




import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
UpperCamelCase__    			=			abspath(join(dirname(dirname(__file__)), """src"""))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action="""ignore""", category=FutureWarning)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict     ):
					from diffusers.utils.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(_lowercase     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[str]     ):
					from diffusers.utils.testing_utils import pytest_terminal_summary_main

					__lowerCAmelCase				   =       terminalreporter.config.getoption("--make-reports"     )
					if make_reports:
										pytest_terminal_summary_main(_lowercase      ,      id=_lowercase     )











"""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		(	_lowercase						:    List[Any]					) ->str:






	'''simple docstring'''






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


if __name__ == "__main__":
		a						: Optional[int]     	=			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						: Tuple     	=			parser.parse_args()
		convert_tf_gptsan_to_pt(args)


