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

from accelerate.utils.dataclasses import DistributedType




class 			__magic_name__	:





   """simple docstring"""




   def __init__(						self       :	Union[str, Any]		,	_lowercase       :	int=2		,	_lowercase       :	List[Any]=3		,	_lowercase       :	Union[str, Any]=64		,	_lowercase       :	List[str]=None				):



    """simple docstring"""



    _UpperCamelCase:  str    						=			np.random.default_rng(_lowercase				)
    _UpperCamelCase:  Union[str, Any]    						=			length
    _UpperCamelCase:  Optional[Any]    						=			rng.normal(size=(length,)				).astype(np.floataa				)
    _UpperCamelCase:  Union[str, Any]    						=			a * self.x + b + rng.normal(scale=0.1		,	size=(length,)				).astype(np.floataa				)



   def __len__(						self       :	Tuple				):



    """simple docstring"""



    return self.length



   def __getitem__(						self       :	Dict		,	_lowercase       :	Dict				):



    """simple docstring"""



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




class 			__magic_name__	(		torch.nn.Module     ):





   """simple docstring"""




   def __init__(						self       :	int		,	_lowercase       :	Optional[int]=0		,	_lowercase       :	List[str]=0		,	_lowercase       :	Dict=False				):



    """simple docstring"""



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



   def 	lowerCAmelCase    (						self       :	List[Any]		,	_lowercase       :	Dict=None				):



    """simple docstring"""



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




class 			__magic_name__	(		torch.nn.Module     ):





   """simple docstring"""




   def __init__(						self       :	Union[str, Any]		,	_lowercase       :	Dict=0		,	_lowercase       :	List[Any]=0		,	_lowercase       :	Tuple=False				):



    """simple docstring"""



    super().__init__()
    _UpperCamelCase:  List[str]    						=			torch.nn.Parameter(torch.tensor(_lowercase				).float()				)
    _UpperCamelCase:  Optional[Any]    						=			torch.nn.Parameter(torch.tensor(_lowercase				).float()				)
    _UpperCamelCase:  Optional[Any]    						=			True



   def 	lowerCAmelCase    (						self       :	Union[str, Any]		,	_lowercase       :	List[Any]=None				):



    """simple docstring"""



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




def    lowerCAmelCase_  (		lowercase:				Dict	,       lowercase:				int = 16		)     ->					Dict:







 '''simple docstring'''
 from datasets import load_dataset
 from transformers import AutoTokenizer

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

 _UpperCamelCase:  int    						=			{v: i for i, v in enumerate(lowercase		)}

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

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

 # Instantiate dataloaders.
 _UpperCamelCase:  List[Any]    						=			DataLoader(tokenized_datasets['''train''']	,       shuffle=lowercase	,       collate_fn=lowercase	,       batch_size=2		)
 _UpperCamelCase:  int    						=			DataLoader(tokenized_datasets['''validation''']	,       shuffle=lowercase	,       collate_fn=lowercase	,       batch_size=1		)

 return train_dataloader, eval_dataloader
from typing import TYPE_CHECKING

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


UpperCAmelCase_           =				{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''EncoderDecoderModel''']

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''TFEncoderDecoderModel''']

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
  from .configuration_encoder_decoder import EncoderDecoderConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_encoder_decoder import EncoderDecoderModel

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
  import sys

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)