



"""simple docstring"""





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def 						__lowerCAmelCase    (		__UpperCamelCase   :      Tuple     ):


						'''simple docstring'''






						snake_case_ :			Any            =      os.path.join(args.tf_model_dir       ,			"""parameters.json"""     )
						snake_case_ :			List[str]            =      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"""     ):
												snake_case_ :			Optional[Any]            =      args.output + """.pt"""
						snake_case_ :			List[Any]            =      OrderedDict()
						with tf.device("""/CPU:0"""     ):
												snake_case_ :			Any            =      tf.train.load_checkpoint(args.tf_model_dir     )
												snake_case_ :			Tuple            =      reader.get_variable_to_shape_map()
												for key_name in shapes.keys():
																		snake_case_ :			Union[str, Any]            =      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"""     ):
																														snake_case_ :			Any            =      int(key_name[9]     )
																								elif key_name.startswith("""pasts/out"""     ):
																														snake_case_ :			Union[str, Any]            =      8
																								snake_case_ :			Optional[Any]            =      """model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
																								snake_case_ :			str            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :			Dict            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/moe"""     ):
																								snake_case_ :			Optional[int]            =      int(key_name[9:].split("""/"""     )[0]     )
																								if key_name.endswith("""/switch_gating/kernel"""     ):
																														snake_case_ :			Dict            =      """model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
																														snake_case_ :			str            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			Union[str, Any]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/softmlp/kernel"""     ):
																														snake_case_ :			Tuple            =      """model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
																														snake_case_ :			Dict            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			List[str]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/wo/kernel"""     ) or key_name.endswith("""/wi/kernel"""     ):
																														snake_case_ :			List[Any]            =      key_name[-9:-7]
																														for i in range(1_6     ):
																																				snake_case_ :			int            =      """model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
																																				snake_case_ :			Union[str, Any]            =      (
																																				    vnp[i].transpose([1, 0]     ).copy()
																																				)  # In Mesh-Tensorflow, it is one array, so it is divided
																																				snake_case_ :			Optional[Any]            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/mlp"""     ):
																								snake_case_ :			int            =      int(key_name[9:].split("""/"""     )[0]     )
																								if key_name.endswith("""/p1/kernel"""     ):
																														snake_case_ :			Optional[int]            =      """model.blocks.%d.feed_forward.mlp.wi.weight""" % player
																														snake_case_ :			List[str]            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			Optional[int]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/p1/bias"""     ):
																														snake_case_ :			str            =      """model.blocks.%d.feed_forward.mlp.wi.bias""" % player
																														snake_case_ :			Optional[int]            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Any            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/p2/kernel"""     ):
																														snake_case_ :			Any            =      """model.blocks.%d.feed_forward.mlp.wo.weight""" % player
																														snake_case_ :			Union[str, Any]            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			str            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/p2/bias"""     ):
																														snake_case_ :			List[Any]            =      """model.blocks.%d.feed_forward.mlp.wo.bias""" % player
																														snake_case_ :			List[str]            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Tuple            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/ln"""     ):
																								snake_case_ :			Optional[Any]            =      int(key_name[8:].split("""/"""     )[0]     )
																								if key_name.endswith("""/b"""     ):
																														snake_case_ :			Optional[int]            =      """model.blocks.%d.feed_forward.norm.bias""" % player
																														snake_case_ :			Tuple            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Optional[Any]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/g"""     ):
																														snake_case_ :			List[Any]            =      """model.blocks.%d.feed_forward.norm.weight""" % player
																														snake_case_ :			Dict            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Optional[int]            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/att"""     ):
																								snake_case_ :			List[Any]            =      int(key_name[9:].split("""/"""     )[0]     )
																								if key_name.endswith("""/qkv/kernel"""     ):
																														snake_case_ :			Tuple            =      vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
																														snake_case_ :			str            =      state[:, 0, :, :]
																														snake_case_ :			str            =      state[:, 1, :, :]
