

# 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    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__=0 ):







							'''simple docstring'''






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

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





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''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(*UpperCamelCase__ )
														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(*UpperCamelCase__ )
														snake_case_     							=       param.transpose(0  ,				1 ).contiguous()
							snake_case_     							=       param.view(*UpperCamelCase__ )
							return param





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       {}

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

														# 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  ,				UpperCamelCase__  ,				UpperCamelCase__ )
																					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(UpperCamelCase__  ,				UpperCamelCase__  ,				3  ,				UpperCamelCase__  ,				UpperCamelCase__ )
																					# 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(UpperCamelCase__  ,				UpperCamelCase__  ,				3  ,				UpperCamelCase__  ,				UpperCamelCase__ )
																					# 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    __lowerCamelCase      (					):







							'''simple docstring'''






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

							snake_case_     							=       input_state_dict.get('args'  ,				UpperCamelCase__ )

							# 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=50257  ,				n_positions=1024  ,				n_embd=1024  ,				n_layer=24  ,				n_head=16  ,				n_inner=4096  ,				activation_function=UpperCamelCase__  ,				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=UpperCamelCase__  ,				summary_activation=UpperCamelCase__  ,				summary_proj_to_labels=UpperCamelCase__  ,				summary_first_dropout=0.1  ,				scale_attn_weights=UpperCamelCase__  ,				use_cache=UpperCamelCase__  ,				bos_token_id=50256  ,				eos_token_id=50256  ,				)
							else:
														snake_case_     							=       GPTaConfig.from_json_file(args.config_file )

							snake_case_     							=       ['GPT2LMHeadModel']

							# Convert.
							print('Converting' )
							snake_case_     							=       convert_megatron_checkpoint(UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ )

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

							# 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(UpperCamelCase__ )
							snake_case_     							=       type(UpperCamelCase__ ).__name__
							snake_case_     							=       tokenizer_class

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

							# Save tokenizer based on args
							print(F'''Adding {tokenizer_class} tokenizer files''' )
							tokenizer.save_pretrained(UpperCamelCase__ )

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


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

if __name__ == "__main__":
					main()

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







import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad







class 					lowercase							(			unittest.TestCase     ):




				def        a				(   self			):
											snake_case_     							=       10




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4]
											snake_case_     							=       [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											snake_case_     							=       [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(snake_case ,  self.block_size ,  0			) ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       ''
											snake_case_							,       snake_case_     							=       process_story(snake_case			)
											self.assertEqual(snake_case ,  []			)
											self.assertEqual(snake_case ,  []			)




				def        a				(   self			):
											snake_case_     							=       (
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											snake_case_							,       snake_case_     							=       process_story(snake_case			)

											snake_case_     							=       [
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(snake_case ,  snake_case			)

											snake_case_     							=       ['It was the best of times.']
											self.assertEqual(snake_case ,  snake_case			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  0			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([1, 2, 3, 4, 23, 23, 23]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  23			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       torch.tensor([8, 2, 3, 4, 1, 1, 1]			)
											snake_case_     							=       torch.tensor([1, 1, 1, 1, 0, 0, 0]			)
											np.testing.assert_array_equal(build_mask(snake_case ,  1			).numpy() ,  expected.numpy()			)




				def        a				(   self			):
											snake_case_     							=       101
											snake_case_     							=       torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]			)
											snake_case_     							=       torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]			)

											snake_case_     							=       compute_token_type_ids(snake_case ,  snake_case			)
											np.testing.assert_array_equal(snake_case ,  snake_case			)




