


'''simple docstring'''
from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 		UpperCamelCase_		(       A ,		A ,		A ):





						"""simple docstring"""




						UpperCAmelCase__						:							Optional[Any]       =  [r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']
						@register_to_config
						def __init__(			self					:       Union[str, Any]						,					_lowerCamelCase					:       Optional[int]						,					_lowerCamelCase					:       Optional[Any]						,					_lowerCamelCase					:       Dict = None						,					_lowerCamelCase					:       int = 5_02_57						,					_lowerCamelCase					:       List[Any] = 10_24						,					_lowerCamelCase					:       Union[str, Any] = 7_68						,					_lowerCamelCase					:       Optional[Any] = 12						,					_lowerCamelCase					:       int = 12						,					_lowerCamelCase					:       Optional[Any] = None						,					_lowerCamelCase					:       int = "gelu_new"						,					_lowerCamelCase					:       str = 0.1						,					_lowerCamelCase					:       List[str] = 0.1						,					_lowerCamelCase					:       List[Any] = 0.1						,					_lowerCamelCase					:       Optional[int] = 1e-5						,					_lowerCamelCase					:       Union[str, Any] = 0.02						,					_lowerCamelCase					:       Dict = True						,					_lowerCamelCase					:       Optional[int] = True						,					_lowerCamelCase					:       Optional[int] = False						,					_lowerCamelCase					:       int = False						,					)				->       int:
										super().__init__()

										__magic_name__							=			prefix_length

										if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
														raise ValueError(
														    f'`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'
														    f' `n_embd`: {n_embd} are not equal.'	)

										__magic_name__							=			prefix_inner_dim
										__magic_name__							=			prefix_hidden_dim

										__magic_name__							=			(
										    nn.Linear(self.prefix_inner_dim						,					self.prefix_hidden_dim	)
										    if self.prefix_hidden_dim is not None
										    else nn.Identity()
										)
										__magic_name__							=			(
										    nn.Linear(self.prefix_hidden_dim						,					_lowerCamelCase	) if self.prefix_hidden_dim is not None else nn.Identity()
										)

										__magic_name__							=			GPTaConfig(
										    vocab_size=_lowerCamelCase						,					n_positions=_lowerCamelCase						,					n_embd=_lowerCamelCase						,					n_layer=_lowerCamelCase						,					n_head=_lowerCamelCase						,					n_inner=_lowerCamelCase						,					activation_function=_lowerCamelCase						,					resid_pdrop=_lowerCamelCase						,					embd_pdrop=_lowerCamelCase						,					attn_pdrop=_lowerCamelCase						,					layer_norm_epsilon=_lowerCamelCase						,					initializer_range=_lowerCamelCase						,					scale_attn_weights=_lowerCamelCase						,					use_cache=_lowerCamelCase						,					scale_attn_by_inverse_layer_idx=_lowerCamelCase						,					reorder_and_upcast_attn=_lowerCamelCase						,					)
										__magic_name__							=			GPTaLMHeadModel(_lowerCamelCase	)
						def        __A       (			self					:       str						,					_lowerCamelCase					:       List[Any]						,					_lowerCamelCase					:       str						,					_lowerCamelCase					:       Tuple = None						,					_lowerCamelCase					:       Optional[Any] = None						,					)				->       List[str]:
										__magic_name__							=			self.transformer.transformer.wte(_lowerCamelCase	)
										__magic_name__							=			self.encode_prefix(_lowerCamelCase	)
										__magic_name__							=			self.decode_prefix(_lowerCamelCase	)
										__magic_name__							=			torch.cat((prefix_embeds, embedding_text)						,					dim=1	)

										if labels is not None:
														__magic_name__							=			self.get_dummy_token(input_ids.shape[0]						,					input_ids.device	)
														__magic_name__							=			torch.cat((dummy_token, input_ids)						,					dim=1	)
										__magic_name__							=			self.transformer(inputs_embeds=_lowerCamelCase						,					labels=_lowerCamelCase						,					attention_mask=_lowerCamelCase	)
										if self.prefix_hidden_dim is not None:
														return out, hidden
										else:
														return out
						def        __A       (			self					:       List[str]						,					_lowerCamelCase					:       Optional[Any]						,					_lowerCamelCase					:       Any	)				->       Union[str, Any]:
										return torch.zeros(_lowerCamelCase						,					self.prefix_length						,					dtype=torch.intaa						,					device=_lowerCamelCase	)
						def        __A       (			self					:       str						,					_lowerCamelCase					:       Tuple	)				->       List[Any]:
										return self.encode_prefix(_lowerCamelCase	)
						@torch.no_grad()
						def        __A       (			self					:       int						,					_lowerCamelCase					:       List[str]						,					_lowerCamelCase					:       Dict						,					_lowerCamelCase					:       Union[str, Any]	)				->       Tuple:
										__magic_name__							=			torch.split(_lowerCamelCase						,					1						,					dim=0	)
										__magic_name__							=			[]
										__magic_name__							=			[]
										for feature in features:
														__magic_name__							=			self.decode_prefix(feature.to(_lowerCamelCase	)	)  # back to the clip feature
														# Only support beam search for now
														__magic_name__							=			self.generate_beam(
														    input_embeds=_lowerCamelCase						,					device=_lowerCamelCase						,					eos_token_id=_lowerCamelCase	)
														generated_tokens.append(output_tokens[0]	)
														generated_seq_lengths.append(seq_lengths[0]	)
										__magic_name__							=			torch.stack(_lowerCamelCase	)
										__magic_name__							=			torch.stack(_lowerCamelCase	)
										return generated_tokens, generated_seq_lengths

