






from __future__ import annotations

import unittest

from transformers import DistilBertConfig, 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.models.distilbert.modeling_tf_distilbert import (
           TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFDistilBertForMaskedLM,
           TFDistilBertForMultipleChoice,
           TFDistilBertForQuestionAnswering,
           TFDistilBertForSequenceClassification,
           TFDistilBertForTokenClassification,
           TFDistilBertModel,
       )
class        a       :



      """simple docstring"""


      def __init__( self   :     List[Any]						, __lowercase   :     Optional[Any]						, )				-> int:
        __UpperCAmelCase				:      str	 =    parent
        __UpperCAmelCase				:      str	 =    13
        __UpperCAmelCase				:      List[str]	 =    7
        __UpperCAmelCase				:      int	 =    True
        __UpperCAmelCase				:      Union[str, Any]	 =    True
        __UpperCAmelCase				:      Union[str, Any]	 =    False
        __UpperCAmelCase				:      Dict	 =    True
        __UpperCAmelCase				:      List[str]	 =    99
        __UpperCAmelCase				:      str	 =    32
        __UpperCAmelCase				:      str	 =    2
        __UpperCAmelCase				:      Dict	 =    4
        __UpperCAmelCase				:      Tuple	 =    37
        __UpperCAmelCase				:      Tuple	 =    """gelu"""
        __UpperCAmelCase				:      Dict	 =    0.1
        __UpperCAmelCase				:      str	 =    0.1
        __UpperCAmelCase				:      Tuple	 =    512
        __UpperCAmelCase				:      str	 =    16
        __UpperCAmelCase				:      Union[str, Any]	 =    2
        __UpperCAmelCase				:      List[Any]	 =    0.02
        __UpperCAmelCase				:      Optional[Any]	 =    3
        __UpperCAmelCase				:      Tuple	 =    4
        __UpperCAmelCase				:      Dict	 =    None
      def       UpperCAmelCase ( self   :     str   )				-> Optional[int]:
        __UpperCAmelCase				:      List[str]	 =    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size   )

        __UpperCAmelCase				:      Tuple	 =    None
        if self.use_input_mask:
          __UpperCAmelCase				:      Dict	 =    random_attention_mask([self.batch_size, self.seq_length]   )

        __UpperCAmelCase				:      int	 =    None
        __UpperCAmelCase				:      Union[str, Any]	 =    None
        __UpperCAmelCase				:      List[Any]	 =    None
        if self.use_labels:
          __UpperCAmelCase				:      Tuple	 =    ids_tensor([self.batch_size]						, self.type_sequence_label_size   )
          __UpperCAmelCase				:      Union[str, Any]	 =    ids_tensor([self.batch_size, self.seq_length]						, self.num_labels   )
          __UpperCAmelCase				:      Union[str, Any]	 =    ids_tensor([self.batch_size]						, self.num_choices   )

        __UpperCAmelCase				:      int	 =    DistilBertConfig(
            vocab_size=self.vocab_size						, dim=self.hidden_size						, n_layers=self.num_hidden_layers						, n_heads=self.num_attention_heads						, hidden_dim=self.intermediate_size						, hidden_act=self.hidden_act						, dropout=self.hidden_dropout_prob						, attention_dropout=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, initializer_range=self.initializer_range						, )

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
      def       UpperCAmelCase ( self   :     Any						, __lowercase   :     Union[str, Any]						, __lowercase   :     List[str]						, __lowercase   :     Dict						, __lowercase   :     Optional[Any]						, __lowercase   :     int						, __lowercase   :     Dict   )				-> Dict:
        __UpperCAmelCase				:      Tuple	 =    TFDistilBertModel(config=__lowercase   )
        __UpperCAmelCase				:      str	 =    {"""input_ids""": input_ids, """attention_mask""": input_mask}

