



"""simple docstring"""




# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase=0					)  ->      Dict:






      '''simple docstring'''


      if name is None:
            lowercase	:   Optional[Any]											= None
      else:
            lowercase	:   Dict											= '.' * max(0						,   spaces - 2					) + '# {:' + str(50 - spaces					) + 's}'
            lowercase	:   Any											= 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 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					)  ->      Optional[Any]:






      '''simple docstring'''


      lowercase	:   Optional[int]											= param.size()
      if checkpoint_version == 1.0:
            # version 1.0 stores [num_heads * hidden_size * num_splits, :]
            lowercase	:   Optional[int]											= (num_heads, hidden_size, num_splits) + input_shape[1:]
            lowercase	:   int											= param.view(*_UpperCAmelCase					)
            lowercase	:   Dict											= param.transpose(0						,   2					)
            lowercase	:   int											= param.transpose(1						,   2					).contiguous()
      elif checkpoint_version >= 2.0:
            # other versions store [num_heads * num_splits * hidden_size, :]
            lowercase	:   int											= (num_heads, num_splits, hidden_size) + input_shape[1:]
            lowercase	:   Optional[int]											= param.view(*_UpperCAmelCase					)
            lowercase	:   Union[str, Any]											= param.transpose(0						,   1					).contiguous()
      lowercase	:   int											= param.view(*_UpperCAmelCase					)
      return param


def 	lowercase__  (      _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					)  ->      List[Any]:






      '''simple docstring'''


      lowercase	:   Union[str, Any]											= {}

      # old versions did not store training args
      lowercase	:   List[Any]											= 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	:   Optional[Any]											= ds_args.tokenizer_type
            lowercase	:   Any											= ds_args.padded_vocab_size
            lowercase	:   int											= ds_args.max_position_embeddings
            lowercase	:   List[Any]											= ds_args.hidden_size
            lowercase	:   str											= ds_args.num_layers
            lowercase	:   Optional[Any]											= ds_args.num_attention_heads
            lowercase	:   Any											= 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	:   Dict											= config.num_attention_heads
      # The hidden_size per head.
      lowercase	:   Union[str, Any]											= config.hidden_size // heads
      # Megatron-LM checkpoint version
      if "checkpoint_version" in input_state_dict.keys():
            lowercase	:   Optional[Any]											= input_state_dict['checkpoint_version']
      else:
            lowercase	:   List[Any]											= 0.0

      # The model.
      lowercase	:   int											= input_state_dict['model']
      # The language model.
      lowercase	:   Optional[int]											= model['language_model']
      # The embeddings.
      lowercase	:   Optional[int]											= lm['embedding']

      # The word embeddings.
      lowercase	:   int											= embeddings['word_embeddings']['weight']
      # Truncate the embedding table to vocab_size rows.
      lowercase	:   List[Any]											= word_embeddings[: config.vocab_size, :]
      # Store the word embeddings.
      lowercase	:   List[str]											= word_embeddings

      # The position embeddings.
      lowercase	:   List[str]											= 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	:   Any											= pos_embeddings

      # The token-type embeddings.
      lowercase	:   int											= embeddings['tokentype_embeddings']['weight']
      # Store the position embeddings.
      lowercase	:   List[Any]											= tokentype_embeddings

      # The transformer.
      lowercase	:   str											= lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']

      # The regex to extract layer names.
      lowercase	:   int											= re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)'					)

      # The simple map of names for "automated" rules.
      lowercase	:   Union[str, Any]											= {
          '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	:   Optional[Any]											= None

      # Extract the layers.
      for key, val in transformer.items():
            # Match the name.
            lowercase	:   List[Any]											= layer_re.match(_UpperCAmelCase					)

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

            # The index of the layer.
            lowercase	:   Optional[int]											= int(m.group(1					)					)
            # The name of the operation.
            lowercase	:   Optional[int]											= m.group(2					)
            # Is it a weight or a bias?
            lowercase	:   List[Any]											= m.group(3					)

            # The name of the layer.
            lowercase	:   int											= f'''bert.encoder.layer.{layer_idx}'''

            # For layernorm(s), simply store the layer norm.
            if op_name.endswith('layernorm'					):
                  lowercase	:   Any											= 'attention.ln' if op_name.startswith('input'					) else 'ln'
                  lowercase	:   Optional[int]											= 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	:   Tuple											= 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	:   List[Any]											= 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	:   str											= attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
                  lowercase	:   str											= attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
                  lowercase	:   int											= attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

                  # Store.
                  lowercase	:   Optional[Any]											= q
                  lowercase	:   Union[str, Any]											= q_bias
                  lowercase	:   Tuple											= k
                  lowercase	:   int											= k_bias
                  lowercase	:   List[str]											= v
                  lowercase	:   str											= v_bias

                  # Clear the stored tensor.
                  lowercase	:   int											= None

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

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

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

      # Store the matrix and the bias.
      lowercase	:   Optional[Any]											= pooler['dense.weight']
      lowercase	:   Tuple											= pooler['dense.bias']

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

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

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

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

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

      # Store the classifier.
      lowercase	:   int											= binary_head['weight']
      lowercase	:   Union[str, Any]											= binary_head['bias']

      # It should be done!
      return output_state_dict


def 	lowercase__  (      )  ->      List[Any]:






      '''simple docstring'''


      lowercase	:   List[Any]											= 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	:   Dict											= parser.parse_args()

      # Extract the basename.
      lowercase	:   Optional[Any]											= 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	:   int											= torch.load(_UpperCAmelCase						,   map_location='cpu'					)
      else:
            lowercase	:   List[Any]											= torch.load(args.path_to_checkpoint						,   map_location='cpu'					)

      if args.config_file == "":
            # Default config of megatron-bert 345m
            lowercase	:   Any											= 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	:   Any											= input_state_dict['model']['lm_head']['bias'].numel()
      else:
            lowercase	:   List[str]											= MegatronBertConfig.from_json_file(args.config_file					)

      # Convert.
      print('Converting'					)
      lowercase	:   List[Any]											= 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	:   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__":
     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'      )
