



import argparse

import torch

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


logging.set_verbosity_info()
_lowerCAmelCase	:   List[Any]    =   logging.get_logger(__name__)

_lowerCAmelCase	:   Dict    =   [
    ["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     UpperCAmelCase_		(					snake_case__					)	->     List[Any]:



				"""simple docstring"""





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

				for parlai_name, hf_name in PATTERNS:
								lowerCAmelCase__		  =  k.replace(snake_case__    ,			snake_case__					)

				if k.startswith('encoder'					):
								lowerCAmelCase__		  =  k.replace('.attn'    ,			'.self_attn'					)
								lowerCAmelCase__		  =  k.replace('norm1'    ,			'self_attn_layer_norm'					)
								lowerCAmelCase__		  =  k.replace('norm2'    ,			'final_layer_norm'					)
				elif k.startswith('decoder'					):
								lowerCAmelCase__		  =  k.replace('norm1'    ,			'self_attn_layer_norm'					)
								lowerCAmelCase__		  =  k.replace('norm2'    ,			'encoder_attn_layer_norm'					)
								lowerCAmelCase__		  =  k.replace('norm3'    ,			'final_layer_norm'					)
				return k







def     UpperCAmelCase_		(					snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__		  =  [
				    '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__		  =  sd.pop(snake_case__					)
								lowerCAmelCase__		  =  k.replace('layernorm_embedding'    ,			'layer_norm'					)
								assert new_k not in sd
								lowerCAmelCase__		  =  v


_lowerCAmelCase	:   Optional[Any]    =   ["START"]







@torch.no_grad()
def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     Optional[int]:



				"""simple docstring"""





				lowerCAmelCase__		  =  torch.load(snake_case__    ,			map_location='cpu'					)
				lowerCAmelCase__		  =  model['model']
				lowerCAmelCase__		  =  BlenderbotConfig.from_json_file(snake_case__					)
				lowerCAmelCase__		  =  BlenderbotForConditionalGeneration(snake_case__					)
				lowerCAmelCase__		  =  m.model.state_dict().keys()
				lowerCAmelCase__		  =  []
				lowerCAmelCase__		  =  {}
				for k, v in sd.items():
								if k in IGNORE_KEYS:
												continue

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


if __name__ == "__main__":
	_lowerCAmelCase	:   Dict    =   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"
	)
	_lowerCAmelCase	:   Dict    =   parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





import numpy as np







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__ = 1E-12    ,			snake_case__ = 100    ,			)	->     tuple[float, np.ndarray]:



				"""simple docstring"""





				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[1]
				# Ensure proper dimensionality.
				assert np.shape(snake_case__					)[0] == np.shape(snake_case__					)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(snake_case__					) == np.iscomplexobj(snake_case__					)
				lowerCAmelCase__		  =  np.iscomplexobj(snake_case__					)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(snake_case__    ,			input_matrix.conj().T					)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				lowerCAmelCase__		  =  False
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  0
				lowerCAmelCase__		  =  1E12

				while not convergence:
								# Multiple matrix by the vector.
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			snake_case__					)
								# Normalize the resulting output vector.
								lowerCAmelCase__		  =  w / np.linalg.norm(snake_case__					)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								lowerCAmelCase__		  =  vector.conj().T if is_complex else vector.T
								lowerCAmelCase__		  =  np.dot(snake_case__    ,			np.dot(snake_case__    ,			snake_case__					)					)

								# Check convergence.
								lowerCAmelCase__		  =  np.abs(lambda_ - lambda_previous					) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												lowerCAmelCase__		  =  True

								lowerCAmelCase__		  =  lambda_

				if is_complex:
								lowerCAmelCase__		  =  np.real(lambda_					)

				return lambda_, vector







def     UpperCAmelCase_		(					)	->     None:



				"""simple docstring"""





				lowerCAmelCase__		  =  np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]					)
				lowerCAmelCase__		  =  np.array([41, 4, 20]					)
				lowerCAmelCase__		  =  real_input_matrix.astype(np.complexaaa					)
				lowerCAmelCase__		  =  np.triu(1j * complex_input_matrix    ,			1					)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				lowerCAmelCase__		  =  np.array([41, 4, 20]					).astype(np.complexaaa					)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												lowerCAmelCase__		  =  real_input_matrix
												lowerCAmelCase__		  =  real_vector
								elif problem_type == "complex":
												lowerCAmelCase__		  =  complex_input_matrix
												lowerCAmelCase__		  =  complex_vector

								# Our implementation.
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  power_iteration(snake_case__    ,			snake_case__					)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  np.linalg.eigh(snake_case__					)
								# Last eigenvalue is the maximum one.
								lowerCAmelCase__		  =  eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								lowerCAmelCase__		  =  eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max					) <= 1E-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(snake_case__					) - np.abs(snake_case__					)					) <= 1E-6


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	test_power_iteration()
