






"""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 						lowercase__(A					,		A					,		A=0						)				->Tuple:




						"""simple docstring"""



						if name is None:
												lowercase__				:      Optional[Any]=       None
						else:
												lowercase__				:      List[str]=       "." * max(0					,		spaces - 2						) + "# {:" + str(50 - spaces						) + "s}"
												lowercase__				:      List[str]=       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[int]:




						"""simple docstring"""



						lowercase__				:      List[Any]=       param.size()
						if checkpoint_version == 1.0:
												# version 1.0 stores [num_heads * hidden_size * num_splits, :]
												lowercase__				:      Any=       (num_heads, hidden_size, num_splits) + input_shape[1:]
												lowercase__				:      List[Any]=       param.view(*A						)
												lowercase__				:      List[str]=       param.transpose(0					,		2						)
												lowercase__				:      Optional[Any]=       param.transpose(1					,		2						).contiguous()
						elif checkpoint_version >= 2.0:
												# other versions store [num_heads * num_splits * hidden_size, :]
												lowercase__				:      str=       (num_heads, num_splits, hidden_size) + input_shape[1:]
												lowercase__				:      Any=       param.view(*A						)
												lowercase__				:      List[str]=       param.transpose(0					,		1						).contiguous()
						lowercase__				:      Any=       param.view(*A						)
						return param





def 						lowercase__(A					,		A					,		A						)				->Tuple:




						"""simple docstring"""



						lowercase__				:      List[str]=       {}

						# old versions did not store training args
						lowercase__				:      str=       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))

												lowercase__				:      Optional[Any]=       ds_args.tokenizer_type
												lowercase__				:      Optional[int]=       ds_args.padded_vocab_size
												lowercase__				:      str=       ds_args.max_position_embeddings
												lowercase__				:      Union[str, Any]=       ds_args.hidden_size
												lowercase__				:      List[str]=       ds_args.num_layers
												lowercase__				:      Any=       ds_args.num_attention_heads
												lowercase__				:      Optional[int]=       ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
												# pprint(config)

						# The number of heads.
						lowercase__				:      List[str]=       config.num_attention_heads
						# The hidden_size per head.
						lowercase__				:      List[Any]=       config.hidden_size // heads
						# Megatron-LM checkpoint version
						if "checkpoint_version" in input_state_dict.keys():
												lowercase__				:      Any=       input_state_dict["checkpoint_version"]
						else:
												lowercase__				:      int=       0.0

						# The model.
						lowercase__				:      Optional[int]=       input_state_dict["model"]
						# The language model.
						lowercase__				:      Dict=       model["language_model"]
						# The embeddings.
						lowercase__				:      Optional[Any]=       lm["embedding"]

						# The word embeddings.
						lowercase__				:      Tuple=       embeddings["word_embeddings"]["weight"]
						# Truncate the embedding table to vocab_size rows.
						lowercase__				:      Any=       word_embeddings[: config.vocab_size, :]
						# Store the word embeddings.
						lowercase__				:      Optional[int]=       word_embeddings

						# The position embeddings.
						lowercase__				:      Any=       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.
						lowercase__				:      int=       pos_embeddings

						# The token-type embeddings.
						lowercase__				:      Any=       embeddings["tokentype_embeddings"]["weight"]
						# Store the position embeddings.
						lowercase__				:      Tuple=       tokentype_embeddings

						# The transformer.
						lowercase__				:      List[Any]=       lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

						# The regex to extract layer names.
						lowercase__				:      Optional[Any]=       re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"						)

						# The simple map of names for "automated" rules.
						lowercase__				:      int=       {
						    "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.
						lowercase__				:      Tuple=       None

						# Extract the layers.
						for key, val in transformer.items():
												# Match the name.
												lowercase__				:      List[str]=       layer_re.match(A						)

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

												# The index of the layer.
												lowercase__				:      str=       int(m.group(1						)						)
												# The name of the operation.
												lowercase__				:      Optional[int]=       m.group(2						)
												# Is it a weight or a bias?
												lowercase__				:      int=       m.group(3						)

												# The name of the layer.
												lowercase__				:      Dict=       f'''bert.encoder.layer.{layer_idx}'''

												# For layernorm(s), simply store the layer norm.
												if op_name.endswith("layernorm"						):
																		lowercase__				:      Tuple=       "attention.ln" if op_name.startswith("input"						) else "ln"
																		lowercase__				:      Dict=       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, ""

																		lowercase__				:      List[Any]=       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.
																		lowercase__				:      Optional[Any]=       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.
																		lowercase__				:      List[Any]=       attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
																		lowercase__				:      Tuple=       attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
																		lowercase__				:      Any=       attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

																		lowercase__				:      List[str]=       fix_query_key_value_ordering(A					,		A					,		3					,		A					,		A						)
																		# Split the bias.
																		lowercase__				:      Tuple=       out_val[0 * config.hidden_size : 1 * config.hidden_size]
																		lowercase__				:      Tuple=       out_val[1 * config.hidden_size : 2 * config.hidden_size]
																		lowercase__				:      Dict=       out_val[2 * config.hidden_size : 3 * config.hidden_size]

