import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig




def      lowerCAmelCase_				(  __A,						__A,						__A=0							)  ->       List[Any]:







      '''simple docstring'''


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

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




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       Union[str, Any]:







      '''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(*__A							)
      UpperCAmelCase__			=						param.transpose(0,						1							).contiguous()
      UpperCAmelCase__			=						param.view(*__A							)
      return param




def      lowerCAmelCase_				(  __A,						__A							)  ->       Optional[int]:







      '''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(__A							):
            UpperCAmelCase__			=						torch.tril(torch.ones((n_positions, n_positions),						dtype=torch.bool							)							)
            UpperCAmelCase__			=						causal_mask.view(1,						1,						__A,						__A							)
            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(__A,						3,						__A,						__A							)
            UpperCAmelCase__			=						vala.transpose(0,						1							).contiguous()

            UpperCAmelCase__			=						sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
            UpperCAmelCase__			=						fix_query_key_value_ordering(__A,						3,						__A,						__A							)
            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_				(  __A,						__A							)  ->       Dict:







      '''simple docstring'''


      UpperCAmelCase__			=						64_000
      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"] == 4_096:
            UpperCAmelCase__			=						1  # <|endoftext|>
            UpperCAmelCase__			=						1  # <|endoftext|>
            UpperCAmelCase__			=						0  # <unk>
      else:
            UpperCAmelCase__			=						2  # <s>
            UpperCAmelCase__			=						3  # <|endoftext|>
            UpperCAmelCase__			=						0  # <pad>

      return config_hf




def      lowerCAmelCase_				(  __A							)  ->       str:







      '''simple docstring'''


      print(__A							)

      UpperCAmelCase__			=						args.checkpoint_path
      UpperCAmelCase__			=						args.save_path
      if isfile(__A							):
            raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""							)

      # Load the model.
      UpperCAmelCase__			=						torch.load(__A,						map_location="cpu"							)

      # Load the config.
      UpperCAmelCase__			=						checkpoint["hyper_parameters"]["cfg"]
      UpperCAmelCase__			=						GPTaConfig()
      UpperCAmelCase__			=						copy_config(config_hf=__A,						config_megatron=__A							)
      UpperCAmelCase__			=						["GPT2LMHeadModel"]

      UpperCAmelCase__			=						checkpoint["state_dict"]

      # Convert.
      print("Converting"							)
      UpperCAmelCase__			=						convert_megatron_checkpoint(__A,						__A							)

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

      UpperCAmelCase__			=						"GPTSw3Tokenizer"

      # Store the config to file.
      print("Saving config"							)
      config_hf.save_pretrained(__A							)

      # Store the state_dict to file.
      UpperCAmelCase__			=						os.path.join(__A,						"pytorch_model.bin"							)
      print(f"""Saving checkpoint to \"{output_checkpoint_file}\""""							)
      torch.save(__A,						__A							)


if __name__ == "__main__":
      UpperCamelCase__       							=      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')
      UpperCamelCase__       							=      parser.parse_args()
      main(_args)




import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class   A							(       UpperCAmelCase_  ):


   def     lowercase_   (self  :		Dict ,						__UpperCAmelCase  :		Optional[int] ,						__UpperCAmelCase  :		List[str]=False	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__	,   UpperCAmelCase__	,   UpperCAmelCase__			=						(
             filename_skip_take["filename"],
             filename_skip_take["skip"] if "skip" in filename_skip_take else None,
             filename_skip_take["take"] if "take" in filename_skip_take else None,
         )
         open(os.path.join(__UpperCAmelCase	) ,						"wb"	).close()
         UpperCAmelCase__			=						pa.Table.from_pydict({"filename": [Path(__UpperCAmelCase	).name] * 1_0_0}	)
         if take == -1:
               UpperCAmelCase__			=						len(__UpperCAmelCase	) - skip
         if skip is not None and take is not None:
               UpperCAmelCase__			=						pa_table.slice(__UpperCAmelCase ,						__UpperCAmelCase	)
         return pa_table





class   A							(       UpperCAmelCase_  ):


   def     lowercase_   (self  :		Tuple	)		->       Tuple:
         """simple docstring"""



