




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class    SCREAMING_SNAKE_CASE_				(       _UpperCamelCase	,  unittest.TestCase   ):



	"""simple docstring"""


	__magic_name__   :       int      	=		KandinskyInpaintPipeline
	__magic_name__   :       Union[str, Any]      	=		['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
	__magic_name__   :       int      	=		[
	    'prompt',
	    'negative_prompt',
	    'image_embeds',
	    'negative_image_embeds',
	    'image',
	    'mask_image',
	]
	__magic_name__   :       str      	=		[
	    'generator',
	    'height',
	    'width',
	    'latents',
	    'guidance_scale',
	    'negative_prompt',
	    'num_inference_steps',
	    'return_dict',
	    'guidance_scale',
	    'num_images_per_prompt',
	    'output_type',
	    'return_dict',
	]
	__magic_name__   :       Any      	=		False






	@property
	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Any:







						"""simple docstring"""







						return 32






	@property
	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Tuple:







						"""simple docstring"""







						return 32






	@property
	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Any:







						"""simple docstring"""







						return self.time_input_dim






	@property
	def 					lowerCamelCase__     (					self		:						List[str]	)	->       Any:







						"""simple docstring"""







						return self.time_input_dim * 4






	@property
	def 					lowerCamelCase__     (					self		:						List[str]	)	->       Optional[Any]:







						"""simple docstring"""







						return 100






	@property
	def 					lowerCamelCase__     (					self		:						Tuple	)	->       str:







						"""simple docstring"""







						__UpperCamelCase					:	Optional[Any]             =      XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
						return tokenizer






	@property
	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       str:







						"""simple docstring"""







						torch.manual_seed(0	)
						__UpperCamelCase					:	List[str]             =      MCLIPConfig(
						    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=1005					,     )

						__UpperCamelCase					:	str             =      MultilingualCLIP(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      text_encoder.eval()

						return text_encoder






	@property
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       str:







						"""simple docstring"""







						torch.manual_seed(0	)

						__UpperCamelCase					:	Any             =      {
						    """in_channels""": 9,
						    # Out channels is double in channels because predicts mean and variance
						    """out_channels""": 8,
						    """addition_embed_type""": """text_image""",
						    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
						    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
						    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
						    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
						    """layers_per_block""": 1,
						    """encoder_hid_dim""": self.text_embedder_hidden_size,
						    """encoder_hid_dim_type""": """text_image_proj""",
						    """cross_attention_dim""": self.cross_attention_dim,
						    """attention_head_dim""": 4,
						    """resnet_time_scale_shift""": """scale_shift""",
						    """class_embed_type""": None,
						}

						__UpperCamelCase					:	Union[str, Any]             =      UNetaDConditionModel(**lowerCAmelCase	)
						return model






	@property
	def 					lowerCamelCase__     (					self		:						List[Any]	)	->       List[Any]:







						"""simple docstring"""







						return {
						    "block_out_channels": [32, 64],
						    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
						    "in_channels": 3,
						    "latent_channels": 4,
						    "layers_per_block": 1,
						    "norm_num_groups": 8,
						    "norm_type": "spatial",
						    "num_vq_embeddings": 12,
						    "out_channels": 3,
						    "up_block_types": [
						        "AttnUpDecoderBlock2D",
						        "UpDecoderBlock2D",
						    ],
						    "vq_embed_dim": 4,
						}






	@property
	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       int:







						"""simple docstring"""







						torch.manual_seed(0	)
						__UpperCamelCase					:	Dict             =      VQModel(**self.dummy_movq_kwargs	)
						return model






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







						"""simple docstring"""







						__UpperCamelCase					:	Optional[int]             =      self.dummy_text_encoder
						__UpperCamelCase					:	List[Any]             =      self.dummy_tokenizer
						__UpperCamelCase					:	List[Any]             =      self.dummy_unet
						__UpperCamelCase					:	Any             =      self.dummy_movq

						__UpperCamelCase					:	Optional[Any]             =      DDIMScheduler(
						    num_train_timesteps=1000					,     beta_schedule="""linear"""					,     beta_start=0.0_00_85					,     beta_end=0.0_12					,     clip_sample=lowerCAmelCase					,     set_alpha_to_one=lowerCAmelCase					,     steps_offset=1					,     prediction_type="""epsilon"""					,     thresholding=lowerCAmelCase					,     )

