




'''simple docstring'''

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

from accelerate.utils.dataclasses import DistributedType



class 		SCREAMING_SNAKE_CASE__  :





			def __init__(      self						:    List[Any]      ,		a_						:    Optional[Any]=2      ,		a_						:    Tuple=3      ,		a_						:    Any=64      ,		a_						:    str=None							):



						"""simple docstring"""




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





			def __len__(      self						:    Optional[Any]							):



						"""simple docstring"""




						return self.length






			def __getitem__(      self						:    Optional[Any]      ,		a_						:    List[str]							):



						"""simple docstring"""




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







class 		SCREAMING_SNAKE_CASE__  (			torch.nn.Module	):





			def __init__(      self						:    Any      ,		a_						:    List[str]=0      ,		a_						:    Union[str, Any]=0      ,		a_						:    Tuple=False							):



						"""simple docstring"""




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






			def 							A (      self						:    List[str]      ,		a_						:    int=None							):



						"""simple docstring"""




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







class 		SCREAMING_SNAKE_CASE__  (			torch.nn.Module	):





			def __init__(      self						:    Dict      ,		a_						:    Dict=0      ,		a_						:    Any=0      ,		a_						:    Optional[Any]=False							):



						"""simple docstring"""




						super().__init__()
						__snake_case			=		torch.nn.Parameter(torch.tensor(a_							).float()							)
						__snake_case			=		torch.nn.Parameter(torch.tensor(a_							).float()							)
						__snake_case			=		True






			def 							A (      self						:    Dict      ,		a_						:    Any=None							):



						"""simple docstring"""




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







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

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

			__snake_case			=		{v: i for i, v in enumerate(_UpperCAmelCase						)}

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

			def collate_fn(_UpperCAmelCase		:   Optional[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(_UpperCAmelCase		,				padding="max_length"		,				max_length=1_28		,				return_tensors="pt"						)
						return tokenizer.pad(_UpperCAmelCase		,				padding="longest"		,				return_tensors="pt"						)

			# Instantiate dataloaders.
			__snake_case			=		DataLoader(tokenized_datasets["train"]		,				shuffle=_UpperCAmelCase		,				collate_fn=_UpperCAmelCase		,				batch_size=2						)
			__snake_case			=		DataLoader(tokenized_datasets["validation"]		,				shuffle=_UpperCAmelCase		,				collate_fn=_UpperCAmelCase		,				batch_size=1						)

			return train_dataloader, eval_dataloader









import qiskit
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      a		:Tuple									=			qiskit.Aer.get_backend('''aer_simulator'''		)

      # Create a Quantum Circuit acting on the q register
      a		:str									=			qiskit.QuantumCircuit(UpperCAmelCase_					,    UpperCAmelCase_		)

      # Apply X (NOT) Gate to Qubits 0 & 1
      circuit.x(0		)
      circuit.x(1		)

      # Map the quantum measurement to the classical bits
      circuit.measure([0, 1]					,    [0, 1]		)

      # Execute the circuit on the qasm simulator
      a		:Optional[Any]									=			qiskit.execute(UpperCAmelCase_					,    UpperCAmelCase_					,    shots=1000		)

      # Return the histogram data of the results of the experiment.
      return job.result().get_counts(UpperCAmelCase_		)


if __name__ == "__main__":
     snake_case							:       Any										=	single_qubit_measure(2, 2)
     print(F"""Total count for various states are: {counts}""")





