
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase							:   int					=     logging.get_logger(__name__)

__lowercase							:   str					=     [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]




def lowercase_	(   _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					if k == "embeddings.weight":
										return "shared.weight"

					for parlai_name, hf_name in PATTERNS:
										lowerCamelCase_					:      str     =		k.replace(_A			,       _A				)

					if k.startswith('''encoder'''				):
										lowerCamelCase_					:      int     =		k.replace('''.attn'''			,       '''.self_attn'''				)
										lowerCamelCase_					:      Any     =		k.replace('''norm1'''			,       '''self_attn_layer_norm'''				)
										lowerCamelCase_					:      Any     =		k.replace('''norm2'''			,       '''final_layer_norm'''				)
					elif k.startswith('''decoder'''				):
										lowerCamelCase_					:      str     =		k.replace('''norm1'''			,       '''self_attn_layer_norm'''				)
										lowerCamelCase_					:      Tuple     =		k.replace('''norm2'''			,       '''encoder_attn_layer_norm'''				)
										lowerCamelCase_					:      Optional[int]     =		k.replace('''norm3'''			,       '''final_layer_norm'''				)
					return k




def lowercase_	(   _lowercase				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      str     =		[
					    '''model.encoder.layernorm_embedding.weight''',
					    '''model.encoder.layernorm_embedding.bias''',
					    '''model.decoder.layernorm_embedding.weight''',
					    '''model.decoder.layernorm_embedding.bias''',
					]
					for k in keys:
										lowerCamelCase_					:      Optional[Any]     =		sd.pop(_A				)
										lowerCamelCase_					:      int     =		k.replace('''layernorm_embedding'''			,       '''layer_norm'''				)
										assert new_k not in sd
										lowerCamelCase_					:      List[str]     =		v


__lowercase							:   Optional[int]					=     ['START']




@torch.no_grad()
def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							Optional[Any]:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		torch.load(_A			,       map_location='''cpu'''				)
					lowerCamelCase_					:      int     =		model['''model''']
					lowerCamelCase_					:      List[Any]     =		BlenderbotConfig.from_json_file(_A				)
					lowerCamelCase_					:      Optional[int]     =		BlenderbotForConditionalGeneration(_A				)
					lowerCamelCase_					:      List[str]     =		m.model.state_dict().keys()
					lowerCamelCase_					:      Optional[int]     =		[]
					lowerCamelCase_					:      str     =		{}
					for k, v in sd.items():
										if k in IGNORE_KEYS:
															continue

										lowerCamelCase_					:      Tuple     =		rename_state_dict_key(_A				)
										if new_k not in valid_keys:
															failures.append([k, new_k]				)
										else:
															lowerCamelCase_					:      List[Any]     =		v
					if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
										rename_layernorm_keys(_A				)
					m.model.load_state_dict(_A			,       strict=_A				)
					m.half()
					m.save_pretrained(_A				)


if __name__ == "__main__":
					__lowercase							:   Any					=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
					parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
					parser.add_argument(
					    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
					)
					__lowercase							:   Dict					=     parser.parse_args()
					convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)




'''simple docstring'''


import qiskit




def lowercase_	(   _lowercase			,       _lowercase				)     ->							qiskit.result.counts.Counts:
					'''simple docstring'''

					lowerCamelCase_					:      Optional[Any]     =		qiskit.Aer.get_backend('''aer_simulator'''				)

					# Create a Quantum Circuit acting on the q register
					lowerCamelCase_					:      Optional[int]     =		qiskit.QuantumCircuit(_lowercase			,       _lowercase				)

					# Apply X (NOT) Gate to Qubits 0 & 1
					circuit.x(0				)
					circuit.x(1				)

					# Map the quantum measurement to the classical bits
					circuit.measure([0, 1]			,       [0, 1]				)

					# Execute the circuit on the qasm simulator
					lowerCamelCase_					:      Optional[int]     =		qiskit.execute(_lowercase			,       _lowercase			,       shots=1_000				)

					# Return the histogram data of the results of the experiment.
					return job.result().get_counts(_lowercase				)


if __name__ == "__main__":
					__lowercase							:   List[Any]					=     single_qubit_measure(2, 2)
					print(f'Total count for various states are: {counts}')


