
'''simple docstring'''

from __future__ import annotations

import math

class       UpperCAmelCase					:





		"""simple docstring"""





		def __init__(     self	,       _snake_case  )					->     int:
				_UpperCamelCase							:			List[str]     =   size
				# approximate the overall size of segment tree with given value
				_UpperCamelCase							:			Tuple     =   [0 for i in range(0	,       4 * size  )]
				# create array to store lazy update
				_UpperCamelCase							:			List[str]     =   [0 for i in range(0	,       4 * size  )]
				_UpperCamelCase							:			List[str]     =   [0 for i in range(0	,       4 * size  )]  # flag for lazy update
		def _lowercase	(     self	,       _snake_case  )					->     List[str]:
				return idx * 2
		def _lowercase	(     self	,       _snake_case  )					->     Any:
				return idx * 2 + 1
		def _lowercase	(     self	,       _snake_case	,       _snake_case	,       _snake_case	,       _snake_case  )					->     Any:


				if left_element == right_element:
						_UpperCamelCase							:			Optional[int]     =   a[left_element - 1]
				else:
						_UpperCamelCase							:			Union[str, Any]     =   (left_element + right_element) // 2
						self.build(self.left(__lowerCAmelCase  )	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase  )
						self.build(self.right(__lowerCAmelCase  )	,       mid + 1	,       __lowerCAmelCase	,       __lowerCAmelCase  )
						_UpperCamelCase							:			List[Any]     =   max(
						    self.segment_tree[self.left(__lowerCAmelCase  )]	,       self.segment_tree[self.right(__lowerCAmelCase  )]  )
		def _lowercase	(     self	,       _snake_case	,       _snake_case	,       _snake_case	,       _snake_case	,       _snake_case	,       _snake_case  )					->     Optional[int]:


				if self.flag[idx] is True:
						_UpperCamelCase							:			List[Any]     =   self.lazy[idx]
						_UpperCamelCase							:			Union[str, Any]     =   False
						if left_element != right_element:
								_UpperCamelCase							:			Optional[int]     =   self.lazy[idx]
								_UpperCamelCase							:			Optional[Any]     =   self.lazy[idx]
								_UpperCamelCase							:			List[Any]     =   True
								_UpperCamelCase							:			Any     =   True

				if right_element < a or left_element > b:
						return True
				if left_element >= a and right_element <= b:
						_UpperCamelCase							:			Optional[Any]     =   val
						if left_element != right_element:
								_UpperCamelCase							:			Dict     =   val
								_UpperCamelCase							:			List[str]     =   val
								_UpperCamelCase							:			List[Any]     =   True
								_UpperCamelCase							:			List[Any]     =   True
						return True
				_UpperCamelCase							:			Union[str, Any]     =   (left_element + right_element) // 2
				self.update(self.left(__lowerCAmelCase  )	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase  )
				self.update(self.right(__lowerCAmelCase  )	,       mid + 1	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase  )
				_UpperCamelCase							:			List[Any]     =   max(
				    self.segment_tree[self.left(__lowerCAmelCase  )]	,       self.segment_tree[self.right(__lowerCAmelCase  )]  )
				return True
		def _lowercase	(     self	,       _snake_case	,       _snake_case	,       _snake_case	,       _snake_case	,       _snake_case  )					->     List[str]:


				if self.flag[idx] is True:
						_UpperCamelCase							:			Optional[Any]     =   self.lazy[idx]
						_UpperCamelCase							:			List[str]     =   False
						if left_element != right_element:
								_UpperCamelCase							:			Dict     =   self.lazy[idx]
								_UpperCamelCase							:			str     =   self.lazy[idx]
								_UpperCamelCase							:			Optional[int]     =   True
								_UpperCamelCase							:			int     =   True
				if right_element < a or left_element > b:
						return -math.inf
				if left_element >= a and right_element <= b:
						return self.segment_tree[idx]
				_UpperCamelCase							:			str     =   (left_element + right_element) // 2
				_UpperCamelCase							:			int     =   self.query(self.left(__lowerCAmelCase  )	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase  )
				_UpperCamelCase							:			int     =   self.query(self.right(__lowerCAmelCase  )	,       mid + 1	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase  )
				return max(__lowerCAmelCase	,       __lowerCAmelCase  )




		def __str__(     self  )					->     int:
				return str([self.query(1	,       1	,       self.size	,       __lowerCAmelCase	,       __lowerCAmelCase  ) for i in range(1	,       self.size + 1  )]  )


if __name__ == "__main__":
	_UpperCAmelCase :   str			    =	[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
	_UpperCAmelCase :   Optional[Any]			    =	15
	_UpperCAmelCase :   Tuple			    =	SegmentTree(size)
	segt.build(1, 1, size, A)
	print(segt.query(1, 1, size, 4, 6))
	print(segt.query(1, 1, size, 7, 11))
	print(segt.query(1, 1, size, 7, 12))
	segt.update(1, 1, size, 1, 3, 111)
	print(segt.query(1, 1, size, 1, 15))
	segt.update(1, 1, size, 7, 8, 235)
	print(segt)






'''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__				(     _A:   Dict					)       ->      Dict:

		'''simple docstring'''


		lowerCAmelCase     =    os.path.join(args.tf_model_dir ,  """parameters.json"""					)
		lowerCAmelCase     =    json.loads(open(_A					).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(_A					).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(_A					)
						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(_A					)
								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(_A					)
								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(_A					)
						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(_A					)
								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(_A					)
								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(_A					)
								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(_A					)
						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(_A					)
								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(_A					)
						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(_A					)
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
										lowerCAmelCase     =    """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
										lowerCAmelCase     =    torch.tensor(_A					)
								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(_A					)
						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(_A					)
								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(_A					)
						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(_A					)
								if key_name.startswith("""model/wte"""					):
										lowerCAmelCase     =    """lm_head.weight"""
										lowerCAmelCase     =    vnp.copy()  # same in embedded
										lowerCAmelCase     =    torch.tensor(_A					)
						elif key_name.startswith("""model/wob"""					):
								lowerCAmelCase     =    """final_logits_bias"""
								lowerCAmelCase     =    vnp.copy()  # same in embedded
								lowerCAmelCase     =    state.reshape((1, -1)					)
								lowerCAmelCase     =    torch.tensor(_A					)
						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(_A					)
						elif key_name == "model/dense_1/bias":
								lowerCAmelCase     =    """model.last_project.bias"""
								lowerCAmelCase     =    vnp.copy()  # same because it is one dimensional
								lowerCAmelCase     =    torch.tensor(_A					)
		torch.save(_A ,  args.output					)


if __name__ == "__main__":
						__lowercase        =    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''')
						__lowercase        =    parser.parse_args()
						convert_tf_gptsan_to_pt(args)




