



import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
   from ... import PreTrainedTokenizerBase, TensorType

_UpperCAmelCase     =			logging.get_logger(__name__)
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				str     						=					'vision-encoder-decoder'
  UpperCamelCase	:				Union[str, Any]     						=					True



  def __init__(						self :					Optional[Any]       ,		**lowerCAmelCase :					int		)     ->					int:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase		)
     if "encoder" not in kwargs or "decoder" not in kwargs:
        raise ValueError(
            f'''A configuraton of type {self.model_type} cannot be instantiated because '''
            f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''		)

     SCREAMING_SNAKE_CASE_: int     			=kwargs.pop("""encoder"""		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=encoder_config.pop("""model_type"""		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=kwargs.pop("""decoder"""		)
     SCREAMING_SNAKE_CASE_: str     			=decoder_config.pop("""model_type"""		)

     SCREAMING_SNAKE_CASE_: List[str]     			=AutoConfig.for_model(lowerCAmelCase       ,		**lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Tuple     			=AutoConfig.for_model(lowerCAmelCase       ,		**lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=True



  @classmethod
  def 	lowerCamelCase__       (						cls :					Tuple       ,		lowerCAmelCase :					PretrainedConfig       ,		lowerCAmelCase :					PretrainedConfig       ,		**lowerCAmelCase :					Any		)     ->					PretrainedConfig:


     '''simple docstring'''




     logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""		)
     SCREAMING_SNAKE_CASE_: Any     			=True
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=True

     return cls(encoder=encoder_config.to_dict()       ,		decoder=decoder_config.to_dict()       ,		**lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=copy.deepcopy(self.__dict__		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=self.encoder.to_dict()
     SCREAMING_SNAKE_CASE_: Tuple     			=self.decoder.to_dict()
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.__class__.model_type
     return output







class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Any     						=					version.parse('1.11'       )



  @property
  def 	lowerCamelCase__       (						self :					Dict		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




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



  @property
  def 	lowerCamelCase__       (						self :					int		)     ->					float:


     '''simple docstring'''




     return 1E-4





  @property
  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




     return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}		)







class 					a	(  UpperCAmelCase__       ):



  @property
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Mapping[str, Mapping[int, str]]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=OrderedDict()
     SCREAMING_SNAKE_CASE_: Optional[Any]     			={0: """batch""", 1: """past_decoder_sequence + sequence"""}
     SCREAMING_SNAKE_CASE_: Dict     			={0: """batch""", 1: """past_decoder_sequence + sequence"""}
     SCREAMING_SNAKE_CASE_: int     			={0: """batch""", 1: """encoder_sequence"""}

     return common_inputs





  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					"PreTrainedTokenizerBase"       ,		lowerCAmelCase :					int = -1       ,		lowerCAmelCase :					int = -1       ,		lowerCAmelCase :					bool = False       ,		lowerCAmelCase :					Optional["TensorType"] = None       ,		)     ->					Mapping[str, Any]:


     '''simple docstring'''




     import torch

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=OrderedDict()

     SCREAMING_SNAKE_CASE_: Dict     			=super().generate_dummy_inputs(
         lowerCAmelCase       ,		batch_size=lowerCAmelCase       ,		seq_length=lowerCAmelCase       ,		is_pair=lowerCAmelCase       ,		framework=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: int     			=dummy_input["""input_ids"""].shape
     SCREAMING_SNAKE_CASE_: Any     			=(batch, encoder_sequence, self._config.encoder_hidden_size)
     SCREAMING_SNAKE_CASE_: List[str]     			=dummy_input.pop("""input_ids"""		)
     SCREAMING_SNAKE_CASE_: Tuple     			=dummy_input.pop("""attention_mask"""		)
     SCREAMING_SNAKE_CASE_: Tuple     			=torch.zeros(lowerCAmelCase		)

     return common_inputs







class 					a	(  UpperCAmelCase__       ):



  @property
  def 	lowerCamelCase__       (						self :					Tuple		)     ->					None:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					PretrainedConfig		)     ->					OnnxConfig:


     '''simple docstring'''




     return VisionEncoderDecoderEncoderOnnxConfig(lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					PretrainedConfig       ,		lowerCAmelCase :					PretrainedConfig       ,		lowerCAmelCase :					str = "default"		)     ->					OnnxConfig:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=encoder_config.hidden_size
     return VisionEncoderDecoderDecoderOnnxConfig(lowerCAmelCase       ,		lowerCAmelCase		)








"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 					a	:



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					List[str]=2       ,		lowerCAmelCase :					int=3       ,		lowerCAmelCase :					Optional[Any]=64       ,		lowerCAmelCase :					Union[str, Any]=None		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=np.random.default_rng(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=length
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rng.normal(size=(length,)		).astype(np.floataa		)
     SCREAMING_SNAKE_CASE_: Tuple     			=a * self.x + b + rng.normal(scale=0.1       ,		size=(length,)		).astype(np.floataa		)



  def __len__(						self :					List[Any]		)     ->					str:


     '''simple docstring'''




     return self.length





  def __getitem__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any		)     ->					List[str]:


     '''simple docstring'''




     return {"x": self.x[i], "y": self.y[i]}







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Optional[int]       ,		lowerCAmelCase :					str=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					Optional[int]=False		)     ->					Tuple:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: int     			=torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
     SCREAMING_SNAKE_CASE_: Dict     			=True





  def 	lowerCamelCase__       (						self :					str       ,		lowerCAmelCase :					Tuple=None		)     ->					int:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=False
     return x * self.a[0] + self.b[0]







class 					a	(  torch.nn.Module       ):



  def __init__(						self :					Union[str, Any]       ,		lowerCAmelCase :					Any=0       ,		lowerCAmelCase :					List[Any]=0       ,		lowerCAmelCase :					List[Any]=False		)     ->					str:


     '''simple docstring'''




     super().__init__()
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=torch.nn.Parameter(torch.tensor(lowerCAmelCase		).float()		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=True





  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					int=None		)     ->					Any:


     '''simple docstring'''




     if self.first_batch:
        print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
        SCREAMING_SNAKE_CASE_: Optional[int]     			=False
     return x * self.a + self.b







def      __magic_name__				(							lowercase							,			lowercase = 16 ):
   from datasets import load_dataset
   from transformers import AutoTokenizer

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

   SCREAMING_SNAKE_CASE_: List[Any]     			={v: i for i, v in enumerate(lowercase )}

   def tokenize_function(lowercase ):
      # max_length=None => use the model max length (it's actually the default)
      SCREAMING_SNAKE_CASE_: Dict     			=tokenizer(
          examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=lowercase							,			max_length=lowercase							,			padding="""max_length""" )
      if "label" in examples:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=[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
   SCREAMING_SNAKE_CASE_: List[Any]     			=datasets.map(
       lowercase							,			batched=lowercase							,			remove_columns=["""sentence1""", """sentence2""", """label"""]							,			)

   def collate_fn(lowercase ):
      # 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(lowercase							,			padding="""max_length"""							,			max_length=128							,			return_tensors="""pt""" )
      return tokenizer.pad(lowercase							,			padding="""longest"""							,			return_tensors="""pt""" )

   # Instantiate dataloaders.
   SCREAMING_SNAKE_CASE_: Optional[int]     			=DataLoader(tokenized_datasets["""train"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=2 )
   SCREAMING_SNAKE_CASE_: Dict     			=DataLoader(tokenized_datasets["""validation"""]							,			shuffle=lowercase							,			collate_fn=lowercase							,			batch_size=1 )

   return train_dataloader, eval_dataloader



