

'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A_	= {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_	= ["EfficientFormerImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_	= [
				    "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "EfficientFormerForImageClassification",
				    "EfficientFormerForImageClassificationWithTeacher",
				    "EfficientFormerModel",
				    "EfficientFormerPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_	= [
				    "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFEfficientFormerForImageClassification",
				    "TFEfficientFormerForImageClassificationWithTeacher",
				    "TFEfficientFormerModel",
				    "TFEfficientFormerPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_efficientformer import EfficientFormerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_efficientformer import (
								    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EfficientFormerForImageClassification,
								    EfficientFormerForImageClassificationWithTeacher,
								    EfficientFormerModel,
								    EfficientFormerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_efficientformer import (
								    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFEfficientFormerForImageClassification,
								    TFEfficientFormerForImageClassificationWithTeacher,
								    TFEfficientFormerModel,
								    TFEfficientFormerPreTrainedModel,
								)

else:
				import sys

				A_	= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









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

from accelerate.utils.dataclasses import DistributedType





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Any							,							_lowerCAmelCase    :						Optional[int]=2							,							_lowerCAmelCase    :						Any=3							,							_lowerCAmelCase    :						Tuple=64							,							_lowerCAmelCase    :						List[str]=None		):
       SCREAMING_SNAKE_CASE_      		=     np.random.default_rng(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     length
       SCREAMING_SNAKE_CASE_      		=     rng.normal(size=(length,)		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     a * self.x + b + rng.normal(scale=0.1							,							size=(length,)		).astype(np.floataa		)



 def __len__(				self    :						Optional[int]		):
       return self.length




 def __getitem__(				self    :						str							,							_lowerCAmelCase    :						Union[str, Any]		):
       return {"x": self.x[i], "y": self.y[i]}





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Tuple							,							_lowerCAmelCase    :						Dict=0							,							_lowerCAmelCase    :						List[str]=0							,							_lowerCAmelCase    :						str=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Union[str, Any]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a[0] + self.b[0]





class 			lowerCamelCase_				( torch.nn.Module   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Any=0							,							_lowerCAmelCase    :						Optional[Any]=False		):
       super().__init__()
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     torch.nn.Parameter(torch.tensor(_lowerCAmelCase		).float()		)
       SCREAMING_SNAKE_CASE_      		=     True




 def  lowerCAmelCase_    (				self    :						Optional[Any]							,							_lowerCAmelCase    :						Optional[int]=None		):
       if self.first_batch:
             print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"		)
             SCREAMING_SNAKE_CASE_      		=     False
       return x * self.a + self.b


def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      int = 16				)   ->					Union[str, Any]:
      from datasets import load_dataset
      from transformers import AutoTokenizer

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

      SCREAMING_SNAKE_CASE_      		=     {v: i for i, v in enumerate(__UpperCAmelCase				)}

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

      def collate_fn(__UpperCAmelCase		:      Dict				):
            # 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.
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['train']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=2				)
      SCREAMING_SNAKE_CASE_      		=     DataLoader(tokenized_datasets['validation']     ,     shuffle=__UpperCAmelCase     ,     collate_fn=__UpperCAmelCase     ,     batch_size=1				)

      return train_dataloader, eval_dataloader