



'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						int							)      ->						List[str]:







	"""simple docstring"""
	lowerCAmelCase   =     os.path.join(args.tf_model_dir							,		"""parameters.json"""							)
	lowerCAmelCase   =     json.loads(open(_UpperCAmelCase							).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"""							):
		lowerCAmelCase   =     args.output + '.pt'
	lowerCAmelCase   =     OrderedDict()
	with tf.device("""/CPU:0"""							):
		lowerCAmelCase   =     tf.train.load_checkpoint(args.tf_model_dir							)
		lowerCAmelCase   =     reader.get_variable_to_shape_map()
		for key_name in shapes.keys():
			lowerCAmelCase   =     reader.get_tensor(_UpperCAmelCase							).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"""							):
					lowerCAmelCase   =     int(key_name[9]							)
				elif key_name.startswith("""pasts/out"""							):
					lowerCAmelCase   =     8
				lowerCAmelCase   =     'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
				lowerCAmelCase   =     vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif key_name.startswith("""model/moe"""							):
				lowerCAmelCase   =     int(key_name[9:].split("""/"""							)[0]							)
				if key_name.endswith("""/switch_gating/kernel"""							):
					lowerCAmelCase   =     'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
					lowerCAmelCase   =     vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				elif key_name.endswith("""/softmlp/kernel"""							):
					lowerCAmelCase   =     'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
					lowerCAmelCase   =     vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				elif key_name.endswith("""/wo/kernel"""							) or key_name.endswith("""/wi/kernel"""							):
					lowerCAmelCase   =     key_name[-9:-7]
					for i in range(16							):
						lowerCAmelCase   =     'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
						lowerCAmelCase   =     (
						    vnp[i].transpose([1, 0]							).copy()
						)  # In Mesh-Tensorflow, it is one array, so it is divided
						lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif key_name.startswith("""model/mlp"""							):
				lowerCAmelCase   =     int(key_name[9:].split("""/"""							)[0]							)
				if key_name.endswith("""/p1/kernel"""							):
					lowerCAmelCase   =     'model.blocks.%d.feed_forward.mlp.wi.weight' % player
					lowerCAmelCase   =     vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				elif key_name.endswith("""/p1/bias"""							):
					lowerCAmelCase   =     'model.blocks.%d.feed_forward.mlp.wi.bias' % player
					lowerCAmelCase   =     vnp.copy()  # same because it is one dimensional
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				elif key_name.endswith("""/p2/kernel"""							):
					lowerCAmelCase   =     'model.blocks.%d.feed_forward.mlp.wo.weight' % player
					lowerCAmelCase   =     vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				elif key_name.endswith("""/p2/bias"""							):
					lowerCAmelCase   =     'model.blocks.%d.feed_forward.mlp.wo.bias' % player
					lowerCAmelCase   =     vnp.copy()  # same because it is one dimensional
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif key_name.startswith("""model/ln"""							):
				lowerCAmelCase   =     int(key_name[8:].split("""/"""							)[0]							)
				if key_name.endswith("""/b"""							):
					lowerCAmelCase   =     'model.blocks.%d.feed_forward.norm.bias' % player
					lowerCAmelCase   =     vnp.copy()  # same because it is one dimensional
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				elif key_name.endswith("""/g"""							):
					lowerCAmelCase   =     'model.blocks.%d.feed_forward.norm.weight' % player
					lowerCAmelCase   =     vnp.copy()  # same because it is one dimensional
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif key_name.startswith("""model/att"""							):
				lowerCAmelCase   =     int(key_name[9:].split("""/"""							)[0]							)
				if key_name.endswith("""/qkv/kernel"""							):
					lowerCAmelCase   =     vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
					lowerCAmelCase   =     state[:, 0, :, :]
					lowerCAmelCase   =     state[:, 1, :, :]
					lowerCAmelCase   =     state[:, 2, :, :]
					lowerCAmelCase   =     (
					    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
					lowerCAmelCase   =     (
					    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
					lowerCAmelCase   =     (
					    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
					lowerCAmelCase   =     'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
					lowerCAmelCase   =     'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
					lowerCAmelCase   =     'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				elif key_name.endswith("""/o/kernel"""							):
					lowerCAmelCase   =     'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
					lowerCAmelCase   =     (
					    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]							).transpose([1, 0]							).copy()
					)  # Mesh-Tensorflow is a diagonal matrix
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif key_name.startswith("""model/an"""							):
				lowerCAmelCase   =     int(key_name[8:].split("""/"""							)[0]							)
				if key_name.endswith("""/b"""							):
					lowerCAmelCase   =     'model.blocks.%d.self_attn.norm.bias' % player
					lowerCAmelCase   =     vnp.copy()  # same because it is one dimensional
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				elif key_name.endswith("""/g"""							):
					lowerCAmelCase   =     'model.blocks.%d.self_attn.norm.weight' % player
					lowerCAmelCase   =     vnp.copy()  # same because it is one dimensional
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif (
			    key_name.startswith("""model/wte"""							)
			    or key_name.startswith("""model/wpe"""							)
			    or key_name.startswith("""model/ete"""							)
			):
				lowerCAmelCase   =     {'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
				    key_name[-3:]
				]
				lowerCAmelCase   =     'model.%s.weight' % nlayer
				lowerCAmelCase   =     vnp.copy()  # same in embedded
				lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
				if key_name.startswith("""model/wte"""							):
					lowerCAmelCase   =     'lm_head.weight'
					lowerCAmelCase   =     vnp.copy()  # same in embedded
					lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif key_name.startswith("""model/wob"""							):
				lowerCAmelCase   =     'final_logits_bias'
				lowerCAmelCase   =     vnp.copy()  # same in embedded
				lowerCAmelCase   =     state.reshape((1, -1)							)
				lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif key_name == "model/dense/kernel":
				lowerCAmelCase   =     'model.last_project.weight'
				lowerCAmelCase   =     vnp.transpose([1, 0]							).copy()  # Mesh-Tensorflow is a diagonal matrix
				lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
			elif key_name == "model/dense_1/bias":
				lowerCAmelCase   =     'model.last_project.bias'
				lowerCAmelCase   =     vnp.copy()  # same because it is one dimensional
				lowerCAmelCase   =     torch.tensor(_UpperCAmelCase							)
	torch.save(_UpperCAmelCase							,		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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase     		=   logging.get_logger(__name__)

class __snake_case(				_lowerCAmelCase ):


						'''simple docstring'''



						UpperCAmelCase							:   Optional[int]									=		"timm_backbone"


						def __init__( self     ,	A_=None     ,	A_=3     ,	A_=True     ,	A_=True     ,	A_=None     ,	**A_     ,	)     ->     int:
							super().__init__(**A_				)
							lowerCAmelCase   =     backbone
							lowerCAmelCase   =     num_channels
							lowerCAmelCase   =     features_only
							lowerCAmelCase   =     use_pretrained_backbone
							lowerCAmelCase   =     True
							lowerCAmelCase   =     out_indices if out_indices is not None else (-1,)