'''simple docstring'''




import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
 raise Exception('''requires fairseq >= 0.9.0''')


logging.set_verbosity_info()
snake_case  =			logging.get_logger(__name__)

snake_case  =			'''Hello world! cécé herlolip'''







def 			A_   ( _lowerCamelCase			:							str  ,  _lowerCamelCase			:							str  ,  _lowerCamelCase			:							bool    ):
      _lowerCAmelCase   ,  _lowerCAmelCase		   =   os.path.split(_lowerCamelCase    )
      _lowerCAmelCase		   =   DataaVecTextModel.from_pretrained(
          _lowerCamelCase  ,  checkpoint_file=_lowerCamelCase    )
      dataavec.eval()  # disable dropout
      _lowerCAmelCase		   =   dataavec.models[0]
      _lowerCAmelCase		   =   dataavec_model.encoder.sentence_encoder
      _lowerCAmelCase		   =   DataaVecTextConfig(
          vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings  ,  hidden_size=dataavec_model.args.encoder_embed_dim  ,  num_hidden_layers=dataavec_model.args.encoder_layers  ,  num_attention_heads=dataavec_model.args.encoder_attention_heads  ,  intermediate_size=dataavec_model.args.encoder_ffn_embed_dim  ,  max_position_embeddings=514  ,  type_vocab_size=1  ,  layer_norm_eps=1e-5  ,  )
      if classification_head:
            _lowerCAmelCase		   =   dataavec.model.classification_heads['mnli'].out_proj.weight.shape[0]
      print('Our BERT config:'  ,  _lowerCamelCase    )

      _lowerCAmelCase		   =   DataaVecTextForSequenceClassification(_lowerCamelCase    ) if classification_head else DataaVecTextForMaskedLM(_lowerCamelCase    )
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      _lowerCAmelCase		   =   dataavec_sent_encoder.embed_tokens.weight
      _lowerCAmelCase		   =   dataavec_sent_encoder.embed_positions.weight
      _lowerCAmelCase		   =   torch.zeros_like(
          model.dataavec_text.embeddings.token_type_embeddings.weight    )  # just zero them out b/c data2vec doesn't use them.
      _lowerCAmelCase		   =   dataavec_sent_encoder.layernorm_embedding.weight
      _lowerCAmelCase		   =   dataavec_sent_encoder.layernorm_embedding.bias

      for i in range(config.num_hidden_layers    ):
            # Encoder: start of layer
            _lowerCAmelCase		   =   model.dataavec_text.encoder.layer[i]
            _lowerCAmelCase		   =   dataavec_sent_encoder.layers[i]

            # self attention
            _lowerCAmelCase		   =   layer.attention.self
            assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
                (config.hidden_size, config.hidden_size)    ), (
                "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
                F" {torch.Size((config.hidden_size, config.hidden_size)    )}"
            )
            assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
                (config.hidden_size, config.hidden_size)    ), (
                "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
                F" {torch.Size((config.hidden_size, config.hidden_size)    )}"
            )
            assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
                (config.hidden_size, config.hidden_size)    ), (
                "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
                F" {torch.Size((config.hidden_size, config.hidden_size)    )}"
            )

            _lowerCAmelCase		   =   dataavec_layer.self_attn.q_proj.weight
            _lowerCAmelCase		   =   dataavec_layer.self_attn.q_proj.bias
            _lowerCAmelCase		   =   dataavec_layer.self_attn.k_proj.weight
            _lowerCAmelCase		   =   dataavec_layer.self_attn.k_proj.bias
            _lowerCAmelCase		   =   dataavec_layer.self_attn.v_proj.weight
            _lowerCAmelCase		   =   dataavec_layer.self_attn.v_proj.bias

            # self-attention output
            _lowerCAmelCase		   =   layer.attention.output
            assert (
                self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
            ), F"Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"
            _lowerCAmelCase		   =   dataavec_layer.self_attn.out_proj.weight
            _lowerCAmelCase		   =   dataavec_layer.self_attn.out_proj.bias
            _lowerCAmelCase		   =   dataavec_layer.self_attn_layer_norm.weight
            _lowerCAmelCase		   =   dataavec_layer.self_attn_layer_norm.bias

            # intermediate
            _lowerCAmelCase		   =   layer.intermediate
            assert (
                intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
            ), F"Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"
            _lowerCAmelCase		   =   dataavec_layer.fca.weight
            _lowerCAmelCase		   =   dataavec_layer.fca.bias

            # output
            _lowerCAmelCase		   =   layer.output
            assert (
                bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
            ), F"Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"
            _lowerCAmelCase		   =   dataavec_layer.fca.weight
            _lowerCAmelCase		   =   dataavec_layer.fca.bias
            _lowerCAmelCase		   =   dataavec_layer.final_layer_norm.weight
            _lowerCAmelCase		   =   dataavec_layer.final_layer_norm.bias
            # end of layer

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

      # Let's check that we get the same results.
      _lowerCAmelCase		   =   dataavec.encode(_lowerCamelCase    ).unsqueeze(0    )  # batch of size 1

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

      pathlib.Path(_lowerCamelCase    ).mkdir(parents=_lowerCamelCase  ,  exist_ok=_lowerCamelCase    )
      print(F"Saving model to {pytorch_dump_folder_path}"    )
      model.save_pretrained(_lowerCamelCase    )


if __name__ == "__main__":
 snake_case  =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--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.'''
 )
 snake_case  =			parser.parse_args()
 convert_dataavec_checkpoint_to_pytorch(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )






'''simple docstring'''




import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute







def 			A_   ( _lowerCamelCase			:							float = np.pi / 2  ,  _lowerCamelCase			:							float = np.pi / 2  ,  _lowerCamelCase			:							float = np.pi / 2    ):
      _lowerCAmelCase		   =   QuantumRegister(3  ,  'qr'    )  # Define the number of quantum bits
      _lowerCAmelCase		   =   ClassicalRegister(1  ,  'cr'    )  # Define the number of classical bits

      _lowerCAmelCase		   =   QuantumCircuit(_lowerCamelCase  ,  _lowerCamelCase    )  # Define the quantum circuit.

      # Build the circuit
      quantum_circuit.u(_lowerCamelCase  ,  _lowerCamelCase  ,  _lowerCamelCase  ,  0    )  # Quantum State to teleport
      quantum_circuit.h(1    )  # add hadamard gate
      quantum_circuit.cx(
          1  ,  2    )  # add control gate with qubit 1 as control and 2 as target.
      quantum_circuit.cx(0  ,  1    )
      quantum_circuit.h(0    )
      quantum_circuit.cz(0  ,  2    )  # add control z gate.
      quantum_circuit.cx(1  ,  2    )

      quantum_circuit.measure([2]  ,  [0]    )  # measure the qubit.

      # Simulate the circuit using qasm simulator
      _lowerCAmelCase		   =   Aer.get_backend('aer_simulator'    )
      _lowerCAmelCase		   =   execute(_lowerCamelCase  ,  _lowerCamelCase  ,  shots=1_000    )

      return job.result().get_counts(_lowerCamelCase    )


if __name__ == "__main__":
 print(
     '''Total count for teleported state is: '''
     f'''{quantum_teleportation(np.pi/2, np.pi/2, np.pi/2)}'''
 )





