



"""simple docstring"""
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__a      =				logging.get_logger(__name__)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Union[str, Any]						=			DPTConfig(embedding_type="""hybrid"""	)

						if "large" in checkpoint_url:
												snake_case_ :List[Any]						=			1024
												snake_case_ :int						=			4096
												snake_case_ :int						=			24
												snake_case_ :Tuple						=			16
												snake_case_ :Any						=			[5, 11, 17, 23]
												snake_case_ :List[Any]						=			[256, 512, 1024, 1024]
												snake_case_ :str						=			(1, 384, 384)

						if "nyu" or "midas" in checkpoint_url:
												snake_case_ :List[str]						=			768
												snake_case_ :Any						=			[1, 1, 1, 0.5]
												snake_case_ :Optional[Any]						=			[256, 512, 768, 768]
												snake_case_ :Optional[Any]						=			150
												snake_case_ :List[str]						=			16
												snake_case_ :Optional[Any]						=			(1, 384, 384)
												snake_case_ :Tuple						=			False
												snake_case_ :List[Any]						=			"""project"""

						if "ade" in checkpoint_url:
												snake_case_ :Dict						=			True
												snake_case_ :Optional[int]						=			768
												snake_case_ :int						=			[1, 1, 1, 0.5]
												snake_case_ :Any						=			150
												snake_case_ :Optional[Any]						=			16
												snake_case_ :List[Any]						=			"""huggingface/label-files"""
												snake_case_ :Any						=			"""ade20k-id2label.json"""
												snake_case_ :Optional[Any]						=			json.load(open(cached_download(hf_hub_url(_lowercase,					_lowercase,					repo_type="""dataset"""	)	),					"""r"""	)	)
												snake_case_ :Union[str, Any]						=			{int(_lowercase	): v for k, v in idalabel.items()}
												snake_case_ :Union[str, Any]						=			idalabel
												snake_case_ :str						=			{v: k for k, v in idalabel.items()}
												snake_case_ :List[str]						=			[1, 150, 480, 480]

						return config, expected_shape
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(_lowercase,					_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												snake_case_ :str						=			name.replace("""pretrained.model""",					"""dpt.encoder"""	)
						if "pretrained.model" in name:
												snake_case_ :Optional[Any]						=			name.replace("""pretrained.model""",					"""dpt.embeddings"""	)
						if "patch_embed" in name:
												snake_case_ :List[str]						=			name.replace("""patch_embed""",					""""""	)
						if "pos_embed" in name:
												snake_case_ :int						=			name.replace("""pos_embed""",					"""position_embeddings"""	)
						if "attn.proj" in name:
												snake_case_ :Union[str, Any]						=			name.replace("""attn.proj""",					"""attention.output.dense"""	)
						if "proj" in name and "project" not in name:
												snake_case_ :str						=			name.replace("""proj""",					"""projection"""	)
						if "blocks" in name:
												snake_case_ :Dict						=			name.replace("""blocks""",					"""layer"""	)
						if "mlp.fc1" in name:
												snake_case_ :int						=			name.replace("""mlp.fc1""",					"""intermediate.dense"""	)
						if "mlp.fc2" in name:
												snake_case_ :int						=			name.replace("""mlp.fc2""",					"""output.dense"""	)
						if "norm1" in name and "backbone" not in name:
												snake_case_ :Optional[int]						=			name.replace("""norm1""",					"""layernorm_before"""	)
						if "norm2" in name and "backbone" not in name:
												snake_case_ :str						=			name.replace("""norm2""",					"""layernorm_after"""	)
						if "scratch.output_conv" in name:
												snake_case_ :List[str]						=			name.replace("""scratch.output_conv""",					"""head"""	)
						if "scratch" in name:
												snake_case_ :int						=			name.replace("""scratch""",					"""neck"""	)
						if "layer1_rn" in name:
												snake_case_ :Tuple						=			name.replace("""layer1_rn""",					"""convs.0"""	)
						if "layer2_rn" in name:
												snake_case_ :List[str]						=			name.replace("""layer2_rn""",					"""convs.1"""	)
						if "layer3_rn" in name:
												snake_case_ :Tuple						=			name.replace("""layer3_rn""",					"""convs.2"""	)
						if "layer4_rn" in name:
												snake_case_ :Optional[int]						=			name.replace("""layer4_rn""",					"""convs.3"""	)
						if "refinenet" in name:
												snake_case_ :Union[str, Any]						=			int(name[len("""neck.refinenet"""	) : len("""neck.refinenet"""	) + 1]	)
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												snake_case_ :Optional[Any]						=			name.replace(f"""refinenet{layer_idx}""",					f"""fusion_stage.layers.{abs(layer_idx-4	)}"""	)
						if "out_conv" in name:
												snake_case_ :str						=			name.replace("""out_conv""",					"""projection"""	)
						if "resConfUnit1" in name:
												snake_case_ :Union[str, Any]						=			name.replace("""resConfUnit1""",					"""residual_layer1"""	)
						if "resConfUnit2" in name:
												snake_case_ :int						=			name.replace("""resConfUnit2""",					"""residual_layer2"""	)
						if "conv1" in name:
												snake_case_ :int						=			name.replace("""conv1""",					"""convolution1"""	)
						if "conv2" in name:
												snake_case_ :str						=			name.replace("""conv2""",					"""convolution2"""	)
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												snake_case_ :Optional[Any]						=			name.replace("""pretrained.act_postprocess1.0.project.0""",					"""neck.reassemble_stage.readout_projects.0.0"""	)
						if "pretrained.act_postprocess2.0.project.0" in name:
												snake_case_ :List[str]						=			name.replace("""pretrained.act_postprocess2.0.project.0""",					"""neck.reassemble_stage.readout_projects.1.0"""	)
						if "pretrained.act_postprocess3.0.project.0" in name:
												snake_case_ :Optional[int]						=			name.replace("""pretrained.act_postprocess3.0.project.0""",					"""neck.reassemble_stage.readout_projects.2.0"""	)
						if "pretrained.act_postprocess4.0.project.0" in name:
												snake_case_ :int						=			name.replace("""pretrained.act_postprocess4.0.project.0""",					"""neck.reassemble_stage.readout_projects.3.0"""	)

