




import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__            =     logging.get_logger(__name__)







def       _a (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Any=False , SCREAMING_SNAKE_CASE_				:							List[Any]=False	):
					__lowerCAmelCase							  =			"backbone." if is_semantic else ""

					__lowerCAmelCase							  =			[]
					for i in range(config.num_hidden_layers	):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""")	)
										rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""")	)
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""")	)
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""")	)
										rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""")	)
										rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""")	)
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""")	)
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""")	)
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""")	)
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""")	)

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (F"""{prefix}cls_token""", "beit.embeddings.cls_token"),
					        (F"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
					        (F"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
					    ]	)

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ("mask_token", "beit.embeddings.mask_token"),
										        (
										            "rel_pos_bias.relative_position_bias_table",
										            "beit.encoder.relative_position_bias.relative_position_bias_table",
										        ),
										        (
										            "rel_pos_bias.relative_position_index",
										            "beit.encoder.relative_position_bias.relative_position_index",
										        ),
										        ("norm.weight", "layernorm.weight"),
										        ("norm.bias", "layernorm.bias"),
										    ]	)
					elif is_semantic:
										# semantic segmentation classification heads
										rename_keys.extend(
										    [
										        ("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
										        ("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
										        ("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
										        ("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
										    ]	)
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ("fc_norm.weight", "beit.pooler.layernorm.weight"),
										        ("fc_norm.bias", "beit.pooler.layernorm.bias"),
										        ("head.weight", "classifier.weight"),
										        ("head.bias", "classifier.bias"),
										    ]	)

					return rename_keys







def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							Union[str, Any]=False , SCREAMING_SNAKE_CASE_				:							Dict=False	):
					for i in range(config.num_hidden_layers	):
										__lowerCAmelCase							  =			"backbone." if is_semantic else ""
										# queries, keys and values
										__lowerCAmelCase							  =			state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""	)
										__lowerCAmelCase							  =			state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""	)
										__lowerCAmelCase							  =			state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""	)

										__lowerCAmelCase							  =			in_proj_weight[
										    : config.hidden_size, :
										]
										__lowerCAmelCase							  =			q_bias
										__lowerCAmelCase							  =			in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__lowerCAmelCase							  =			in_proj_weight[
										    -config.hidden_size :, :
										]
										__lowerCAmelCase							  =			v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										__lowerCAmelCase							  =			state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""	)
										__lowerCAmelCase							  =			state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""	)

										__lowerCAmelCase							  =			gamma_a
										__lowerCAmelCase							  =			gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															__lowerCAmelCase							  =			state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""	)
															__lowerCAmelCase							  =			state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""	)

															__lowerCAmelCase							  =			table
															__lowerCAmelCase							  =			index







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Union[str, Any]	):
					__lowerCAmelCase							  =			dct.pop(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			val







def       _a (		):
					__lowerCAmelCase							  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
					__lowerCAmelCase							  =			Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_	).raw	)
					return im







@torch.no_grad()
def       _a (		SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							Any	):
					__lowerCAmelCase							  =			BeitConfig()
					__lowerCAmelCase							  =			False
					__lowerCAmelCase							  =			False
					__lowerCAmelCase							  =			"huggingface/label-files"
					# set config parameters based on URL
					if checkpoint_url[-9:-4] == "pt22k":
										# masked image modeling
										__lowerCAmelCase							  =			True
										__lowerCAmelCase							  =			True
										__lowerCAmelCase							  =			True
					elif checkpoint_url[-9:-4] == "ft22k":
										# intermediate fine-tuning on ImageNet-22k
										__lowerCAmelCase							  =			True
										__lowerCAmelCase							  =			2_18_41
										__lowerCAmelCase							  =			"imagenet-22k-id2label.json"
										__lowerCAmelCase							  =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="dataset"	) , "r"	)	)
										__lowerCAmelCase							  =			{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
										# this dataset contains 21843 labels but the model only has 21841
										# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
										del idalabel[92_05]
										del idalabel[1_50_27]
										__lowerCAmelCase							  =			idalabel
										__lowerCAmelCase							  =			{v: k for k, v in idalabel.items()}
					elif checkpoint_url[-8:-4] == "to1k":
										# fine-tuning on ImageNet-1k
										__lowerCAmelCase							  =			True
										__lowerCAmelCase							  =			10_00
										__lowerCAmelCase							  =			"imagenet-1k-id2label.json"
										__lowerCAmelCase							  =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="dataset"	) , "r"	)	)
										__lowerCAmelCase							  =			{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
										__lowerCAmelCase							  =			idalabel
										__lowerCAmelCase							  =			{v: k for k, v in idalabel.items()}
										if "384" in checkpoint_url:
															__lowerCAmelCase							  =			3_84
										if "512" in checkpoint_url:
															__lowerCAmelCase							  =			5_12
					elif "ade20k" in checkpoint_url:
										# fine-tuning
										__lowerCAmelCase							  =			True
										__lowerCAmelCase							  =			1_50
										__lowerCAmelCase							  =			"ade20k-id2label.json"
										__lowerCAmelCase							  =			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="dataset"	) , "r"	)	)
										__lowerCAmelCase							  =			{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
										__lowerCAmelCase							  =			idalabel
										__lowerCAmelCase							  =			{v: k for k, v in idalabel.items()}
										__lowerCAmelCase							  =			6_40
										__lowerCAmelCase							  =			True
					else:
										raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"	)

