

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	str,      UpperCamelCase_					:	Union[str, Any]=0)     ->						Tuple:
   '''simple docstring'''


   if name is None:
      __lowercase								=						None
   else:
      __lowercase								=						"." * max(0,      spaces - 2) + "# {:" + str(50 - spaces) + "s}"
      __lowercase								=						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   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	int)     ->						Dict:
   '''simple docstring'''


   __lowercase								=						param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      __lowercase								=						(num_heads, hidden_size, num_splits) + input_shape[1:]
      __lowercase								=						param.view(*UpperCamelCase_)
      __lowercase								=						param.transpose(0,      2)
      __lowercase								=						param.transpose(1,      2).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      __lowercase								=						(num_heads, num_splits, hidden_size) + input_shape[1:]
      __lowercase								=						param.view(*UpperCamelCase_)
      __lowercase								=						param.transpose(0,      1).contiguous()
   __lowercase								=						param.view(*UpperCamelCase_)
   return param




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Tuple)     ->						Optional[int]:
   '''simple docstring'''


   __lowercase								=						{}

   # old versions did not store training args
   __lowercase								=						input_state_dict.get("args",      UpperCamelCase_)
   if ds_args is not None:
      # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
      # from pprint import pprint
      # pprint(vars(ds_args))

      __lowercase								=						ds_args.padded_vocab_size
      __lowercase								=						ds_args.max_position_embeddings
      __lowercase								=						ds_args.hidden_size
      __lowercase								=						ds_args.num_layers
      __lowercase								=						ds_args.num_attention_heads
      __lowercase								=						ds_args.ffn_hidden_size
      # pprint(config)

   # The number of heads.
   __lowercase								=						config.n_head
   # The hidden_size per head.
   __lowercase								=						config.n_embd // config.n_head
   # Megatron-LM checkpoint version
   if "checkpoint_version" in input_state_dict.keys():
      __lowercase								=						input_state_dict["checkpoint_version"]
   else:
      __lowercase								=						0.0

   # The model.
   __lowercase								=						input_state_dict["model"]
   # The language model.
   __lowercase								=						model["language_model"]
   # The embeddings.
   __lowercase								=						lm["embedding"]

   # The word embeddings.
   __lowercase								=						embeddings["word_embeddings"]["weight"]
   # Truncate the embedding table to vocab_size rows.
   __lowercase								=						word_embeddings[: config.vocab_size, :]
   __lowercase								=						word_embeddings

   # The position embeddings.
   __lowercase								=						embeddings["position_embeddings"]["weight"]
   # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
   __lowercase								=						pos_embeddings.size(0)
   if n_positions != config.n_positions:
      raise ValueError(
          F"""pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match""")
   # Store the position embeddings.
   __lowercase								=						pos_embeddings

   # The transformer.
   __lowercase								=						lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

   # The regex to extract layer names.
   __lowercase								=						re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)")

   # The simple map of names for "automated" rules.
   __lowercase								=						{
       "attention.dense": ".attn.c_proj.",
       "self_attention.dense": ".attn.c_proj.",
       "mlp.dense_h_to_4h": ".mlp.c_fc.",
       "mlp.dense_4h_to_h": ".mlp.c_proj.",
   }

   # Extract the layers.
   for key, val in transformer.items():
      # Match the name.
      __lowercase								=						layer_re.match(UpperCamelCase_)

      # Stop if that's not a layer
      if m is None:
         break

      # The index of the layer.
      __lowercase								=						int(m.group(1))
      # The name of the operation.
      __lowercase								=						m.group(2)
      # Is it a weight or a bias?
      __lowercase								=						m.group(3)

      # The name of the layer.
      __lowercase								=						F"""transformer.h.{layer_idx}"""

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith("layernorm"):
         __lowercase								=						"ln_1" if op_name.startswith("input") else "ln_2"
         __lowercase								=						val

      # Transpose the QKV matrix.
      elif (
          op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
      ) and weight_or_bias == "weight":
         # Insert a tensor of 1x1xDxD bias.
         __lowercase								=						torch.tril(torch.ones((n_positions, n_positions),      dtype=torch.floataa)).view(
             1,      1,      UpperCamelCase_,      UpperCamelCase_)
         __lowercase								=						causal_mask

