





"""simple docstring"""




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
   from tokenizers import Tokenizer
   from tokenizers.models import WordPiece

class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        A_    :			List[str]									=    list(tokenizer_class.max_model_input_sizes.keys()    )
        for model_name in sa_models[:1]:
          A_    :			List[Any]									=    tokenizer_class.from_pretrained(snake_case_    )
          self.assertIsNotNone(snake_case_    )
          self.assertIsInstance(snake_case_   , snake_case_    )
          self.assertIsInstance(snake_case_   , snake_case_    )

          for special_tok in tokenizer.all_special_tokens:
            self.assertIsInstance(snake_case_   , snake_case_    )
            A_    :			Optional[int]									=    tokenizer.convert_tokens_to_ids(snake_case_    )
            self.assertIsInstance(snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_ = None    ):

        """simple docstring"""

        A_    :			Tuple									=    pickle.dumps(snake_case_    )
        self.assertIsNotNone(snake_case_    )

        A_    :			Tuple									=    pickle.loads(snake_case_    )

        # Ensure is_fast is correctly restored
        self.assertEqual(be_restored.is_fast   , be_original.is_fast    )

        # Ensure encodings are potentially correctly restored
        if be_original.is_fast:
          self.assertIsNotNone(be_restored.encodings    )
        else:
          self.assertIsNone(be_restored.encodings    )

        # Ensure the keys are the same
        for original_v, restored_v in zip(be_original.values()   , be_restored.values()    ):
          if equal_op:
            self.assertTrue(equal_op(snake_case_   , snake_case_    )    )
          else:
            self.assertEqual(snake_case_   , snake_case_    )

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.check_tokenizer_from_pretrained(snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.assertEqual(TensorType('tf'    )   , TensorType.TENSORFLOW    )
        self.assertEqual(TensorType('pt'    )   , TensorType.PYTORCH    )
        self.assertEqual(TensorType('np'    )   , TensorType.NUMPY    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import numpy as np

        A_    :			List[str]									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			Optional[int]									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        # Python no tensor
        with self.subTest('BatchEncoding (Python, return_tensors=None)'    ):
          self.assert_dump_and_restore(tokenizer_p('Small example to encode'    )    )

        with self.subTest('BatchEncoding (Python, return_tensors=NUMPY)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.NUMPY    )   , np.array_equal    )

        with self.subTest('BatchEncoding (Rust, return_tensors=None)'    ):
          self.assert_dump_and_restore(tokenizer_r('Small example to encode'    )    )

        with self.subTest('BatchEncoding (Rust, return_tensors=NUMPY)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.NUMPY    )   , np.array_equal    )

      @require_tf
      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import tensorflow as tf

        def tf_array_equals(snake_case_   , snake_case_    ):
          return tf.reduce_all(tf.equal(snake_case_   , snake_case_    )    )

        A_    :			Dict									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			int									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('BatchEncoding (Python, return_tensors=TENSORFLOW)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.TENSORFLOW    )   , snake_case_    )

        with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.TENSORFLOW    )   , snake_case_    )

      @require_torch
      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import torch

        A_    :			Optional[int]									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			List[Any]									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('BatchEncoding (Python, return_tensors=PYTORCH)'    ):
          self.assert_dump_and_restore(
              tokenizer_p('Small example to encode'   , return_tensors=TensorType.PYTORCH    )   , torch.equal    )

        with self.subTest('BatchEncoding (Rust, return_tensors=PYTORCH)'    ):
          self.assert_dump_and_restore(
              tokenizer_r('Small example to encode'   , return_tensors=TensorType.PYTORCH    )   , torch.equal    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BertTokenizer.from_pretrained('bert-base-cased'    )
        A_    :			Any									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )

        with self.subTest('Python Tokenizer'    ):
          self.assertFalse(tokenizer_p('Small example to_encode'    ).is_fast    )

        with self.subTest('Rust Tokenizer'    ):
          self.assertTrue(tokenizer_r('Small example to_encode'    ).is_fast    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BertTokenizerFast.from_pretrained('bert-base-cased'    )
        A_    :			Any									=    tokenizer_r(['Test', '\xad', 'test']   , is_split_into_words=snake_case_    )

