

"""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_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    (       snake_case__						,			snake_case__						,			snake_case__=0      ):




			'''simple docstring'''


			if name is None:
						SCREAMING_SNAKE_CASE__									= None
			else:
						SCREAMING_SNAKE_CASE__									= """.""" * max(0						,			spaces - 2      ) + """# {:""" + str(50 - spaces      ) + """s}"""
						SCREAMING_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 		A    (       snake_case__						,			snake_case__						,			snake_case__						,			snake_case__						,			snake_case__      ):




			'''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(*snake_case__      )
						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(*snake_case__      )
						SCREAMING_SNAKE_CASE__									= param.transpose(0						,			1      ).contiguous()
			SCREAMING_SNAKE_CASE__									= param.view(*snake_case__      )
			return param

def 		A    (       snake_case__						,			snake_case__						,			snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= {}

			# old versions did not store training args
			SCREAMING_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))

						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(snake_case__      )

						# 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(snake_case__						,			snake_case__						,			3						,			snake_case__						,			snake_case__      )
									# 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(snake_case__						,			snake_case__						,			3						,			snake_case__						,			snake_case__      )
									# 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    (       ):




			'''simple docstring'''


			SCREAMING_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 ZIP file containing the checkpoint"""      )
			parser.add_argument(
			    """--config_file"""						,			default=""""""						,			type=snake_case__						,			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(snake_case__						,			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(snake_case__						,			snake_case__						,			snake_case__      )

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

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

			# Store the state_dict to file.
			SCREAMING_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"""




class 				lowerCamelCase  :

			def __init__(   self			:  Dict    )				->      None:
						SCREAMING_SNAKE_CASE__									= [0, 1]





			def    SCREAMING_SNAKE_CASE			(   self			:  Optional[int]      ,			__UpperCAmelCase			:  int    )				->      list:
						if (difference := index - (len(self.sequence    ) - 2)) >= 1:
									for _ in range(__UpperCAmelCase    ):
												self.sequence.append(self.sequence[-1] + self.sequence[-2]    )
						return self.sequence[:index]

def 		A    (       ):




			'''simple docstring'''


			print(
			    """Fibonacci Series Using Dynamic Programming\n"""						,			"""Enter the index of the Fibonacci number you want to calculate """						,			"""in the prompt below. (To exit enter exit or Ctrl-C)\n"""						,			sep=""""""						,			)
			SCREAMING_SNAKE_CASE__									= Fibonacci()

			while True:
						SCREAMING_SNAKE_CASE__									= input(""">> """      )
						if prompt in {"exit", "quit"}:
									break

						try:
									SCREAMING_SNAKE_CASE__									= int(snake_case__      )
						except ValueError:
									print("""Enter a number or 'exit'"""      )
									continue

						print(fibonacci.get(snake_case__      )      )


if __name__ == "__main__":
						main()


