


'''simple docstring'''






import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)






def 	_A				(lowerCAmelCase__  :Optional[int]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							checkpoints.load_tax_checkpoint(lowerCAmelCase__		)
     _a			      =							flatten_dict(lowerCAmelCase__		)
     return flax_params






def 	_A				(lowerCAmelCase__  :int		)   ->  Optional[int]:




     '''simple docstring'''



     _a			      =							{}

     _a			      =							{
         'token_embedder': 'embeddings',
         'encoder_norm': 'layernorm',
         'kernel': 'weight',
         '.out': '.output',
         'scale': 'weight',
         'embedders_0.pos_embedding': 'row_embedder.weight',
         'embedders_1.pos_embedding': 'column_embedder.weight',
     }

     _a			      =							{
         'query': 'attention.query',
         'key': 'attention.key',
         'value': 'attention.value',
         'output.dense': 'output',
         'encoder_decoder_attention.o': 'encoder_decoder_attention.attention.o',
         'pre_self_attention_layer_norm': 'self_attention.layer_norm',
         'pre_cross_attention_layer_norm': 'encoder_decoder_attention.layer_norm',
         'mlp.': 'mlp.DenseReluDense.',
         'pre_mlp_layer_norm': 'mlp.layer_norm',
         'self_attention.o': 'self_attention.attention.o',
         'decoder.embeddings.embedding': 'decoder.embed_tokens.weight',
         'decoder.relpos_bias.rel_embedding': 'decoder.layer.0.self_attention.attention.relative_attention_bias.weight',
         'decoder.decoder_norm.weight': 'decoder.final_layer_norm.weight',
         'decoder.logits_dense.weight': 'decoder.lm_head.weight',
     }

     for key in flax_dict.keys():
          if "target" in key:
               # remove the first prefix from the key
               _a			      =							'.'.join(key[1:]		)

               # rename the key
               for old, new in CONVERSION_MAPPING.items():
                    _a			      =							new_key.replace(lowerCAmelCase__				,						lowerCAmelCase__		)

               if "decoder" in new_key:
                    for old, new in DECODER_CONVERSION_MAPPING.items():
                         _a			      =							new_key.replace(lowerCAmelCase__				,						lowerCAmelCase__		)

               if "layers" in new_key and "decoder" not in new_key:
                    # use regex to replace the layer number
                    _a			      =							re.sub(r'layers_(\d+)'				,						r'layer.\1'				,						lowerCAmelCase__		)
                    _a			      =							new_key.replace('encoder'				,						'encoder.encoder'		)

               elif "layers" in new_key and "decoder" in new_key:
                    # use regex to replace the layer number
                    _a			      =							re.sub(r'layers_(\d+)'				,						r'layer.\1'				,						lowerCAmelCase__		)

               _a			      =							flax_dict[key]

     _a			      =							{}
     # convert converted_dict into torch format
     for key in converted_dict.keys():
          if ("embed_tokens" not in key) and ("embedder" not in key):
               _a			      =							torch.from_numpy(converted_dict[key].T		)
          else:
               _a			      =							torch.from_numpy(converted_dict[key]		)

     return converted_torch_dict






def 	_A				(lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :str				,						lowerCAmelCase__  :int=False				,						lowerCAmelCase__  :List[Any]=False		)   ->  Dict:




     '''simple docstring'''



     _a			      =							get_flax_param(lowerCAmelCase__		)

     if not use_large:
          _a			      =							PixaStructVisionConfig()
          _a			      =							PixaStructTextConfig()
     else:
          _a			      =							PixaStructVisionConfig(
              hidden_size=15_36				,						d_ff=39_68				,						num_attention_heads=24				,						num_hidden_layers=18		)
          _a			      =							PixaStructTextConfig(hidden_size=15_36				,						d_ff=39_68				,						num_heads=24				,						num_layers=18		)
     _a			      =							PixaStructConfig(
         vision_config=encoder_config.to_dict()				,						text_config=decoder_config.to_dict()				,						is_vqa=lowerCAmelCase__		)

     _a			      =							PixaStructForConditionalGeneration(lowerCAmelCase__		)

     _a			      =							rename_and_convert_flax_params(lowerCAmelCase__		)
     model.load_state_dict(lowerCAmelCase__		)

     _a			      =							AutoTokenizer.from_pretrained('ybelkada/test-pix2struct-tokenizer'		)
     _a			      =							PixaStructImageProcessor()
     _a			      =							PixaStructProcessor(image_processor=lowerCAmelCase__				,						tokenizer=lowerCAmelCase__		)

     if use_large:
          _a			      =							40_96

     _a			      =							True

     # mkdir if needed
     os.makedirs(lowerCAmelCase__				,						exist_ok=lowerCAmelCase__		)

     model.save_pretrained(lowerCAmelCase__		)
     processor.save_pretrained(lowerCAmelCase__		)

     print('Model saved in {}'.format(lowerCAmelCase__		)		)


if __name__ == "__main__":
 a_					:    Any          =    argparse.ArgumentParser()
 parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--use_large", action="store_true", help="Use large model.")
 parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
 a_					:    str          =    parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )





'''simple docstring'''






import numpy as np
import qiskit






def 	_A				(lowerCAmelCase__  :int = 8				,						lowerCAmelCase__  :int | None = None		)   ->  str:




     '''simple docstring'''



     _a			      =							np.random.default_rng(seed=lowerCAmelCase__		)

     # Roughly 25% of the qubits will contribute to the key.
     # So we take more than we need.
     _a			      =							6 * key_len
     # Measurement basis for Alice's qubits.
     _a			      =							rng.integers(2				,						size=lowerCAmelCase__		)
     # The set of states Alice will prepare.
     _a			      =							rng.integers(2				,						size=lowerCAmelCase__		)
     # Measurement basis for Bob's qubits.
     _a			      =							rng.integers(2				,						size=lowerCAmelCase__		)

     # Quantum Circuit to simulate BB84
     _a			      =							qiskit.QuantumCircuit(lowerCAmelCase__				,						name='BB84'		)

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

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

     bbaa_circ.barrier()
     bbaa_circ.measure_all()

     # Simulate the quantum circuit.
     _a			      =							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.
     _a			      =							qiskit.execute(lowerCAmelCase__				,						lowerCAmelCase__				,						shots=1				,						seed_simulator=lowerCAmelCase__		)
     # Returns the result of measurement.
     _a			      =							job.result().get_counts(lowerCAmelCase__		).most_frequent()

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

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


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

 testmod()