        self.assertEqual(encoded.word_to_tokens(0    )   , TokenSpan(start=1   , end=2    )    )
        self.assertEqual(encoded.word_to_tokens(1    )   , snake_case_    )
        self.assertEqual(encoded.word_to_tokens(2    )   , TokenSpan(start=2   , end=3    )    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			str									=    batch.convert_to_tensors(tensor_type='np'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Any									=    batch.convert_to_tensors(tensor_type='np'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			List[Any]									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Dict									=    batch.convert_to_tensors(tensor_type='np'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_torch
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			Union[str, Any]									=    batch.convert_to_tensors(tensor_type='pt'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			str									=    batch.convert_to_tensors(tensor_type='pt'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			str									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Any									=    batch.convert_to_tensors(tensor_type='pt'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_tf
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			Dict									=    batch.convert_to_tensors(tensor_type='tf'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Optional[int]									=    batch.convert_to_tensors(tensor_type='tf'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			int									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Union[str, Any]									=    batch.convert_to_tensors(tensor_type='tf'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      @require_flax
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}    )
        A_    :			List[str]									=    batch.convert_to_tensors(tensor_type='jax'    )
        self.assertEqual(tensor_batch['inputs'].shape   , (2, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (2,)    )
        # test converting the converted
        with CaptureStderr() as cs:
          A_    :			Optional[Any]									=    batch.convert_to_tensors(tensor_type='jax'    )
        self.assertFalse(len(cs.err    )   , msg=F"""should have no warning, but got {cs.err}"""    )

        A_    :			Union[str, Any]									=    BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}    )
        A_    :			Optional[Any]									=    batch.convert_to_tensors(tensor_type='jax'   , prepend_batch_axis=snake_case_    )
        self.assertEqual(tensor_batch['inputs'].shape   , (1, 3)    )
        self.assertEqual(tensor_batch['labels'].shape   , (1,)    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    [{'input_ids': np.array([0, 1, 2]    )}, {'input_ids': np.array([0, 1, 2, 3]    )}]
        A_    :			List[str]									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , np.ndarray    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			Optional[Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='np'    )
        self.assertTrue(isinstance(batch['input_ids']   , np.ndarray    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_torch
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import torch

        A_    :			Tuple									=    [{'input_ids': torch.tensor([0, 1, 2]    )}, {'input_ids': torch.tensor([0, 1, 2, 3]    )}]
        A_    :			int									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Any									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , torch.Tensor    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			int									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='pt'    )
        self.assertTrue(isinstance(batch['input_ids']   , torch.Tensor    )    )
        self.assertEqual(batch['input_ids'].tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_tf
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        import tensorflow as tf

        A_    :			Dict									=    [{'input_ids': tf.constant([0, 1, 2]    )}, {'input_ids': tf.constant([0, 1, 2, 3]    )}]
        A_    :			Optional[int]									=    BertTokenizer.from_pretrained('bert-base-cased'    )

        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_    )
        self.assertTrue(isinstance(batch['input_ids']   , tf.Tensor    )    )
        self.assertEqual(batch['input_ids'].numpy().tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )
        A_    :			Union[str, Any]									=    tokenizer.pad(snake_case_   , padding=snake_case_   , return_tensors='tf'    )
        self.assertTrue(isinstance(batch['input_ids']   , tf.Tensor    )    )
        self.assertEqual(batch['input_ids'].numpy().tolist()   , [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			str									=    Tokenizer(WordPiece(unk_token='[UNK]'    )    )
        PreTrainedTokenizerFast(tokenizer_object=snake_case_    )

      @require_tokenizers
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Dict									=    Tokenizer(WordPiece(unk_token='[UNK]'    )    )
        with tempfile.TemporaryDirectory() as tmpdirname:
          bert_tokenizer.save(os.path.join(snake_case_   , 'tokenizer.json'    )    )
          PreTrainedTokenizerFast(tokenizer_file=os.path.join(snake_case_   , 'tokenizer.json'    )    )







"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=0       )       ->						str:
    """simple docstring"""

    if name is None:
        UpperCamelCase__								=     None
    else:
        UpperCamelCase__								=     '.' * max(0			,						spaces - 2       ) + '# {:' + str(50 - spaces       ) + 's}'
        UpperCamelCase__								=     fmt.format(SCREAMING_SNAKE_CASE       )

    # Print and recurse (if needed).
    if isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ):
        if msg is not None:
            print(SCREAMING_SNAKE_CASE       )
        for k in val.keys():
            recursive_print(SCREAMING_SNAKE_CASE			,						val[k]			,						spaces + 2       )
    elif isinstance(SCREAMING_SNAKE_CASE			,						torch.Tensor       ):
        print(SCREAMING_SNAKE_CASE			,						':'			,						val.size()       )
    else:
        print(SCREAMING_SNAKE_CASE			,						':'			,						SCREAMING_SNAKE_CASE       )







