
import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil

@pytest.fixture
def        lowerCamelCase_      (			UpperCAmelCase_							:				str       ):
						lowercase       :    Dict											=  tmp_path / '''text.txt'''
						lowercase       :    List[Any]											=  textwrap.dedent(
						    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        '''       )
						with open(UpperCAmelCase_							,			'''w'''							,			encoding='''utf-8'''       ) as f:
												f.write(UpperCAmelCase_       )
						return str(UpperCAmelCase_       )

@pytest.fixture
def        lowerCamelCase_      (			UpperCAmelCase_							:				List[Any]							,			UpperCAmelCase_							:				List[str]       ):
						lowercase       :    int											=  tmp_path / '''text_with_image.txt'''
						with open(UpperCAmelCase_							,			'''w'''							,			encoding='''utf-8'''       ) as f:
												f.write(UpperCAmelCase_       )
						return str(UpperCAmelCase_       )

@pytest.mark.parametrize('''keep_linebreaks'''							,			[True, False]       )
def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				str       ):
						with open(UpperCAmelCase_							,			encoding='''utf-8'''       ) as f:
												lowercase       :    Union[str, Any]											=  f.read().splitlines(keepends=UpperCAmelCase_       )
						lowercase       :    List[str]											=  Text(keep_linebreaks=UpperCAmelCase_							,			encoding='''utf-8'''       )
						lowercase       :    List[Any]											=  text._generate_tables([[text_file]]       )
						lowercase       :    Tuple											=  pa.concat_tables([table for _, table in generator]       ).to_pydict()['''text''']
						assert generated_content == expected_content

@require_pil
def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple       ):
						with open(UpperCAmelCase_							,			encoding='''utf-8'''       ) as f:
												lowercase       :    Any											=  f.read().splitlines()[0]
						lowercase       :    List[str]											=  Text(encoding='''utf-8'''							,			features=Features({'''image''': Image()}       )       )
						lowercase       :    Any											=  text._generate_tables([[text_file_with_image]]       )
						lowercase       :    List[str]											=  pa.concat_tables([table for _, table in generator]       )
						assert pa_table.schema.field('''image'''       ).type == Image()()
						lowercase       :    Union[str, Any]											=  pa_table.to_pydict()['''image''']
						assert generated_content == [{"path": image_file, "bytes": None}]




@pytest.mark.parametrize('''sample_by'''							,			['''line''', '''paragraph''', '''document''']       )
def        lowerCamelCase_      (			UpperCAmelCase_							:				Any							,			UpperCAmelCase_							:				List[Any]       ):
						with open(UpperCAmelCase_							,			encoding='''utf-8'''       ) as f:
												lowercase       :    int											=  f.read()
						if sample_by == "line":
												lowercase       :    Optional[Any]											=  expected_content.splitlines()
						elif sample_by == "paragraph":
												lowercase       :    Optional[int]											=  expected_content.split('''\n\n'''       )
						elif sample_by == "document":
												lowercase       :    str											=  [expected_content]
						lowercase       :    Dict											=  Text(sample_by=UpperCAmelCase_							,			encoding='''utf-8'''							,			chunksize=1_00       )
						lowercase       :    Any											=  text._generate_tables([[text_file]]       )
						lowercase       :    Dict											=  pa.concat_tables([table for _, table in generator]       ).to_pydict()['''text''']
						assert generated_content == expected_content




import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Optional[Any]							,			UpperCAmelCase_							:				int=0       ):
						# Format the message.
						if name is None:
												lowercase       :    List[str]											=  None
						else:
												lowercase       :    str											=  '''.''' * max(0							,			spaces - 2       ) + '''# {:''' + str(50 - spaces       ) + '''s}'''
												lowercase       :    List[str]											=  fmt.format(UpperCAmelCase_       )

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

def        lowerCamelCase_      (			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				Dict							,			UpperCAmelCase_							:				Tuple							,			UpperCAmelCase_							:				Tuple       ):
						# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
						# for compatibility with later versions of NVIDIA Megatron-LM.
						# The inverse operation is performed inside Megatron-LM to read checkpoints:
						# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
						# If param is the weight tensor of the self-attention block, the returned tensor
						# will have to be transposed one more time to be read by HuggingFace GPT2.
						lowercase       :    Optional[Any]											=  param.size()
						# other versions store [num_heads * num_splits * hidden_size, :]
						lowercase       :    Any											=  (num_heads, num_splits, hidden_size) + input_shape[1:]
						lowercase       :    Optional[int]											=  param.view(*UpperCAmelCase_       )
						lowercase       :    List[Any]											=  param.transpose(0							,			1       ).contiguous()
						lowercase       :    int											=  param.view(*UpperCAmelCase_       )
						return param

