



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


UpperCAmelCase_		 =					[
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
UpperCAmelCase_		 =					[
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

UpperCAmelCase_		 =					(
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

UpperCAmelCase_		 =					(
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

UpperCAmelCase_		 =					[
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Dict	,   _snake_case	:Optional[int]				)  ->     Optional[Any]:
   for tf_name, hf_name in patterns:
      _A      =       k.replace(_snake_case	,   _snake_case				)
   return k

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:dict	,   _snake_case	:dict				)  ->     BigBirdPegasusForConditionalGeneration:
   _A      =       BigBirdPegasusConfig(**_snake_case				)
   _A      =       BigBirdPegasusForConditionalGeneration(_snake_case				)
   _A      =       torch_model.state_dict()
   _A      =       {}

   # separating decoder weights
   _A      =       {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''				)}
   _A      =       {k: tf_weights[k] for k in tf_weights if not k.startswith('''pegasus/decoder'''				)}

   for k, v in tqdm(decoder_weights.items()	,   '''tf -> hf conversion'''				):
      _A      =       [k.endswith(_snake_case				) for ending in KEYS_TO_IGNORE]
      if any(_snake_case				):
         continue
      _A      =       DECODER_PATTERNS
      _A      =       rename_state_dict_key(_snake_case	,   _snake_case				)
      if new_k not in state_dict:
         raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''				)
      if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value''']				):
         _A      =       v.T
      _A      =       torch.from_numpy(_snake_case				)
      assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

   for k, v in tqdm(remaining_weights.items()	,   '''tf -> hf conversion'''				):
      _A      =       [k.endswith(_snake_case				) for ending in KEYS_TO_IGNORE]
      if any(_snake_case				):
         continue
      _A      =       REMAINING_PATTERNS
      _A      =       rename_state_dict_key(_snake_case	,   _snake_case				)
      if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
         raise ValueError(F'''could not find new key {new_k} in state dict. (converted from {k})'''				)
      if any(True if i in k else False for i in ['''dense''', '''query''', '''key''', '''value''']				):
         _A      =       v.T
      _A      =       torch.from_numpy(_snake_case				)
      if k != "pegasus/embeddings/position_embeddings":
         assert v.shape == state_dict[new_k].shape, F'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''

   _A      =       mapping['''model.embed_positions.weight''']
   _A      =       mapping.pop('''model.embed_positions.weight'''				)
   _A  ,    _A      =       torch_model.load_state_dict(_snake_case	,   strict=_snake_case				)
   _A      =       [
       k
       for k in missing
       if k
       not in [
           '''final_logits_bias''',
           '''model.encoder.embed_tokens.weight''',
           '''model.decoder.embed_tokens.weight''',
           '''lm_head.weight''',
       ]
   ]
   assert unexpected_missing == [], F'''no matches found for the following torch keys {unexpected_missing}'''
   assert extra == [], F'''no matches found for the following tf keys {extra}'''
   return torch_model

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]				)  ->     Dict:
   _A      =       tf.train.list_variables(_snake_case				)
   _A      =       {}
   _A      =       ['''global_step''']
   for name, shape in tqdm(_snake_case	,   desc='''converting tf checkpoint to dict'''				):
      _A      =       any(pat in name for pat in ignore_name				)
      if skip_key:
         continue
      _A      =       tf.train.load_variable(_snake_case	,   _snake_case				)
      _A      =       array
   return tf_weights

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str	,   _snake_case	:str	,   _snake_case	:dict				)  ->     str:
   _A      =       get_tf_weights_as_numpy(_snake_case				)
   _A      =       convert_bigbird_pegasus(_snake_case	,   _snake_case				)
   torch_model.save_pretrained(_snake_case				)


if __name__ == "__main__":
   UpperCAmelCase_		 =					argparse.ArgumentParser()
   parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
   parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
   UpperCAmelCase_		 =					parser.parse_args()
   UpperCAmelCase_		 =					{}
   convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)








import math

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

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 3				)  ->     qiskit.result.counts.Counts:
   if isinstance(_snake_case	,   _snake_case				):
      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(_snake_case				) != number_of_qubits:
      raise ValueError('''number of qubits must be exact integer.'''				)
   if number_of_qubits > 10:
      raise ValueError('''number of qubits too large to simulate(>10).'''				)

   _A      =       QuantumRegister(_snake_case	,   '''qr'''				)
   _A      =       ClassicalRegister(_snake_case	,   '''cr'''				)

   _A      =       QuantumCircuit(_snake_case	,   _snake_case				)

   _A      =       number_of_qubits

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

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

   # measure all the qubits
   quantum_circuit.measure(_snake_case	,   _snake_case				)
   # simulate with 10000 shots
   _A      =       Aer.get_backend('''qasm_simulator'''				)
   _A      =       execute(_snake_case	,   _snake_case	,   shots=10_000				)

   return job.result().get_counts(_snake_case				)


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



