




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()







class     A(		unittest.TestCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		StableDiffusionLDMaDPipeline
 UpperCamelCase		  =		TEXT_TO_IMAGE_PARAMS
 UpperCamelCase		  =		TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCamelCase		  =		TEXT_TO_IMAGE_IMAGE_PARAMS

 def 							a__					(							self   :    List[str]    )  ->      Optional[Any]:

       """simple docstring"""






       torch.manual_seed(0    )
       lowerCamelCase_							=       UNetaDConditionModel(
           block_out_channels=(32, 64)		,				layers_per_block=2		,				sample_size=32		,				in_channels=4		,				out_channels=4		,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')		,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')		,				cross_attention_dim=32		,				)
       lowerCamelCase_							=       DDIMScheduler(
           beta_start=0.00085		,				beta_end=0.012		,				beta_schedule='scaled_linear'		,				clip_sample=A_		,				set_alpha_to_one=A_		,				)
       torch.manual_seed(0    )
       lowerCamelCase_							=       AutoencoderKL(
           block_out_channels=[32, 64]		,				in_channels=6		,				out_channels=6		,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']		,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']		,				latent_channels=4		,				)
       torch.manual_seed(0    )
       lowerCamelCase_							=       CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=32		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=1000		,				)
       lowerCamelCase_							=       CLIPTextModel(A_    )
       lowerCamelCase_							=       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

       lowerCamelCase_							=       {
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components

 def 							a__					(							self   :    Any		,				A_   :    List[str]		,				A_   :    List[str]=0    )  ->      int:

       """simple docstring"""






       if str(A_    ).startswith('mps'    ):
             lowerCamelCase_							=       torch.manual_seed(A_    )
       else:
             lowerCamelCase_							=       torch.Generator(device=A_    ).manual_seed(A_    )
       lowerCamelCase_							=       {
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'guidance_scale': 6.0,
           'output_type': 'numpy',
       }
       return inputs

 def 							a__					(							self   :    Dict    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       'cpu'  # ensure determinism for the device-dependent torch.Generator

       lowerCamelCase_							=       self.get_dummy_components()
       lowerCamelCase_							=       StableDiffusionLDMaDPipeline(**A_    )
       lowerCamelCase_							=       ldmad_pipe.to(A_    )
       ldmad_pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       self.get_dummy_inputs(A_    )
       lowerCamelCase_							=       ldmad_pipe(**A_    )
       lowerCamelCase_	,							lowerCamelCase_							=       output.rgb, output.depth

       lowerCamelCase_							=       rgb[0, -3:, -3:, -1]
       lowerCamelCase_							=       depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       lowerCamelCase_							=       np.array(
           [0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]    )
       lowerCamelCase_							=       np.array([103.46727, 85.812004, 87.849236]    )

       assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb    ).max() < 1E-2
       assert np.abs(image_slice_depth.flatten() - expected_slice_depth    ).max() < 1E-2

 def 							a__					(							self   :    Union[str, Any]    )  ->      int:

       """simple docstring"""






       lowerCamelCase_							=       self.get_dummy_components()
       lowerCamelCase_							=       StableDiffusionLDMaDPipeline(**A_    )
       lowerCamelCase_							=       ldmad_pipe.to(A_    )
       ldmad_pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       self.get_dummy_inputs(A_    )
       lowerCamelCase_							=       3 * [inputs['prompt']]

       # forward
       lowerCamelCase_							=       ldmad_pipe(**A_    )
       lowerCamelCase_	,							lowerCamelCase_							=       output.rgb, output.depth
       lowerCamelCase_							=       rgb_slice_a[0, -3:, -3:, -1]
       lowerCamelCase_							=       depth_slice_a[0, -3:, -1]

       lowerCamelCase_							=       self.get_dummy_inputs(A_    )
       lowerCamelCase_							=       3 * [inputs.pop('prompt'    )]

       lowerCamelCase_							=       ldmad_pipe.tokenizer(
           A_		,				padding='max_length'		,				max_length=ldmad_pipe.tokenizer.model_max_length		,				truncation=A_		,				return_tensors='pt'		,				)
       lowerCamelCase_							=       text_inputs['input_ids'].to(A_    )

       lowerCamelCase_							=       ldmad_pipe.text_encoder(A_    )[0]

       lowerCamelCase_							=       prompt_embeds

       # forward
       lowerCamelCase_							=       ldmad_pipe(**A_    )
       lowerCamelCase_	,							lowerCamelCase_							=       output.rgb, output.depth
       lowerCamelCase_							=       rgb_slice_a[0, -3:, -3:, -1]
       lowerCamelCase_							=       depth_slice_a[0, -3:, -1]

       assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()    ).max() < 1E-4
       assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()    ).max() < 1E-4





 def 							a__					(							self   :    Optional[int]    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowerCamelCase_							=       self.get_dummy_components()
       lowerCamelCase_							=       PNDMScheduler(skip_prk_steps=A_    )
       lowerCamelCase_							=       StableDiffusionLDMaDPipeline(**A_    )
       lowerCamelCase_							=       ldmad_pipe.to(A_    )
       ldmad_pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       self.get_dummy_inputs(A_    )
       lowerCamelCase_							=       'french fries'
       lowerCamelCase_							=       ldmad_pipe(**A_		,				negative_prompt=A_    )

       lowerCamelCase_	,							lowerCamelCase_							=       output.rgb, output.depth
       lowerCamelCase_							=       rgb[0, -3:, -3:, -1]
       lowerCamelCase_							=       depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       lowerCamelCase_							=       np.array(
           [0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]    )
       lowerCamelCase_							=       np.array([107.84738, 84.62802, 89.962135]    )
       assert np.abs(rgb_slice.flatten() - expected_slice_rgb    ).max() < 1E-2
       assert np.abs(depth_slice.flatten() - expected_slice_depth    ).max() < 1E-2