					# size of the architecture
					if "base" in checkpoint_url:
										pass
					elif "large" in checkpoint_url:
										__lowerCAmelCase							  =			10_24
										__lowerCAmelCase							  =			40_96
										__lowerCAmelCase							  =			24
										__lowerCAmelCase							  =			16
										if "ade20k" in checkpoint_url:
															__lowerCAmelCase							  =			6_40
															__lowerCAmelCase							  =			[7, 11, 15, 23]
					else:
										raise ValueError("Should either find 'base' or 'large' in checkpoint URL"	)

					# load state_dict of original model, remove and rename some keys
					__lowerCAmelCase							  =			torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ , map_location="cpu" , check_hash=SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			state_dict["model"] if "ade20k" not in checkpoint_url else state_dict["state_dict"]

					__lowerCAmelCase							  =			create_rename_keys(SCREAMING_SNAKE_CASE_ , has_lm_head=SCREAMING_SNAKE_CASE_ , is_semantic=SCREAMING_SNAKE_CASE_	)
					for src, dest in rename_keys:
										rename_key(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					read_in_q_k_v(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , has_lm_head=SCREAMING_SNAKE_CASE_ , is_semantic=SCREAMING_SNAKE_CASE_	)
					if is_semantic:
										# add prefix to decoder keys
										for key, val in state_dict.copy().items():
															__lowerCAmelCase							  =			state_dict.pop(SCREAMING_SNAKE_CASE_	)
															if key.startswith("backbone.fpn"	):
																				__lowerCAmelCase							  =			key.replace("backbone.fpn" , "fpn"	)
															__lowerCAmelCase							  =			val

    # load HuggingFace model
					if checkpoint_url[-9:-4] == "pt22k":
										__lowerCAmelCase							  =			BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE_	)
					elif "ade20k" in checkpoint_url:
										__lowerCAmelCase							  =			BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE_	)
					else:
										__lowerCAmelCase							  =			BeitForImageClassification(SCREAMING_SNAKE_CASE_	)
					model.eval()
					model.load_state_dict(SCREAMING_SNAKE_CASE_	)

					# Check outputs on an image
					if is_semantic:
										__lowerCAmelCase							  =			BeitImageProcessor(size=config.image_size , do_center_crop=SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			load_dataset("hf-internal-testing/fixtures_ade20k" , split="test"	)
										__lowerCAmelCase							  =			Image.open(ds[0]["file"]	)
					else:
										__lowerCAmelCase							  =			BeitImageProcessor(
										    size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			prepare_img()

					__lowerCAmelCase							  =			image_processor(images=SCREAMING_SNAKE_CASE_ , return_tensors="pt"	)
					__lowerCAmelCase							  =			encoding["pixel_values"]

