"""simple docstring"""






import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




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


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





"""simple docstring"""






import math




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:

				if initial_intensity < 0:
								raise ValueError("The value of intensity cannot be negative"       )
								# handling of negative values of initial intensity
				if angle < 0 or angle > 360:
								raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"       )
								# handling of values out of allowed range
				return initial_intensity * (math.cos(math.radians(__UpperCAmelCase       )       ) ** 2)


if __name__ == "__main__":
						import doctest

						doctest.testmod(name='malus_law')