																		# Store.
																		lowercase__				:      Optional[Any]=       q
																		lowercase__				:      Optional[Any]=       q_bias
																		lowercase__				:      Optional[int]=       k
																		lowercase__				:      Tuple=       k_bias
																		lowercase__				:      Optional[Any]=       v
																		lowercase__				:      Optional[int]=       v_bias

																		# Clear the stored tensor.
																		lowercase__				:      Dict=       None

												# Copy weights and biases as is.
												elif weight_or_bias in ["weight", "bias"]:
																		lowercase__				:      Tuple=       megatron_to_transformers[op_name]
																		lowercase__				:      int=       val

    # The final layernorm.
						lowercase__				:      Tuple=       transformer["final_layernorm.weight"]
						lowercase__				:      Tuple=       transformer["final_layernorm.bias"]

						# The pooler.
						lowercase__				:      int=       lm["pooler"]

						# Store the matrix and the bias.
						lowercase__				:      Union[str, Any]=       pooler["dense.weight"]
						lowercase__				:      Optional[Any]=       pooler["dense.bias"]

						# The LM head from Megatron (for RACE).
						lowercase__				:      Dict=       model["lm_head"]

						# The transform matrix.
						lowercase__				:      List[str]=       lm_head["dense.weight"]
						lowercase__				:      Union[str, Any]=       lm_head["dense.bias"]

						# The transform LN.
						lowercase__				:      Dict=       lm_head["layernorm.weight"]
						lowercase__				:      Union[str, Any]=       lm_head["layernorm.bias"]

						# For the decoder, we replicate the weights.
						lowercase__				:      Union[str, Any]=       word_embeddings
						lowercase__				:      Any=       lm_head["bias"]

						# The classifier from Megatron (for MLNI).
						lowercase__				:      List[str]=       model["binary_head"]

						# Store the classifier.
						lowercase__				:      Optional[Any]=       binary_head["weight"]
						lowercase__				:      Optional[int]=       binary_head["bias"]

						# It should be done!
						return output_state_dict





def 						lowercase__()				->List[str]:




						"""simple docstring"""



						lowercase__				:      Any=       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."					,		)
						lowercase__				:      Optional[Any]=       parser.parse_args()

						# Extract the basename.
						lowercase__				:      Any=       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:
																								lowercase__				:      Tuple=       torch.load(A					,		map_location="cpu"						)
						else:
												lowercase__				:      List[Any]=       torch.load(args.path_to_checkpoint					,		map_location="cpu"						)

						if args.config_file == "":
												# Default config of megatron-bert 345m
												lowercase__				:      Any=       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
												lowercase__				:      List[Any]=       input_state_dict["model"]["lm_head"]["bias"].numel()
						else:
												lowercase__				:      List[str]=       MegatronBertConfig.from_json_file(args.config_file						)

						# Convert.
						print("Converting"						)
						lowercase__				:      str=       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.
						lowercase__				:      List[str]=       os.path.join(A					,		"pytorch_model.bin"						)
						print(f'''Saving checkpoint to "{output_checkpoint_file}"'''						)
						torch.save(A					,		A						)


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

if __name__ == "__main__":
			main()

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








import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
lowerCamelCase_  :       Tuple					      =     False
lowerCamelCase_  :       int					      =     Config.from_pretrained("""unc-nlp/frcnn-vg-finetuned""")
lowerCamelCase_  :       Any					      =     datasets.Features(
    OrderedDict(
        {
            """attr_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """attr_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """boxes""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype="""float32"""),
            """img_id""": datasets.Value("""int32"""),
            """obj_ids""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """obj_probs""": datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value("""float32""")),
            """roi_features""": datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 20_48), dtype="""float32"""),
            """sizes""": datasets.Sequence(length=2, feature=datasets.Value("""float32""")),
            """preds_per_image""": datasets.Value(dtype="""int32"""),
        }
    )
)

class 				_UpperCamelCase :






