






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
							raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
__UpperCamelCase					:		Optional[int]	  =						logging.get_logger(__name__)

__UpperCamelCase					:		int	  =						"""Hello world! cécé herlolip"""
def      a_ (      _A      ,   _A      ,   _A  )			->			int:







				"""simple docstring"""
				snake_case__									=     FairseqRobertaModel.from_pretrained(_A  )
				roberta.eval()  # disable dropout
				snake_case__									=     roberta.model.encoder.sentence_encoder
				snake_case__									=     XLMRobertaConfig(
				    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings      ,   hidden_size=roberta.cfg.model.encoder_embed_dim      ,   num_hidden_layers=roberta.cfg.model.encoder_layers      ,   num_attention_heads=roberta.cfg.model.encoder_attention_heads      ,   intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim      ,   max_position_embeddings=514      ,   type_vocab_size=1      ,   layer_norm_eps=1e-5      ,   )
				if classification_head:
								snake_case__									=     roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

				print('Our RoBERTa config:'      ,   _A  )

				snake_case__									=     XLMRobertaXLForSequenceClassification(_A  ) if classification_head else XLMRobertaXLForMaskedLM(_A  )
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__									=     roberta_sent_encoder.embed_tokens.weight
				snake_case__									=     roberta_sent_encoder.embed_positions.weight
				snake_case__									=     torch.zeros_like(
				    model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.

				snake_case__									=     roberta_sent_encoder.layer_norm.weight
				snake_case__									=     roberta_sent_encoder.layer_norm.bias

				for i in range(config.num_hidden_layers  ):
								# Encoder: start of layer
								snake_case__									=     model.roberta.encoder.layer[i]
								snake_case__									=     roberta_sent_encoder.layers[i]

								snake_case__									=     layer.attention
								snake_case__									=     roberta_layer.self_attn_layer_norm.weight
								snake_case__									=     roberta_layer.self_attn_layer_norm.bias

								# self attention
								snake_case__									=     layer.attention.self
								assert (
								    roberta_layer.self_attn.k_proj.weight.data.shape
								    == roberta_layer.self_attn.q_proj.weight.data.shape
								    == roberta_layer.self_attn.v_proj.weight.data.shape
								    == torch.Size((config.hidden_size, config.hidden_size)  )
								)

								snake_case__									=     roberta_layer.self_attn.q_proj.weight
								snake_case__									=     roberta_layer.self_attn.q_proj.bias
								snake_case__									=     roberta_layer.self_attn.k_proj.weight
								snake_case__									=     roberta_layer.self_attn.k_proj.bias
								snake_case__									=     roberta_layer.self_attn.v_proj.weight
								snake_case__									=     roberta_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__									=     layer.attention.output
								assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
								snake_case__									=     roberta_layer.self_attn.out_proj.weight
								snake_case__									=     roberta_layer.self_attn.out_proj.bias

								# this one is final layer norm
								snake_case__									=     roberta_layer.final_layer_norm.weight
								snake_case__									=     roberta_layer.final_layer_norm.bias

								# intermediate
								snake_case__									=     layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								snake_case__									=     roberta_layer.fca.weight
								snake_case__									=     roberta_layer.fca.bias

								# output
								snake_case__									=     layer.output
								assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
								snake_case__									=     roberta_layer.fca.weight
								snake_case__									=     roberta_layer.fca.bias
								# end of layer

				if classification_head:
								snake_case__									=     roberta.model.classification_heads['mnli'].dense.weight
								snake_case__									=     roberta.model.classification_heads['mnli'].dense.bias
								snake_case__									=     roberta.model.classification_heads['mnli'].out_proj.weight
								snake_case__									=     roberta.model.classification_heads['mnli'].out_proj.bias
				else:
								# LM Head
								snake_case__									=     roberta.model.encoder.lm_head.dense.weight
								snake_case__									=     roberta.model.encoder.lm_head.dense.bias
								snake_case__									=     roberta.model.encoder.lm_head.layer_norm.weight
								snake_case__									=     roberta.model.encoder.lm_head.layer_norm.bias
								snake_case__									=     roberta.model.encoder.lm_head.weight
								snake_case__									=     roberta.model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__									=     roberta.encode(_A  ).unsqueeze(0  )  # batch of size 1

				snake_case__									=     model(_A  )[0]
				if classification_head:
								snake_case__									=     roberta.model.classification_heads['mnli'](roberta.extract_features(_A  )  )
				else:
								snake_case__									=     roberta.model(_A  )[0]
				print(our_output.shape      ,   their_output.shape  )
				snake_case__									=     torch.max(torch.abs(our_output - their_output  )  ).item()
				print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
				snake_case__									=     torch.allclose(_A      ,   _A      ,   atol=1e-3  )
				print('Do both models output the same tensors?'      ,   '🔥' if success else '💩'  )
				if not success:
								raise Exception('Something went wRoNg'  )

				pathlib.Path(_A  ).mkdir(parents=_A      ,   exist_ok=_A  )
				print(f'''Saving model to {pytorch_dump_folder_path}'''  )
				model.save_pretrained(_A  )


if __name__ == "__main__":
							__UpperCamelCase					:		int	  =						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
							)
							__UpperCamelCase					:		int	  =						parser.parse_args()
							convert_xlm_roberta_xl_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)












import qiskit
def      a_ (      _A = 2  )			->			qiskit.result.counts.Counts:







				"""simple docstring"""
				snake_case__									=     qubits

				# Using Aer's simulator
				snake_case__									=     qiskit.Aer.get_backend('aer_simulator'  )

				# Creating a Quantum Circuit acting on the q register
				snake_case__									=     qiskit.QuantumCircuit(_A      ,   _A  )

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

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

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

				# 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
				snake_case__									=     qiskit.execute(_A      ,   _A      ,   shots=1000  )

				return job.result().get_counts(_A  )


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