        __UpperCAmelCase				:      Union[str, Any]	 =    model(__lowercase   )

        __UpperCAmelCase				:      str	 =    [input_ids, input_mask]

        __UpperCAmelCase				:      Union[str, Any]	 =    model(__lowercase   )

        self.parent.assertEqual(result.last_hidden_state.shape						, (self.batch_size, self.seq_length, self.hidden_size)   )
      def       UpperCAmelCase ( self   :     Optional[int]						, __lowercase   :     Optional[int]						, __lowercase   :     str						, __lowercase   :     Dict						, __lowercase   :     str						, __lowercase   :     Dict						, __lowercase   :     Tuple   )				-> List[str]:
        __UpperCAmelCase				:      Dict	 =    TFDistilBertForMaskedLM(config=__lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    {"""input_ids""": input_ids, """attention_mask""": input_mask}
        __UpperCAmelCase				:      Tuple	 =    model(__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.vocab_size)   )
      def       UpperCAmelCase ( self   :     Union[str, Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     Optional[Any]						, __lowercase   :     str						, __lowercase   :     str						, __lowercase   :     Dict						, __lowercase   :     Optional[Any]   )				-> Union[str, Any]:
        __UpperCAmelCase				:      int	 =    TFDistilBertForQuestionAnswering(config=__lowercase   )
        __UpperCAmelCase				:      Tuple	 =    {
            """input_ids""": input_ids,
            """attention_mask""": input_mask,
        }
        __UpperCAmelCase				:      int	 =    model(__lowercase   )
        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   :     int						, __lowercase   :     Optional[Any]						, __lowercase   :     Tuple						, __lowercase   :     List[str]						, __lowercase   :     Any						, __lowercase   :     Any						, __lowercase   :     Optional[int]   )				-> Optional[Any]:
        __UpperCAmelCase				:      Optional[int]	 =    self.num_labels
        __UpperCAmelCase				:      Optional[int]	 =    TFDistilBertForSequenceClassification(__lowercase   )
        __UpperCAmelCase				:      Optional[int]	 =    {"""input_ids""": input_ids, """attention_mask""": input_mask}
        __UpperCAmelCase				:      Any	 =    model(__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_labels)   )
      def       UpperCAmelCase ( self   :     List[Any]						, __lowercase   :     str						, __lowercase   :     Optional[int]						, __lowercase   :     Union[str, Any]						, __lowercase   :     Union[str, Any]						, __lowercase   :     Any						, __lowercase   :     List[str]   )				-> Dict:
        __UpperCAmelCase				:      List[Any]	 =    self.num_choices
        __UpperCAmelCase				:      Dict	 =    TFDistilBertForMultipleChoice(__lowercase   )
        __UpperCAmelCase				:      Union[str, Any]	 =    tf.tile(tf.expand_dims(__lowercase						, 1   )						, (1, self.num_choices, 1)   )
        __UpperCAmelCase				:      Optional[Any]	 =    tf.tile(tf.expand_dims(__lowercase						, 1   )						, (1, self.num_choices, 1)   )
        __UpperCAmelCase				:      List[Any]	 =    {
            """input_ids""": multiple_choice_inputs_ids,
            """attention_mask""": multiple_choice_input_mask,
        }
        __UpperCAmelCase				:      Optional[int]	 =    model(__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.num_choices)   )
      def       UpperCAmelCase ( self   :     Tuple						, __lowercase   :     Optional[Any]						, __lowercase   :     List[Any]						, __lowercase   :     Optional[int]						, __lowercase   :     Dict						, __lowercase   :     Union[str, Any]						, __lowercase   :     Optional[int]   )				-> Any:
        __UpperCAmelCase				:      List[str]	 =    self.num_labels
        __UpperCAmelCase				:      Optional[Any]	 =    TFDistilBertForTokenClassification(__lowercase   )
        __UpperCAmelCase				:      Any	 =    {"""input_ids""": input_ids, """attention_mask""": input_mask}
        __UpperCAmelCase				:      List[str]	 =    model(__lowercase   )
        self.parent.assertEqual(result.logits.shape						, (self.batch_size, self.seq_length, self.num_labels)   )




