


# 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      _lowerCAmelCase  (							lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:Any						,  lowerCAmelCase_							:str=0							)->Optional[int]:
   '''simple docstring'''






   if name is None:
      snake_case_         =   None
   else:
      snake_case_         =   "." * max(0						,  spaces - 2							) + "# {:" + str(50 - spaces							) + "s}"
      snake_case_         =   fmt.format(lowerCAmelCase_							)

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



def      _lowerCAmelCase  (							lowerCAmelCase_							:int						,  lowerCAmelCase_							:Tuple						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Union[str, Any]						,  lowerCAmelCase_							:Optional[Any]							)->Optional[Any]:
   '''simple docstring'''






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



def      _lowerCAmelCase  (							lowerCAmelCase_							:str						,  lowerCAmelCase_							:Optional[int]						,  lowerCAmelCase_							:int							)->Optional[int]:
   '''simple docstring'''






   snake_case_         =   {}

   # old versions did not store training args
   snake_case_         =   input_state_dict.get("args"						,  lowerCAmelCase_							)
   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))

      snake_case_         =   ds_args.tokenizer_type
      snake_case_         =   ds_args.padded_vocab_size
      snake_case_         =   ds_args.max_position_embeddings
      snake_case_         =   ds_args.hidden_size
      snake_case_         =   ds_args.num_layers
      snake_case_         =   ds_args.num_attention_heads
      snake_case_         =   ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
      # pprint(config)

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

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

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

   # The position embeddings.
   snake_case_         =   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.
   snake_case_         =   pos_embeddings

   # The token-type embeddings.
   snake_case_         =   embeddings["tokentype_embeddings"]["weight"]
   # Store the position embeddings.
   snake_case_         =   tokentype_embeddings

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

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

   # The simple map of names for "automated" rules.
   snake_case_         =   {
       "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.
   snake_case_         =   None

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

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

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

      # The name of the layer.
      snake_case_         =   F'''bert.encoder.layer.{layer_idx}'''

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith("layernorm"							):
         snake_case_         =   "attention.ln" if op_name.startswith("input"							) else "ln"
         snake_case_         =   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, ""

         snake_case_         =   fix_query_key_value_ordering(lowerCAmelCase_						,  lowerCAmelCase_						,  3						,  lowerCAmelCase_						,  lowerCAmelCase_							)
         # Store the tensor as we need the bias as well to interleave QKV and biases.
         snake_case_         =   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.
         snake_case_         =   attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
         snake_case_         =   attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
         snake_case_         =   attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

         # Clear the stored tensor.
         snake_case_         =   None

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

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

   # The pooler.
   snake_case_         =   lm["pooler"]

   # Store the matrix and the bias.
   snake_case_         =   pooler["dense.weight"]
   snake_case_         =   pooler["dense.bias"]

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

   # The transform matrix.
   snake_case_         =   lm_head["dense.weight"]
   snake_case_         =   lm_head["dense.bias"]

   # The transform LN.
   snake_case_         =   lm_head["layernorm.weight"]
   snake_case_         =   lm_head["layernorm.bias"]

   # For the decoder, we replicate the weights.
   snake_case_         =   word_embeddings
   snake_case_         =   lm_head["bias"]

   # The classifier from Megatron (for MLNI).
   snake_case_         =   model["binary_head"]

   # Store the classifier.
   snake_case_         =   binary_head["weight"]
   snake_case_         =   binary_head["bias"]

   # It should be done!
   return output_state_dict



def      _lowerCAmelCase  (							)->Any:
   '''simple docstring'''






   snake_case_         =   argparse.ArgumentParser()
   parser.add_argument("--print-checkpoint-structure"						,  action="store_true"							)
   parser.add_argument("path_to_checkpoint"						,  type=lowerCAmelCase_						,  help="Path to the ZIP file containing the checkpoint"							)
   parser.add_argument(
       "--config_file"						,  default=""						,  type=lowerCAmelCase_						,  help="An optional config json file describing the pre-trained model."						,  )
   snake_case_         =   parser.parse_args()

   # Extract the basename.
   snake_case_         =   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:
            snake_case_         =   torch.load(lowerCAmelCase_						,  map_location="cpu"							)
   else:
      snake_case_         =   torch.load(args.path_to_checkpoint						,  map_location="cpu"							)

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

   # Convert.
   print("Converting"							)
   snake_case_         =   convert_megatron_checkpoint(lowerCAmelCase_						,  lowerCAmelCase_						,  lowerCAmelCase_							)

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

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

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


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

if __name__ == "__main__":
   main()

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








import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey


class       __lowerCAmelCase       :






  """simple docstring"""





  def __init__(       self    :    Optional[Any]      ,			_lowerCAmelCase    :    Union[str, Any]      ,			_lowerCAmelCase    :    int					)				->	Tuple:




     """simple docstring"""
     snake_case_         =   0
     # max greyscale value for #FFFFFF
     snake_case_         =   int(self.get_greyscale(2_5_5      ,			2_5_5      ,			2_5_5					)					)

     if not self.min_threshold < threshold < self.max_threshold:
        snake_case_         =   F'''Factor value should be from 0 to {self.max_threshold}'''
        raise ValueError(_lowerCAmelCase					)

     snake_case_         =   input_img
     snake_case_         =   threshold
     snake_case_      ,      snake_case_         =   self.input_img.shape[1], self.input_img.shape[0]

     # error table size (+4 columns and +1 row) greater than input image because of
     # lack of if statements
     snake_case_         =   [
         [0 for _ in range(self.height + 4					)] for __ in range(self.width + 1					)
     ]
     snake_case_         =   np.ones((self.width, self.height, 3)      ,			np.uinta					) * 2_5_5



  @classmethod
  def    lowerCAmelCase__					(       cls    :    Optional[Any]      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int      ,			_lowerCAmelCase    :    int					)				->	float:




     """simple docstring"""
     return 0.114 * blue + 0.587 * green + 0.2_126 * red



  def    lowerCAmelCase__					(       self    :    Union[str, Any]					)				->	None:




     """simple docstring"""
     for y in range(self.height					):
        for x in range(self.width					):
           snake_case_         =   int(self.get_greyscale(*self.input_img[y][x]					)					)
           if self.threshold > greyscale + self.error_table[y][x]:
              snake_case_         =   (0, 0, 0)
              snake_case_         =   greyscale + self.error_table[x][y]
           else:
              snake_case_         =   (2_5_5, 2_5_5, 2_5_5)
              snake_case_         =   greyscale + self.error_table[x][y] - 2_5_5
           self.error_table[y][x + 1] += int(8 / 3_2 * current_error					)
           self.error_table[y][x + 2] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x] += int(8 / 3_2 * current_error					)
           self.error_table[y + 1][x + 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x + 2] += int(2 / 3_2 * current_error					)
           self.error_table[y + 1][x - 1] += int(4 / 3_2 * current_error					)
           self.error_table[y + 1][x - 2] += int(2 / 3_2 * current_error					)


if __name__ == "__main__":
   # create Burke's instances with original images in greyscale
   SCREAMING_SNAKE_CASE		:Dict											=       [
       Burkes(imread('''image_data/lena.jpg''', 1), threshold)
       for threshold in (1, 1_26, 1_30, 1_40)
   ]

   for burkes in burkes_instances:
      burkes.process()

   for burkes in burkes_instances:
      imshow(
          F'''Original image with dithering threshold: {burkes.threshold}''',
          burkes.output_img,
      )

   waitKey(0)
   destroyAllWindows()