def        lowerCamelCase_      (			UpperCAmelCase_							:				List[str]							,			UpperCAmelCase_							:				Union[str, Any]       ):
						lowercase       :    str											=  config.n_positions
						lowercase       :    Union[str, Any]											=  config.n_layer
						lowercase       :    Optional[int]											=  config.vocab_size
						lowercase       :    Union[str, Any]											=  config.n_head
						lowercase       :    int											=  config.n_embd // config.n_head

						lowercase       :    Optional[Any]											=  sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
						lowercase       :    Optional[int]											=  {
						    '''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'''],
						}

						lowercase       :    Dict											=  '''model.language_model.encoder.layers.'''
						for i in range(UpperCAmelCase_       ):
												lowercase       :    List[str]											=  torch.tril(torch.ones((n_positions, n_positions)							,			dtype=torch.bool       )       )
												lowercase       :    List[str]											=  causal_mask.view(1							,			1							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    List[str]											=  causal_mask
												lowercase       :    Optional[int]											=  torch.tensor(-1E4							,			dtype=torch.bfloataa       )

												lowercase       :    List[str]											=  sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

												lowercase       :    Any											=  sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
												lowercase       :    Optional[int]											=  fix_query_key_value_ordering(UpperCAmelCase_							,			3							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    Tuple											=  vala.transpose(0							,			1       ).contiguous()

												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
												lowercase       :    Any											=  fix_query_key_value_ordering(UpperCAmelCase_							,			3							,			UpperCAmelCase_							,			UpperCAmelCase_       )
												lowercase       :    int											=  vala

												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
												    0							,			1       )
												lowercase       :    Optional[int]											=  sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
												lowercase       :    Tuple											=  sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
												lowercase       :    Union[str, Any]											=  sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
												lowercase       :    Optional[Any]											=  sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0							,			1       )
												lowercase       :    List[Any]											=  sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
												lowercase       :    Dict											=  sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
												    0							,			1       )
												lowercase       :    str											=  sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']

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

						return sd_hf

def        lowerCamelCase_      (			UpperCAmelCase_							:				str							,			UpperCAmelCase_							:				str       ):
						lowercase       :    str											=  6_40_00
						lowercase       :    Union[str, Any]											=  config_megatron['''encoder_seq_length''']
						lowercase       :    Union[str, Any]											=  config_megatron['''hidden_size''']
						lowercase       :    Optional[Any]											=  config_megatron['''num_layers''']
						lowercase       :    int											=  config_megatron['''num_attention_heads''']
						lowercase       :    str											=  config_megatron['''ffn_hidden_size''']
						lowercase       :    Union[str, Any]											=  '''gelu'''
						lowercase       :    Tuple											=  0.1
						lowercase       :    str											=  0.1
						lowercase       :    str											=  0.1
						lowercase       :    Dict											=  config_megatron['''layernorm_epsilon''']  # 1e-5
						lowercase       :    str											=  config_megatron['''init_method_std''']  # 0.02
						lowercase       :    Optional[int]											=  config_megatron['''apply_query_key_layer_scaling''']  # True
						lowercase       :    Union[str, Any]											=  True
						lowercase       :    int											=  True

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

						return config_hf

def        lowerCamelCase_      (			UpperCAmelCase_							:				Tuple       ):
						print(UpperCAmelCase_       )

						lowercase       :    List[Any]											=  args.checkpoint_path
						lowercase       :    Dict											=  args.save_path
						if isfile(UpperCAmelCase_       ):
												raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''       )

						# Load the model.
						lowercase       :    Optional[int]											=  torch.load(UpperCAmelCase_							,			map_location='''cpu'''       )

						# Load the config.
						lowercase       :    Any											=  checkpoint['''hyper_parameters''']['''cfg''']
						lowercase       :    Dict											=  GPTaConfig()
						lowercase       :    Dict											=  copy_config(config_hf=UpperCAmelCase_							,			config_megatron=UpperCAmelCase_       )
						lowercase       :    Any											=  ['''GPT2LMHeadModel''']

						lowercase       :    List[Any]											=  checkpoint['''state_dict''']

						# Convert.
						print('''Converting'''       )
						lowercase       :    Dict											=  convert_megatron_checkpoint(UpperCAmelCase_							,			UpperCAmelCase_       )

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

						lowercase       :    List[Any]											=  '''GPTSw3Tokenizer'''

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

						# Store the state_dict to file.
						lowercase       :    Union[str, Any]											=  os.path.join(UpperCAmelCase_							,			'''pytorch_model.bin'''       )
						print(f'''Saving checkpoint to "{output_checkpoint_file}"'''       )
						torch.save(UpperCAmelCase_							,			UpperCAmelCase_       )


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