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

    UpperCamelCase__								=     param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    UpperCamelCase__								=     (num_heads, num_splits, hidden_size) + input_shape[1:]
    UpperCamelCase__								=     param.view(*SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     param.transpose(0			,						1       ).contiguous()
    UpperCamelCase__								=     param.view(*SCREAMING_SNAKE_CASE       )
    return param







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

    UpperCamelCase__								=     config.n_positions
    UpperCamelCase__								=     config.n_layer
    UpperCamelCase__								=     config.vocab_size
    UpperCamelCase__								=     config.n_head
    UpperCamelCase__								=     config.n_embd // config.n_head

    UpperCamelCase__								=     sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
    UpperCamelCase__								=     {
        'transformer.wte.weight': word_embeddings,
        'transformer.wpe.weight': sd_megatron['model.language_model.embedding.position_embeddings.weight'],
        'transformer.ln_f.weight': sd_megatron['model.language_model.encoder.final_layernorm.weight'],
        'transformer.ln_f.bias': sd_megatron['model.language_model.encoder.final_layernorm.bias'],
    }

    UpperCamelCase__								=     'model.language_model.encoder.layers.'
    for i in range(SCREAMING_SNAKE_CASE       ):
        UpperCamelCase__								=     torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool       )       )
        UpperCamelCase__								=     causal_mask.view(1			,						1			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     causal_mask
        UpperCamelCase__								=     torch.tensor(-1E4			,						dtype=torch.bfloataa       )

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.bias']

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala.transpose(0			,						1       ).contiguous()

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.dense.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.post_attention_layernorm.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_h_to_4h.bias']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0			,						1       )
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.mlp.dense_4h_to_h.bias']

    # For LM head, transformers' wants the matrix to weight embeddings.
    UpperCamelCase__								=     word_embeddings

    return sd_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    UpperCamelCase__								=     6_40_00
    UpperCamelCase__								=     config_megatron['encoder_seq_length']
    UpperCamelCase__								=     config_megatron['hidden_size']
    UpperCamelCase__								=     config_megatron['num_layers']
    UpperCamelCase__								=     config_megatron['num_attention_heads']
    UpperCamelCase__								=     config_megatron['ffn_hidden_size']
    UpperCamelCase__								=     'gelu'
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     0.1
    UpperCamelCase__								=     config_megatron['layernorm_epsilon']  # 1e-5
    UpperCamelCase__								=     config_megatron['init_method_std']  # 0.02
    UpperCamelCase__								=     config_megatron['apply_query_key_layer_scaling']  # True
    UpperCamelCase__								=     True
    UpperCamelCase__								=     True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 40_96:
        UpperCamelCase__								=     1  # <|endoftext|>
        UpperCamelCase__								=     1  # <|endoftext|>
        UpperCamelCase__								=     0  # <unk>
    else:
        UpperCamelCase__								=     2  # <s>
        UpperCamelCase__								=     3  # <|endoftext|>
        UpperCamelCase__								=     0  # <pad>

    return config_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    print(SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     args.checkpoint_path
    UpperCamelCase__								=     args.save_path
    if isfile(SCREAMING_SNAKE_CASE       ):
        raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'       )

    # Load the model.
    UpperCamelCase__								=     torch.load(SCREAMING_SNAKE_CASE			,						map_location='cpu'       )

    # Load the config.
    UpperCamelCase__								=     checkpoint['hyper_parameters']['cfg']
    UpperCamelCase__								=     GPTaConfig()
    UpperCamelCase__								=     copy_config(config_hf=SCREAMING_SNAKE_CASE			,						config_megatron=SCREAMING_SNAKE_CASE       )
    UpperCamelCase__								=     ['GPT2LMHeadModel']

    UpperCamelCase__								=     checkpoint['state_dict']

    # Convert.
    print('Converting'       )
    UpperCamelCase__								=     convert_megatron_checkpoint(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     'GPTSw3Tokenizer'

    # Store the config to file.
    print('Saving config'       )
    config_hf.save_pretrained(SCREAMING_SNAKE_CASE       )

    # Store the state_dict to file.
    UpperCamelCase__								=     os.path.join(SCREAMING_SNAKE_CASE			,						'pytorch_model.bin'       )
    print(F'Saving checkpoint to "{output_checkpoint_file}"'       )
    torch.save(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
  A__       :			Optional[Any]=						argparse.ArgumentParser()
  parser.add_argument(
      """--checkpoint_path""",
      type=str,
      required=True,
      help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
  )
  parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
  parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
  A__       :			Any=						parser.parse_args()
  main(_args)