						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												snake_case_ :Optional[Any]						=			name.replace("""pretrained.act_postprocess1.3""",					"""neck.reassemble_stage.layers.0.projection"""	)
						if "pretrained.act_postprocess1.4" in name:
												snake_case_ :Optional[Any]						=			name.replace("""pretrained.act_postprocess1.4""",					"""neck.reassemble_stage.layers.0.resize"""	)
						if "pretrained.act_postprocess2.3" in name:
												snake_case_ :int						=			name.replace("""pretrained.act_postprocess2.3""",					"""neck.reassemble_stage.layers.1.projection"""	)
						if "pretrained.act_postprocess2.4" in name:
												snake_case_ :Optional[int]						=			name.replace("""pretrained.act_postprocess2.4""",					"""neck.reassemble_stage.layers.1.resize"""	)
						if "pretrained.act_postprocess3.3" in name:
												snake_case_ :List[str]						=			name.replace("""pretrained.act_postprocess3.3""",					"""neck.reassemble_stage.layers.2.projection"""	)
						if "pretrained.act_postprocess4.3" in name:
												snake_case_ :Tuple						=			name.replace("""pretrained.act_postprocess4.3""",					"""neck.reassemble_stage.layers.3.projection"""	)
						if "pretrained.act_postprocess4.4" in name:
												snake_case_ :str						=			name.replace("""pretrained.act_postprocess4.4""",					"""neck.reassemble_stage.layers.3.resize"""	)
						if "pretrained" in name:
												snake_case_ :List[str]						=			name.replace("""pretrained""",					"""dpt"""	)
						if "bn" in name:
												snake_case_ :Optional[int]						=			name.replace("""bn""",					"""batch_norm"""	)
						if "head" in name:
												snake_case_ :Dict						=			name.replace("""head""",					"""head.head"""	)
						if "encoder.norm" in name:
												snake_case_ :Optional[int]						=			name.replace("""encoder.norm""",					"""layernorm"""	)
						if "auxlayer" in name:
												snake_case_ :List[str]						=			name.replace("""auxlayer""",					"""auxiliary_head.head"""	)
						if "backbone" in name:
												snake_case_ :List[str]						=			name.replace("""backbone""",					"""backbone.bit.encoder"""	)

						if ".." in name:
												snake_case_ :str						=			name.replace("""..""",					"""."""	)