         UpperCAmelCase__			=						"my_name"
         UpperCAmelCase__			=						SplitInfo(name="train" ,						num_examples=1_0_0	)
         UpperCAmelCase__			=						SplitInfo(name="test" ,						num_examples=1_0_0	)
         UpperCAmelCase__			=						[train_info, test_info]
         UpperCAmelCase__			=						SplitDict()
         split_dict.add(__UpperCAmelCase	)
         split_dict.add(__UpperCAmelCase	)
         UpperCAmelCase__			=						DatasetInfo(splits=__UpperCAmelCase	)

         with tempfile.TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__			=						ReaderTest(__UpperCAmelCase ,						__UpperCAmelCase	)

               UpperCAmelCase__			=						"test[:33%]"
               UpperCAmelCase__			=						Dataset(**reader.read(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase	)	)
               self.assertEqual(dset["filename"][0] ,						f"""{name}-test"""	)
               self.assertEqual(dset.num_rows ,						3_3	)
               self.assertEqual(dset.num_columns ,						1	)

               UpperCAmelCase__			=						["train", "test[:33%]"]
               UpperCAmelCase__			=						[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"	)]
               for instructions in [instructionsa, instructionsa]:
                     UpperCAmelCase__			=						[reader.read(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase	) for instr in instructions]
                     UpperCAmelCase__	,   UpperCAmelCase__			=						(Dataset(**__UpperCAmelCase	) for dataset_kwargs in datasets_kwargs)
                     self.assertEqual(train_dset["filename"][0] ,						f"""{name}-train"""	)
                     self.assertEqual(train_dset.num_rows ,						1_0_0	)
                     self.assertEqual(train_dset.num_columns ,						1	)
                     self.assertIsInstance(train_dset.split ,						__UpperCAmelCase	)
                     self.assertEqual(str(train_dset.split	) ,						"train"	)
                     self.assertEqual(test_dset["filename"][0] ,						f"""{name}-test"""	)
                     self.assertEqual(test_dset.num_rows ,						3_3	)
                     self.assertEqual(test_dset.num_columns ,						1	)
                     self.assertIsInstance(test_dset.split ,						__UpperCAmelCase	)
                     self.assertEqual(str(test_dset.split	) ,						"test[:33%]"	)
                     del train_dset, test_dset


   def     lowercase_   (self  :		Any	)		->       Any:
         """simple docstring"""



         UpperCAmelCase__			=						"my_name"
         UpperCAmelCase__			=						SplitInfo(name="train" ,						num_examples=1_0_0_0 ,						shard_lengths=[1_0_0] * 1_0	)
         UpperCAmelCase__			=						[train_info]
         UpperCAmelCase__			=						SplitDict()
         split_dict.add(__UpperCAmelCase	)
         UpperCAmelCase__			=						DatasetInfo(splits=__UpperCAmelCase	)

         with tempfile.TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__			=						ReaderTest(__UpperCAmelCase ,						__UpperCAmelCase	)

               UpperCAmelCase__			=						"train[:33%]"
               UpperCAmelCase__			=						Dataset(**reader.read(__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase	)	)
               self.assertEqual(dset["filename"][0] ,						f"""{name}-train-00000-of-00010"""	)
               self.assertEqual(dset["filename"][-1] ,						f"""{name}-train-00003-of-00010"""	)
               self.assertEqual(dset.num_rows ,						3_3_0	)
               self.assertEqual(dset.num_columns ,						1	)





   def     lowercase_   (self  :		Dict	)		->       Dict:
         """simple docstring"""



         UpperCAmelCase__			=						SplitInfo(name="train" ,						num_examples=1_0_0	)
         UpperCAmelCase__			=						SplitInfo(name="test" ,						num_examples=1_0_0	)
         UpperCAmelCase__			=						SplitDict()
         split_dict.add(__UpperCAmelCase	)
         split_dict.add(__UpperCAmelCase	)
         UpperCAmelCase__			=						DatasetInfo(splits=__UpperCAmelCase	)

         with tempfile.TemporaryDirectory() as tmp_dir:
               UpperCAmelCase__			=						ReaderTest(__UpperCAmelCase ,						__UpperCAmelCase	)

               UpperCAmelCase__			=						[
                   {"filename": os.path.join(__UpperCAmelCase ,						"train"	)},
                   {"filename": os.path.join(__UpperCAmelCase ,						"test"	), "skip": 1_0, "take": 1_0},
               ]
               UpperCAmelCase__			=						Dataset(**reader.read_files(__UpperCAmelCase ,						original_instructions="train+test[10:20]"	)	)
               self.assertEqual(dset.num_rows ,						1_1_0	)
               self.assertEqual(dset.num_columns ,						1	)
               del dset