@slow
@require_torch_gpu
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    Any    )  ->      Tuple:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 def 							a__					(							self   :    Dict		,				A_   :    Any		,				A_   :    int="cpu"		,				A_   :    int=torch.floataa		,				A_   :    Dict=0    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       torch.Generator(device=A_    ).manual_seed(A_    )
       lowerCamelCase_							=       np.random.RandomState(A_    ).standard_normal((1, 4, 64, 64)    )
       lowerCamelCase_							=       torch.from_numpy(A_    ).to(device=A_		,				dtype=A_    )
       lowerCamelCase_							=       {
           'prompt': 'a photograph of an astronaut riding a horse',
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 3,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs





 def 							a__					(							self   :    Dict    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'    )
       lowerCamelCase_							=       ldmad_pipe.to(A_    )
       ldmad_pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       self.get_inputs(A_    )
       lowerCamelCase_							=       ldmad_pipe(**A_    )
       lowerCamelCase_	,							lowerCamelCase_							=       output.rgb, output.depth
       lowerCamelCase_							=       rgb[0, -3:, -3:, -1].flatten()
       lowerCamelCase_							=       rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512)

       lowerCamelCase_							=       np.array(
           [0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]    )
       lowerCamelCase_							=       np.array(
           [0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]    )
       assert np.abs(rgb_slice - expected_slice_rgb    ).max() < 3E-3
       assert np.abs(depth_slice - expected_slice_depth    ).max() < 3E-3







@nightly
@require_torch_gpu
class     A(		unittest.TestCase  ):



 '''simple docstring'''


 def 							a__					(							self   :    List[str]    )  ->      List[str]:

       """simple docstring"""






       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()

 def 							a__					(							self   :    Union[str, Any]		,				A_   :    Optional[Any]		,				A_   :    str="cpu"		,				A_   :    List[Any]=torch.floataa		,				A_   :    Dict=0    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       torch.Generator(device=A_    ).manual_seed(A_    )
       lowerCamelCase_							=       np.random.RandomState(A_    ).standard_normal((1, 4, 64, 64)    )
       lowerCamelCase_							=       torch.from_numpy(A_    ).to(device=A_		,				dtype=A_    )
       lowerCamelCase_							=       {
           'prompt': 'a photograph of an astronaut riding a horse',
           'latents': latents,
           'generator': generator,
           'num_inference_steps': 50,
           'guidance_scale': 7.5,
           'output_type': 'numpy',
       }
       return inputs

 def 							a__					(							self   :    Any    )  ->      Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d'    ).to(A_    )
       ldmad_pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       self.get_inputs(A_    )
       lowerCamelCase_							=       ldmad_pipe(**A_    )
       lowerCamelCase_	,							lowerCamelCase_							=       output.rgb, output.depth

       lowerCamelCase_							=       0.495586
       lowerCamelCase_							=       0.33795515
       lowerCamelCase_							=       112.48518
       lowerCamelCase_							=       98.489746
       assert np.abs(expected_rgb_mean - rgb.mean()    ) < 1E-3
       assert np.abs(expected_rgb_std - rgb.std()    ) < 1E-3
       assert np.abs(expected_depth_mean - depth.mean()    ) < 1E-3
       assert np.abs(expected_depth_std - depth.std()    ) < 1E-3





 def 							a__					(							self   :    List[str]    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       StableDiffusionLDMaDPipeline.from_pretrained('Intel/ldm3d-4c'    ).to(A_    )
       ldmad_pipe.set_progress_bar_config(disable=A_    )

       lowerCamelCase_							=       self.get_inputs(A_    )
       lowerCamelCase_							=       ldmad_pipe(**A_    )
       lowerCamelCase_	,							lowerCamelCase_							=       output.rgb, output.depth

       lowerCamelCase_							=       0.4194127
       lowerCamelCase_							=       0.35375586
       lowerCamelCase_							=       0.5638502
       lowerCamelCase_							=       0.34686103

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512, 1)
       assert np.abs(expected_rgb_mean - rgb.mean()    ) < 1E-3
       assert np.abs(expected_rgb_std - rgb.std()    ) < 1E-3
       assert np.abs(expected_depth_mean - depth.mean()    ) < 1E-3
       assert np.abs(expected_depth_std - depth.std()    ) < 1E-3





import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch


def 				__A(lowerCAmelCase			)		->					Dict:

		"""simple docstring"""






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


if __name__ == "__main__":
		lowerCamelCase__						=				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__						=				parser.parse_args()
		convert_tf_gptsan_to_pt(args)