						__UpperCamelCase					:	List[Any]             =      {
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """movq""": movq,
						}

						return components






	def 					lowerCamelCase__     (					self		:						Dict					,     lowerCAmelCase		:						Optional[int]					,     lowerCAmelCase		:						Dict=0	)	->       List[str]:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase	)	).to(lowerCAmelCase	)
						__UpperCamelCase					:	Dict             =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1	)	).to(lowerCAmelCase	)
						# create init_image
						__UpperCamelCase					:	str             =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase	)	).to(lowerCAmelCase	)
						__UpperCamelCase					:	List[Any]             =      image.cpu().permute(0					,     2					,     3					,     1	)[0]
						__UpperCamelCase					:	str             =      Image.fromarray(np.uinta(lowerCAmelCase	)	).convert("""RGB"""	).resize((256, 256)	)
						# create mask
						__UpperCamelCase					:	List[str]             =      np.ones((64, 64)					,     dtype=np.floataa	)
						__UpperCamelCase					:	Optional[Any]             =      0

						if str(lowerCAmelCase	).startswith("""mps"""	):
											__UpperCamelCase					:	Union[str, Any]             =      torch.manual_seed(lowerCAmelCase	)
						else:
											__UpperCamelCase					:	int             =      torch.Generator(device=lowerCAmelCase	).manual_seed(lowerCAmelCase	)
						__UpperCamelCase					:	List[str]             =      {
						    """prompt""": """horse""",
						    """image""": init_image,
						    """mask_image""": mask,
						    """image_embeds""": image_embeds,
						    """negative_image_embeds""": negative_image_embeds,
						    """generator""": generator,
						    """height""": 64,
						    """width""": 64,
						    """num_inference_steps""": 2,
						    """guidance_scale""": 4.0,
						    """output_type""": """np""",
						}
						return inputs






	def 					lowerCamelCase__     (					self		:						Optional[Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	str             =      """cpu"""

						__UpperCamelCase					:	Optional[Any]             =      self.get_dummy_components()

						__UpperCamelCase					:	Dict             =      self.pipeline_class(**lowerCAmelCase	)
						__UpperCamelCase					:	Optional[int]             =      pipe.to(lowerCAmelCase	)

						pipe.set_progress_bar_config(disable=lowerCAmelCase	)

						__UpperCamelCase					:	Any             =      pipe(**self.get_dummy_inputs(lowerCAmelCase	)	)
						__UpperCamelCase					:	Union[str, Any]             =      output.images

						__UpperCamelCase					:	Optional[Any]             =      pipe(
						    **self.get_dummy_inputs(lowerCAmelCase	)					,     return_dict=lowerCAmelCase					,     )[0]

						__UpperCamelCase					:	Optional[int]             =      image[0, -3:, -3:, -1]
						__UpperCamelCase					:	Dict             =      image_from_tuple[0, -3:, -3:, -1]

						print(F'''image.shape {image.shape}'''	)

						assert image.shape == (1, 64, 64, 3)

						__UpperCamelCase					:	List[str]             =      np.array(
						    [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]	)

						assert (
						    np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
						assert (
						    np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2
						), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''






	def 					lowerCamelCase__     (					self		:						List[str]	)	->       Dict:







						"""simple docstring"""







						super().test_inference_batch_single_identical(expected_max_diff=3E-3	)






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



	"""simple docstring"""


	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       Optional[Any]:







						"""simple docstring"""







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






	def 					lowerCamelCase__     (					self		:						Tuple	)	->       Union[str, Any]:







						"""simple docstring"""







						__UpperCamelCase					:	List[Any]             =      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
						    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""	)

						__UpperCamelCase					:	Optional[Any]             =      load_image(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""	)
						__UpperCamelCase					:	str             =      np.ones((768, 768)					,     dtype=np.floataa	)
						__UpperCamelCase					:	int             =      0

