

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class 						__magic_name__    :


 def __init__(						self		: int			,						lowerCAmelCase__		: Any=2			,						lowerCAmelCase__		: List[str]=3			,						lowerCAmelCase__		: Union[str, Any]=6_4			,						lowerCAmelCase__		: Tuple=None    )    ->      Optional[Any]:
   UpperCAmelCase					      =  np.random.default_rng(lowercase_    )
   UpperCAmelCase					      =  length
   UpperCAmelCase					      =  rng.normal(size=(length,)    ).astype(np.floataa    )
   UpperCAmelCase					      =  a * self.x + b + rng.normal(scale=0.1			,						size=(length,)    ).astype(np.floataa    )


 def __len__(						self		: str    )    ->      Dict:
   return self.length


 def __getitem__(						self		: List[str]			,						lowerCAmelCase__		: Optional[int]    )    ->      Optional[int]:
   return {"x": self.x[i], "y": self.y[i]}






class 						__magic_name__    (       torch.nn.Module    ):


 def __init__(						self		: Any			,						lowerCAmelCase__		: Any=0			,						lowerCAmelCase__		: Any=0			,						lowerCAmelCase__		: List[Any]=False    )    ->      Union[str, Any]:
   super().__init__()
   UpperCAmelCase					      =  torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
   UpperCAmelCase					      =  torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
   UpperCAmelCase					      =  True


 def _UpperCamelCase	(						self		: Any			,						lowerCAmelCase__		: Union[str, Any]=None    )    ->      Union[str, Any]:
   if self.first_batch:
     print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"    )
     UpperCAmelCase					      =  False
   return x * self.a[0] + self.b[0]






class 						__magic_name__    (       torch.nn.Module    ):


 def __init__(						self		: Any			,						lowerCAmelCase__		: List[str]=0			,						lowerCAmelCase__		: Optional[int]=0			,						lowerCAmelCase__		: str=False    )    ->      Tuple:
   super().__init__()
   UpperCAmelCase					      =  torch.nn.Parameter(torch.tensor(lowercase_    ).float()    )
   UpperCAmelCase					      =  torch.nn.Parameter(torch.tensor(lowercase_    ).float()    )
   UpperCAmelCase					      =  True


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: int=None    )    ->      List[str]:
   if self.first_batch:
     print(f"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"    )
     UpperCAmelCase					      =  False
   return x * self.a + self.b

def      _lowerCAmelCase(	__A   ,       __A = 16			):
  from datasets import load_dataset
  from transformers import AutoTokenizer

  UpperCAmelCase					      =  AutoTokenizer.from_pretrained("bert-base-cased"			)
  UpperCAmelCase					      =  {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
  UpperCAmelCase					      =  load_dataset("csv"   ,       data_files=__SCREAMING_SNAKE_CASE			)
  UpperCAmelCase					      =  datasets["train"].unique("label"			)

  UpperCAmelCase					      =  {v: i for i, v in enumerate(__SCREAMING_SNAKE_CASE			)}

  def tokenize_function(__A			):
    # max_length=None => use the model max length (it's actually the default)
    UpperCAmelCase					      =  tokenizer(
        examples["sentence1"]   ,       examples["sentence2"]   ,       truncation=__SCREAMING_SNAKE_CASE   ,       max_length=__SCREAMING_SNAKE_CASE   ,       padding="max_length"			)
    if "label" in examples:
      UpperCAmelCase					      =  [label_to_id[l] for l in examples["label"]]
    return outputs

  # Apply the method we just defined to all the examples in all the splits of the dataset
  UpperCAmelCase					      =  datasets.map(
      __SCREAMING_SNAKE_CASE   ,       batched=__SCREAMING_SNAKE_CASE   ,       remove_columns=["sentence1", "sentence2", "label"]   ,       )

  def collate_fn(__A			):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    if accelerator.distributed_type == DistributedType.TPU:
      return tokenizer.pad(__SCREAMING_SNAKE_CASE   ,       padding="max_length"   ,       max_length=128   ,       return_tensors="pt"			)
    return tokenizer.pad(__SCREAMING_SNAKE_CASE   ,       padding="longest"   ,       return_tensors="pt"			)

