from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFDebertaVaForMaskedLM,
					    TFDebertaVaForQuestionAnswering,
					    TFDebertaVaForSequenceClassification,
					    TFDebertaVaForTokenClassification,
					    TFDebertaVaModel,
					)
class     lowerCAmelCase_     :







							def __init__(   self      :     Dict ,			_A      :     Optional[Any] ,			_A      :     Dict=13 ,			_A      :     int=7 ,			_A      :     List[str]=True ,			_A      :     Optional[int]=True ,			_A      :     Union[str, Any]=True ,			_A      :     Optional[Any]=True ,			_A      :     List[Any]=99 ,			_A      :     Any=32 ,			_A      :     Union[str, Any]=2 ,			_A      :     Optional[int]=4 ,			_A      :     int=37 ,			_A      :     Any="gelu" ,			_A      :     int=0.1 ,			_A      :     Dict=0.1 ,			_A      :     Any=512 ,			_A      :     List[Any]=16 ,			_A      :     Tuple=2 ,			_A      :     List[Any]=0.02 ,			_A      :     List[Any]=False ,			_A      :     int=True ,			_A      :     Union[str, Any]="None" ,			_A      :     Optional[Any]=3 ,			_A      :     Dict=4 ,			_A      :     Any=None ,			):
												_UpperCamelCase			     =     parent
												_UpperCamelCase			     =     batch_size
												_UpperCamelCase			     =     seq_length
												_UpperCamelCase			     =     is_training
												_UpperCamelCase			     =     use_input_mask
												_UpperCamelCase			     =     use_token_type_ids
												_UpperCamelCase			     =     use_labels
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     hidden_size
												_UpperCamelCase			     =     num_hidden_layers
												_UpperCamelCase			     =     num_attention_heads
												_UpperCamelCase			     =     intermediate_size
												_UpperCamelCase			     =     hidden_act
												_UpperCamelCase			     =     hidden_dropout_prob
												_UpperCamelCase			     =     attention_probs_dropout_prob
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     type_vocab_size
												_UpperCamelCase			     =     type_sequence_label_size
												_UpperCamelCase			     =     initializer_range
												_UpperCamelCase			     =     num_labels
												_UpperCamelCase			     =     num_choices
												_UpperCamelCase			     =     relative_attention
												_UpperCamelCase			     =     position_biased_input
												_UpperCamelCase			     =     pos_att_type
												_UpperCamelCase			     =     scope







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.vocab_size		)

												_UpperCamelCase			     =     None
												if self.use_input_mask:
																	_UpperCamelCase			     =     random_attention_mask([self.batch_size, self.seq_length]		)

												_UpperCamelCase			     =     None
												if self.use_token_type_ids:
																	_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.type_vocab_size		)

												_UpperCamelCase			     =     None
												_UpperCamelCase			     =     None
												_UpperCamelCase			     =     None
												if self.use_labels:
																	_UpperCamelCase			     =     ids_tensor([self.batch_size] ,			self.type_sequence_label_size		)
																	_UpperCamelCase			     =     ids_tensor([self.batch_size, self.seq_length] ,			self.num_labels		)

												_UpperCamelCase			     =     DebertaVaConfig(
												    vocab_size=self.vocab_size ,			hidden_size=self.hidden_size ,			num_hidden_layers=self.num_hidden_layers ,			num_attention_heads=self.num_attention_heads ,			intermediate_size=self.intermediate_size ,			hidden_act=self.hidden_act ,			hidden_dropout_prob=self.hidden_dropout_prob ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob ,			max_position_embeddings=self.max_position_embeddings ,			type_vocab_size=self.type_vocab_size ,			relative_attention=self.relative_attention ,			position_biased_input=self.position_biased_input ,			initializer_range=self.initializer_range ,			return_dict=_A ,			)

												return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Optional[Any] ,			_A      :     Tuple ,			_A      :     Optional[Any] ,			_A      :     str ,			_A      :     str ,			_A      :     Optional[Any] ,			_A      :     Dict		):
												_UpperCamelCase			     =     TFDebertaVaModel(config=_A		)
												_UpperCamelCase			     =     {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

												_UpperCamelCase			     =     [input_ids, input_mask]
												_UpperCamelCase			     =     model(_A		)

