






# 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 		A_			(		lowercase_					,       lowercase_					,       lowercase_=0	)							->Optional[Any]:







 """simple docstring"""

 if name is None:
  SCREAMING_SNAKE_CASE							  =						None
 else:
  SCREAMING_SNAKE_CASE							  =						'.' * max(0					,       spaces - 2	) + '# {:' + str(5_0 - spaces	) + 's}'
  SCREAMING_SNAKE_CASE							  =						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 		A_			(		lowercase_					,       lowercase_					,       lowercase_					,       lowercase_					,       lowercase_	)							->Dict:







 """simple docstring"""

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





def 		A_			(		lowercase_					,       lowercase_					,       lowercase_	)							->Optional[Any]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						{}

 # old versions did not store training args
 SCREAMING_SNAKE_CASE							  =						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))

  SCREAMING_SNAKE_CASE							  =						ds_args.tokenizer_type
  SCREAMING_SNAKE_CASE							  =						ds_args.padded_vocab_size
  SCREAMING_SNAKE_CASE							  =						ds_args.max_position_embeddings
  SCREAMING_SNAKE_CASE							  =						ds_args.hidden_size
  SCREAMING_SNAKE_CASE							  =						ds_args.num_layers
  SCREAMING_SNAKE_CASE							  =						ds_args.num_attention_heads
  SCREAMING_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.
 SCREAMING_SNAKE_CASE							  =						config.num_attention_heads
 # The hidden_size per head.
 SCREAMING_SNAKE_CASE							  =						config.hidden_size // heads
 # Megatron-LM checkpoint version
 if "checkpoint_version" in input_state_dict.keys():
  SCREAMING_SNAKE_CASE							  =						input_state_dict['checkpoint_version']
 else:
  SCREAMING_SNAKE_CASE							  =						0.0

 # The model.
 SCREAMING_SNAKE_CASE							  =						input_state_dict['model']
 # The language model.
 SCREAMING_SNAKE_CASE							  =						model['language_model']
 # The embeddings.
 SCREAMING_SNAKE_CASE							  =						lm['embedding']

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

 # The position embeddings.
 SCREAMING_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.
 SCREAMING_SNAKE_CASE							  =						pos_embeddings

 # The token-type embeddings.
 SCREAMING_SNAKE_CASE							  =						embeddings['tokentype_embeddings']['weight']
 # Store the position embeddings.
 SCREAMING_SNAKE_CASE							  =						tokentype_embeddings

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

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

 # The simple map of names for "automated" rules.
 SCREAMING_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.
 SCREAMING_SNAKE_CASE							  =						None

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

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

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

  # The name of the layer.
  SCREAMING_SNAKE_CASE							  =						f'''bert.encoder.layer.{layer_idx}'''

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

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

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

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

   # Clear the stored tensor.
   SCREAMING_SNAKE_CASE							  =						None

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

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

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

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

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

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

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

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

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

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

 # It should be done!
 return output_state_dict





def 		A_			(		)							->Any:







 """simple docstring"""

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

 # Extract the basename.
 SCREAMING_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:
    SCREAMING_SNAKE_CASE							  =						torch.load(lowercase_					,       map_location='cpu'	)
 else:
  SCREAMING_SNAKE_CASE							  =						torch.load(args.path_to_checkpoint					,       map_location='cpu'	)

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

 # Convert.
 print('Converting'	)
 SCREAMING_SNAKE_CASE							  =						convert_megatron_checkpoint(lowercase_					,       lowercase_					,       lowercase_	)

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

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

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


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

if __name__ == "__main__":
      main()

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












from datetime import date

import requests

__UpperCAmelCase      				=							"https://api.carbonintensity.org.uk/intensity"





def 		A_			(		)							->str:







 """simple docstring"""

 SCREAMING_SNAKE_CASE							  =						requests.get(lowercase_	).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def 		A_			(		lowercase_					,       lowercase_	)							->list:







 """simple docstring"""

 return requests.get(f'''{BASE_URL}/{start}/{end}'''	).json()["data"]


if __name__ == "__main__":
      for entry in fetch_from_to(start=date(2_0_2_0, 1_0, 1), end=date(2_0_2_0, 1_0, 3)):
            print("from {from} to {to}: {intensity[actual]}".format(**entry))
      print(f'{fetch_last_half_hour() = }')




