





"""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  :   int		,    snake_case  :   Dict		,    snake_case  :   Tuple=0						)->						List[str]:
       # Format the message.
       if name is None:
              _lowerCamelCase             =   None
       else:
              _lowerCamelCase             =   """.""" * max(0		,    spaces - 2						) + """# {:""" + str(50 - spaces						) + """s}"""
              _lowerCamelCase             =   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  :   Optional[int]		,    snake_case  :   int		,    snake_case  :   Union[str, Any]		,    snake_case  :   Optional[Any]		,    snake_case  :   int						)->						Dict:
       # 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.
       _lowerCamelCase             =   param.size()
       if checkpoint_version == 1.0:
              # version 1.0 stores [num_heads * hidden_size * num_splits, :]
              _lowerCamelCase             =   (num_heads, hidden_size, num_splits) + input_shape[1:]
              _lowerCamelCase             =   param.view(*snake_case						)
              _lowerCamelCase             =   param.transpose(0		,    2						)
              _lowerCamelCase             =   param.transpose(1		,    2						).contiguous()
       elif checkpoint_version >= 2.0:
              # other versions store [num_heads * num_splits * hidden_size, :]
              _lowerCamelCase             =   (num_heads, num_splits, hidden_size) + input_shape[1:]
              _lowerCamelCase             =   param.view(*snake_case						)
              _lowerCamelCase             =   param.transpose(0		,    1						).contiguous()
       _lowerCamelCase             =   param.view(*snake_case						)
       return param





def SCREAMING_SNAKE_CASE_  (			snake_case  :   Union[str, Any]		,    snake_case  :   Optional[int]		,    snake_case  :   Tuple						)->						Union[str, Any]:
       # The converted output model.
       _lowerCamelCase             =   {}

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

              _lowerCamelCase             =   ds_args.padded_vocab_size
              _lowerCamelCase             =   ds_args.max_position_embeddings
              _lowerCamelCase             =   ds_args.hidden_size
              _lowerCamelCase             =   ds_args.num_layers
              _lowerCamelCase             =   ds_args.num_attention_heads
              _lowerCamelCase             =   ds_args.ffn_hidden_size
              # pprint(config)

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

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

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

       # The position embeddings.
       _lowerCamelCase             =   embeddings["""position_embeddings"""]["""weight"""]
       # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
       _lowerCamelCase             =   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.
       _lowerCamelCase             =   pos_embeddings

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

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

       # The simple map of names for "automated" rules.
       _lowerCamelCase             =   {
           """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.
              _lowerCamelCase             =   layer_re.match(snake_case						)

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

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

              # The name of the layer.
              _lowerCamelCase             =   f'transformer.h.{layer_idx}'

              # For layernorm(s), simply store the layer norm.
              if op_name.endswith('layernorm'						):
                     _lowerCamelCase             =   """ln_1""" if op_name.startswith('input'						) else """ln_2"""
                     _lowerCamelCase             =   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.
                     _lowerCamelCase             =   torch.tril(torch.ones((n_positions, n_positions)		,    dtype=torch.floataa						)						).view(
                         1		,    1		,    snake_case		,    snake_case						)
                     _lowerCamelCase             =   causal_mask

                     # Insert a "dummy" tensor for masked_bias.
                     _lowerCamelCase             =   torch.tensor(-1e4		,    dtype=torch.floataa						)
                     _lowerCamelCase             =   masked_bias

                     _lowerCamelCase             =   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.
                     _lowerCamelCase             =   out_val.transpose(0		,    1						).contiguous()
                     # Store.
                     _lowerCamelCase             =   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":
                     _lowerCamelCase             =   fix_query_key_value_ordering(snake_case		,    snake_case		,    3		,    snake_case		,    snake_case						)
                     # Store. No change of shape.
                     _lowerCamelCase             =   out_val

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

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

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

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

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

       # It should be done!
       return output_state_dict





def SCREAMING_SNAKE_CASE_  (			)->						Union[str, Any]:
       # Create the argument parser.
       _lowerCamelCase             =   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.'		,    )
       _lowerCamelCase             =   parser.parse_args()

       # Extract the basename.
       _lowerCamelCase             =   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:
                            _lowerCamelCase             =   torch.load(snake_case		,    map_location='cpu'						)
       else:
              _lowerCamelCase             =   torch.load(args.path_to_checkpoint		,    map_location='cpu'						)

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

              # Spell out all parameters in case the defaults change.
              _lowerCamelCase             =   GPTaConfig(
                  vocab_size=50_257		,    n_positions=1_024		,    n_embd=1_024		,    n_layer=24		,    n_head=16		,    n_inner=4_096		,    activation_function=snake_case		,    resid_pdrop=0.1		,    embd_pdrop=0.1		,    attn_pdrop=0.1		,    layer_norm_epsilon=1e-5		,    initializer_range=0.0_2		,    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=50_256		,    eos_token_id=50_256		,    )
       else:
              _lowerCamelCase             =   GPTaConfig.from_json_file(args.config_file						)

       _lowerCamelCase             =   ["""GPT2LMHeadModel"""]

       # Convert.
       print('Converting'						)
       _lowerCamelCase             =   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:
              _lowerCamelCase             =   ds_args.tokenizer_type
              if tokenizer_type == "GPT2BPETokenizer":
                     _lowerCamelCase             =   """gpt2"""
              elif tokenizer_type == "PretrainedFromHF":
                     _lowerCamelCase             =   ds_args.tokenizer_name_or_path
              else:
                     raise ValueError(f'Unrecognized tokenizer_type {tokenizer_type}'						)
       else:
              _lowerCamelCase             =   """gpt2"""

       _lowerCamelCase             =   AutoTokenizer.from_pretrained(snake_case						)
       _lowerCamelCase             =   type(snake_case						).__name__
       _lowerCamelCase             =   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.
       _lowerCamelCase             =   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()

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












from __future__ import annotations

from fractions import Fraction




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				return (
				    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
				)




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						List[str]										=[]
				__magic_name__						:						List[Any]										=11
				__magic_name__						:						Tuple										=int("""1""" + """0""" * digit_len )
				for num in range(lowerCamelCase						,  lowerCamelCase ):
								while den <= 99:
												if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																if is_digit_cancelling(lowerCamelCase						,  lowerCamelCase ):
																				solutions.append(F"{num}/{den}" )
												den += 1
								num += 1
								__magic_name__						:						List[str]										=10
				return solutions




def 		lowerCAmelCase_ (						lowerCamelCase = 2 ):
				__magic_name__						:						str										=1.0
				for fraction in fraction_list(lowerCamelCase ):
								__magic_name__						:						int										=Fraction(lowerCamelCase )
								result *= frac.denominator / frac.numerator
				return int(lowerCamelCase )


if __name__ == "__main__":
							print(solution())




