


"""simple docstring"""







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

from accelerate.utils.dataclasses import DistributedType






class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A=2			,   __A=3			,   __A=64			,   __A=None						) ->					Union[str, Any]:
     a					=np.random.default_rng(__snake_case						)
     a					=length
     a					=rng.normal(size=(length,)						).astype(np.floataa						)
     a					=a * self.x + b + rng.normal(scale=0.1			,   size=(length,)						).astype(np.floataa						)


 def __len__(		self						) ->					List[Any]:
     return self.length





 def __getitem__(		self			,   __A						) ->					Dict:
     return {"x": self.x[i], "y": self.y[i]}






class 				__A    (	torch.nn.Module      ):






 """simple docstring"""




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





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=None						) ->					Tuple:
     if self.first_batch:
         print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''						)
         a					=False
     return x * self.a[0] + self.b[0]






class 				__A    (	torch.nn.Module      ):






 """simple docstring"""




 def __init__(		self			,   __A=0			,   __A=0			,   __A=False						) ->					int:
     super().__init__()
     a					=torch.nn.Parameter(torch.tensor(__snake_case						).float()						)
     a					=torch.nn.Parameter(torch.tensor(__snake_case						).float()						)
     a					=True





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=None						) ->					List[Any]:
     if self.first_batch:
         print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''						)
         a					=False
     return x * self.a + self.b

def _A						(						lowercase	,   lowercase = 16		):







    """simple docstring"""
    from datasets import load_dataset
    from transformers import AutoTokenizer

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

    a					={v: i for i, v in enumerate(_A		)}

    def tokenize_function(lowercase		):
        # max_length=None => use the model max length (it's actually the default)
        a					=tokenizer(
            examples['''sentence1''']	,   examples['''sentence2''']	,   truncation=_A	,   max_length=_A	,   padding='''max_length'''		)
        if "label" in examples:
            a					=[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
    a					=datasets.map(
        _A	,   batched=_A	,   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(_A	,   padding='''max_length'''	,   max_length=1_28	,   return_tensors='''pt'''		)
        return tokenizer.pad(_A	,   padding='''longest'''	,   return_tensors='''pt'''		)

    # Instantiate dataloaders.
    a					=DataLoader(tokenized_datasets['''train''']	,   shuffle=_A	,   collate_fn=_A	,   batch_size=2		)
    a					=DataLoader(tokenized_datasets['''validation''']	,   shuffle=_A	,   collate_fn=_A	,   batch_size=1		)

    return train_dataloader, eval_dataloader





"""simple docstring"""







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowerCamelCase_       :       int      						=  logging.get_logger(__name__)






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "upernet"


 def __init__(		self			,   __A=None			,   __A=512			,   __A=0.02			,   __A=[1, 2, 3, 6]			,   __A=True			,   __A=0.4			,   __A=384			,   __A=256			,   __A=1			,   __A=False			,   __A=255			,   **__A			,   ) ->					Tuple:
     super().__init__(**__A						)

     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=['''stage1''', '''stage2''', '''stage3''', '''stage4''']						)
     elif isinstance(__A			,   __A						):
         a					=backbone_config.get('''model_type'''						)
         a					=CONFIG_MAPPING[backbone_model_type]
         a					=config_class.from_dict(__A						)

     a					=backbone_config
     a					=hidden_size
     a					=initializer_range
     a					=pool_scales
     a					=use_auxiliary_head
     a					=auxiliary_loss_weight
     a					=auxiliary_in_channels
     a					=auxiliary_channels
     a					=auxiliary_num_convs
     a					=auxiliary_concat_input
     a					=loss_ignore_index





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=copy.deepcopy(self.__dict__						)
     a					=self.backbone_config.to_dict()
     a					=self.__class__.model_type
     return output