


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       :		Optional[Any] ,   _UpperCAmelCase       :		List[Any]=2 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Dict=64 ,   _UpperCAmelCase       :		str=None						)		->  str:




							"""simple docstring"""


							lowercase__	     =						np.random.default_rng(_UpperCAmelCase						)
							lowercase__	     =						length
							lowercase__	     =						rng.normal(size=(length,)						).astype(np.floataa						)
							lowercase__	     =						a * self.x + b + rng.normal(scale=0.1 ,   size=(length,)						).astype(np.floataa						)

					def __len__(self       :		Optional[Any]						)		->  Optional[Any]:




							"""simple docstring"""


							return self.length


					def __getitem__(self       :		Union[str, Any] ,   _UpperCAmelCase       :		Dict						)		->  Tuple:




							"""simple docstring"""


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






class 				A       (			torch.nn.Module				):
					'''simple docstring'''






					def __init__(self       :		str ,   _UpperCAmelCase       :		str=0 ,   _UpperCAmelCase       :		Any=0 ,   _UpperCAmelCase       :		Dict=False						)		->  Any:




							"""simple docstring"""


							super().__init__()
							lowercase__	     =						torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
							lowercase__	     =						torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
							lowercase__	     =						True


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




							"""simple docstring"""


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






class 				A       (			torch.nn.Module				):
					'''simple docstring'''






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




							"""simple docstring"""


							super().__init__()
							lowercase__	     =						torch.nn.Parameter(torch.tensor(_UpperCAmelCase						).float()						)
							lowercase__	     =						torch.nn.Parameter(torch.tensor(_UpperCAmelCase						).float()						)
							lowercase__	     =						True


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




							"""simple docstring"""


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






def 					UpperCamelCase   (      __magic_name__	:				Dict       ,       __magic_name__	:				int = 16	)  ->    Union[str, Any]:
		"""simple docstring"""




		from datasets import load_dataset
		from transformers import AutoTokenizer

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

		lowercase__	     =						{v: i for i, v in enumerate(__magic_name__	)}

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

		def collate_fn(__magic_name__	:				List[str]	):
				# 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(__magic_name__       ,       padding="""max_length"""       ,       max_length=128       ,       return_tensors="""pt"""	)
				return tokenizer.pad(__magic_name__       ,       padding="""longest"""       ,       return_tensors="""pt"""	)

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

		return train_dataloader, eval_dataloader





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


A			:						Any        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''poolformer'''

					def __init__(self       :		Dict ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		str=16 ,   _UpperCAmelCase       :		Any=16 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Union[str, Any]=4.0 ,   _UpperCAmelCase       :		str=[2, 2, 6, 2] ,   _UpperCAmelCase       :		int=[64, 128, 320, 512] ,   _UpperCAmelCase       :		Union[str, Any]=[7, 3, 3, 3] ,   _UpperCAmelCase       :		List[Any]=[4, 2, 2, 2] ,   _UpperCAmelCase       :		Union[str, Any]=[2, 1, 1, 1] ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Dict=0.0 ,   _UpperCAmelCase       :		Union[str, Any]="gelu" ,   _UpperCAmelCase       :		Optional[Any]=True ,   _UpperCAmelCase       :		Tuple=1E-5 ,   _UpperCAmelCase       :		Tuple=0.02 ,   **_UpperCAmelCase       :		Union[str, Any] ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						num_channels
							lowercase__	     =						patch_size
							lowercase__	     =						stride
							lowercase__	     =						padding
							lowercase__	     =						pool_size
							lowercase__	     =						hidden_sizes
							lowercase__	     =						mlp_ratio
							lowercase__	     =						depths
							lowercase__	     =						patch_sizes
							lowercase__	     =						strides
							lowercase__	     =						num_encoder_blocks
							lowercase__	     =						drop_path_rate
							lowercase__	     =						hidden_act
							lowercase__	     =						use_layer_scale
							lowercase__	     =						layer_scale_init_value
							lowercase__	     =						initializer_range
							super().__init__(**_UpperCAmelCase						)






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					version.parse('''1.11'''				)

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




							"""simple docstring"""


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


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




							"""simple docstring"""


							return 2E-3