																														snake_case_ :			Optional[Any]            =      state[:, 2, :, :]
																														snake_case_ :			Any            =      (
																														    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
																														snake_case_ :			str            =      (
																														    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
																														snake_case_ :			List[str]            =      (
																														    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
																														snake_case_ :			str            =      """model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
																														snake_case_ :			Optional[Any]            =      torch.tensor(__UpperCamelCase     )
																														snake_case_ :			Dict            =      """model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
																														snake_case_ :			int            =      torch.tensor(__UpperCamelCase     )
																														snake_case_ :			Tuple            =      """model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
																														snake_case_ :			Tuple            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/o/kernel"""     ):
																														snake_case_ :			List[str]            =      """model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
																														snake_case_ :			Dict            =      (
																														    vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]     ).transpose([1, 0]     ).copy()
																														)  # Mesh-Tensorflow is a diagonal matrix
																														snake_case_ :			Union[str, Any]            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/an"""     ):
																								snake_case_ :			Optional[int]            =      int(key_name[8:].split("""/"""     )[0]     )
																								if key_name.endswith("""/b"""     ):
																														snake_case_ :			Optional[int]            =      """model.blocks.%d.self_attn.norm.bias""" % player
																														snake_case_ :			Optional[Any]            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Optional[Any]            =      torch.tensor(__UpperCamelCase     )
																								elif key_name.endswith("""/g"""     ):
																														snake_case_ :			str            =      """model.blocks.%d.self_attn.norm.weight""" % player
																														snake_case_ :			str            =      vnp.copy()  # same because it is one dimensional
																														snake_case_ :			Dict            =      torch.tensor(__UpperCamelCase     )
																		elif (
																		    key_name.startswith("""model/wte"""     )
																		    or key_name.startswith("""model/wpe"""     )
																		    or key_name.startswith("""model/ete"""     )
																		):
																								snake_case_ :			Optional[int]            =      {"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
																								    key_name[-3:]
																								]
																								snake_case_ :			Union[str, Any]            =      """model.%s.weight""" % nlayer
																								snake_case_ :			Optional[Any]            =      vnp.copy()  # same in embedded
																								snake_case_ :			str            =      torch.tensor(__UpperCamelCase     )
																								if key_name.startswith("""model/wte"""     ):
																														snake_case_ :			Optional[int]            =      """lm_head.weight"""
																														snake_case_ :			Tuple            =      vnp.copy()  # same in embedded
																														snake_case_ :			Union[str, Any]            =      torch.tensor(__UpperCamelCase     )
																		elif key_name.startswith("""model/wob"""     ):
																								snake_case_ :			List[str]            =      """final_logits_bias"""
																								snake_case_ :			Optional[int]            =      vnp.copy()  # same in embedded
																								snake_case_ :			List[str]            =      state.reshape((1, -1)     )
																								snake_case_ :			str            =      torch.tensor(__UpperCamelCase     )
																		elif key_name == "model/dense/kernel":
																								snake_case_ :			Tuple            =      """model.last_project.weight"""
																								snake_case_ :			List[str]            =      vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
																								snake_case_ :			Tuple            =      torch.tensor(__UpperCamelCase     )
																		elif key_name == "model/dense_1/bias":
																								snake_case_ :			Tuple            =      """model.last_project.bias"""
																								snake_case_ :			Tuple            =      vnp.copy()  # same because it is one dimensional
																								snake_case_ :			int            =      torch.tensor(__UpperCamelCase     )
						torch.save(__UpperCamelCase       ,			args.output     )


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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    if exponent == 1:
        return base
    if exponent % 2 == 0:
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						exponent // 2			,						SCREAMING_SNAKE_CASE_			) % modulo_value
        return (x * x) % modulo_value
    else:
        return (base * _modexpt(SCREAMING_SNAKE_CASE_			,						exponent - 1			,						SCREAMING_SNAKE_CASE_			)) % modulo_value




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_ = 1_7_7_7			,						SCREAMING_SNAKE_CASE_ = 1_8_5_5			,						SCREAMING_SNAKE_CASE_ = 8			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						base
    for _ in range(1			,						SCREAMING_SNAKE_CASE_			):
        UpperCamelCase_			  =						_modexpt(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						1_0**digits			)

    return result


if __name__ == "__main__":
 print(F'''{solution() = }''')
