

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()
a_      =     logging.get_logger(__name__)

a_      =     'Hello world! cécé herlolip'

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							FairseqRobertaModel.from_pretrained(_a)
	roberta.eval()  # disable dropout
	SCREAMING_SNAKE_CASE :   int					  =							roberta.model.encoder.sentence_encoder
	SCREAMING_SNAKE_CASE :   List[str]					  =							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:
		SCREAMING_SNAKE_CASE :   List[str]					  =							roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("Our RoBERTa config:"				,      _a)

	SCREAMING_SNAKE_CASE :   Any					  =							XLMRobertaXLForSequenceClassification(_a) if classification_head else XLMRobertaXLForMaskedLM(_a)
	model.eval()

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

	SCREAMING_SNAKE_CASE :   Any					  =							roberta_sent_encoder.layer_norm.weight
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							roberta_sent_encoder.layer_norm.bias

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

		SCREAMING_SNAKE_CASE :   RobertaAttention					  =							layer.attention
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta_layer.self_attn_layer_norm.weight
		SCREAMING_SNAKE_CASE :   str					  =							roberta_layer.self_attn_layer_norm.bias

		# self attention
		SCREAMING_SNAKE_CASE :   BertSelfAttention					  =							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))
		)

		SCREAMING_SNAKE_CASE :   Tuple					  =							roberta_layer.self_attn.q_proj.weight
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta_layer.self_attn.q_proj.bias
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta_layer.self_attn.k_proj.weight
		SCREAMING_SNAKE_CASE :   str					  =							roberta_layer.self_attn.k_proj.bias
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							roberta_layer.self_attn.v_proj.weight
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							roberta_layer.self_attn.v_proj.bias

		# self-attention output
		SCREAMING_SNAKE_CASE :   BertSelfOutput					  =							layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta_layer.self_attn.out_proj.weight
		SCREAMING_SNAKE_CASE :   List[Any]					  =							roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta_layer.final_layer_norm.weight
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta_layer.final_layer_norm.bias

		# intermediate
		SCREAMING_SNAKE_CASE :   BertIntermediate					  =							layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta_layer.fca.weight
		SCREAMING_SNAKE_CASE :   str					  =							roberta_layer.fca.bias

		# output
		SCREAMING_SNAKE_CASE :   BertOutput					  =							layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		SCREAMING_SNAKE_CASE :   Tuple					  =							roberta_layer.fca.weight
		SCREAMING_SNAKE_CASE :   int					  =							roberta_layer.fca.bias
		# end of layer

	if classification_head:
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							roberta.model.classification_heads["mnli"].dense.weight
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							roberta.model.classification_heads["mnli"].dense.bias
		SCREAMING_SNAKE_CASE :   List[Any]					  =							roberta.model.classification_heads["mnli"].out_proj.weight
		SCREAMING_SNAKE_CASE :   Tuple					  =							roberta.model.classification_heads["mnli"].out_proj.bias
	else:
		# LM Head
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							roberta.model.encoder.lm_head.dense.weight
		SCREAMING_SNAKE_CASE :   int					  =							roberta.model.encoder.lm_head.dense.bias
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta.model.encoder.lm_head.layer_norm.weight
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							roberta.model.encoder.lm_head.layer_norm.bias
		SCREAMING_SNAKE_CASE :   Dict					  =							roberta.model.encoder.lm_head.weight
		SCREAMING_SNAKE_CASE :   Tuple					  =							roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	SCREAMING_SNAKE_CASE :   torch.Tensor					  =							roberta.encode(_a).unsqueeze(0)  # batch of size 1

	SCREAMING_SNAKE_CASE :   List[str]					  =							model(_a)[0]
	if classification_head:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta.model.classification_heads["mnli"](roberta.extract_features(_a))
	else:
		SCREAMING_SNAKE_CASE :   Optional[int]					  =							roberta.model(_a)[0]
	print(our_output.shape				,      their_output.shape)
	SCREAMING_SNAKE_CASE :   str					  =							torch.max(torch.abs(our_output - their_output)).item()
	print(f"max_absolute_diff = {max_absolute_diff}")  # ~ 1e-7
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							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__":
			a_      =     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.'
			)
			a_      =     parser.parse_args()
			convert_xlm_roberta_xl_checkpoint_to_pytorch(
			    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
			)
'''simple docstring'''







import numpy as np
import qiskit


def    lowerCamelCase       (			UpperCAmelCase__				:  int = 8				,						UpperCAmelCase__				:  int | None = None		)			->	str:


							'''simple docstring'''






							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = np.random.default_rng(seed=UpperCAmelCase__		)

							# Roughly 25% of the qubits will contribute to the key.
							# So we take more than we need.
							SCREAMING_SNAKE_CASE__     :Optional[int]						    = 6 * key_len
							# Measurement basis for Alice's qubits.
							SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = rng.integers(2				,						size=UpperCAmelCase__		)
							# The set of states Alice will prepare.
							SCREAMING_SNAKE_CASE__     :List[Any]						    = rng.integers(2				,						size=UpperCAmelCase__		)
							# Measurement basis for Bob's qubits.
							SCREAMING_SNAKE_CASE__     :str						    = rng.integers(2				,						size=UpperCAmelCase__		)

							# Quantum Circuit to simulate BB84
							SCREAMING_SNAKE_CASE__     :int						    = qiskit.QuantumCircuit(UpperCAmelCase__				,						name='BB84'		)

							# Alice prepares her qubits according to rules above.
							for index, _ in enumerate(UpperCAmelCase__		):
														if alice_state[index] == 1:
																					bbaa_circ.x(UpperCAmelCase__		)
														if alice_basis[index] == 1:
																					bbaa_circ.h(UpperCAmelCase__		)
							bbaa_circ.barrier()

							# Bob measures the received qubits according to rules above.
							for index, _ in enumerate(UpperCAmelCase__		):
														if bob_basis[index] == 1:
																					bbaa_circ.h(UpperCAmelCase__		)

							bbaa_circ.barrier()
							bbaa_circ.measure_all()

							# Simulate the quantum circuit.
							SCREAMING_SNAKE_CASE__     :str						    = qiskit.Aer.get_backend('aer_simulator'		)
							# We only need to run one shot because the key is unique.
							# Multiple shots will produce the same key.
							SCREAMING_SNAKE_CASE__     :int						    = qiskit.execute(UpperCAmelCase__				,						UpperCAmelCase__				,						shots=1				,						seed_simulator=UpperCAmelCase__		)
							# Returns the result of measurement.
							SCREAMING_SNAKE_CASE__     :List[Any]						    = job.result().get_counts(UpperCAmelCase__		).most_frequent()

							# Extracting the generated key from the simulation results.
							# Only keep measurement results where Alice and Bob chose the same basis.
							SCREAMING_SNAKE_CASE__     :Any						    = ''.join(
							    [
							        result_bit
							        for alice_basis_bit, bob_basis_bit, result_bit in zip(
							            UpperCAmelCase__				,						UpperCAmelCase__				,						UpperCAmelCase__		)
							        if alice_basis_bit == bob_basis_bit
							    ]		)

							# Get final key. Pad with 0 if too short, otherwise truncate.
							SCREAMING_SNAKE_CASE__     :Optional[Any]						    = gen_key[:key_len] if len(UpperCAmelCase__		) >= key_len else gen_key.ljust(UpperCAmelCase__				,						'0'		)
							return key


if __name__ == "__main__":
		print(f"The generated key is : {bbaa(8, seed=0)}")
		from doctest import testmod

		testmod()