						if "stem.conv" in name:
												snake_case_ :Optional[Any]						=			name.replace("""stem.conv""",					"""bit.embedder.convolution"""	)
						if "blocks" in name:
												snake_case_ :int						=			name.replace("""blocks""",					"""layers"""	)
						if "convolution" in name and "backbone" in name:
												snake_case_ :Any						=			name.replace("""convolution""",					"""conv"""	)
						if "layer" in name and "backbone" in name:
												snake_case_ :Optional[int]						=			name.replace("""layer""",					"""layers"""	)
						if "backbone.bit.encoder.bit" in name:
												snake_case_ :Any						=			name.replace("""backbone.bit.encoder.bit""",					"""backbone.bit"""	)
						if "embedder.conv" in name:
												snake_case_ :List[Any]						=			name.replace("""embedder.conv""",					"""embedder.convolution"""	)
						if "backbone.bit.encoder.stem.norm" in name:
												snake_case_ :Any						=			name.replace("""backbone.bit.encoder.stem.norm""",					"""backbone.bit.embedder.norm"""	)
						return name
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						for i in range(config.num_hidden_layers	):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												snake_case_ :str						=			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.weight"""	)
												snake_case_ :List[str]						=			state_dict.pop(f"""dpt.encoder.layer.{i}.attn.qkv.bias"""	)
												# next, add query, keys and values (in that order) to the state dict
												snake_case_ :List[Any]						=			in_proj_weight[: config.hidden_size, :]
												snake_case_ :Union[str, Any]						=			in_proj_bias[: config.hidden_size]
												snake_case_ :List[str]						=			in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												snake_case_ :List[Any]						=			in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												snake_case_ :Tuple						=			in_proj_weight[
												    -config.hidden_size :, :
												]
												snake_case_ :Optional[int]						=			in_proj_bias[-config.hidden_size :]
def    A_  (			):







						'''simple docstring'''


						snake_case_ :Tuple						=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						snake_case_ :List[Any]						=			Image.open(requests.get(_lowercase,					stream=_lowercase	).raw	)
						return im
@torch.no_grad()
def    A_  (			_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_,       snake_case_ :int						=			get_dpt_config(_lowercase	)
						# load original state_dict from URL
						# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
						snake_case_ :Any						=			torch.load(_lowercase,					map_location="""cpu"""	)
						# remove certain keys
						remove_ignore_keys_(_lowercase	)
						# rename keys
						for key in state_dict.copy().keys():
												snake_case_ :Any						=			state_dict.pop(_lowercase	)
												snake_case_ :int						=			val
						# read in qkv matrices
						read_in_q_k_v(_lowercase,					_lowercase	)

						# load HuggingFace model
						snake_case_ :Tuple						=			DPTForSemanticSegmentation(_lowercase	) if """ade""" in checkpoint_url else DPTForDepthEstimation(_lowercase	)
						model.load_state_dict(_lowercase	)
						model.eval()

						# Check outputs on an image
						snake_case_ :List[str]						=			480 if """ade""" in checkpoint_url else 384
						snake_case_ :Any						=			DPTImageProcessor(size=_lowercase	)

						snake_case_ :Any						=			prepare_img()
						snake_case_ :Tuple						=			image_processor(_lowercase,					return_tensors="""pt"""	)

						# forward pass
						snake_case_ :str						=			model(**_lowercase	).logits if """ade""" in checkpoint_url else model(**_lowercase	).predicted_depth

						if show_prediction:
												snake_case_ :Union[str, Any]						=			(
												    torch.nn.functional.interpolate(
												        outputs.unsqueeze(1	),					size=(image.size[1], image.size[0]),					mode="""bicubic""",					align_corners=_lowercase,					)
												    .squeeze()
												    .cpu()
												    .numpy()
												)

												Image.fromarray((prediction / prediction.max()) * 255	).show()

						if pytorch_dump_folder_path is not None:
												Path(_lowercase	).mkdir(exist_ok=_lowercase	)
												print(f"""Saving model to {pytorch_dump_folder_path}"""	)
												model.save_pretrained(_lowercase	)
												print(f"""Saving image processor to {pytorch_dump_folder_path}"""	)
												image_processor.save_pretrained(_lowercase	)

						if push_to_hub:
												model.push_to_hub("""ybelkada/dpt-hybrid-midas"""	)
												image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
						    type=str,
						    help="URL of the original DPT checkpoint you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default=None,
						    type=str,
						    required=False,
						    help="Path to the output PyTorch model directory.",
						)
						parser.add_argument(
						    "--push_to_hub",
						    action="store_true",
						)
						parser.add_argument(
						    "--model_name",
						    default="dpt-large",
						    type=str,
						    help="Name of the model, in case you're pushing to the hub.",
						)
						parser.add_argument(
						    "--show_prediction",
						    action="store_true",
						)

						__a      =				parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)





"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def    A_  (			_lowercase	):







						'''simple docstring'''


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