         # Insert a "dummy" tensor for masked_bias.
         __lowercase								=						torch.tensor(-1E4,      dtype=torch.floataa)
         __lowercase								=						masked_bias

         __lowercase								=						fix_query_key_value_ordering(UpperCamelCase_,      UpperCamelCase_,      3,      UpperCamelCase_,      UpperCamelCase_)
         # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
         __lowercase								=						out_val.transpose(0,      1).contiguous()
         # Store.
         __lowercase								=						out_val

      # Transpose the bias.
      elif (
          op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
      ) and weight_or_bias == "bias":
         __lowercase								=						fix_query_key_value_ordering(UpperCamelCase_,      UpperCamelCase_,      3,      UpperCamelCase_,      UpperCamelCase_)
         # Store. No change of shape.
         __lowercase								=						out_val

      # Transpose the weights.
      elif weight_or_bias == "weight":
         __lowercase								=						megatron_to_transformers[op_name]
         __lowercase								=						val.transpose(0,      1)

      # Copy the bias.
      elif weight_or_bias == "bias":
         __lowercase								=						megatron_to_transformers[op_name]
         __lowercase								=						val

    # DEBUG.
   assert config.n_layer == layer_idx + 1

   # The final layernorm.
   __lowercase								=						transformer["final_layernorm.weight"]
   __lowercase								=						transformer["final_layernorm.bias"]

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

   # It should be done!
   return output_state_dict




def   _A  (  )     ->						Union[str, Any]:
   '''simple docstring'''


   __lowercase								=						argparse.ArgumentParser()
   parser.add_argument("--print-checkpoint-structure",      action="store_true")
   parser.add_argument(
       "path_to_checkpoint",      type=UpperCamelCase_,      help="Path to the checkpoint file (.zip archive or direct .pt file)",      )
   parser.add_argument(
       "--config_file",      default="",      type=UpperCamelCase_,      help="An optional config json file describing the pre-trained model.",      )
   __lowercase								=						parser.parse_args()

   # Extract the basename.
   __lowercase								=						os.path.dirname(args.path_to_checkpoint)

   # Load the model.
   # the .zip is very optional, let's keep it for backward compatibility
   print(F"""Extracting PyTorch state dictionary from {args.path_to_checkpoint}""")
   if args.path_to_checkpoint.endswith(".zip"):
      with zipfile.ZipFile(args.path_to_checkpoint,      "r") as checkpoint:
         with checkpoint.open("release/mp_rank_00/model_optim_rng.pt") as pytorch_dict:
            __lowercase								=						torch.load(UpperCamelCase_,      map_location="cpu")
   else:
      __lowercase								=						torch.load(args.path_to_checkpoint,      map_location="cpu")

   __lowercase								=						input_state_dict.get("args",      UpperCamelCase_)

   # Read the config, or default to the model released by NVIDIA.
   if args.config_file == "":
      if ds_args is not None:
         if ds_args.bias_gelu_fusion:
            __lowercase								=						"gelu_fast"
         elif ds_args.openai_gelu:
            __lowercase								=						"gelu_new"
         else:
            __lowercase								=						"gelu"
      else:
         # in the very early days this used to be "gelu_new"
         __lowercase								=						"gelu_new"

      # Spell out all parameters in case the defaults change.
      __lowercase								=						GPTaConfig(
          vocab_size=50257,      n_positions=1024,      n_embd=1024,      n_layer=24,      n_head=16,      n_inner=4096,      activation_function=UpperCamelCase_,      resid_pdrop=0.1,      embd_pdrop=0.1,      attn_pdrop=0.1,      layer_norm_epsilon=1E-5,      initializer_range=0.02,      summary_type="cls_index",      summary_use_proj=UpperCamelCase_,      summary_activation=UpperCamelCase_,      summary_proj_to_labels=UpperCamelCase_,      summary_first_dropout=0.1,      scale_attn_weights=UpperCamelCase_,      use_cache=UpperCamelCase_,      bos_token_id=50256,      eos_token_id=50256,      )
   else:
      __lowercase								=						GPTaConfig.from_json_file(args.config_file)