  # Instantiate dataloaders.
  UpperCAmelCase					      =  DataLoader(tokenized_datasets["train"]   ,       shuffle=__SCREAMING_SNAKE_CASE   ,       collate_fn=__SCREAMING_SNAKE_CASE   ,       batch_size=2			)
  UpperCAmelCase					      =  DataLoader(tokenized_datasets["validation"]   ,       shuffle=__SCREAMING_SNAKE_CASE   ,       collate_fn=__SCREAMING_SNAKE_CASE   ,       batch_size=1			)

  return train_dataloader, eval_dataloader






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


lowerCAmelCase__       = logging.get_logger(__name__)

lowerCAmelCase__       = {
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						"""lxmert"""
 UpperCAmelCase							    =						{}


 def __init__(						self		: int			,						lowerCAmelCase__		: Any=3_0_5_2_2			,						lowerCAmelCase__		: List[str]=7_6_8			,						lowerCAmelCase__		: Union[str, Any]=1_2			,						lowerCAmelCase__		: List[Any]=9_5_0_0			,						lowerCAmelCase__		: Any=1_6_0_0			,						lowerCAmelCase__		: Union[str, Any]=4_0_0			,						lowerCAmelCase__		: Tuple=3_0_7_2			,						lowerCAmelCase__		: Dict="gelu"			,						lowerCAmelCase__		: Tuple=0.1			,						lowerCAmelCase__		: Tuple=0.1			,						lowerCAmelCase__		: int=5_1_2			,						lowerCAmelCase__		: List[str]=2			,						lowerCAmelCase__		: List[str]=0.02			,						lowerCAmelCase__		: str=1e-1_2			,						lowerCAmelCase__		: str=9			,						lowerCAmelCase__		: int=5			,						lowerCAmelCase__		: Optional[int]=5			,						lowerCAmelCase__		: List[Any]=2_0_4_8			,						lowerCAmelCase__		: Any=4			,						lowerCAmelCase__		: Dict=6.67			,						lowerCAmelCase__		: Any=True			,						lowerCAmelCase__		: Union[str, Any]=True			,						lowerCAmelCase__		: Any=True			,						lowerCAmelCase__		: Tuple=True			,						lowerCAmelCase__		: Optional[Any]=True			,						lowerCAmelCase__		: Optional[int]=True			,						lowerCAmelCase__		: Tuple=True			,						**lowerCAmelCase__		: List[Any]			,						)    ->      Dict:
   UpperCAmelCase					      =  vocab_size
   UpperCAmelCase					      =  hidden_size
   UpperCAmelCase					      =  num_attention_heads
   UpperCAmelCase					      =  hidden_act
   UpperCAmelCase					      =  intermediate_size
   UpperCAmelCase					      =  hidden_dropout_prob
   UpperCAmelCase					      =  attention_probs_dropout_prob
   UpperCAmelCase					      =  max_position_embeddings
   UpperCAmelCase					      =  type_vocab_size
   UpperCAmelCase					      =  initializer_range
   UpperCAmelCase					      =  layer_norm_eps
   UpperCAmelCase					      =  num_qa_labels
   UpperCAmelCase					      =  num_object_labels
   UpperCAmelCase					      =  num_attr_labels
   UpperCAmelCase					      =  l_layers
   UpperCAmelCase					      =  x_layers
   UpperCAmelCase					      =  r_layers
   UpperCAmelCase					      =  visual_feat_dim
   UpperCAmelCase					      =  visual_pos_dim
   UpperCAmelCase					      =  visual_loss_normalizer
   UpperCAmelCase					      =  task_matched
   UpperCAmelCase					      =  task_mask_lm
   UpperCAmelCase					      =  task_obj_predict
   UpperCAmelCase					      =  task_qa
   UpperCAmelCase					      =  visual_obj_loss
   UpperCAmelCase					      =  visual_attr_loss
   UpperCAmelCase					      =  visual_feat_loss
   UpperCAmelCase					      =  {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
   super().__init__(**lowerCAmelCase__    )



