
# 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_gpt2/convert_megatron_gpt2_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 AutoTokenizer, GPTaConfig

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=0 )		->						List[str]:


  '''simple docstring'''






  if name is None:
    lowercase__	: List[Any]     		=   None
  else:
    lowercase__	: Any     		=   """.""" * max(0			,			spaces - 2 ) + """# {:""" + str(50 - spaces ) + """s}"""
    lowercase__	: int     		=   fmt.format(lowercase_ )

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

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  lowercase__	: List[Any]     		=   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__	: str     		=   param.view(*lowercase_ )
    lowercase__	: Dict     		=   param.transpose(0			,			2 )
    lowercase__	: Tuple     		=   param.transpose(1			,			2 ).contiguous()
  elif checkpoint_version >= 2.0:
    # other versions store [num_heads * num_splits * hidden_size, :]
    lowercase__	: Optional[Any]     		=   (num_heads, num_splits, hidden_size) + input_shape[1:]
    lowercase__	: int     		=   param.view(*lowercase_ )
    lowercase__	: List[Any]     		=   param.transpose(0			,			1 ).contiguous()
  lowercase__	: List[str]     		=   param.view(*lowercase_ )
  return param

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Union[str, Any]     		=   {}

  # old versions did not store training args
  lowercase__	: Optional[int]     		=   input_state_dict.get("""args"""			,			lowercase_ )
  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[int]     		=   ds_args.padded_vocab_size
    lowercase__	: List[str]     		=   ds_args.max_position_embeddings
    lowercase__	: Union[str, Any]     		=   ds_args.hidden_size
    lowercase__	: Union[str, Any]     		=   ds_args.num_layers
    lowercase__	: List[Any]     		=   ds_args.num_attention_heads
    lowercase__	: Any     		=   ds_args.ffn_hidden_size
    # pprint(config)

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

  # The model.
  lowercase__	: Tuple     		=   input_state_dict["""model"""]
  # The language model.
  lowercase__	: List[str]     		=   model["""language_model"""]
  # The embeddings.
  lowercase__	: Dict     		=   lm["""embedding"""]

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

  # The position embeddings.
  lowercase__	: Tuple     		=   embeddings["""position_embeddings"""]["""weight"""]
  # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
  lowercase__	: List[str]     		=   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__	: Optional[Any]     		=   pos_embeddings

  # The transformer.
  lowercase__	: List[str]     		=   lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

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

  # The simple map of names for "automated" rules.
  lowercase__	: Optional[int]     		=   {
      """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__	: int     		=   layer_re.match(lowercase_ )

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

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

    # The name of the layer.
    lowercase__	: List[str]     		=   F'transformer.h.{layer_idx}'

    # For layernorm(s), simply store the layer norm.
    if op_name.endswith("""layernorm""" ):
      lowercase__	: Optional[Any]     		=   """ln_1""" if op_name.startswith("""input""" ) else """ln_2"""
      lowercase__	: str     		=   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__	: List[str]     		=   torch.tril(torch.ones((n_positions, n_positions)			,			dtype=torch.floataa ) ).view(
          1			,			1			,			lowercase_			,			lowercase_ )
      lowercase__	: Any     		=   causal_mask

      # Insert a "dummy" tensor for masked_bias.
      lowercase__	: Dict     		=   torch.tensor(-1E4			,			dtype=torch.floataa )
      lowercase__	: Tuple     		=   masked_bias

      lowercase__	: List[Any]     		=   fix_query_key_value_ordering(lowercase_			,			lowercase_			,			3			,			lowercase_			,			lowercase_ )
      # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
      lowercase__	: Optional[int]     		=   out_val.transpose(0			,			1 ).contiguous()
      # Store.
      lowercase__	: int     		=   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__	: List[str]     		=   fix_query_key_value_ordering(lowercase_			,			lowercase_			,			3			,			lowercase_			,			lowercase_ )
      # Store. No change of shape.
      lowercase__	: Dict     		=   out_val

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

    # Copy the bias.
    elif weight_or_bias == "bias":
      lowercase__	: List[str]     		=   megatron_to_transformers[op_name]
      lowercase__	: Dict     		=   val

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

  # The final layernorm.
  lowercase__	: Union[str, Any]     		=   transformer["""final_layernorm.weight"""]
  lowercase__	: List[Any]     		=   transformer["""final_layernorm.bias"""]

  # For LM head, transformers' wants the matrix to weight embeddings.
  lowercase__	: Union[str, Any]     		=   word_embeddings

  # It should be done!
  return output_state_dict

def 		UpperCamelCase						(   )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   argparse.ArgumentParser()
  parser.add_argument("""--print-checkpoint-structure"""			,			action="""store_true""" )
  parser.add_argument(
      """path_to_checkpoint"""			,			type=lowercase_			,			help="""Path to the checkpoint file (.zip archive or direct .pt file)"""			,			)
  parser.add_argument(
      """--config_file"""			,			default=""""""			,			type=lowercase_			,			help="""An optional config json file describing the pre-trained model."""			,			)
  lowercase__	: Optional[int]     		=   parser.parse_args()

  # Extract the basename.
  lowercase__	: Union[str, 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__	: Union[str, Any]     		=   torch.load(lowercase_			,			map_location="""cpu""" )
  else:
    lowercase__	: int     		=   torch.load(args.path_to_checkpoint			,			map_location="""cpu""" )

  lowercase__	: Optional[int]     		=   input_state_dict.get("""args"""			,			lowercase_ )

