

'''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		,					UpperCamelCase__=2		,					UpperCamelCase__=3		,					UpperCamelCase__=64		,					UpperCamelCase__=None							)    ->   Tuple:





           '''simple docstring'''
           A_						       =   np.random.default_rng(UpperCamelCase__							)
           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							)    ->   Dict:





           '''simple docstring'''
           return self.length







    def __getitem__(       self		,					UpperCamelCase__							)    ->   Tuple:





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


class        A__   (   torch.nn.Module      ):



    def __init__(       self		,					UpperCamelCase__=0		,					UpperCamelCase__=0		,					UpperCamelCase__=False							)    ->   str:





           '''simple docstring'''
           super().__init__()
           A_						       =   torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
           A_						       =   torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
           A_						       =   True







    def        snake_case_      (       self		,					UpperCamelCase__=None							)    ->   Dict:





           '''simple docstring'''
           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      ):



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





           '''simple docstring'''
           super().__init__()
           A_						       =   torch.nn.Parameter(torch.tensor(UpperCamelCase__							).float()							)
           A_						       =   torch.nn.Parameter(torch.tensor(UpperCamelCase__							).float()							)
           A_						       =   True







    def        snake_case_      (       self		,					UpperCamelCase__=None							)    ->   Union[str, Any]:





           '''simple docstring'''
           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    UpperCAmelCase__     (   UpperCAmelCase__,							UpperCAmelCase__ = 16					)      ->      Any:
       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=UpperCAmelCase__					)
       A_						       =   datasets["""train"""].unique("""label"""					)

       A_						       =   {v: i for i, v in enumerate(UpperCAmelCase__					)}

       def tokenize_function(UpperCAmelCase__					):
              # max_length=None => use the model max length (it's actually the default)
              A_						       =   tokenizer(
                  examples["""sentence1"""],							examples["""sentence2"""],							truncation=UpperCAmelCase__,							max_length=UpperCAmelCase__,							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(
           UpperCAmelCase__,							batched=UpperCAmelCase__,							remove_columns=["""sentence1""", """sentence2""", """label"""],							)

       def collate_fn(UpperCAmelCase__					):
              # 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(UpperCAmelCase__,							padding="""max_length""",							max_length=1_28,							return_tensors="""pt"""					)
              return tokenizer.pad(UpperCAmelCase__,							padding="""longest""",							return_tensors="""pt"""					)

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

       return train_dataloader, eval_dataloader



'''simple docstring'''







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 ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase									=     logging.get_logger(__name__)

__lowerCamelCase									=     {
    '''microsoft/resnet-50''': '''https://huggingface.co/microsoft/resnet-50/blob/main/config.json''',
}


class        A__   (   _snake_case    ,    _snake_case      ):
    lowercase						       =		"resnet"
    lowercase						       =		["basic", "bottleneck"]



    def __init__(       self		,					UpperCamelCase__=3		,					UpperCamelCase__=64		,					UpperCamelCase__=[256, 512, 1024, 2048]		,					UpperCamelCase__=[3, 4, 6, 3]		,					UpperCamelCase__="bottleneck"		,					UpperCamelCase__="relu"		,					UpperCamelCase__=False		,					UpperCamelCase__=None		,					UpperCamelCase__=None		,					**UpperCamelCase__		,					)    ->   List[str]:





           '''simple docstring'''
           super().__init__(**UpperCamelCase__							)
           if layer_type not in self.layer_types:
                  raise ValueError(f'''layer_type={layer_type} is not one of {",".join(self.layer_types							)}'''							)
           A_						       =   num_channels
           A_						       =   embedding_size
           A_						       =   hidden_sizes
           A_						       =   depths
           A_						       =   layer_type
           A_						       =   hidden_act
           A_						       =   downsample_in_first_stage
           A_						       =   ["""stem"""] + [f'''stage{idx}''' for idx in range(1		,					len(UpperCamelCase__							) + 1							)]
           A_       ,      A_						       =   get_aligned_output_features_output_indices(
               out_features=UpperCamelCase__		,					out_indices=UpperCamelCase__		,					stage_names=self.stage_names							)


class        A__   (   _snake_case      ):
    lowercase						       =		version.parse("1.11"      )



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





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







    @property
    def        snake_case_      (       self							)    ->   float:





           '''simple docstring'''
           return 1e-3
