






"""simple docstring"""



import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    str						,   __snake_case     :    List[str]=0       ):





  '''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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    Optional[Any]						,   __snake_case     :    Dict						,   __snake_case     :    str						,   __snake_case     :    Any       ):





  '''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 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    int						,   __snake_case     :    Union[str, Any]       ):





  '''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.tokenizer_type
    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 if 'ffn_hidden_size' in ds_args else 4 * ds_args.hidden_size
    # pprint(config)

  # The number of heads.
  lowercase				       =			config.num_attention_heads
  # The hidden_size per head.
  lowercase				       =			config.hidden_size // heads
  # 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, :]
  # Store the word embeddings.
  lowercase				       =			word_embeddings

  # The position embeddings.
  lowercase				       =			embeddings['position_embeddings']['weight']
  assert pos_embeddings.size(0       ) == config.max_position_embeddings and pos_embeddings.size(1       ) == config.hidden_size
  # Store the position embeddings.
  lowercase				       =			pos_embeddings

  # The token-type embeddings.
  lowercase				       =			embeddings['tokentype_embeddings']['weight']
  # Store the position embeddings.
  lowercase				       =			tokentype_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': '.attention.output.dense.',
      'self_attention.dense': '.attention.output.dense.',
      'mlp.dense_h_to_4h': '.intermediate.dense.',
      'mlp.dense_4h_to_h': '.output.dense.',
  }

  # Keep track of the attention/query/value tensor.
  lowercase				       =			None

  # 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'bert.encoder.layer.{layer_idx}'

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith('layernorm'       ):
      lowercase				       =			'attention.ln' if op_name.startswith('input'       ) else 'ln'
      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":
      # Make sure the QKV pointer is nil.
      assert attention_qkv_weight is None, ""

      lowercase				       =			fix_query_key_value_ordering(_UpperCAmelCase						,   _UpperCAmelCase						,   3						,   _UpperCAmelCase						,   _UpperCAmelCase       )
      # Store the tensor as we need the bias as well to interleave QKV and biases.
      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":
      # Make sure we read the weight tensor.
      assert attention_qkv_weight is not None, ""

      # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
      lowercase				       =			attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
      lowercase				       =			attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
      lowercase				       =			attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

      lowercase				       =			fix_query_key_value_ordering(_UpperCAmelCase						,   _UpperCAmelCase						,   3						,   _UpperCAmelCase						,   _UpperCAmelCase       )
      # Split the bias.
      lowercase				       =			out_val[0 * config.hidden_size : 1 * config.hidden_size]
      lowercase				       =			out_val[1 * config.hidden_size : 2 * config.hidden_size]
      lowercase				       =			out_val[2 * config.hidden_size : 3 * config.hidden_size]

      # Store.
      lowercase				       =			q
      lowercase				       =			q_bias
      lowercase				       =			k
      lowercase				       =			k_bias
      lowercase				       =			v
      lowercase				       =			v_bias

      # Clear the stored tensor.
      lowercase				       =			None

    # Copy weights and biases as is.
    elif weight_or_bias in ["weight", "bias"]:
      lowercase				       =			megatron_to_transformers[op_name]
      lowercase				       =			val

    # The final layernorm.
  lowercase				       =			transformer['final_layernorm.weight']
  lowercase				       =			transformer['final_layernorm.bias']

  # The pooler.
  lowercase				       =			lm['pooler']

  # Store the matrix and the bias.
  lowercase				       =			pooler['dense.weight']
  lowercase				       =			pooler['dense.bias']

  # The LM head from Megatron (for RACE).
  lowercase				       =			model['lm_head']

  # The transform matrix.
  lowercase				       =			lm_head['dense.weight']
  lowercase				       =			lm_head['dense.bias']

  # The transform LN.
  lowercase				       =			lm_head['layernorm.weight']
  lowercase				       =			lm_head['layernorm.bias']

  # For the decoder, we replicate the weights.
  lowercase				       =			word_embeddings
  lowercase				       =			lm_head['bias']

  # The classifier from Megatron (for MLNI).
  lowercase				       =			model['binary_head']

  # Store the classifier.
  lowercase				       =			binary_head['weight']
  lowercase				       =			binary_head['bias']

  # It should be done!
  return output_state_dict
def 		_SCREAMING_SNAKE_CASE					(       ):





  '''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 ZIP file containing the checkpoint'       )
  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'       )

  if args.config_file == "":
    # Default config of megatron-bert 345m
    lowercase				       =			MegatronBertConfig()

    # different megatron-bert-*-345m models have different vocab sizes, so override the default
    # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
    lowercase				       =			input_state_dict['model']['lm_head']['bias'].numel()
  else:
    lowercase				       =			MegatronBertConfig.from_json_file(args.config_file       )

  # 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       )

  # Store the config to file.
  print('Saving config'       )
  config.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 unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image


class 	a__		(    unittest.TestCase,	SCREAMING_SNAKE_CASE__  ):


   def 							lowercase					(				self   :				Dict      )     ->							List[str]:
         lowercase	:   str											= load_tool('image-captioning'      )
         self.tool.setup()
         lowercase	:   List[str]											= load_tool('image-captioning',  remote=lowerCAmelCase      )


   def 							lowercase					(				self   :				str      )     ->							int:
         lowercase	:   Union[str, Any]											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   List[str]											= self.tool(lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )


   def 							lowercase					(				self   :				str      )     ->							List[str]:
         lowercase	:   str											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   Tuple											= self.remote_tool(lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )


   def 							lowercase					(				self   :				Tuple      )     ->							Union[str, Any]:
         lowercase	:   Tuple											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   Union[str, Any]											= self.tool(image=lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )






   def 							lowercase					(				self   :				str      )     ->							Optional[int]:
         lowercase	:   List[str]											= Image.open(Path(get_tests_dir('fixtures/tests_samples/COCO'      )      ) / '000000039769.png'      )
         lowercase	:   str											= self.remote_tool(image=lowerCAmelCase      )
         self.assertEqual(lowerCAmelCase,  'two cats sleeping on a couch'      )