   __lowercase								=						["GPT2LMHeadModel"]

   # Convert.
   print("Converting")
   __lowercase								=						convert_megatron_checkpoint(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)

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

   # Add tokenizer class info to config
   # see https://github.com/huggingface/transformers/issues/13906)
   if ds_args is not None:
      __lowercase								=						ds_args.tokenizer_type
      if tokenizer_type == "GPT2BPETokenizer":
         __lowercase								=						"gpt2"
      elif tokenizer_type == "PretrainedFromHF":
         __lowercase								=						ds_args.tokenizer_name_or_path
      else:
         raise ValueError(F"""Unrecognized tokenizer_type {tokenizer_type}""")
   else:
      __lowercase								=						"gpt2"

   __lowercase								=						AutoTokenizer.from_pretrained(UpperCamelCase_)
   __lowercase								=						type(UpperCamelCase_).__name__
   __lowercase								=						tokenizer_class

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

   # Save tokenizer based on args
   print(F"""Adding {tokenizer_class} tokenizer files""")
   tokenizer.save_pretrained(UpperCamelCase_)

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


####################################################################################################

if __name__ == "__main__":
   main()

####################################################################################################







"""simple docstring"""





import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_a							=							'bert-base-cased'
_a							=							'google/pegasus-xsum'
_a							=							[' Sam ate lunch today.', 'Sams lunch ingredients.']
_a							=							['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_a							=							'patrickvonplaten/t5-tiny-random'
_a							=							'sshleifer/bart-tiny-random'
_a							=							'sshleifer/tiny-mbart'
_a							=							'sshleifer/tiny-marian-en-de'




def   _A  (  UpperCamelCase_					:	Path,      UpperCamelCase_					:	list)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						"\n".join(UpperCamelCase_)
   Path(UpperCamelCase_).open("w").writelines(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''simple docstring'''


   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(UpperCamelCase_,      F"""{split}.source"""),      UpperCamelCase_)
      _dump_articles(os.path.join(UpperCamelCase_,      F"""{split}.target"""),      UpperCamelCase_)
   return tmp_dir




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 @slow
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __lowercase		,__lowercase								=						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, src_lang=UpperCAmelCase__, tgt_lang=UpperCAmelCase__, )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=2, collate_fn=train_dataset.collate_fn     )
    for batch in dataloader:
       assert isinstance(UpperCAmelCase__, UpperCAmelCase__     )
       assert batch["attention_mask"].shape == batch["input_ids"].shape
       # show that articles were trimmed.
       assert batch["input_ids"].shape[1] == max_src_len
       # show that targets are the same len
       assert batch["labels"].shape[1] == max_tgt_len
       if tok_name != MBART_TINY:
          continue
       # check language codes in correct place
       __lowercase								=						shift_tokens_right(batch["labels"], tokenizer.pad_token_id     )
       assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
       assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
       assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
       assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

       break  # No need to test every batch


 @parameterized.expand([BART_TINY, BERT_BASE_CASED]     )
 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						LegacySeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=2_0, max_target_length=UpperCAmelCase__, )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=2, collate_fn=train_dataset.collate_fn     )
    for batch in dataloader:
       assert batch["attention_mask"].shape == batch["input_ids"].shape
       # show that articles were trimmed.
       assert batch["input_ids"].shape[1] == max_len_source
       assert 2_0 >= batch["input_ids"].shape[1]  # trimmed significantly
       # show that targets were truncated
       assert batch["labels"].shape[1] == trunc_target  # Truncated
       assert max_len_target > trunc_target  # Truncated
       break  # No need to test every batch


 def  _lowercase			(      self							:      str     ):
    __lowercase								=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"     )