						@torch.no_grad()
						def        __A       (			self					:       Union[str, Any]						,					_lowerCamelCase					:       Any=None						,					_lowerCamelCase					:       List[Any]=None						,					_lowerCamelCase					:       List[str]=None						,					_lowerCamelCase					:       List[Any] = 5						,					_lowerCamelCase					:       Any = 67						,					_lowerCamelCase					:       Tuple = 1.0						,					_lowerCamelCase					:       int = None						,					)				->       List[str]:
										__magic_name__							=			eos_token_id
										__magic_name__							=			None
										__magic_name__							=			None
										__magic_name__							=			torch.ones(_lowerCamelCase						,					device=_lowerCamelCase						,					dtype=torch.int	)
										__magic_name__							=			torch.zeros(_lowerCamelCase						,					device=_lowerCamelCase						,					dtype=torch.bool	)

										if input_embeds is not None:
														__magic_name__							=			input_embeds
										else:
														__magic_name__							=			self.transformer.transformer.wte(_lowerCamelCase	)

										for i in range(_lowerCamelCase	):
														__magic_name__							=			self.transformer(inputs_embeds=_lowerCamelCase	)
														__magic_name__							=			outputs.logits
														__magic_name__							=			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
														__magic_name__							=			logits.softmax(-1	).log()

														if scores is None:
																		__magic_name__							=			logits.topk(_lowerCamelCase						,					-1	)
																		__magic_name__							=			generated.expand(_lowerCamelCase						,					*generated.shape[1:]	)
																		__magic_name__							=			next_tokens.permute(1						,					0	), scores.squeeze(0	)
																		if tokens is None:
																						__magic_name__							=			next_tokens
																		else:
																						__magic_name__							=			tokens.expand(_lowerCamelCase						,					*tokens.shape[1:]	)
																						__magic_name__							=			torch.cat((tokens, next_tokens)						,					dim=1	)
														else:
																		__magic_name__							=			-float(np.inf	)
																		__magic_name__							=			0
																		__magic_name__							=			scores[:, None] + logits
																		seq_lengths[~is_stopped] += 1
																		__magic_name__							=			scores_sum / seq_lengths[:, None]
																		__magic_name__							=			scores_sum_average.view(-1	).topk(_lowerCamelCase						,					-1	)
																		__magic_name__							=			next_tokens // scores_sum.shape[1]
																		__magic_name__							=			seq_lengths[next_tokens_source]
																		__magic_name__							=			next_tokens % scores_sum.shape[1]
																		__magic_name__							=			next_tokens.unsqueeze(1	)
																		__magic_name__							=			tokens[next_tokens_source]
																		__magic_name__							=			torch.cat((tokens, next_tokens)						,					dim=1	)
																		__magic_name__							=			generated[next_tokens_source]
																		__magic_name__							=			scores_sum_average * seq_lengths
																		__magic_name__							=			is_stopped[next_tokens_source]

														__magic_name__							=			self.transformer.transformer.wte(next_tokens.squeeze()	).view(generated.shape[0]						,					1						,					-1	)
														__magic_name__							=			torch.cat((generated, next_token_embed)						,					dim=1	)
														__magic_name__							=			is_stopped + next_tokens.eq(_lowerCamelCase	).squeeze()
														if is_stopped.all():
																		break

										__magic_name__							=			scores / seq_lengths
										__magic_name__							=			scores.argsort(descending=_lowerCamelCase	)
										# tokens tensors are already padded to max_seq_length
										__magic_name__							=			[tokens[i] for i in order]
										__magic_name__							=			torch.stack(_lowerCamelCase						,					dim=0	)
										__magic_name__							=			torch.tensor([seq_lengths[i] for i in order]						,					dtype=seq_lengths.dtype	)
										return output_texts, seq_lengths









"""simple docstring"""






import qiskit







def 	_snake_case     (							lowercase__ = 2						):
				_lowerCamelCase	:     Optional[Any]       =       qubits

				# Using Aer's simulator
				_lowerCamelCase	:     Dict       =       qiskit.Aer.get_backend('aer_simulator'						)

				# Creating a Quantum Circuit acting on the q register
				_lowerCamelCase	:     Dict       =       qiskit.QuantumCircuit(lowercase__				,       lowercase__						)

				# Adding a H gate on qubit 0 (now q0 in superposition)
				circuit.h(0						)

				for i in range(1				,       lowercase__						):
								# Adding CX (CNOT) gate
								circuit.cx(i - 1				,       lowercase__						)

				# Mapping the quantum measurement to the classical bits
				circuit.measure(list(range(lowercase__						)						)				,       list(range(lowercase__						)						)						)

				# Now measuring any one qubit would affect other qubits to collapse
				# their super position and have same state as the measured one.

				# Executing the circuit on the simulator
				_lowerCamelCase	:     Optional[Any]       =       qiskit.execute(lowercase__				,       lowercase__				,       shots=1000						)

				return job.result().get_counts(lowercase__						)


if __name__ == "__main__":
			print(F"Total count for various states are: {quantum_entanglement(3)}")