



'''simple docstring'''





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

from accelerate.utils.dataclasses import DistributedType






class 						__a :



	def __init__(						self							:    List[str]				,	snake_case_							:    Optional[Any]=2				,	snake_case_							:    str=3				,	snake_case_							:    Union[str, Any]=64				,	snake_case_							:    Dict=None)->							str:
						__lowerCAmelCase	   =np.random.default_rng(snake_case_)
						__lowerCAmelCase	   =length
						__lowerCAmelCase	   =rng.normal(size=(length,)).astype(np.floataa)
						__lowerCAmelCase	   =a * self.x + b + rng.normal(scale=0.1				,	size=(length,)).astype(np.floataa)



	def __len__(						self							:    Tuple)->							Dict:
						return self.length



	def __getitem__(						self							:    List[str]				,	snake_case_							:    Any)->							Union[str, Any]:
						return {"x": self.x[i], "y": self.y[i]}






class 						__a (     torch.nn.Module				):



	def __init__(						self							:    List[str]				,	snake_case_							:    Dict=0				,	snake_case_							:    Any=0				,	snake_case_							:    Any=False)->							int:
						super().__init__()
						__lowerCAmelCase	   =torch.nn.Parameter(torch.tensor([2, 3]).float())
						__lowerCAmelCase	   =torch.nn.Parameter(torch.tensor([2, 3]).float())
						__lowerCAmelCase	   =True



	def 		UpperCamelCase							(						self							:    Any				,	snake_case_							:    str=None)->							Union[str, Any]:
						if self.first_batch:
											print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
											__lowerCAmelCase	   =False
						return x * self.a[0] + self.b[0]






class 						__a (     torch.nn.Module				):



	def __init__(						self							:    str				,	snake_case_							:    Optional[Any]=0				,	snake_case_							:    List[str]=0				,	snake_case_							:    int=False)->							Optional[Any]:
						super().__init__()
						__lowerCAmelCase	   =torch.nn.Parameter(torch.tensor(snake_case_).float())
						__lowerCAmelCase	   =torch.nn.Parameter(torch.tensor(snake_case_).float())
						__lowerCAmelCase	   =True



	def 		UpperCamelCase							(						self							:    str				,	snake_case_							:    List[str]=None)->							Union[str, Any]:
						if self.first_batch:
											print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""")
											__lowerCAmelCase	   =False
						return x * self.a + self.b






def 					__lowerCAmelCase		(  __lowerCamelCase	:    Union[str, Any]				,   __lowerCamelCase	:    int = 16	)  ->						List[Any]:
					from datasets import load_dataset
					from transformers import AutoTokenizer

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

					__lowerCAmelCase	   ={v: i for i, v in enumerate(__lowerCamelCase	)}

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

					def collate_fn(__lowerCamelCase	:    Union[str, Any]	):
										# 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.
					__lowerCAmelCase	   =DataLoader(tokenized_datasets["""train"""]				,   shuffle=__lowerCamelCase				,   collate_fn=__lowerCamelCase				,   batch_size=2	)
					__lowerCAmelCase	   =DataLoader(tokenized_datasets["""validation"""]				,   shuffle=__lowerCamelCase				,   collate_fn=__lowerCamelCase				,   batch_size=1	)

					return train_dataloader, eval_dataloader









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_	       =			{
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase_	       =			[
				    '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''Blip2Model''',
				    '''Blip2QFormerModel''',
				    '''Blip2PreTrainedModel''',
				    '''Blip2ForConditionalGeneration''',
				    '''Blip2VisionModel''',
				]

if TYPE_CHECKING:
				from .configuration_blip_a import (
				    BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BlipaConfig,
				    BlipaQFormerConfig,
				    BlipaVisionConfig,
				)
				from .processing_blip_a import BlipaProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_blip_a import (
								    BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BlipaForConditionalGeneration,
								    BlipaModel,
								    BlipaPreTrainedModel,
								    BlipaQFormerModel,
								    BlipaVisionModel,
								)

else:
				import sys

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