						__UpperCamelCase					:	int             =      """a hat"""

						__UpperCamelCase					:	Tuple             =      KandinskyPriorPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-prior"""					,     torch_dtype=torch.floataa	)
						pipe_prior.to(lowerCAmelCase	)

						__UpperCamelCase					:	Dict             =      KandinskyInpaintPipeline.from_pretrained(
						    """kandinsky-community/kandinsky-2-1-inpaint"""					,     torch_dtype=torch.floataa	)
						__UpperCamelCase					:	Optional[Any]             =      pipeline.to(lowerCAmelCase	)
						pipeline.set_progress_bar_config(disable=lowerCAmelCase	)

						__UpperCamelCase					:	int             =      torch.Generator(device="""cpu"""	).manual_seed(0	)
						__UpperCamelCase      ,			__UpperCamelCase					:	List[Any]             =      pipe_prior(
						    lowerCAmelCase					,     generator=lowerCAmelCase					,     num_inference_steps=5					,     negative_prompt=""""""					,     ).to_tuple()

						__UpperCamelCase					:	Any             =      pipeline(
						    lowerCAmelCase					,     image=lowerCAmelCase					,     mask_image=lowerCAmelCase					,     image_embeds=lowerCAmelCase					,     negative_image_embeds=lowerCAmelCase					,     generator=lowerCAmelCase					,     num_inference_steps=100					,     height=768					,     width=768					,     output_type="""np"""					,     )

						__UpperCamelCase					:	Dict             =      output.images[0]

						assert image.shape == (768, 768, 3)

						assert_mean_pixel_difference(lowerCAmelCase					,     lowerCAmelCase	)












import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
	from transformers import LlamaTokenizerFast
except ImportError as e:
	warnings.warn(e)
	warnings.warn(
	    '''The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion'''
	)
	a__		     =     None
a__		     =     {
    '''7B''': 1_1008,
    '''13B''': 1_3824,
    '''30B''': 1_7920,
    '''65B''': 2_2016,
    '''70B''': 2_8672,
}
a__		     =     {
    '''7B''': 1,
    '''7Bf''': 1,
    '''13B''': 2,
    '''13Bf''': 2,
    '''30B''': 4,
    '''65B''': 8,
    '''70B''': 8,
    '''70Bf''': 8,
}





def 				A__ (snake_case   :		str		,	snake_case   :		Optional[Any]=1		,	snake_case   :		Optional[int]=2_56				)     ->						Dict:
					return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3				)				) + multiple_of - 1) // multiple_of)





def 				A__ (snake_case   :		Dict				)     ->						Optional[Any]:
					with open(snake_case		,	"""r"""				) as f:
										return json.load(snake_case				)





def 				A__ (snake_case   :		List[Any]		,	snake_case   :		Any				)     ->						Dict:
					with open(snake_case		,	"""w"""				) as f:
										json.dump(snake_case		,	snake_case				)





def 				A__ (snake_case   :		Tuple		,	snake_case   :		List[Any]		,	snake_case   :		List[str]		,	snake_case   :		str=True				)     ->						int:
					os.makedirs(snake_case		,	exist_ok=snake_case				)
					__UpperCamelCase					:	Optional[int]             =      os.path.join(snake_case		,	"""tmp"""				)
					os.makedirs(snake_case		,	exist_ok=snake_case				)

					__UpperCamelCase					:	Any             =      read_json(os.path.join(snake_case		,	"""params.json"""				)				)
					__UpperCamelCase					:	Dict             =      NUM_SHARDS[model_size]
					__UpperCamelCase					:	List[Any]             =      params["""n_layers"""]
					__UpperCamelCase					:	Any             =      params["""n_heads"""]
					__UpperCamelCase					:	Optional[int]             =      n_heads // num_shards
					__UpperCamelCase					:	Tuple             =      params["""dim"""]
					__UpperCamelCase					:	Optional[int]             =      dim // n_heads
					__UpperCamelCase					:	Dict             =      10000.0
					__UpperCamelCase					:	str             =      1.0 / (base ** (torch.arange(0		,	snake_case		,	2				).float() / dims_per_head))

					if "n_kv_heads" in params:
										__UpperCamelCase					:	Any             =      params["""n_kv_heads"""]  # for GQA / MQA
										__UpperCamelCase					:	Optional[int]             =      n_heads_per_shard // num_key_value_heads
										__UpperCamelCase					:	List[Any]             =      dim // num_key_value_heads
					else:  # compatibility with other checkpoints
										__UpperCamelCase					:	Optional[Any]             =      n_heads
										__UpperCamelCase					:	Any             =      n_heads_per_shard
										__UpperCamelCase					:	str             =      dim

