
'''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 snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					List[str]				       =							checkpoints.load_tax_checkpoint(lowercase__		)
  UpperCAmelCase__      :					Union[str, Any]				       =							flatten_dict(lowercase__		)
  return flax_params







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Any				       =							{}

  UpperCAmelCase__      :					Optional[int]				       =							{
      "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",
  }

  UpperCAmelCase__      :					List[Any]				       =							{
      "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
      UpperCAmelCase__      :					List[str]				       =							".".join(key[1:]		)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        UpperCAmelCase__      :					List[Any]				       =							new_key.replace(lowercase__   ,       lowercase__		)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          UpperCAmelCase__      :					Dict				       =							new_key.replace(lowercase__   ,       lowercase__		)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        UpperCAmelCase__      :					Any				       =							re.sub(R"layers_(\d+)"   ,       R"layer.\1"   ,       lowercase__		)
        UpperCAmelCase__      :					Tuple				       =							new_key.replace("encoder"   ,       "encoder.encoder"		)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        UpperCAmelCase__      :					str				       =							re.sub(R"layers_(\d+)"   ,       R"layer.\1"   ,       lowercase__		)

      UpperCAmelCase__      :					Any				       =							flax_dict[key]

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

  return converted_torch_dict







def snake_case_     (					lowercase__   ,       lowercase__   ,       lowercase__=False   ,       lowercase__=False		):
  UpperCAmelCase__      :					str				       =							get_flax_param(lowercase__		)

  if not use_large:
    UpperCAmelCase__      :					Any				       =							PixaStructVisionConfig()
    UpperCAmelCase__      :					Optional[Any]				       =							PixaStructTextConfig()
  else:
    UpperCAmelCase__      :					Union[str, Any]				       =							PixaStructVisionConfig(
        hidden_size=1_5_3_6   ,       d_ff=3_9_6_8   ,       num_attention_heads=2_4   ,       num_hidden_layers=1_8		)
    UpperCAmelCase__      :					List[str]				       =							PixaStructTextConfig(hidden_size=1_5_3_6   ,       d_ff=3_9_6_8   ,       num_heads=2_4   ,       num_layers=1_8		)
  UpperCAmelCase__      :					Any				       =							PixaStructConfig(
      vision_config=encoder_config.to_dict()   ,       text_config=decoder_config.to_dict()   ,       is_vqa=lowercase__		)

  UpperCAmelCase__      :					List[Any]				       =							PixaStructForConditionalGeneration(lowercase__		)

  UpperCAmelCase__      :					int				       =							rename_and_convert_flax_params(lowercase__		)
  model.load_state_dict(lowercase__		)

  UpperCAmelCase__      :					Tuple				       =							AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"		)
  UpperCAmelCase__      :					List[str]				       =							PixaStructImageProcessor()
  UpperCAmelCase__      :					Tuple				       =							PixaStructProcessor(image_processor=lowercase__   ,       tokenizer=lowercase__		)

  if use_large:
    UpperCAmelCase__      :					int				       =							4_0_9_6

  UpperCAmelCase__      :					List[str]				       =							True

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

  model.save_pretrained(lowercase__		)
  processor.save_pretrained(lowercase__		)

  print("Model saved in {}".format(lowercase__		)		)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE     =       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.""")
      SCREAMING_SNAKE_CASE     =       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 math

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







def snake_case_     (					lowercase__ = 3		):
  if isinstance(lowercase__   ,       lowercase__		):
    raise TypeError("number of qubits must be a integer."		)
  if number_of_qubits <= 0:
    raise ValueError("number of qubits must be > 0."		)
  if math.floor(lowercase__		) != number_of_qubits:
    raise ValueError("number of qubits must be exact integer."		)
  if number_of_qubits > 1_0:
    raise ValueError("number of qubits too large to simulate(>10)."		)

  UpperCAmelCase__      :					Optional[int]				       =							QuantumRegister(lowercase__   ,       "qr"		)
  UpperCAmelCase__      :					List[Any]				       =							ClassicalRegister(lowercase__   ,       "cr"		)

  UpperCAmelCase__      :					List[str]				       =							QuantumCircuit(lowercase__   ,       lowercase__		)

  UpperCAmelCase__      :					Union[str, Any]				       =							number_of_qubits

  for i in range(lowercase__		):
    quantum_circuit.h(number_of_qubits - i - 1		)
    counter -= 1
    for j in range(lowercase__		):
      quantum_circuit.cp(np.pi / 2 ** (counter - j)   ,       lowercase__   ,       lowercase__		)

  for k in range(number_of_qubits // 2		):
    quantum_circuit.swap(lowercase__   ,       number_of_qubits - k - 1		)

  # measure all the qubits
  quantum_circuit.measure(lowercase__   ,       lowercase__		)
  # simulate with 10000 shots
  UpperCAmelCase__      :					Union[str, Any]				       =							Aer.get_backend("qasm_simulator"		)
  UpperCAmelCase__      :					Optional[int]				       =							execute(lowercase__   ,       lowercase__   ,       shots=1_0_0_0_0		)

  return job.result().get_counts(lowercase__		)


if __name__ == "__main__":
      print(
          F'Total count for quantum fourier transform state is: \
    {quantum_fourier_transform(3)}'
      )



