



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

from accelerate.utils.dataclasses import DistributedType


class   snake_case_ :




       """simple docstring"""






       def __init__(     self     ,lowercase=2     ,lowercase=3     ,lowercase=64     ,lowercase=None):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		np.random.default_rng(lowercase)
             UpperCAmelCase_  :	int				      =		length
             UpperCAmelCase_  :	Tuple				      =		rng.normal(size=(length,)).astype(np.floataa)
             UpperCAmelCase_  :	List[Any]				      =		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   snake_case_ (torch.nn.Module  ):




       """simple docstring"""






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

             """simple docstring"""





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



       def  A_   (     self     ,lowercase=None):

             """simple docstring"""





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


class   snake_case_ (torch.nn.Module  ):




       """simple docstring"""






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

             """simple docstring"""





             super().__init__()
             UpperCAmelCase_  :	Union[str, Any]				      =		torch.nn.Parameter(torch.tensor(lowercase).float())
             UpperCAmelCase_  :	Optional[int]				      =		torch.nn.Parameter(torch.tensor(lowercase).float())
             UpperCAmelCase_  :	Any				      =		True



       def  A_   (     self     ,lowercase=None):

             """simple docstring"""





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







def    _snake_case (       __snake_case					,  __snake_case = 1_6    )     ->     Optional[Any]:






      '''simple docstring'''







      from datasets import load_dataset
      from transformers import AutoTokenizer

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

      UpperCAmelCase_  :	List[str]				      =		{v: i for i, v in enumerate(__snake_case    )}

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

      def collate_fn(__snake_case    ):
            # 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(__snake_case					,  padding="max_length"					,  max_length=1_2_8					,  return_tensors="pt"    )
            return tokenizer.pad(__snake_case					,  padding="longest"					,  return_tensors="pt"    )

      # Instantiate dataloaders.
      UpperCAmelCase_  :	List[str]				      =		DataLoader(tokenized_datasets["train"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  batch_size=2    )
      UpperCAmelCase_  :	Optional[int]				      =		DataLoader(tokenized_datasets["validation"]					,  shuffle=__snake_case					,  collate_fn=__snake_case					,  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_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase         =			{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoFormerForCausalLM''',
       '''RoFormerForMaskedLM''',
       '''RoFormerForMultipleChoice''',
       '''RoFormerForQuestionAnswering''',
       '''RoFormerForSequenceClassification''',
       '''RoFormerForTokenClassification''',
       '''RoFormerLayer''',
       '''RoFormerModel''',
       '''RoFormerPreTrainedModel''',
       '''load_tf_weights_in_roformer''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRoFormerForCausalLM''',
       '''TFRoFormerForMaskedLM''',
       '''TFRoFormerForMultipleChoice''',
       '''TFRoFormerForQuestionAnswering''',
       '''TFRoFormerForSequenceClassification''',
       '''TFRoFormerForTokenClassification''',
       '''TFRoFormerLayer''',
       '''TFRoFormerModel''',
       '''TFRoFormerPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __lowerCamelCase         =			[
       '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxRoFormerForMaskedLM''',
       '''FlaxRoFormerForMultipleChoice''',
       '''FlaxRoFormerForQuestionAnswering''',
       '''FlaxRoFormerForSequenceClassification''',
       '''FlaxRoFormerForTokenClassification''',
       '''FlaxRoFormerModel''',
       '''FlaxRoFormerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

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