  # 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__	: int     		=   """gelu_fast"""
      elif ds_args.openai_gelu:
        lowercase__	: Tuple     		=   """gelu_new"""
      else:
        lowercase__	: Union[str, Any]     		=   """gelu"""
    else:
      # in the very early days this used to be "gelu_new"
      lowercase__	: Optional[int]     		=   """gelu_new"""

    # Spell out all parameters in case the defaults change.
    lowercase__	: Tuple     		=   GPTaConfig(
        vocab_size=5_02_57			,			n_positions=10_24			,			n_embd=10_24			,			n_layer=24			,			n_head=16			,			n_inner=40_96			,			activation_function=lowercase_			,			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=lowercase_			,			summary_activation=lowercase_			,			summary_proj_to_labels=lowercase_			,			summary_first_dropout=0.1			,			scale_attn_weights=lowercase_			,			use_cache=lowercase_			,			bos_token_id=5_02_56			,			eos_token_id=5_02_56			,			)
  else:
    lowercase__	: List[str]     		=   GPTaConfig.from_json_file(args.config_file )

  lowercase__	: Optional[Any]     		=   ["""GPT2LMHeadModel"""]

  # Convert.
  print("""Converting""" )
  lowercase__	: Dict     		=   convert_megatron_checkpoint(lowercase_			,			lowercase_			,			lowercase_ )

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

  # Add tokenizer class info to config
  # see https://github.com/huggingface/transformers/issues/13906)
  if ds_args is not None:
    lowercase__	: Dict     		=   ds_args.tokenizer_type
    if tokenizer_type == "GPT2BPETokenizer":
      lowercase__	: List[Any]     		=   """gpt2"""
    elif tokenizer_type == "PretrainedFromHF":
      lowercase__	: str     		=   ds_args.tokenizer_name_or_path
    else:
      raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}' )
  else:
    lowercase__	: Union[str, Any]     		=   """gpt2"""

  lowercase__	: List[Any]     		=   AutoTokenizer.from_pretrained(lowercase_ )
  lowercase__	: int     		=   type(lowercase_ ).__name__
  lowercase__	: str     		=   tokenizer_class

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

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

  # Store the state_dict to file.
  lowercase__	: Optional[Any]     		=   os.path.join(lowercase_			,			"""pytorch_model.bin""" )
  print(F'Saving checkpoint to "{output_checkpoint_file}"' )
  torch.save(lowercase_			,			lowercase_ )


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

if __name__ == "__main__":
     main()

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








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class    _snake_case (						unittest.TestCase				):


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_=13       ,	SCREAMING_SNAKE_CASE_=3       ,	SCREAMING_SNAKE_CASE_=2_24       ,	SCREAMING_SNAKE_CASE_=30       ,	SCREAMING_SNAKE_CASE_=4_00       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5]       ,	):


   '''simple docstring'''




   lowercase__	: List[str]     		=   size if size is not None else {"""height""": 18, """width""": 18}
   lowercase__	: int     		=   parent
   lowercase__	: Union[str, Any]     		=   batch_size
   lowercase__	: List[str]     		=   num_channels
   lowercase__	: str     		=   image_size
   lowercase__	: int     		=   min_resolution
   lowercase__	: Dict     		=   max_resolution
   lowercase__	: Tuple     		=   do_resize
   lowercase__	: Union[str, Any]     		=   size
   lowercase__	: Any     		=   do_normalize
   lowercase__	: Tuple     		=   image_mean
   lowercase__	: str     		=   image_std



 def   lowercase__  (       self):


   '''simple docstring'''




   return {
       "image_mean": self.image_mean,
       "image_std": self.image_std,
       "do_normalize": self.do_normalize,
       "do_resize": self.do_resize,
       "size": self.size,
   }



@require_torch
@require_vision
class    _snake_case (						UpperCAmelCase_	,    unittest.TestCase				):
 __lowerCAmelCase	:  Optional[Any]	   =      ViTImageProcessor if is_vision_available() else None


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   EfficientFormerImageProcessorTester(self)


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return self.image_proc_tester.prepare_image_processor_dict()


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   self.image_processing_class(**self.image_processor_dict)
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_mean"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""image_std"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_normalize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""do_resize"""))
   self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_       ,	"""size"""))


 def   lowercase__  (       self):


   '''simple docstring'''




   pass


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PIL images
   lowercase__	: List[Any]     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	Image.Image)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: str     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Tuple     		=   self.image_processing_class(**self.image_processor_dict)
   # create random numpy tensors
   lowercase__	: str     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	numpify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	np.ndarray)

   # Test not batched input
   lowercase__	: Optional[int]     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Dict     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.image_processing_class(**self.image_processor_dict)
   # create random PyTorch tensors
   lowercase__	: Dict     		=   prepare_image_inputs(self.image_proc_tester       ,	equal_resolution=SCREAMING_SNAKE_CASE_       ,	torchify=SCREAMING_SNAKE_CASE_)
   for image in image_inputs:
     self.assertIsInstance(SCREAMING_SNAKE_CASE_       ,	torch.Tensor)

   # Test not batched input
   lowercase__	: int     		=   image_processor(image_inputs[0]       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           1,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)

   # Test batched
   lowercase__	: Any     		=   image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors="""pt""").pixel_values
   self.assertEqual(
       encoded_images.shape       ,	(
           self.image_proc_tester.batch_size,
           self.image_proc_tester.num_channels,
           self.image_proc_tester.size["""height"""],
           self.image_proc_tester.size["""width"""],
       )       ,	)