												_UpperCamelCase			     =     model(_A		)

												self.parent.assertEqual(result.last_hidden_state.shape ,			(self.batch_size, self.seq_length, self.hidden_size)		)







							def  UpperCamelCase_						(   self      :     Optional[Any] ,			_A      :     int ,			_A      :     List[str] ,			_A      :     str ,			_A      :     Tuple ,			_A      :     List[Any] ,			_A      :     Dict ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     TFDebertaVaForMaskedLM(config=_A		)
												_UpperCamelCase			     =     {
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}
												_UpperCamelCase			     =     model(_A		)
												self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.vocab_size)		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     str ,			_A      :     str ,			_A      :     Union[str, Any] ,			_A      :     int ,			_A      :     Optional[Any] ,			_A      :     str ,			_A      :     Dict		):
												_UpperCamelCase			     =     self.num_labels
												_UpperCamelCase			     =     TFDebertaVaForSequenceClassification(config=_A		)
												_UpperCamelCase			     =     {
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}

												_UpperCamelCase			     =     model(_A		)
												self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.num_labels)		)







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     List[str] ,			_A      :     Tuple ,			_A      :     int ,			_A      :     int ,			_A      :     Tuple ,			_A      :     Tuple ,			_A      :     int		):
												_UpperCamelCase			     =     self.num_labels
												_UpperCamelCase			     =     TFDebertaVaForTokenClassification(config=_A		)
												_UpperCamelCase			     =     {
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}
												_UpperCamelCase			     =     model(_A		)
												self.parent.assertEqual(result.logits.shape ,			(self.batch_size, self.seq_length, self.num_labels)		)







							def  UpperCamelCase_						(   self      :     Dict ,			_A      :     Any ,			_A      :     Union[str, Any] ,			_A      :     List[str] ,			_A      :     Dict ,			_A      :     Tuple ,			_A      :     Any ,			_A      :     Union[str, Any]		):
												_UpperCamelCase			     =     TFDebertaVaForQuestionAnswering(config=_A		)
												_UpperCamelCase			     =     {
												    '''input_ids''': input_ids,
												    '''attention_mask''': input_mask,
												    '''token_type_ids''': token_type_ids,
												}

												_UpperCamelCase			     =     model(_A		)
												self.parent.assertEqual(result.start_logits.shape ,			(self.batch_size, self.seq_length)		)
												self.parent.assertEqual(result.end_logits.shape ,			(self.batch_size, self.seq_length)		)







							def  UpperCamelCase_						(   self      :     Union[str, Any]		):
												_UpperCamelCase			     =     self.prepare_config_and_inputs()
												(
												    (
												    _UpperCamelCase
												)     ,    (
												    _UpperCamelCase
												)     ,    (
												    _UpperCamelCase
												)     ,    (
												    _UpperCamelCase
												)     ,    (
												    _UpperCamelCase
												)     ,    (
												    _UpperCamelCase
												)     ,    (
												    _UpperCamelCase
												)     ,    
												)			     =     config_and_inputs
												_UpperCamelCase			     =     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
												return config, inputs_dict




@require_tf
class     lowerCAmelCase_     (       __lowercase,  __lowercase,  unittest.TestCase ):
							UpperCAmelCase				   =     (
							    (
							        TFDebertaVaModel,
							        TFDebertaVaForMaskedLM,
							        TFDebertaVaForQuestionAnswering,
							        TFDebertaVaForSequenceClassification,
							        TFDebertaVaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							UpperCAmelCase				   =     (
							    {
							        "feature-extraction": TFDebertaVaModel,
							        "fill-mask": TFDebertaVaForMaskedLM,
							        "question-answering": TFDebertaVaForQuestionAnswering,
							        "text-classification": TFDebertaVaForSequenceClassification,
							        "token-classification": TFDebertaVaForTokenClassification,
							        "zero-shot": TFDebertaVaForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							UpperCAmelCase				   =     False
							UpperCAmelCase				   =     False







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     TFDebertaVaModelTester(self		)
												_UpperCamelCase			     =     ConfigTester(self ,			config_class=_A ,			hidden_size=37		)







							def  UpperCamelCase_						(   self      :     Dict		):
												self.config_tester.run_common_tests()







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_A		)







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*_A		)







							def  UpperCamelCase_						(   self      :     Dict		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*_A		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*_A		)







							def  UpperCamelCase_						(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*_A		)







							@slow
							def  UpperCamelCase_						(   self      :     int		):
												_UpperCamelCase			     =     TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''		)
												self.assertIsNotNone(_A		)




