



# 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 			__lowercase     (				a__							,     a__							,     a__=0     )       ->				List[Any]:
							# Format the message.
							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(a__     )

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






def 			__lowercase     (				a__							,     a__							,     a__							,     a__							,     a__     )       ->				Optional[Any]:
							# 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 BERT.
							__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(*a__     )
														__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(*a__     )
														__SCREAMING_SNAKE_CASE								=      param.transpose(0							,     1     ).contiguous()
							__SCREAMING_SNAKE_CASE								=      param.view(*a__     )
							return param






def 			__lowercase     (				a__							,     a__							,     a__     )       ->				Optional[Any]:
							# The converted output model.
							__SCREAMING_SNAKE_CASE								=      {}

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

														# 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(a__							,     a__							,     3							,     a__							,     a__     )
																					# 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(a__							,     a__							,     3							,     a__							,     a__     )
																					# 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 			__lowercase     (				)       ->				Union[str, Any]:
							# Create the argument parser.
							__SCREAMING_SNAKE_CASE								=      argparse.ArgumentParser()
							parser.add_argument('--print-checkpoint-structure'							,     action='store_true'     )
							parser.add_argument('path_to_checkpoint'							,     type=a__							,     help='Path to the ZIP file containing the checkpoint'     )
							parser.add_argument(
							    '--config_file'							,     default=''							,     type=a__							,     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(a__							,     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(a__							,     a__							,     a__     )

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

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

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


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

if __name__ == "__main__":
			main()

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











import unittest

from cer import CER


lowerCAmelCase__	:  Dict 		=CER()

class      UpperCAmelCase_						(     unittest.TestCase ):

						'''simple docstring'''




						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['White House']
													__SCREAMING_SNAKE_CASE								=      ['white house']
													# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_8_1_8_1_8_1_8_1_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['were wolf']
													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_1_1_1_1_1_1	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['weae     wolf']
													# S = 1, D = 1, I = 0, N = 8, CER = 0.25
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.2_5	) < 1e-6	)

													# consecutive whitespaces case 1
													__SCREAMING_SNAKE_CASE								=      ['were wolf']
													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
													# S = 0, D = 0, I = 0, N = 9, CER = 0
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)

													# consecutive whitespaces case 2
													__SCREAMING_SNAKE_CASE								=      ['were   wolf']
													__SCREAMING_SNAKE_CASE								=      ['were               wolf']
													# S = 0, D = 0, I = 0, N = 9, CER = 0
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['weaewolf']
													# S = 1, D = 0, I = 0, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['wereawolf']
													# S = 0, D = 1, I = 0, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      ['wereolf']
													# S = 0, D = 0, I = 1, N = 8, CER = 0.125
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.1_2_5	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertEqual(_A   ,							0.0	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertEqual(_A   ,							0.0	)

													__SCREAMING_SNAKE_CASE								=      ['werewolf', 'I am your father', 'doge']
													__SCREAMING_SNAKE_CASE								=      ['werxwolf', 'I       am your father', 'doge']
													# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.0_3_5_7_1_4_2_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['My hovercraft', 'is full of eels']
													__SCREAMING_SNAKE_CASE								=      ['My hovercraft is full', ' of eels']
													# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
													# whitespace at the front of " of eels" will be strip during preporcessing
													# so need to insert 2 whitespaces
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A   ,							concatenate_texts=_A	)
													self.assertTrue(abs(char_error_rate - 0.0_7_1_4_2_8	) < 1e-6	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
													__SCREAMING_SNAKE_CASE								=      [' 能吞虾玻璃而 不霜身体啦']
													# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃', '而不伤身体']
													__SCREAMING_SNAKE_CASE								=      ['我    能 吞 下 玻 璃', '而不伤身体']
													# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertTrue(abs(char_error_rate - 0.4_5_4_5_4_5_4_5_4_5_4_5	) < 1e-6	)

													__SCREAMING_SNAKE_CASE								=      ['我能吞下玻璃而不伤身体']
													__SCREAMING_SNAKE_CASE								=      cer.compute(predictions=_A   ,							references=_A	)
													self.assertFalse(_A   ,							0.0	)






						def  _A			(					self	):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE								=      ['']
													__SCREAMING_SNAKE_CASE								=      ['Hypothesis']
													with self.assertRaises(_A	):
																				cer.compute(predictions=_A   ,							references=_A	)


if __name__ == "__main__":
			unittest.main()