    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    __lowercase								=						tmp_dir.joinpath("train.source"     ).open().readlines()
    __lowercase								=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
    pack_data_dir(UpperCAmelCase__, UpperCAmelCase__, 1_2_8, UpperCAmelCase__     )
    __lowercase								=						{x.name for x in tmp_dir.iterdir()}
    __lowercase								=						{x.name for x in save_dir.iterdir()}
    __lowercase								=						save_dir.joinpath("train.source"     ).open().readlines()
    # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
    # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
    assert len(UpperCAmelCase__     ) < len(UpperCAmelCase__     )
    assert len(UpperCAmelCase__     ) == 1
    assert len(packed_examples[0]     ) == sum(len(UpperCAmelCase__     ) for x in orig_examples     )
    assert orig_paths == new_paths


 @pytest.mark.skipif(not FAIRSEQ_AVAILABLE, reason="This test requires fairseq"     )
 def  _lowercase			(      self							:      List[str]     ):
    if not FAIRSEQ_AVAILABLE:
       return
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=6_4     )
    __lowercase								=						6_4
    __lowercase								=						ds.make_dynamic_sampler(UpperCAmelCase__, required_batch_size_multiple=UpperCAmelCase__     )
    __lowercase								=						[len(UpperCAmelCase__     ) for x in batch_sampler]
    assert len(set(UpperCAmelCase__     )     ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(UpperCAmelCase__     ) == len(UpperCAmelCase__     )  # no dropped or added examples
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_sampler=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						[]
    __lowercase								=						[]
    for batch in data_loader:
       __lowercase								=						batch["input_ids"].shape
       __lowercase								=						src_shape[0]
       assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
       __lowercase								=						np.product(batch["input_ids"].shape     )
       num_src_per_batch.append(UpperCAmelCase__     )
       if num_src_tokens > (max_tokens * 1.1):
          failures.append(UpperCAmelCase__     )
    assert num_src_per_batch[0] == max(UpperCAmelCase__     )
    if failures:
       raise AssertionError(F"""too many tokens in {len(UpperCAmelCase__     )} batches"""     )


 def  _lowercase			(      self							:      Any     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=5_1_2     )
    __lowercase								=						2
    __lowercase								=						ds.make_sortish_sampler(UpperCAmelCase__, shuffle=UpperCAmelCase__     )

    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2, sampler=UpperCAmelCase__     )

    __lowercase								=						tokenizer.pad_token_id

    def count_pad_tokens(UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any]="input_ids"     ):
       return [batch[k].eq(UpperCAmelCase__     ).sum().item() for batch in data_loader]

    assert sum(count_pad_tokens(UpperCAmelCase__, k="labels"     )     ) < sum(count_pad_tokens(UpperCAmelCase__, k="labels"     )     )
    assert sum(count_pad_tokens(UpperCAmelCase__     )     ) < sum(count_pad_tokens(UpperCAmelCase__     )     )
    assert len(UpperCAmelCase__     ) == len(UpperCAmelCase__     )


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      int=1_0_0_0, UpperCAmelCase__							:      str=1_2_8     ):
    if os.getenv("USE_REAL_DATA", UpperCAmelCase__     ):
       __lowercase								=						"examples/seq2seq/wmt_en_ro"
       __lowercase								=						max_len * 2 * 6_4
       if not Path(UpperCAmelCase__     ).joinpath("train.len"     ).exists():
          save_len_file(UpperCAmelCase__, UpperCAmelCase__     )
    else:
       __lowercase								=						"examples/seq2seq/test_data/wmt_en_ro"
       __lowercase								=						max_len * 4
       save_len_file(UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, n_obs=UpperCAmelCase__, )
    return ds, max_tokens, tokenizer


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset()
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=0, add_extra_examples=UpperCAmelCase__     )     )
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=1, add_extra_examples=UpperCAmelCase__     )     )
    assert idsa.intersection(UpperCAmelCase__     ) == set()



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__, use_fast=UpperCAmelCase__     )
    if tok_name == MBART_TINY:
       __lowercase								=						SeqaSeqDataset(
           UpperCAmelCase__, data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     ), type_path="train", max_source_length=4, max_target_length=8, src_lang="EN", tgt_lang="FR", )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
       __lowercase								=						SeqaSeqDataset(
           UpperCAmelCase__, data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     ), type_path="train", max_source_length=4, max_target_length=8, )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
       assert len(UpperCAmelCase__     ) == 1 if tok_name == BART_TINY else len(UpperCAmelCase__     ) == 0




