"""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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case=0):
     # Format the message.
     if name is None:
          __snake_case				=							None
     else:
          __snake_case				=							'''.''' * max(0,      spaces - 2) + '''# {:''' + str(50 - spaces) + '''s}'''
          __snake_case				=							fmt.format(snake_case)

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





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case):
     # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
     # for compatibility with later versions of NVIDIA Megatron-LM.
     # The inverse operation is performed inside Megatron-LM to read checkpoints:
     # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
     # If param is the weight tensor of the self-attention block, the returned tensor
     # will have to be transposed one more time to be read by HuggingFace GPT2.
     __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(*snake_case)
          __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(*snake_case)
          __snake_case				=							param.transpose(0,      1).contiguous()
     __snake_case				=							param.view(*snake_case)
     return param





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case):
     # The converted output model.
     __snake_case				=							{}

     # old versions did not store training args
     __snake_case				=							input_state_dict.get('''args''',      snake_case)
     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.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
          # pprint(config)

     # The number of heads.
     __snake_case				=							config.n_head
     # The hidden_size per head.
     __snake_case				=							config.n_embd // config.n_head
     # 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, :]
     __snake_case				=							word_embeddings

     # The position embeddings.
     __snake_case				=							embeddings['''position_embeddings''']['''weight''']
     # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
     __snake_case				=							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.
     __snake_case				=							pos_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''': '''.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.
          __snake_case				=							layer_re.match(snake_case)

          # 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"transformer.h.{layer_idx}"

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith('''layernorm'''):
               __snake_case				=							'''ln_1''' if op_name.startswith('''input''') else '''ln_2'''
               __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":
               # Insert a tensor of 1x1xDxD bias.
               __snake_case				=							torch.tril(torch.ones((n_positions, n_positions),      dtype=torch.floataa)).view(
                   1,      1,      snake_case,      snake_case)
               __snake_case				=							causal_mask

               # Insert a "dummy" tensor for masked_bias.
               __snake_case				=							torch.tensor(-1E4,      dtype=torch.floataa)
               __snake_case				=							masked_bias

               __snake_case				=							fix_query_key_value_ordering(snake_case,      snake_case,      3,      snake_case,      snake_case)
               # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
               __snake_case				=							out_val.transpose(0,      1).contiguous()
               # Store.
               __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":
               __snake_case				=							fix_query_key_value_ordering(snake_case,      snake_case,      3,      snake_case,      snake_case)
               # Store. No change of shape.
               __snake_case				=							out_val

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

          # Copy the bias.
          elif weight_or_bias == "bias":
               __snake_case				=							megatron_to_transformers[op_name]
               __snake_case				=							val

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

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

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

     # It should be done!
     return output_state_dict





def    SCREAMING_SNAKE_CASE       (     ):
     # Create the argument parser.
     __snake_case				=							argparse.ArgumentParser()
     parser.add_argument('''--print-checkpoint-structure''',      action='''store_true''')
     parser.add_argument(
         '''path_to_checkpoint''',      type=snake_case,      help='''Path to the checkpoint file (.zip archive or direct .pt file)''',      )
     parser.add_argument(
         '''--config_file''',      default='''''',      type=snake_case,      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(snake_case,      map_location='''cpu''')
     else:
          __snake_case				=							torch.load(args.path_to_checkpoint,      map_location='''cpu''')

     __snake_case				=							input_state_dict.get('''args''',      snake_case)

     # 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:
                    __snake_case				=							'''gelu_fast'''
               elif ds_args.openai_gelu:
                    __snake_case				=							'''gelu_new'''
               else:
                    __snake_case				=							'''gelu'''
          else:
               # in the very early days this used to be "gelu_new"
               __snake_case				=							'''gelu_new'''

          # Spell out all parameters in case the defaults change.
          __snake_case				=							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=snake_case,      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=snake_case,      summary_activation=snake_case,      summary_proj_to_labels=snake_case,      summary_first_dropout=0.1,      scale_attn_weights=snake_case,      use_cache=snake_case,      bos_token_id=5_02_56,      eos_token_id=5_02_56,      )
     else:
          __snake_case				=							GPTaConfig.from_json_file(args.config_file)

     __snake_case				=							['''GPT2LMHeadModel''']

     # Convert.
     print('''Converting''')
     __snake_case				=							convert_megatron_checkpoint(snake_case,      snake_case,      snake_case)

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

     # Add tokenizer class info to config
     # see https://github.com/huggingface/transformers/issues/13906)
     if ds_args is not None:
          __snake_case				=							ds_args.tokenizer_type
          if tokenizer_type == "GPT2BPETokenizer":
               __snake_case				=							'''gpt2'''
          elif tokenizer_type == "PretrainedFromHF":
               __snake_case				=							ds_args.tokenizer_name_or_path
          else:
               raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}")
     else:
          __snake_case				=							'''gpt2'''

     __snake_case				=							AutoTokenizer.from_pretrained(snake_case)
     __snake_case				=							type(snake_case).__name__
     __snake_case				=							tokenizer_class

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

     # Save tokenizer based on args
     print(f"Adding {tokenizer_class} tokenizer files")
     tokenizer.save_pretrained(snake_case)

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


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

if __name__ == "__main__":
       main()

####################################################################################################
"""simple docstring"""



def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							len(snake_case)
     for i in range(length - 1):
          __snake_case				=							i
          for k in range(i + 1,      snake_case):
               if collection[k] < collection[least]:
                    __snake_case				=							k
          if least != i:
               __snake_case		,	__snake_case				=							(collection[i], collection[least])
     return collection


if __name__ == "__main__":
       __lowercase		:		Optional[Any]														=							input("Enter numbers separated by a comma:\n").strip()
       __lowercase		:		Tuple														=							[int(item) for item in user_input.split(",")]
       print(selection_sort(unsorted))