			'''simple docstring'''

			def __init__(				self       :				Optional[Any]  ,		snake_case_       :				Optional[Any]=sys.argv[1:]					):
							UpperCamelCase_:							str		=  None
							UpperCamelCase_:							List[str]		=  None
							UpperCamelCase_:							Tuple		=  None
							UpperCamelCase_:							Tuple		=  1
							UpperCamelCase_,      UpperCamelCase_:							Optional[Any]		=  getopt.getopt(snake_case_  ,		"""i:o:b:s"""  ,		["""inputdir=""", """outfile=""", """batch_size=""", """subset_list="""]					)
							for opt, arg in opts:
											if opt in ("-i", "--inputdir"):
															UpperCamelCase_:							Dict		=  arg
											elif opt in ("-o", "--outfile"):
															UpperCamelCase_:							Optional[Any]		=  arg
											elif opt in ("-b", "--batch_size"):
															UpperCamelCase_:							List[str]		=  int(snake_case_					)
											elif opt in ("-s", "--subset_list"):
															UpperCamelCase_:							Dict		=  arg

							assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
							assert outputfile is not None and not os.path.isfile(snake_case_					), f'''{outputfile}'''
							if subset_list is not None:
											with open(os.path.realpath(snake_case_					)					) as f:
															UpperCamelCase_:							List[Any]		=  {self._vqa_file_split()[0] for x in tryload(snake_case_					)}
							else:
											UpperCamelCase_:							List[str]		=  None

							UpperCamelCase_:							Tuple		=  CONFIG
							if torch.cuda.is_available():
											UpperCamelCase_:							Dict		=  """cuda"""
							UpperCamelCase_:							int		=  os.path.realpath(snake_case_					)
							UpperCamelCase_:							List[str]		=  os.path.realpath(snake_case_					)
							UpperCamelCase_:							str		=  Preprocess(self.config					)
							UpperCamelCase_:							int		=  GeneralizedRCNN.from_pretrained("""unc-nlp/frcnn-vg-finetuned"""  ,		config=self.config					)
							UpperCamelCase_:							Union[str, Any]		=  batch_size if batch_size != 0 else 1
							UpperCamelCase_:							Dict		=  DEFAULT_SCHEMA

			def 					lowerCAmelCase__		(				self       :				Union[str, Any]  ,		snake_case_       :				int					):
							UpperCamelCase_:							int		=  int(file.split("""."""					)[0].split("""_"""					)[-1]					)
							UpperCamelCase_:							int		=  os.path.join(self.inputdir  ,		snake_case_					)
							return (img_id, filepath)

			@property
			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Dict		=  []
							for i, file in enumerate(os.listdir(self.inputdir					)					):
											if self.subset_list is not None and i not in self.subset_list:
															continue
											batch.append(self._vqa_file_split(snake_case_					)					)
											if len(snake_case_					) == self.batch:
															UpperCamelCase_:							Dict		=  batch
															UpperCamelCase_:							List[Any]		=  []
															yield list(map(snake_case_  ,		zip(*snake_case_					)					)					)

							for i in range(1					):
											yield list(map(snake_case_  ,		zip(*snake_case_					)					)					)




			def __call__(				self       :				Optional[int]					):
							# make writer
							if not TEST:
											UpperCamelCase_:							Optional[Any]		=  datasets.ArrowWriter(features=self.schema  ,		path=self.outputfile					)
							# do file generator
							for i, (img_ids, filepaths) in enumerate(self.file_generator					):
											UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_:							Dict		=  self.preprocess(snake_case_					)
											UpperCamelCase_:							Optional[Any]		=  self.model(
											    snake_case_  ,		snake_case_  ,		scales_yx=snake_case_  ,		padding="""max_detections"""  ,		max_detections=self.config.MAX_DETECTIONS  ,		pad_value=0  ,		return_tensors="""np"""  ,		location="""cpu"""  ,		)
											UpperCamelCase_:							Tuple		=  output_dict.pop("""normalized_boxes"""					)
											if not TEST:
															UpperCamelCase_:							Union[str, Any]		=  np.array(snake_case_					)
															UpperCamelCase_:							int		=  self.schema.encode_batch(snake_case_					)
															writer.write_batch(snake_case_					)
											if TEST:
															break
											# finalizer the writer
							if not TEST:
											UpperCamelCase_,      UpperCamelCase_:							int		=  writer.finalize()
											print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''					)







def 				A__    (  lowerCamelCase  )     ->		Tuple:
				try:
								UpperCamelCase_:							str		=  json.load(lowerCamelCase  )
								try:
												UpperCamelCase_:							List[Any]		=  list(data.keys()  )
								except Exception:
												UpperCamelCase_:							Tuple		=  [d["""img_id"""] for d in data]
				except Exception:
								try:
												UpperCamelCase_:							int		=  eval(stream.read()  )
								except Exception:
												UpperCamelCase_:							Union[str, Any]		=  stream.read().split("""\n"""  )
				return data


if __name__ == "__main__":
							lowerCamelCase_  :       Optional[Any]					      =     Extract(sys.argv[1:])
							extract()
							if not TEST:
														lowerCamelCase_  :       List[Any]					      =     datasets.Dataset.from_file(extract.outputfile)
														# wala!
														# print(np.array(dataset[0:2]["roi_features"]).shape)






