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

from accelerate.utils.dataclasses import DistributedType





class     lowerCamelCase      :







	def __init__(     self      :Optional[int]   ,     lowercase      :str=2   ,     lowercase      :Optional[Any]=3   ,     lowercase      :Optional[int]=6_4   ,     lowercase      :Tuple=None			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= np.random.default_rng(lowercase			)
				SCREAMING_SNAKE_CASE     						= length
				SCREAMING_SNAKE_CASE     						= rng.normal(size=(length,)			).astype(np.floataa			)
				SCREAMING_SNAKE_CASE     						= a * self.x + b + rng.normal(scale=0.1   ,     size=(length,)			).astype(np.floataa			)







	def __len__(     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				return self.length







	def __getitem__(     self      :int   ,     lowercase      :Optional[int]			)		->      List[str]:







				"""simple docstring"""






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





class     lowerCamelCase      (       torch.nn.Module					):







	def __init__(     self      :Dict   ,     lowercase      :int=0   ,     lowercase      :Optional[Any]=0   ,     lowercase      :Union[str, Any]=False			)		->      Tuple:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE     						= torch.nn.Parameter(torch.tensor([2, 3]			).float()			)
				SCREAMING_SNAKE_CASE     						= torch.nn.Parameter(torch.tensor([2, 3]			).float()			)
				SCREAMING_SNAKE_CASE     						= True







	def  snake_case__     (     self      :Optional[int]   ,     lowercase      :Optional[Any]=None			)		->      Tuple:







				"""simple docstring"""






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





class     lowerCamelCase      (       torch.nn.Module					):







	def __init__(     self      :List[Any]   ,     lowercase      :Dict=0   ,     lowercase      :Tuple=0   ,     lowercase      :Tuple=False			)		->      List[Any]:







				"""simple docstring"""






				super().__init__()
				SCREAMING_SNAKE_CASE     						= torch.nn.Parameter(torch.tensor(lowercase			).float()			)
				SCREAMING_SNAKE_CASE     						= torch.nn.Parameter(torch.tensor(lowercase			).float()			)
				SCREAMING_SNAKE_CASE     						= True







	def  snake_case__     (     self      :int   ,     lowercase      :int=None			)		->      Optional[int]:







				"""simple docstring"""






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



def    a      ( a		,						a = 16	)     ->str:




			'''simple docstring'''


			from datasets import load_dataset
			from transformers import AutoTokenizer

			SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained('''bert-base-cased'''	)
			SCREAMING_SNAKE_CASE     						= {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
			SCREAMING_SNAKE_CASE     						= load_dataset('''csv'''		,						data_files=a	)
			SCREAMING_SNAKE_CASE     						= datasets['''train'''].unique('''label'''	)

			SCREAMING_SNAKE_CASE     						= {v: i for i, v in enumerate(a	)}

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

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

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE     						= DataLoader(tokenized_datasets['''train''']		,						shuffle=a		,						collate_fn=a		,						batch_size=2	)
			SCREAMING_SNAKE_CASE     						= DataLoader(tokenized_datasets['''validation''']		,						shuffle=a		,						collate_fn=a		,						batch_size=1	)

			return train_dataloader, eval_dataloader

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase					   =       logging.get_logger(__name__)

__lowerCAmelCase					   =       {
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}





class     lowerCamelCase      (       __lowerCamelCase					):
	UpperCamelCase_  :					Union[str, Any]    		=	'biogpt'







	def __init__(     self      :Tuple   ,     lowercase      :Union[str, Any]=4_2_3_8_4   ,     lowercase      :str=1_0_2_4   ,     lowercase      :Any=2_4   ,     lowercase      :List[str]=1_6   ,     lowercase      :List[str]=4_0_9_6   ,     lowercase      :int="gelu"   ,     lowercase      :List[Any]=0.1   ,     lowercase      :int=0.1   ,     lowercase      :int=1_0_2_4   ,     lowercase      :Optional[Any]=0.02   ,     lowercase      :Union[str, Any]=1e-12   ,     lowercase      :Tuple=True   ,     lowercase      :int=True   ,     lowercase      :Optional[int]=0.0   ,     lowercase      :Any=0.0   ,     lowercase      :str=1   ,     lowercase      :Any=0   ,     lowercase      :int=2   ,     **lowercase      :Tuple   ,     )		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= vocab_size
				SCREAMING_SNAKE_CASE     						= max_position_embeddings
				SCREAMING_SNAKE_CASE     						= hidden_size
				SCREAMING_SNAKE_CASE     						= num_hidden_layers
				SCREAMING_SNAKE_CASE     						= num_attention_heads
				SCREAMING_SNAKE_CASE     						= intermediate_size
				SCREAMING_SNAKE_CASE     						= hidden_act
				SCREAMING_SNAKE_CASE     						= hidden_dropout_prob
				SCREAMING_SNAKE_CASE     						= attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE     						= initializer_range
				SCREAMING_SNAKE_CASE     						= layer_norm_eps
				SCREAMING_SNAKE_CASE     						= scale_embedding
				SCREAMING_SNAKE_CASE     						= use_cache
				SCREAMING_SNAKE_CASE     						= layerdrop
				SCREAMING_SNAKE_CASE     						= activation_dropout
				super().__init__(pad_token_id=lowercase   ,     bos_token_id=lowercase   ,     eos_token_id=lowercase   ,     **lowercase			)