@pytest.mark.parametrize("in_memory",						[False, True]							)
def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       List[Any]:







      '''simple docstring'''


      UpperCAmelCase__			=						arrow_file
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            UpperCAmelCase__			=						ArrowReader.read_table(__A,						in_memory=__A							)
      assert table.shape == dataset.data.shape
      assert set(table.column_names							) == set(dataset.data.column_names							)
      assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict




@pytest.mark.parametrize("in_memory",						[False, True]							)
def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       Tuple:







      '''simple docstring'''


      UpperCAmelCase__			=						arrow_file
      UpperCAmelCase__			=						ArrowReader("",						__A							)
      with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
            UpperCAmelCase__			=						reader.read_files([{"filename": filename}],						in_memory=__A							)
      assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
      UpperCAmelCase__			=						dataset_kwargs["arrow_table"]
      assert table.shape == dataset.data.shape
      assert set(table.column_names							) == set(dataset.data.column_names							)
      assert dict(table.to_pydict()							) == dict(dataset.data.to_pydict()							)  # to_pydict returns OrderedDict




def      lowerCAmelCase_				(  )  ->       Any:







      '''simple docstring'''


      assert ReadInstruction("train",						to=10,						unit="abs"							).to_spec() == "train[:10]"
      assert ReadInstruction("train",						from_=-80,						to=10,						unit="%"							).to_spec() == "train[-80%:10%]"

      UpperCAmelCase__			=						"train+test"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_test

      UpperCAmelCase__			=						"train[2:10]"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_abs

      UpperCAmelCase__			=						"train[15%:-20%]"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_pct

      UpperCAmelCase__			=						"train[:10%](closest)"
      assert ReadInstruction.from_spec(__A							).to_spec() == "train[:10%]"

      UpperCAmelCase__			=						"train[:10%](pct1_dropremainder)"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_pct_rounding

      UpperCAmelCase__			=						"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
      assert ReadInstruction.from_spec(__A							).to_spec() == spec_train_test_pct_rounding




def      lowerCAmelCase_				(  )  ->       int:







      '''simple docstring'''


      UpperCAmelCase__			=						"dummy"
      UpperCAmelCase__			=						[SplitInfo(name="train",						num_examples=100							)]
      UpperCAmelCase__			=						"train[:33%]"
      UpperCAmelCase__			=						"arrow"
      UpperCAmelCase__			=						"prefix"

      UpperCAmelCase__			=						make_file_instructions(__A,						__A,						__A,						__A,						__A							)
      assert isinstance(__A,						__A							)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(__A,						f"""{name}-train.arrow"""							), "skip": 0, "take": 33}
      ]

      UpperCAmelCase__			=						[SplitInfo(name="train",						num_examples=100,						shard_lengths=[10] * 10							)]
      UpperCAmelCase__			=						make_file_instructions(__A,						__A,						__A,						__A,						__A							)
      assert isinstance(__A,						__A							)
      assert file_instructions.num_examples == 33
      assert file_instructions.file_instructions == [
          {"filename": os.path.join(__A,						f"""{name}-train-00000-of-00010.arrow"""							), "skip": 0, "take": -1},
          {"filename": os.path.join(__A,						f"""{name}-train-00001-of-00010.arrow"""							), "skip": 0, "take": -1},
          {"filename": os.path.join(__A,						f"""{name}-train-00002-of-00010.arrow"""							), "skip": 0, "take": -1},
          {"filename": os.path.join(__A,						f"""{name}-train-00003-of-00010.arrow"""							), "skip": 0, "take": 3},
      ]







@pytest.mark.parametrize("name, expected_exception",						[(None, TypeError), ("", ValueError)]							)
def      lowerCAmelCase_				(  __A,						__A							)  ->       Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__			=						[SplitInfo(name="train",						num_examples=100							)]
      UpperCAmelCase__			=						"train"
      UpperCAmelCase__			=						"arrow"
      UpperCAmelCase__			=						"prefix_path"
      with pytest.raises(__A							):
            UpperCAmelCase__			=						make_file_instructions(__A,						__A,						__A,						__A,						__A							)



