

'''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_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(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_=0	):


 if name is None:
  UpperCAmelCase       :	List[str]											= None
 else:
  UpperCAmelCase       :	Optional[Any]											= '.' * max(0  ,       spaces - 2	) + '# {:' + str(50 - spaces	) + 's}'
  UpperCAmelCase       :	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       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	int											= param.size()
 if checkpoint_version == 1.0:
  # version 1.0 stores [num_heads * hidden_size * num_splits, :]
  UpperCAmelCase       :	str											= (num_heads, hidden_size, num_splits) + input_shape[1:]
  UpperCAmelCase       :	List[Any]											= param.view(*UpperCamelCase_	)
  UpperCAmelCase       :	List[str]											= param.transpose(0  ,       2	)
  UpperCAmelCase       :	Optional[Any]											= param.transpose(1  ,       2	).contiguous()
 elif checkpoint_version >= 2.0:
  # other versions store [num_heads * num_splits * hidden_size, :]
  UpperCAmelCase       :	Optional[Any]											= (num_heads, num_splits, hidden_size) + input_shape[1:]
  UpperCAmelCase       :	str											= param.view(*UpperCamelCase_	)
  UpperCAmelCase       :	Optional[Any]											= param.transpose(0  ,       1	).contiguous()
 UpperCAmelCase       :	List[Any]											= param.view(*UpperCamelCase_	)
 return param




def       UpperCamelCase(     UpperCAmelCase_  ,       UpperCAmelCase_  ,       UpperCAmelCase_	):
 UpperCAmelCase       :	List[str]											= {}

 # old versions did not store training args
 UpperCAmelCase       :	Optional[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))

  UpperCAmelCase       :	str											= ds_args.padded_vocab_size
  UpperCAmelCase       :	int											= ds_args.max_position_embeddings
  UpperCAmelCase       :	Union[str, Any]											= ds_args.hidden_size
  UpperCAmelCase       :	str											= ds_args.num_layers
  UpperCAmelCase       :	Tuple											= ds_args.num_attention_heads
  UpperCAmelCase       :	Optional[int]											= ds_args.ffn_hidden_size
  # pprint(config)

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

 # The model.
 UpperCAmelCase       :	List[str]											= input_state_dict['model']
 # The language model.
 UpperCAmelCase       :	Any											= model['language_model']
 # The embeddings.
 UpperCAmelCase       :	int											= lm['embedding']

 # The word embeddings.
 UpperCAmelCase       :	Optional[Any]											= embeddings['word_embeddings']['weight']
 # Truncate the embedding table to vocab_size rows.
 UpperCAmelCase       :	List[Any]											= word_embeddings[: config.vocab_size, :]
 UpperCAmelCase       :	Optional[Any]											= word_embeddings

 # The position embeddings.
 UpperCAmelCase       :	Any											= embeddings['position_embeddings']['weight']
 # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
 UpperCAmelCase       :	Dict											= 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.
 UpperCAmelCase       :	List[str]											= pos_embeddings

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

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

 # The simple map of names for "automated" rules.
 UpperCAmelCase       :	str											= {
     '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.
  UpperCAmelCase       :	Dict											= layer_re.match(UpperCamelCase_	)

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

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

  # The name of the layer.
  UpperCAmelCase       :	List[Any]											= F"""transformer.h.{layer_idx}"""

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

   # Insert a "dummy" tensor for masked_bias.
   UpperCAmelCase       :	int											= torch.tensor(-1E4  ,       dtype=torch.floataa	)
   UpperCAmelCase       :	List[str]											= masked_bias

   UpperCAmelCase       :	List[str]											= fix_query_key_value_ordering(UpperCamelCase_  ,       UpperCamelCase_  ,       3  ,       UpperCamelCase_  ,       UpperCamelCase_	)
   # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
   UpperCAmelCase       :	Any											= out_val.transpose(0  ,       1	).contiguous()
   # Store.
   UpperCAmelCase       :	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":
   UpperCAmelCase       :	Any											= fix_query_key_value_ordering(UpperCamelCase_  ,       UpperCamelCase_  ,       3  ,       UpperCamelCase_  ,       UpperCamelCase_	)
   # Store. No change of shape.
   UpperCAmelCase       :	List[str]											= out_val

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

  # Copy the bias.
  elif weight_or_bias == "bias":
   UpperCAmelCase       :	Dict											= megatron_to_transformers[op_name]
   UpperCAmelCase       :	Union[str, Any]											= val

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

 # The final layernorm.
 UpperCAmelCase       :	Tuple											= transformer['final_layernorm.weight']
 UpperCAmelCase       :	Union[str, Any]											= transformer['final_layernorm.bias']

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

 # It should be done!
 return output_state_dict




def       UpperCamelCase(     ):
 UpperCAmelCase       :	Optional[int]											= 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.'  ,       )
 UpperCAmelCase       :	Any											= parser.parse_args()

 # Extract the basename.
 UpperCAmelCase       :	int											= 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:
    UpperCAmelCase       :	int											= torch.load(UpperCamelCase_  ,       map_location='cpu'	)
 else:
  UpperCAmelCase       :	Dict											= torch.load(args.path_to_checkpoint  ,       map_location='cpu'	)

