



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def 	snake_case__       (					__lowercase       )		->		List[Any]:







						"""simple docstring"""

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


if __name__ == "__main__":
		snake_case    :						Optional[Any]  =						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')
		snake_case    :						List[str]  =						parser.parse_args()
		convert_tf_gptsan_to_pt(args)




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 							lowerCAmelCase__  (	unittest.TestCase						):







					@slow
					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,		return_dict=_A).to(_A)
											A__	:							Any						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							int						    = tokenizer("Hello there"			,		return_tensors="pt").input_ids
											A__	:							List[str]						    = tokenizer("Hi I am"			,		return_tensors="pt").input_ids

											A__	:							int						    = model(input_ids.to(_A)			,		labels=labels.to(_A)).loss
											A__	:							Optional[int]						    = -(labels.shape[-1] * loss.item())

											A__	:							List[str]						    = -84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)