






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     ,						_lowerCAmelCase=2     ,						_lowerCAmelCase=3     ,						_lowerCAmelCase=64     ,						_lowerCAmelCase=None		):



          '''simple docstring'''
          lowerCAmelCase__ :Optional[Any]   				=					np.random.default_rng(snake_case_		)
          lowerCAmelCase__ :Optional[Any]   				=					length
          lowerCAmelCase__ :Union[str, Any]   				=					rng.normal(size=(length,)		).astype(np.floataa		)
          lowerCAmelCase__ :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     ,						_lowerCAmelCase		):



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







class 						_UpperCAmelCase	(	torch.nn.Module		):




      """simple docstring"""



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



          '''simple docstring'''
          super().__init__()
          lowerCAmelCase__ :int   				=					torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
          lowerCAmelCase__ :Optional[Any]   				=					torch.nn.Parameter(torch.tensor([2, 3]		).float()		)
          lowerCAmelCase__ :List[Any]   				=					True



      def       snake_case_   ( self     ,						_lowerCAmelCase=None		):



          '''simple docstring'''

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







class 						_UpperCAmelCase	(	torch.nn.Module		):




      """simple docstring"""



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



          '''simple docstring'''
          super().__init__()
          lowerCAmelCase__ :Any   				=					torch.nn.Parameter(torch.tensor(snake_case_		).float()		)
          lowerCAmelCase__ :Dict   				=					torch.nn.Parameter(torch.tensor(snake_case_		).float()		)
          lowerCAmelCase__ :List[Any]   				=					True



      def       snake_case_   ( self     ,						_lowerCAmelCase=None		):



          '''simple docstring'''

          if self.first_batch:
              print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''		)
              lowerCAmelCase__ :Optional[int]   				=					False
          return x * self.a + self.b





def 					snake_case__				(				UpperCAmelCase				:	str     ,      UpperCAmelCase				:	str = 1_6							):
    from datasets import load_dataset
    from transformers import AutoTokenizer

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

    lowerCAmelCase__ :Any   				=					{v: i for i, v in enumerate(_UpperCamelCase							)}

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

    def collate_fn(UpperCAmelCase				:	str							):
        # 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_2_8     ,      return_tensors="pt"							)
        return tokenizer.pad(_UpperCamelCase     ,      padding="longest"     ,      return_tensors="pt"							)

    # Instantiate dataloaders.
    lowerCAmelCase__ :List[str]   				=					DataLoader(tokenized_datasets["train"]     ,      shuffle=_UpperCamelCase     ,      collate_fn=_UpperCamelCase     ,      batch_size=2							)
    lowerCAmelCase__ :Union[str, Any]   				=					DataLoader(tokenized_datasets["validation"]     ,      shuffle=_UpperCamelCase     ,      collate_fn=_UpperCamelCase     ,      batch_size=1							)

    return train_dataloader, eval_dataloader







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	a			:			Dict												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


