

'''simple docstring'''





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 							_lowerCAmelCase (      __magic_name__	:    str	)							->     Union[str, Any]:
						lowercase   :       Union[str, Any]								=os.path.join(args.tf_model_dir      ,   '''parameters.json'''	)
						lowercase   :       List[str]								=json.loads(open(__magic_name__	).read()	)
						if not params:
												raise ValueError(
												    f'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''	)
						if not args.output.endswith('''.pt'''	):
												lowercase   :       Tuple								=args.output + '''.pt'''
						lowercase   :       int								=OrderedDict()
						with tf.device('''/CPU:0'''	):
												lowercase   :       List[Any]								=tf.train.load_checkpoint(args.tf_model_dir	)
												lowercase   :       int								=reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		lowercase   :       Any								=reader.get_tensor(__magic_name__	).astype(np.floataa	)
																		if key_name.endswith('''/adam_m'''	) or key_name.endswith('''/adam_v'''	):
																								continue
																		if key_name.startswith('''pasts/'''	):
																								if key_name.startswith('''pasts/mlp'''	):
																														lowercase   :       int								=int(key_name[9]	)
																								elif key_name.startswith('''pasts/out'''	):
																														lowercase   :       Union[str, Any]								=8
																								lowercase   :       Any								='''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								lowercase   :       Dict								=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								lowercase   :       List[str]								=torch.tensor(__magic_name__	)
																		elif key_name.startswith('''model/moe'''	):
																								lowercase   :       Union[str, Any]								=int(key_name[9:].split('''/'''	)[0]	)
																								if key_name.endswith('''/switch_gating/kernel'''	):
																														lowercase   :       Dict								='''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
																														lowercase   :       Any								=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														lowercase   :       List[Any]								=torch.tensor(__magic_name__	)
																								elif key_name.endswith('''/softmlp/kernel'''	):
																														lowercase   :       Optional[int]								='''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
																														lowercase   :       int								=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														lowercase   :       Optional[Any]								=torch.tensor(__magic_name__	)
																								elif key_name.endswith('''/wo/kernel'''	) or key_name.endswith('''/wi/kernel'''	):
																														lowercase   :       Union[str, Any]								=key_name[-9:-7]
																														for i in range(16	):
																																				lowercase   :       Dict								='''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
																																				lowercase   :       Any								=(
																																				    vnp[i].transpose([1, 0]	).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				lowercase   :       List[str]								=torch.tensor(__magic_name__	)
																		elif key_name.startswith('''model/mlp'''	):
																								lowercase   :       Dict								=int(key_name[9:].split('''/'''	)[0]	)
																								if key_name.endswith('''/p1/kernel'''	):
																														lowercase   :       Any								='''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
																														lowercase   :       str								=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														lowercase   :       Any								=torch.tensor(__magic_name__	)
																								elif key_name.endswith('''/p1/bias'''	):
																														lowercase   :       List[Any]								='''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
																														lowercase   :       Union[str, Any]								=vnp.copy()  # same because it is one dimensional
																														lowercase   :       Union[str, Any]								=torch.tensor(__magic_name__	)
																								elif key_name.endswith('''/p2/kernel'''	):
																														lowercase   :       int								='''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
																														lowercase   :       Tuple								=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																														lowercase   :       int								=torch.tensor(__magic_name__	)
																								elif key_name.endswith('''/p2/bias'''	):
																														lowercase   :       str								='''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
																														lowercase   :       Optional[int]								=vnp.copy()  # same because it is one dimensional
																														lowercase   :       List[Any]								=torch.tensor(__magic_name__	)
																		elif key_name.startswith('''model/ln'''	):
																								lowercase   :       int								=int(key_name[8:].split('''/'''	)[0]	)
																								if key_name.endswith('''/b'''	):
																														lowercase   :       Any								='''model.blocks.%d.feed_forward.norm.bias''' % player
																														lowercase   :       Optional[int]								=vnp.copy()  # same because it is one dimensional
																														lowercase   :       Union[str, Any]								=torch.tensor(__magic_name__	)
																								elif key_name.endswith('''/g'''	):
																														lowercase   :       Optional[Any]								='''model.blocks.%d.feed_forward.norm.weight''' % player
																														lowercase   :       Any								=vnp.copy()  # same because it is one dimensional
																														lowercase   :       List[Any]								=torch.tensor(__magic_name__	)
																		elif key_name.startswith('''model/att'''	):
																								lowercase   :       int								=int(key_name[9:].split('''/'''	)[0]	)
																								if key_name.endswith('''/qkv/kernel'''	):
																														lowercase   :       Optional[int]								=vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														lowercase   :       Dict								=state[:, 0, :, :]
																														lowercase   :       Tuple								=state[:, 1, :, :]
																														lowercase   :       List[Any]								=state[:, 2, :, :]
																														lowercase   :       Optional[int]								=(
																														    state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														lowercase   :       Optional[Any]								=(
																														    state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														lowercase   :       Optional[int]								=(
																														    state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]	)
																														    .transpose([1, 0]	)
																														    .copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														lowercase   :       Dict								='''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
																														lowercase   :       Dict								=torch.tensor(__magic_name__	)
																														lowercase   :       List[Any]								='''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
																														lowercase   :       Optional[Any]								=torch.tensor(__magic_name__	)
																														lowercase   :       Optional[Any]								='''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
																														lowercase   :       Tuple								=torch.tensor(__magic_name__	)
																								elif key_name.endswith('''/o/kernel'''	):
																														lowercase   :       Dict								='''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
																														lowercase   :       List[Any]								=(
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]	).transpose([1, 0]	).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														lowercase   :       str								=torch.tensor(__magic_name__	)
																		elif key_name.startswith('''model/an'''	):
																								lowercase   :       Optional[Any]								=int(key_name[8:].split('''/'''	)[0]	)
																								if key_name.endswith('''/b'''	):
																														lowercase   :       List[str]								='''model.blocks.%d.self_attn.norm.bias''' % player
																														lowercase   :       Union[str, Any]								=vnp.copy()  # same because it is one dimensional
																														lowercase   :       List[str]								=torch.tensor(__magic_name__	)
																								elif key_name.endswith('''/g'''	):
																														lowercase   :       Any								='''model.blocks.%d.self_attn.norm.weight''' % player
																														lowercase   :       Any								=vnp.copy()  # same because it is one dimensional
																														lowercase   :       Optional[Any]								=torch.tensor(__magic_name__	)
																		elif (
																		    key_name.startswith('''model/wte'''	)
																		    or key_name.startswith('''model/wpe'''	)
																		    or key_name.startswith('''model/ete'''	)
																		):
																								lowercase   :       Any								={'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
																								    key_name[-3:]
																								]
																								lowercase   :       Optional[Any]								='''model.%s.weight''' % nlayer
																								lowercase   :       Optional[int]								=vnp.copy()  # same in embedded
																								lowercase   :       List[Any]								=torch.tensor(__magic_name__	)
																								if key_name.startswith('''model/wte'''	):
																														lowercase   :       Tuple								='''lm_head.weight'''
																														lowercase   :       str								=vnp.copy()  # same in embedded
																														lowercase   :       Union[str, Any]								=torch.tensor(__magic_name__	)
																		elif key_name.startswith('''model/wob'''	):
																								lowercase   :       List[str]								='''final_logits_bias'''
																								lowercase   :       Dict								=vnp.copy()  # same in embedded
																								lowercase   :       Tuple								=state.reshape((1, -1)	)
																								lowercase   :       Dict								=torch.tensor(__magic_name__	)
																		elif key_name == "model/dense/kernel":
																								lowercase   :       Dict								='''model.last_project.weight'''
																								lowercase   :       int								=vnp.transpose([1, 0]	).copy()  # Mesh-Tensorflow is a diagonal matrix
																								lowercase   :       Optional[Any]								=torch.tensor(__magic_name__	)
																		elif key_name == "model/dense_1/bias":
																								lowercase   :       List[Any]								='''model.last_project.bias'''
																								lowercase   :       str								=vnp.copy()  # same because it is one dimensional
																								lowercase   :       Optional[Any]								=torch.tensor(__magic_name__	)
						torch.save(__magic_name__      ,   args.output	)