					# permute for sliced rotary
					def permute(snake_case   :		Tuple		,	snake_case   :		Dict=n_heads		,	snake_case   :		str=dim		,	snake_case   :		Optional[Any]=dim				):
										return w.view(snake_case		,	dima // n_heads // 2		,	2		,	snake_case				).transpose(1		,	2				).reshape(snake_case		,	snake_case				)

					print(F'''Fetching all parameters from the checkpoint at {input_base_path}.'''				)
					# Load weights
					if model_size == "7B":
										# Not sharded
										# (The sharded implementation would also work, but this is simpler.)
										__UpperCamelCase					:	Any             =      torch.load(os.path.join(snake_case		,	"""consolidated.00.pth"""				)		,	map_location="""cpu"""				)
					else:
										# Sharded
										__UpperCamelCase					:	Optional[int]             =      [
										    torch.load(os.path.join(snake_case		,	F'''consolidated.{i:02d}.pth'''				)		,	map_location="""cpu"""				)
										    for i in range(snake_case				)
										]
					__UpperCamelCase					:	Optional[int]             =      0
					__UpperCamelCase					:	List[str]             =      {"""weight_map""": {}}
					for layer_i in range(snake_case				):
										__UpperCamelCase					:	Tuple             =      F'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
										if model_size == "7B":
															# Unsharded
															__UpperCamelCase					:	Optional[Any]             =      {
															    F'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
															        loaded[F'''layers.{layer_i}.attention.wq.weight''']				),
															    F'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
															        loaded[F'''layers.{layer_i}.attention.wk.weight''']				),
															    F'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[F'''layers.{layer_i}.attention.wv.weight'''],
															    F'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[F'''layers.{layer_i}.attention.wo.weight'''],
															    F'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w1.weight'''],
															    F'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w2.weight'''],
															    F'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w3.weight'''],
															    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[F'''layers.{layer_i}.attention_norm.weight'''],
															    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[F'''layers.{layer_i}.ffn_norm.weight'''],
															}
										else:
															# Sharded
															# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
															# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
															# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

															__UpperCamelCase					:	int             =      {
															    F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
															        F'''layers.{layer_i}.attention_norm.weight'''
															    ].clone(),
															    F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
															        F'''layers.{layer_i}.ffn_norm.weight'''
															    ].clone(),
															}
															__UpperCamelCase					:	Union[str, Any]             =      permute(
															    torch.cat(
															        [
															            loaded[i][F'''layers.{layer_i}.attention.wq.weight'''].view(snake_case		,	snake_case		,	snake_case				)
															            for i in range(snake_case				)
															        ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)				)
															__UpperCamelCase					:	int             =      permute(
															    torch.cat(
															        [
															            loaded[i][F'''layers.{layer_i}.attention.wk.weight'''].view(
															                snake_case		,	snake_case		,	snake_case				)
															            for i in range(snake_case				)
															        ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)		,	snake_case		,	snake_case		,	snake_case		,	)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [
															        loaded[i][F'''layers.{layer_i}.attention.wv.weight'''].view(
															            snake_case		,	snake_case		,	snake_case				)
															        for i in range(snake_case				)
															    ]		,	dim=0		,	).reshape(snake_case		,	snake_case				)

															__UpperCamelCase					:	str             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(snake_case				)]		,	dim=1				)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(snake_case				)]		,	dim=0				)
															__UpperCamelCase					:	Dict             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(snake_case				)]		,	dim=1				)
															__UpperCamelCase					:	List[str]             =      torch.cat(
															    [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(snake_case				)]		,	dim=0				)

										__UpperCamelCase					:	Dict             =      inv_freq
										for k, v in state_dict.items():
															__UpperCamelCase					:	str             =      filename
															param_count += v.numel()
										torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)

