




import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
UpperCAmelCase__    :							int       		=     """stas/tiny-wmt19-en-ru"""

class   UpperCAmelCase    (      _a ,       unittest.TestCase ):
   __SCREAMING_SNAKE_CASE		:				Optional[Any]      					=       FSMTTokenizer
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       False






   def 			_a    (						self :							Optional[int]  )->      List[Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     _lowercase:			Optional[int]      =     [
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         'w</w>',
         'r</w>',
         't</w>',
         'lo',
         'low',
         'er</w>',
         'low</w>',
         'lowest</w>',
         'newer</w>',
         'wider</w>',
         '<unk>',
     ]
     _lowercase:			str      =     dict(zip(snake_case_    ,       range(len(snake_case_  )  )  )  )
     _lowercase:			Optional[int]      =     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

     _lowercase:			List[str]      =     ['en', 'ru']
     _lowercase:			List[str]      =     {
         'langs': self.langs,
         'src_vocab_size': 10,
         'tgt_vocab_size': 20,
     }

     _lowercase:			List[str]      =     os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES['src_vocab_file']  )
     _lowercase:			Optional[Any]      =     os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES['tgt_vocab_file']  )
     _lowercase:			Optional[Any]      =     os.path.join(self.tmpdirname    ,       'tokenizer_config.json'  )
     _lowercase:			List[Any]      =     os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES['merges_file']  )
     with open(self.src_vocab_file    ,       'w'  ) as fp:
       fp.write(json.dumps(snake_case_  )  )
     with open(self.tgt_vocab_file    ,       'w'  ) as fp:
       fp.write(json.dumps(snake_case_  )  )
     with open(self.merges_file    ,       'w'  ) as fp:
       fp.write('\n'.join(snake_case_  )  )
     with open(snake_case_    ,       'w'  ) as fp:
       fp.write(json.dumps(snake_case_  )  )






   @cached_property
   def 			_a    (						self :							int  )->      List[str]:
     return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'  )






   @cached_property
   def 			_a    (						self :							Optional[int]  )->      Tuple:
     return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'  )






   def 			_a    (						self :							int  )->      Optional[Any]:
     _lowercase:			int      =     FSMTTokenizer.from_pretrained(snake_case_  )
     self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]    ,       ['en', 'ru']  )
     self.assertEqual(tokenizer.src_vocab_size    ,       21  )
     self.assertEqual(tokenizer.tgt_vocab_size    ,       21  )






   def 			_a    (						self :							int  )->      str:
     _lowercase:			str      =     FSMTTokenizer(self.langs    ,       self.src_vocab_file    ,       self.tgt_vocab_file    ,       self.merges_file  )

     _lowercase:			Dict      =     'lower'
     _lowercase:			Union[str, Any]      =     ['low', 'er</w>']
     _lowercase:			Any      =     tokenizer.tokenize(snake_case_  )
     self.assertListEqual(snake_case_    ,       snake_case_  )

     _lowercase:			int      =     tokens + ['<unk>']
     _lowercase:			List[str]      =     [14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_  )    ,       snake_case_  )






   @slow
   def 			_a    (						self :							Optional[int]  )->      Tuple:
     _lowercase:			Optional[int]      =     self.tokenizer_ru_en

     _lowercase:			List[Any]      =     tokenizer.encode('sequence builders'    ,       add_special_tokens=snake_case_  )
     _lowercase:			Optional[Any]      =     tokenizer.encode('multi-sequence build'    ,       add_special_tokens=snake_case_  )

     _lowercase:			Dict      =     tokenizer.build_inputs_with_special_tokens(snake_case_  )
     _lowercase:			int      =     tokenizer.build_inputs_with_special_tokens(snake_case_    ,       snake_case_  )

     assert encoded_sentence == text + [2]
     assert encoded_pair == text + [2] + text_a + [2]






   @slow
   def 			_a    (						self :							Optional[int]  )->      List[Any]:
     _lowercase:			Optional[Any]      =     self.tokenizer_en_ru
     _lowercase:			Any      =     self.tokenizer_ru_en

     _lowercase:			Tuple      =     [
         [
             'Here\'s a little song I wrote. Don\'t worry, be happy.',
             [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
         ],
         ['This is it. No more. I\'m done!', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
     ]

     # if data needs to be recreated or added, run:
     # import torch
     # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
     # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

     for src_text, tgt_input_ids in targets:
       _lowercase:			List[Any]      =     tokenizer_enc.encode(snake_case_    ,       return_tensors=snake_case_  )
       self.assertListEqual(snake_case_    ,       snake_case_  )

       # and decode backward, using the reversed languages model
       _lowercase:			Union[str, Any]      =     tokenizer_dec.decode(snake_case_    ,       skip_special_tokens=snake_case_  )
       self.assertEqual(snake_case_    ,       snake_case_  )






   @slow
   def 			_a    (						self :							Optional[Any]  )->      Optional[Any]:
     _lowercase:			Optional[Any]      =     FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'    ,       do_lower_case=snake_case_  )
     _lowercase:			Dict      =     tokenizer.tokenize('USA is United States of America'  )
     _lowercase:			Union[str, Any]      =     ['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
     self.assertListEqual(snake_case_    ,       snake_case_  )






   @unittest.skip('FSMTConfig.__init__  requires non-optional args'  )
   def 			_a    (						self :							Tuple  )->      Any:
     pass






   @unittest.skip('FSMTConfig.__init__  requires non-optional args'  )
   def 			_a    (						self :							Dict  )->      List[str]:
     pass








"""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)




