowerCamelCase		:	Union[str, Any]							=      encoder_attention_heads
       lowerCamelCase		:	Any							=      decoder_ffn_dim
       lowerCamelCase		:	Dict							=      decoder_layers
       lowerCamelCase		:	Union[str, Any]							=      decoder_attention_heads
       lowerCamelCase		:	Dict							=      dropout
       lowerCamelCase		:	List[str]							=      attention_dropout
       lowerCamelCase		:	Union[str, Any]							=      activation_dropout
       lowerCamelCase		:	Optional[int]							=      activation_function
       lowerCamelCase		:	int							=      init_std
       lowerCamelCase		:	str							=      init_xavier_std
       lowerCamelCase		:	Tuple							=      encoder_layerdrop
       lowerCamelCase		:	str							=      decoder_layerdrop
       lowerCamelCase		:	Tuple							=      encoder_layers
       lowerCamelCase		:	Optional[int]							=      auxiliary_loss
       lowerCamelCase		:	Optional[Any]							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      backbone
       lowerCamelCase		:	Union[str, Any]							=      use_pretrained_backbone
       lowerCamelCase		:	str							=      dilation
       # Hungarian matcher
       lowerCamelCase		:	Optional[Any]							=      class_cost
       lowerCamelCase		:	Dict							=      bbox_cost
       lowerCamelCase		:	Tuple							=      giou_cost
       # Loss coefficients
       lowerCamelCase		:	Union[str, Any]							=      mask_loss_coefficient
       lowerCamelCase		:	Dict							=      dice_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      cls_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      bbox_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      giou_loss_coefficient
       lowerCamelCase		:	Optional[int]							=      focal_alpha
       super().__init__(is_encoder_decoder=__magic_name__    ,  **__magic_name__      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.encoder_attention_heads
      @property
      def 						UpperCamelCase__							(      self      ):
       return self.d_model






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      copy.deepcopy(self.__dict__      )
       if self.backbone_config is not None:
        lowerCamelCase		:	Optional[int]							=      self.backbone_config.to_dict()
       lowerCamelCase		:	Optional[Any]							=      self.__class__.model_type
       return output





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Any           =			version.parse("""1.11""")
      @property
      def 						UpperCamelCase__							(      self      ):
       return OrderedDict(
           [
               ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
               ("""pixel_mask""", {0: """batch"""}),
           ]      )
      @property
      def 						UpperCamelCase__							(      self      ):
       return 1e-5






      @property
      def 						UpperCamelCase__							(      self      ):
       return 1_2