      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> List[Any]:
        __UpperCAmelCase				:      Optional[int]	 =    self.prepare_config_and_inputs()
        ((__UpperCAmelCase) ,					(__UpperCAmelCase) ,					(__UpperCAmelCase) ,					(__UpperCAmelCase) ,					(__UpperCAmelCase) ,					(__UpperCAmelCase))				:      Union[str, Any]	 =    config_and_inputs
        __UpperCAmelCase				:      List[Any]	 =    {"""input_ids""": input_ids, """attention_mask""": input_mask}
        return config, inputs_dict


@require_tf
class        a       (					lowercase__				,			lowercase__				,			unittest.TestCase  ):



      """simple docstring"""


      a    :					Tuple							=      (
          (
              TFDistilBertModel,
              TFDistilBertForMaskedLM,
              TFDistilBertForQuestionAnswering,
              TFDistilBertForSequenceClassification,
              TFDistilBertForTokenClassification,
              TFDistilBertForMultipleChoice,
          )
          if is_tf_available()
          else None
      )
      a    :					Optional[Any]							=      (
          {
              'feature-extraction': TFDistilBertModel,
              'fill-mask': TFDistilBertForMaskedLM,
              'question-answering': TFDistilBertForQuestionAnswering,
              'text-classification': TFDistilBertForSequenceClassification,
              'token-classification': TFDistilBertForTokenClassification,
              'zero-shot': TFDistilBertForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )
      a    :					List[Any]							=      False
      a    :					str							=      False
      def       UpperCAmelCase ( self   :     int   )				-> Optional[Any]:
        __UpperCAmelCase				:      int	 =    TFDistilBertModelTester(self   )
        __UpperCAmelCase				:      Optional[int]	 =    ConfigTester(self						, config_class=__lowercase						, dim=37   )
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Optional[Any]:
        self.config_tester.run_common_tests()
      def       UpperCAmelCase ( self   :     Union[str, Any]   )				-> Union[str, Any]:
        __UpperCAmelCase				:      int	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_model(*__lowercase   )
      def       UpperCAmelCase ( self   :     Optional[int]   )				-> Optional[int]:
        __UpperCAmelCase				:      Any	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_masked_lm(*__lowercase   )
      def       UpperCAmelCase ( self   :     Any   )				-> Any:
        __UpperCAmelCase				:      int	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_question_answering(*__lowercase   )
      def       UpperCAmelCase ( self   :     Dict   )				-> Any:
        __UpperCAmelCase				:      Optional[Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_sequence_classification(*__lowercase   )
      def       UpperCAmelCase ( self   :     List[str]   )				-> Any:
        __UpperCAmelCase				:      Tuple	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_multiple_choice(*__lowercase   )
      def       UpperCAmelCase ( self   :     Any   )				-> Dict:
        __UpperCAmelCase				:      Union[str, Any]	 =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_distilbert_for_token_classification(*__lowercase   )




      @slow
      def       UpperCAmelCase ( self   :     Optional[Any]   )				-> Any:
        for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]   ):
          __UpperCAmelCase				:      List[Any]	 =    TFDistilBertModel.from_pretrained(__lowercase   )
          self.assertIsNotNone(__lowercase   )


@require_tf
class        a       (					unittest.TestCase  ):



      """simple docstring"""


      @slow
      def       UpperCAmelCase ( self   :     Any   )				-> Any:
        __UpperCAmelCase				:      Tuple	 =    TFDistilBertModel.from_pretrained("""distilbert-base-uncased"""   )
        __UpperCAmelCase				:      Optional[Any]	 =    tf.constant([[0, 1, 2, 3, 4, 5]]   )
        __UpperCAmelCase				:      Optional[int]	 =    model(__lowercase   )[0]

        __UpperCAmelCase				:      List[Any]	 =    [1, 6, 768]
        self.assertEqual(output.shape						, __lowercase   )

        __UpperCAmelCase				:      Tuple	 =    tf.constant(
            [
                [
                    [0.19_261_885, -0.13_732_955, 0.4_119_799],
                    [0.22_150_156, -0.07_422_661, 0.39_037_204],
                    [0.22_756_018, -0.0_896_414, 0.3_701_467],
                ]
            ]   )
        tf.debugging.assert_near(output[:, :3, :3]						, __lowercase						, atol=1e-4   )








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()