@require_tf
class     lowerCAmelCase_     (       unittest.TestCase ):







							@unittest.skip(reason='''Model not available yet'''		)
							def  UpperCamelCase_						(   self      :     int		):
												pass







							@slow
							def  UpperCamelCase_						(   self      :     List[str]		):
												_UpperCamelCase			     =     TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''		)
												_UpperCamelCase			     =     tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]		)
												_UpperCamelCase			     =     tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
												_UpperCamelCase			     =     model(_A ,			attention_mask=_A		)[0]

												_UpperCamelCase			     =     tf.constant(
												    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]		)
												tf.debugging.assert_near(output[:, 1:4, 1:4] ,			_A ,			atol=1e-4		)


'''simple docstring'''



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"""
						)
						_lowercase          =						None
_lowercase          =						{
    """7B""": 11008,
    """13B""": 13824,
    """30B""": 17920,
    """65B""": 22016,
    """70B""": 28672,
}
_lowercase          =						{
    """7B""": 1,
    """7Bf""": 1,
    """13B""": 2,
    """13Bf""": 2,
    """30B""": 4,
    """65B""": 8,
    """70B""": 8,
    """70Bf""": 8,
}


def A (__lowerCamelCase :int					,      __lowerCamelCase :Optional[Any]=1					,      __lowerCamelCase :List[Any]=256						):
						return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3						)						) + multiple_of - 1) // multiple_of)


def A (__lowerCamelCase :Any						):
						with open(__lowerCamelCase					,      """r"""						) as f:
												return json.load(__lowerCamelCase						)


def A (__lowerCamelCase :List[Any]					,      __lowerCamelCase :int						):
						with open(__lowerCamelCase					,      """w"""						) as f:
												json.dump(__lowerCamelCase					,      __lowerCamelCase						)


def A (__lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Tuple					,      __lowerCamelCase :Optional[Any]					,      __lowerCamelCase :Tuple=True						):
						os.makedirs(__lowerCamelCase					,      exist_ok=__lowerCamelCase						)
						_lowerCAmelCase    		=					os.path.join(__lowerCamelCase					,      """tmp"""						)
						os.makedirs(__lowerCamelCase					,      exist_ok=__lowerCamelCase						)

						_lowerCAmelCase    		=					read_json(os.path.join(__lowerCamelCase					,      """params.json"""						)						)
						_lowerCAmelCase    		=					NUM_SHARDS[model_size]
						_lowerCAmelCase    		=					params["""n_layers"""]
						_lowerCAmelCase    		=					params["""n_heads"""]
						_lowerCAmelCase    		=					n_heads // num_shards
						_lowerCAmelCase    		=					params["""dim"""]
						_lowerCAmelCase    		=					dim // n_heads
						_lowerCAmelCase    		=					10_000.0
						_lowerCAmelCase    		=					1.0 / (base ** (torch.arange(0					,      __lowerCamelCase					,      2						).float() / dims_per_head))

						if "n_kv_heads" in params:
												_lowerCAmelCase    		=					params["""n_kv_heads"""]  # for GQA / MQA
												_lowerCAmelCase    		=					n_heads_per_shard // num_key_value_heads
												_lowerCAmelCase    		=					dim // num_key_value_heads
						else:  # compatibility with other checkpoints
												_lowerCAmelCase    		=					n_heads
												_lowerCAmelCase    		=					n_heads_per_shard
												_lowerCAmelCase    		=					dim

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

						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.)
												_lowerCAmelCase    		=					torch.load(os.path.join(__lowerCamelCase					,      """consolidated.00.pth"""						)					,      map_location="""cpu"""						)
						else:
												# Sharded
												_lowerCAmelCase    		=					[
												    torch.load(os.path.join(__lowerCamelCase					,      f'consolidated.{i:02d}.pth'						)					,      map_location="""cpu"""						)
												    for i in range(__lowerCamelCase						)
												]
						_lowerCAmelCase    		=					0
						_lowerCAmelCase    		=					{"""weight_map""": {}}
						for layer_i in range(__lowerCamelCase						):
												_lowerCAmelCase    		=					f'pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'
												if model_size == "7B":
																		# Unsharded
																		_lowerCAmelCase    		=					{
																		    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.

