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

from accelerate.utils.dataclasses import DistributedType



class        lowercase_   :



							def __init__(						self				,    lowercase_=2				,    lowercase_=3				,    lowercase_=64				,    lowercase_=None    ):
													_snake_case						:					Tuple    =   np.random.default_rng(lowercase_    )
													_snake_case						:					Optional[Any]    =   length
													_snake_case						:					Dict    =   rng.normal(size=(length,)    ).astype(np.floataa    )
													_snake_case						:					Optional[int]    =   a * self.x + b + rng.normal(scale=0.1				,    size=(length,)    ).astype(np.floataa    )



							def __len__(						self    ):
													return self.length



							def __getitem__(						self				,    lowercase_    ):
													return {"x": self.x[i], "y": self.y[i]}




class        lowercase_   (					torch.nn.Module		):



							def __init__(						self				,    lowercase_=0				,    lowercase_=0				,    lowercase_=False    ):
													super().__init__()
													_snake_case						:					Optional[Any]    =   torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
													_snake_case						:					int    =   torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
													_snake_case						:					List[str]    =   True



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




class        lowercase_   (					torch.nn.Module		):



							def __init__(						self				,    lowercase_=0				,    lowercase_=0				,    lowercase_=False    ):
													super().__init__()
													_snake_case						:					Dict    =   torch.nn.Parameter(torch.tensor(lowercase_    ).float()    )
													_snake_case						:					Optional[int]    =   torch.nn.Parameter(torch.tensor(lowercase_    ).float()    )
													_snake_case						:					Union[str, Any]    =   True



							def 	UpperCamelCase	(						self				,    lowercase_=None    ):
													if self.first_batch:
																			print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""    )
																			_snake_case						:					str    =   False
													return x * self.a + self.b




def  snake_case						(__lowercase ,      __lowercase = 16						)	->	str:

						'''simple docstring'''



						from datasets import load_dataset
						from transformers import AutoTokenizer

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

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

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

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

						return train_dataloader, eval_dataloader
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		'M-CLIP'



							def __init__(						self				,    lowercase_=1_024				,    lowercase_=768				,    **lowercase_    ):
													_snake_case						:					str    =   transformerDimSize
													_snake_case						:					Union[str, Any]    =   imageDimSize
													super().__init__(**lowercase_    )




class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		MCLIPConfig



							def __init__(						self				,    lowercase_				,    *lowercase_				,    **lowercase_    ):
													super().__init__(lowercase_				,    *lowercase_				,    **lowercase_    )
													_snake_case						:					List[Any]    =   XLMRobertaModel(lowercase_    )
													_snake_case						:					int    =   torch.nn.Linear(
													    in_features=config.transformerDimensions				,    out_features=config.numDims    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_    ):
													_snake_case						:					Tuple    =   self.transformer(input_ids=lowercase_				,    attention_mask=lowercase_    )[0]
													_snake_case						:					Tuple    =   (embs * attention_mask.unsqueeze(2    )).sum(dim=1    ) / attention_mask.sum(dim=1    )[:, None]
													return self.LinearTransformation(lowercase_    ), embs