


'''simple docstring'''
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   ,   __lowercase=2   ,   __lowercase=3   ,   __lowercase=64   ,   __lowercase=None    ):

  """simple docstring"""







  __A	:		Any	  =      np.random.default_rng(__lowercase    )
  __A	:		Tuple	  =      length
  __A	:		Dict	  =      rng.normal(size=(length,)    ).astype(np.floataa    )
  __A	:		str	  =      a * self.x + b + rng.normal(scale=0.1   ,   size=(length,)    ).astype(np.floataa    )


 def __len__(   self    ):

  """simple docstring"""







  return self.length







 def __getitem__(   self   ,   __lowercase    ):

  """simple docstring"""







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


class     _lowerCamelCase       (     torch.nn.Module					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase=0   ,   __lowercase=0   ,   __lowercase=False    ):

  """simple docstring"""







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







 def 			snake_case__ (   self   ,   __lowercase=None    ):

  """simple docstring"""







  if self.first_batch:
   print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
   __A	:		int	  =      False
  return x * self.a[0] + self.b[0]


class     _lowerCamelCase       (     torch.nn.Module					):




 '''simple docstring'''


 def __init__(   self   ,   __lowercase=0   ,   __lowercase=0   ,   __lowercase=False    ):

  """simple docstring"""







  super().__init__()
  __A	:		str	  =      torch.nn.Parameter(torch.tensor(__lowercase    ).float()    )
  __A	:		Union[str, Any]	  =      torch.nn.Parameter(torch.tensor(__lowercase    ).float()    )
  __A	:		Dict	  =      True







 def 			snake_case__ (   self   ,   __lowercase=None    ):

  """simple docstring"""







  if self.first_batch:
   print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
   __A	:		int	  =      False
  return x * self.a + self.b


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       int = 16      ):
 from datasets import load_dataset
 from transformers import AutoTokenizer

 __A	:		str	  =      AutoTokenizer.from_pretrained('bert-base-cased'      )
 __A	:		Union[str, Any]	  =      {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
 __A	:		Union[str, Any]	  =      load_dataset('csv',					data_files=UpperCamelCase__      )
 __A	:		Dict	  =      datasets['train'].unique('label'      )

 __A	:		Optional[Any]	  =      {v: i for i, v in enumerate(UpperCamelCase__      )}

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

 def collate_fn(UpperCamelCase__							:       Optional[int]      ):
  # 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=128,					return_tensors='pt'      )
  return tokenizer.pad(UpperCamelCase__,					padding='longest',					return_tensors='pt'      )

 # Instantiate dataloaders.
 __A	:		Optional[int]	  =      DataLoader(tokenized_datasets['train'],					shuffle=UpperCamelCase__,					collate_fn=UpperCamelCase__,					batch_size=2      )
 __A	:		Optional[Any]	  =      DataLoader(tokenized_datasets['validation'],					shuffle=UpperCamelCase__,					collate_fn=UpperCamelCase__,					batch_size=1      )

 return train_dataloader, eval_dataloader










'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCAmelCase_			:  Any										=   {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Tuple										=   ['MaskFormerFeatureExtractor']
 UpperCAmelCase_			:  List[str]										=   ['MaskFormerImageProcessor']


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'MaskFormerForInstanceSegmentation',
     'MaskFormerModel',
     'MaskFormerPreTrainedModel',
 ]
 UpperCAmelCase_			:  Optional[int]										=   [
     'MaskFormerSwinBackbone',
     'MaskFormerSwinModel',
     'MaskFormerSwinPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
 from .configuration_maskformer_swin import MaskFormerSwinConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_maskformer import MaskFormerFeatureExtractor
  from .image_processing_maskformer import MaskFormerImageProcessor
 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_maskformer import (
      MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      MaskFormerForInstanceSegmentation,
      MaskFormerModel,
      MaskFormerPreTrainedModel,
  )
  from .modeling_maskformer_swin import (
      MaskFormerSwinBackbone,
      MaskFormerSwinModel,
      MaskFormerSwinPreTrainedModel,
  )


else:
 import sys

 UpperCAmelCase_			:  Union[str, Any]										=   _LazyModule(__name__, globals()['__file__'], _import_structure)