																		_lowerCAmelCase    		=					{
																		    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(),
																		}
																		_lowerCAmelCase    		=					permute(
																		    torch.cat(
																		        [
																		            loaded[i][f'layers.{layer_i}.attention.wq.weight'].view(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		            for i in range(__lowerCamelCase						)
																		        ]					,      dim=0					,      ).reshape(__lowerCamelCase					,      __lowerCamelCase						)						)
																		_lowerCAmelCase    		=					permute(
																		    torch.cat(
																		        [
																		            loaded[i][f'layers.{layer_i}.attention.wk.weight'].view(
																		                __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		            for i in range(__lowerCamelCase						)
																		        ]					,      dim=0					,      ).reshape(__lowerCamelCase					,      __lowerCamelCase						)					,      __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase					,      )
																		_lowerCAmelCase    		=					torch.cat(
																		    [
																		        loaded[i][f'layers.{layer_i}.attention.wv.weight'].view(
																		            __lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)
																		        for i in range(__lowerCamelCase						)
																		    ]					,      dim=0					,      ).reshape(__lowerCamelCase					,      __lowerCamelCase						)

																		_lowerCAmelCase    		=					torch.cat(
																		    [loaded[i][f'layers.{layer_i}.attention.wo.weight'] for i in range(__lowerCamelCase						)]					,      dim=1						)
																		_lowerCAmelCase    		=					torch.cat(
																		    [loaded[i][f'layers.{layer_i}.feed_forward.w1.weight'] for i in range(__lowerCamelCase						)]					,      dim=0						)
																		_lowerCAmelCase    		=					torch.cat(
																		    [loaded[i][f'layers.{layer_i}.feed_forward.w2.weight'] for i in range(__lowerCamelCase						)]					,      dim=1						)
																		_lowerCAmelCase    		=					torch.cat(
																		    [loaded[i][f'layers.{layer_i}.feed_forward.w3.weight'] for i in range(__lowerCamelCase						)]					,      dim=0						)

												_lowerCAmelCase    		=					inv_freq
												for k, v in state_dict.items():
																		_lowerCAmelCase    		=					filename
																		param_count += v.numel()
												torch.save(__lowerCamelCase					,      os.path.join(__lowerCamelCase					,      __lowerCamelCase						)						)

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

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

						# Write configs
						_lowerCAmelCase    		=					{"""total_size""": param_count * 2}
						write_json(__lowerCamelCase					,      os.path.join(__lowerCamelCase					,      """pytorch_model.bin.index.json"""						)						)
						_lowerCAmelCase    		=					params["""ffn_dim_multiplier"""] if """ffn_dim_multiplier""" in params else 1
						_lowerCAmelCase    		=					params["""multiple_of"""] if """multiple_of""" in params else 256
						_lowerCAmelCase    		=					LlamaConfig(
						    hidden_size=__lowerCamelCase					,      intermediate_size=compute_intermediate_size(__lowerCamelCase					,      __lowerCamelCase					,      __lowerCamelCase						)					,      num_attention_heads=params["""n_heads"""]					,      num_hidden_layers=params["""n_layers"""]					,      rms_norm_eps=params["""norm_eps"""]					,      num_key_value_heads=__lowerCamelCase					,      )
						config.save_pretrained(__lowerCamelCase						)

						# 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."""						)
						_lowerCAmelCase    		=					LlamaForCausalLM.from_pretrained(__lowerCamelCase					,      torch_dtype=torch.floataa					,      low_cpu_mem_usage=__lowerCamelCase						)
						# Avoid saving this as part of the config.
						del model.config._name_or_path

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


def A (__lowerCamelCase :Union[str, Any]					,      __lowerCamelCase :Union[str, Any]						):
						# Initialize the tokenizer based on the `spm` model
						_lowerCAmelCase    		=					LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
						print(f'Saving a {tokenizer_class.__name__} to {tokenizer_path}.'						)
						_lowerCAmelCase    		=					tokenizer_class(__lowerCamelCase						)
						tokenizer.save_pretrained(__lowerCamelCase						)


def A ():
						_lowerCAmelCase    		=					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=__lowerCamelCase					,      help="""Whether or not to save using `safetensors`."""						)
						_lowerCAmelCase    		=					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					,      )
						_lowerCAmelCase    		=					os.path.join(args.input_dir					,      """tokenizer.model"""						)
						write_tokenizer(args.output_dir					,      __lowerCamelCase						)


if __name__ == "__main__":
						main()
