
'''simple docstring'''
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


lowerCAmelCase					:    Union[str, Any]           = [
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
lowerCAmelCase					:    Tuple           = [
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

lowerCAmelCase					:    Union[str, Any]           = (
    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
)

lowerCAmelCase					:    Optional[Any]           = (
    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
)

lowerCAmelCase					:    Union[str, Any]           = [
    '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 		A_(     A						:		Optional[int]		,     A						:		List[str]):
							for tf_name, hf_name in patterns:
														UpperCamelCase   			=     k.replace(A		,     A)
							return k







def 		A_(     A						:		dict		,     A						:		dict):
							UpperCamelCase   			=     BigBirdPegasusConfig(**A)
							UpperCamelCase   			=     BigBirdPegasusForConditionalGeneration(A)
							UpperCamelCase   			=     torch_model.state_dict()
							UpperCamelCase   			=     {}

							# separating decoder weights
							UpperCamelCase   			=     {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder')}
							UpperCamelCase   			=     {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'):
														UpperCamelCase   			=     [k.endswith(A) for ending in KEYS_TO_IGNORE]
														if any(A):
																					continue
														UpperCamelCase   			=     DECODER_PATTERNS
														UpperCamelCase   			=     rename_state_dict_key(A		,     A)
														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']):
																					UpperCamelCase   			=     v.T
														UpperCamelCase   			=     torch.from_numpy(A)
														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'):
														UpperCamelCase   			=     [k.endswith(A) for ending in KEYS_TO_IGNORE]
														if any(A):
																					continue
														UpperCamelCase   			=     REMAINING_PATTERNS
														UpperCamelCase   			=     rename_state_dict_key(A		,     A)
														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']):
																					UpperCamelCase   			=     v.T
														UpperCamelCase   			=     torch.from_numpy(A)
														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}'''

							UpperCamelCase   			=     mapping['model.embed_positions.weight']
							UpperCamelCase   			=     mapping.pop('model.embed_positions.weight')
							UpperCamelCase			,	UpperCamelCase   			=     torch_model.load_state_dict(A		,     strict=A)
							UpperCamelCase   			=     [
							    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 		A_(     A						:		List[str]):
							UpperCamelCase   			=     tf.train.list_variables(A)
							UpperCamelCase   			=     {}
							UpperCamelCase   			=     ['global_step']
							for name, shape in tqdm(A		,     desc='converting tf checkpoint to dict'):
														UpperCamelCase   			=     any(pat in name for pat in ignore_name)
														if skip_key:
																					continue
														UpperCamelCase   			=     tf.train.load_variable(A		,     A)
														UpperCamelCase   			=     array
							return tf_weights







def 		A_(     A						:		str		,     A						:		str		,     A						:		dict):
							UpperCamelCase   			=     get_tf_weights_as_numpy(A)
							UpperCamelCase   			=     convert_bigbird_pegasus(A		,     A)
							torch_model.save_pretrained(A)


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = 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.')
						lowerCAmelCase					:    Dict           = parser.parse_args()
						lowerCAmelCase					:    Any           = {}
						convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)





'''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()