 UpperCAmelCase       :	str											= 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:
    UpperCAmelCase       :	Optional[Any]											= 'gelu_fast'
   elif ds_args.openai_gelu:
    UpperCAmelCase       :	str											= 'gelu_new'
   else:
    UpperCAmelCase       :	List[Any]											= 'gelu'
  else:
   # in the very early days this used to be "gelu_new"
   UpperCAmelCase       :	str											= 'gelu_new'

  # Spell out all parameters in case the defaults change.
  UpperCAmelCase       :	str											= 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=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=5_02_56  ,       eos_token_id=5_02_56  ,       )
 else:
  UpperCAmelCase       :	Optional[Any]											= GPTaConfig.from_json_file(args.config_file	)

 UpperCAmelCase       :	Any											= ['GPT2LMHeadModel']

 # Convert.
 print('Converting'	)
 UpperCAmelCase       :	str											= 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:
  UpperCAmelCase       :	List[Any]											= ds_args.tokenizer_type
  if tokenizer_type == "GPT2BPETokenizer":
   UpperCAmelCase       :	List[str]											= 'gpt2'
  elif tokenizer_type == "PretrainedFromHF":
   UpperCAmelCase       :	List[str]											= ds_args.tokenizer_name_or_path
  else:
   raise ValueError(F"""Unrecognized tokenizer_type {tokenizer_type}"""	)
 else:
  UpperCAmelCase       :	Optional[int]											= 'gpt2'

 UpperCAmelCase       :	List[Any]											= AutoTokenizer.from_pretrained(UpperCamelCase_	)
 UpperCAmelCase       :	Tuple											= type(UpperCamelCase_	).__name__
 UpperCAmelCase       :	Dict											= 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.
 UpperCAmelCase       :	Optional[int]											= os.path.join(UpperCamelCase_  ,       'pytorch_model.bin'	)
 print(F"""Saving checkpoint to \"{output_checkpoint_file}\""""	)
 torch.save(UpperCamelCase_  ,       UpperCamelCase_	)


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

if __name__ == "__main__":
 main()

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






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case								=  logging.get_logger(__name__)




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   List[str]				       =["""pixel_values"""]
	def __init__(			self							:   Dict       ,   UpperCAmelCase_							:   bool = True       ,   UpperCAmelCase_							:   int = 32       ,   UpperCAmelCase_							:   int=PILImageResampling.BILINEAR       ,   UpperCAmelCase_							:   bool = True       ,   **UpperCAmelCase_							:   Any       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor
						SCREAMING_SNAKE_CASE__													= resample
						super().__init__(**UpperCAmelCase_							)
	def      A_				(			self							:   Optional[int]       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   int       ,   UpperCAmelCase_							:   Dict       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   Dict							):
						SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= get_image_size(UpperCAmelCase_							)
						# Rounds the height and width down to the closest multiple of size_divisor
						SCREAMING_SNAKE_CASE__													= height // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= width // size_divisor * size_divisor
						SCREAMING_SNAKE_CASE__													= resize(UpperCAmelCase_       ,   (new_h, new_w)       ,   resample=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						return image
	def      A_				(			self							:   Any       ,   UpperCAmelCase_							:   np.ndarray       ,   UpperCAmelCase_							:   float       ,   UpperCAmelCase_							:   Optional[ChannelDimension] = None       ,   **UpperCAmelCase_							:   List[str]							):
						return rescale(image=UpperCAmelCase_       ,   scale=UpperCAmelCase_       ,   data_format=UpperCAmelCase_       ,   **UpperCAmelCase_							)





	def      A_				(			self							:   Dict       ,   UpperCAmelCase_							:   Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[int] = None       ,   UpperCAmelCase_							:   List[str]=None       ,   UpperCAmelCase_							:   Optional[bool] = None       ,   UpperCAmelCase_							:   Optional[Union[TensorType, str]] = None       ,   UpperCAmelCase_							:   ChannelDimension = ChannelDimension.FIRST       ,   **UpperCAmelCase_							:   List[Any]       ,   ):
						SCREAMING_SNAKE_CASE__													= do_resize if do_resize is not None else self.do_resize
						SCREAMING_SNAKE_CASE__													= do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE__													= size_divisor if size_divisor is not None else self.size_divisor
						SCREAMING_SNAKE_CASE__													= resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
											raise ValueError('size_divisor is required for resizing'							)

						SCREAMING_SNAKE_CASE__													= make_list_of_images(UpperCAmelCase_							)

						if not valid_images(UpperCAmelCase_							):
											raise ValueError('Invalid image(s)'							)

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE__													= [to_numpy_array(UpperCAmelCase_							) for img in images]

						if do_resize:
											SCREAMING_SNAKE_CASE__													= [self.resize(UpperCAmelCase_       ,   size_divisor=UpperCAmelCase_       ,   resample=UpperCAmelCase_							) for image in images]

						if do_rescale:
											SCREAMING_SNAKE_CASE__													= [self.rescale(UpperCAmelCase_       ,   scale=1 / 255							) for image in images]

						SCREAMING_SNAKE_CASE__													= [to_channel_dimension_format(UpperCAmelCase_       ,   UpperCAmelCase_							) for image in images]

						SCREAMING_SNAKE_CASE__													= {'pixel_values': images}
						return BatchFeature(data=UpperCAmelCase_       ,   tensor_type=UpperCAmelCase_							)





