



'''simple docstring'''



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

from accelerate.utils.dataclasses import DistributedType





class _UpperCamelCase		:





				'''simple docstring'''





				def __init__(							self  :  Tuple  ,       _lowerCAmelCase  :  Union[str, Any]=2  ,       _lowerCAmelCase  :  List[Any]=3  ,       _lowerCAmelCase  :  List[Any]=6_4  ,       _lowerCAmelCase  :  int=None):







											'''simple docstring'''

											__lowercase       	=np.random.default_rng(_lowerCAmelCase)
											__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  :  Any):







											'''simple docstring'''

											return self.length

				def __getitem__(							self  :  Optional[int]  ,       _lowerCAmelCase  :  Any):







											'''simple docstring'''

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





class _UpperCamelCase		(     torch.nn.Module		):





				'''simple docstring'''





				def __init__(							self  :  Optional[Any]  ,       _lowerCAmelCase  :  List[Any]=0  ,       _lowerCAmelCase  :  Optional[int]=0  ,       _lowerCAmelCase  :  List[Any]=False):







											'''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  :  Dict  ,       _lowerCAmelCase  :  Union[str, Any]=None):







											'''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 _UpperCamelCase		(     torch.nn.Module		):





				'''simple docstring'''





				def __init__(							self  :  Dict  ,       _lowerCAmelCase  :  Any=0  ,       _lowerCAmelCase  :  Any=0  ,       _lowerCAmelCase  :  Tuple=False):







											'''simple docstring'''

											super().__init__()
											__lowercase       	=torch.nn.Parameter(torch.tensor(_lowerCAmelCase).float())
											__lowercase       	=torch.nn.Parameter(torch.tensor(_lowerCAmelCase).float())
											__lowercase       	=True

				def 				__lowerCamelCase     (							self  :  int  ,       _lowerCAmelCase  :  int=None):







											'''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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = 16					):



							"""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=_lowerCAmelCase					)
							__lowercase       	=datasets['train'].unique('label'					)

							__lowercase       	={v: i for i, v in enumerate(_lowerCAmelCase					)}

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

							def collate_fn(_lowerCAmelCase					):
														# 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(_lowerCAmelCase  ,   padding='max_length'  ,   max_length=128  ,   return_tensors='pt'					)
														return tokenizer.pad(_lowerCAmelCase  ,   padding='longest'  ,   return_tensors='pt'					)

							# Instantiate dataloaders.
							__lowercase       	=DataLoader(tokenized_datasets['train']  ,   shuffle=_lowerCAmelCase  ,   collate_fn=_lowerCAmelCase  ,   batch_size=2					)
							__lowercase       	=DataLoader(tokenized_datasets['validation']  ,   shuffle=_lowerCAmelCase  ,   collate_fn=_lowerCAmelCase  ,   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


lowerCamelCase	 =       {
    """configuration_mask2former""": [
        """MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """Mask2FormerConfig""",
    ],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase	 =       ["""Mask2FormerImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase	 =       [
		    """MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """Mask2FormerForUniversalSegmentation""",
		    """Mask2FormerModel""",
		    """Mask2FormerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_maskaformer import MaskaFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_maskaformer import (
				    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaskaFormerForUniversalSegmentation,
				    MaskaFormerModel,
				    MaskaFormerPreTrainedModel,
				)


else:
		import sys

		lowerCamelCase	 =       _LazyModule(__name__, globals()["""__file__"""], _import_structure)