if __name__ == "__main__":
							UpperCamelCase_							=					argparse.ArgumentParser(
							    description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
							)
							parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
							parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
							UpperCamelCase_							=					parser.parse_args()
							convert_tf_gptsan_to_pt(args)








'''simple docstring'''





from datetime import datetime

import matplotlib.pyplot as plt
import torch






def 							_lowerCAmelCase (      __magic_name__	:    Dict	)							->     Dict:
						for param in module.parameters():
												lowercase   :       List[str]								=False






def 							_lowerCAmelCase (      )							->     List[str]:
						lowercase   :       Dict								='''cuda''' if torch.cuda.is_available() else '''cpu'''
						if torch.backends.mps.is_available() and torch.backends.mps.is_built():
												lowercase   :       Optional[int]								='''mps'''
						if device == "mps":
												print(
												    '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
												    ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
												    ''' with generations.'''	)
						return device






def 							_lowerCAmelCase (      __magic_name__	:    Union[str, Any]	)							->     str:
						lowercase   :       Optional[int]								=plt.imshow(__magic_name__	)
						fig.axes.get_xaxis().set_visible(__magic_name__	)
						fig.axes.get_yaxis().set_visible(__magic_name__	)
						plt.show()







def 							_lowerCAmelCase (      )							->     List[Any]:
						lowercase   :       Any								=datetime.now()
						lowercase   :       Dict								=current_time.strftime('''%H:%M:%S'''	)
						return timestamp