					__UpperCamelCase					:	Dict             =      F'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
					if model_size == "7B":
										# Unsharded
										__UpperCamelCase					:	Dict             =      {
										    """model.embed_tokens.weight""": loaded["""tok_embeddings.weight"""],
										    """model.norm.weight""": loaded["""norm.weight"""],
										    """lm_head.weight""": loaded["""output.weight"""],
										}
					else:
										__UpperCamelCase					:	Dict             =      {
										    """model.norm.weight""": loaded[0]["""norm.weight"""],
										    """model.embed_tokens.weight""": torch.cat(
										        [loaded[i]["""tok_embeddings.weight"""] for i in range(snake_case				)]		,	dim=1				),
										    """lm_head.weight""": torch.cat([loaded[i]["""output.weight"""] for i in range(snake_case				)]		,	dim=0				),
										}

					for k, v in state_dict.items():
										__UpperCamelCase					:	str             =      filename
										param_count += v.numel()
					torch.save(snake_case		,	os.path.join(snake_case		,	snake_case				)				)

					# Write configs
					__UpperCamelCase					:	Union[str, Any]             =      {"""total_size""": param_count * 2}
					write_json(snake_case		,	os.path.join(snake_case		,	"""pytorch_model.bin.index.json"""				)				)
					__UpperCamelCase					:	str             =      params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
					__UpperCamelCase					:	str             =      params["""multiple_of"""] if """multiple_of""" in params else 2_56
					__UpperCamelCase					:	str             =      LlamaConfig(
					    hidden_size=snake_case		,	intermediate_size=compute_intermediate_size(snake_case		,	snake_case		,	snake_case				)		,	num_attention_heads=params["""n_heads"""]		,	num_hidden_layers=params["""n_layers"""]		,	rms_norm_eps=params["""norm_eps"""]		,	num_key_value_heads=snake_case		,	)
					config.save_pretrained(snake_case				)

					# Make space so we can load the model properly now.
					del state_dict
					del loaded
					gc.collect()

					print("""Loading the checkpoint in a Llama model."""				)
					__UpperCamelCase					:	Optional[int]             =      LlamaForCausalLM.from_pretrained(snake_case		,	torch_dtype=torch.floataa		,	low_cpu_mem_usage=snake_case				)
					# Avoid saving this as part of the config.
					del model.config._name_or_path

					print("""Saving in the Transformers format."""				)
					model.save_pretrained(snake_case		,	safe_serialization=snake_case				)
					shutil.rmtree(snake_case				)





def 				A__ (snake_case   :		Any		,	snake_case   :		str				)     ->						Union[str, Any]:
					# Initialize the tokenizer based on the `spm` model
					__UpperCamelCase					:	Union[str, Any]             =      LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
					print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.'''				)
					__UpperCamelCase					:	Tuple             =      tokenizer_class(snake_case				)
					tokenizer.save_pretrained(snake_case				)





def 				A__ ()     ->						int:
					__UpperCamelCase					:	Union[str, Any]             =      argparse.ArgumentParser()
					parser.add_argument(
					    """--input_dir"""		,	help="""Location of LLaMA weights, which contains tokenizer.model and model folders"""		,	)
					parser.add_argument(
					    """--model_size"""		,	choices=["""7B""", """7Bf""", """13B""", """13Bf""", """30B""", """65B""", """70B""", """70Bf""", """tokenizer_only"""]		,	)
					parser.add_argument(
					    """--output_dir"""		,	help="""Location to write HF model and tokenizer"""		,	)
					parser.add_argument("""--safe_serialization"""		,	type=snake_case		,	help="""Whether or not to save using `safetensors`."""				)
					__UpperCamelCase					:	str             =      parser.parse_args()
					if args.model_size != "tokenizer_only":
										write_model(
										    model_path=args.output_dir		,	input_base_path=os.path.join(args.input_dir		,	args.model_size				)		,	model_size=args.model_size		,	safe_serialization=args.safe_serialization		,	)
					__UpperCamelCase					:	int             =      os.path.join(args.input_dir		,	"""tokenizer.model"""				)
					write_tokenizer(args.output_dir		,	snake_case				)


if __name__ == "__main__":
	main()