					__lowerCAmelCase							  =			model(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			outputs.logits

					# verify logits
					__lowerCAmelCase							  =			torch.Size([1, 10_00]	)
					if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"	):
										__lowerCAmelCase							  =			torch.Size([1, 1_96, 81_92]	)
					elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"	):
										__lowerCAmelCase							  =			torch.Size([1, 1_96, 81_92]	)
					elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"	):
										__lowerCAmelCase							  =			torch.Size([1, 2_18_41]	)
										__lowerCAmelCase							  =			torch.tensor([2.22_88, 2.46_71, 0.73_95]	)
										__lowerCAmelCase							  =			23_97
					elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"	):
										__lowerCAmelCase							  =			torch.Size([1, 2_18_41]	)
										__lowerCAmelCase							  =			torch.tensor([1.68_81, -0.27_87, 0.59_01]	)
										__lowerCAmelCase							  =			23_96
					elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"	):
										__lowerCAmelCase							  =			torch.tensor([0.12_41, 0.07_98, -0.65_69]	)
										__lowerCAmelCase							  =			2_85
					elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"	):
										__lowerCAmelCase							  =			torch.tensor([-1.23_85, -1.09_87, -1.01_08]	)
										__lowerCAmelCase							  =			2_81
					elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"	):
										__lowerCAmelCase							  =			torch.tensor([-1.53_03, -0.94_84, -0.31_47]	)
										__lowerCAmelCase							  =			7_61
					elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"	):
										__lowerCAmelCase							  =			torch.tensor([0.46_10, -0.09_28, 0.20_86]	)
										__lowerCAmelCase							  =			7_61
					elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"	):
										__lowerCAmelCase							  =			torch.tensor([-0.48_04, 0.62_57, -0.18_37]	)
										__lowerCAmelCase							  =			7_61
					elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"	):
										__lowerCAmelCase							  =			torch.tensor([[-0.51_22, 0.51_17, -0.21_13]]	)
										__lowerCAmelCase							  =			7_61
					elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"	):
										__lowerCAmelCase							  =			torch.tensor([-0.30_62, 0.72_61, 0.48_52]	)
										__lowerCAmelCase							  =			7_61
					elif checkpoint_url[:-4].endswith("beit_base_patch16_640_pt22k_ft22ktoade20k"	):
										__lowerCAmelCase							  =			(1, 1_50, 1_60, 1_60)
										__lowerCAmelCase							  =			torch.tensor(
										    [
										        [[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
										        [[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
										        [[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
										    ]	)
					elif checkpoint_url[:-4].endswith("beit_large_patch16_640_pt22k_ft22ktoade20k"	):
										__lowerCAmelCase							  =			(1, 1_50, 1_60, 1_60)
										__lowerCAmelCase							  =			torch.tensor(
										    [
										        [[-4.33_05, -2.30_49, -3.01_61], [-2.95_91, -1.53_05, -2.22_51], [-3.41_98, -1.80_04, -2.90_62]],
										        [[-5.89_22, -3.74_35, -4.39_78], [-4.20_63, -2.78_72, -3.47_55], [-4.27_91, -3.18_74, -4.16_81]],
										        [[0.98_95, 4.34_67, 4.76_63], [4.24_76, 5.68_30, 6.15_18], [4.55_50, 6.24_95, 6.51_54]],
										    ]	)
					else:
										raise ValueError("Can't verify logits as model is not supported"	)

					assert logits.shape == expected_shape, "Shape of logits not as expected"
					if not has_lm_head:
										if is_semantic:
															assert torch.allclose(
															    logits[0, :3, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-3	), "First elements of logits not as expected"
										else:
															print("Predicted class idx:" , logits.argmax(-1	).item()	)
															assert torch.allclose(
															    logits[0, :3] , SCREAMING_SNAKE_CASE_ , atol=1E-3	), "First elements of logits not as expected"
															assert logits.argmax(-1	).item() == expected_class_idx, "Predicted class index not as expected"

					Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
					print(F"""Saving model to {pytorch_dump_folder_path}"""	)
					model.save_pretrained(SCREAMING_SNAKE_CASE_	)
					print(F"""Saving image processor to {pytorch_dump_folder_path}"""	)
					image_processor.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
					UpperCamelCase__            =     argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_url""",
					    default="""https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth""",
					    type=str,
					    help="""URL to the original PyTorch checkpoint (.pth file).""",
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
					)
					UpperCamelCase__            =     parser.parse_args()
					convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)










# 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 (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							List[str] , SCREAMING_SNAKE_CASE_				:							List[str]=0	):
					# Format the message.
					if name is None:
										__lowerCAmelCase							  =			None
					else:
										__lowerCAmelCase							  =			"." * max(0 , spaces - 2	) + "# {:" + str(50 - spaces	) + "s}"
										__lowerCAmelCase							  =			fmt.format(SCREAMING_SNAKE_CASE_	)

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







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							int , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							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.
					__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(*SCREAMING_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(*SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			param.transpose(0 , 1	).contiguous()
					__lowerCAmelCase							  =			param.view(*SCREAMING_SNAKE_CASE_	)
					return param







def       _a (		SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Any , SCREAMING_SNAKE_CASE_				:							Optional[Any]	):
					# The converted output model.
					__lowerCAmelCase							  =			{}

					# old versions did not store training args
					__lowerCAmelCase							  =			input_state_dict.get("args" , SCREAMING_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.tokenizer_type
										__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 if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
										# pprint(config)

					# The number of heads.
					__lowerCAmelCase							  =			config.num_attention_heads
					# The hidden_size per head.
					__lowerCAmelCase							  =			config.hidden_size // heads
					# 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, :]
					# Store the word embeddings.
					__lowerCAmelCase							  =			word_embeddings

					# The position embeddings.
					__lowerCAmelCase							  =			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.
					__lowerCAmelCase							  =			pos_embeddings

					# The token-type embeddings.
					__lowerCAmelCase							  =			embeddings["tokentype_embeddings"]["weight"]
					# Store the position embeddings.
					__lowerCAmelCase							  =			tokentype_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": ".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.
					__lowerCAmelCase							  =			None

					# Extract the layers.
					for key, val in transformer.items():
										# Match the name.
										__lowerCAmelCase							  =			layer_re.match(SCREAMING_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"""bert.encoder.layer.{layer_idx}"""

										# For layernorm(s), simply store the layer norm.
										if op_name.endswith("layernorm"	):
															__lowerCAmelCase							  =			"attention.ln" if op_name.startswith("input"	) else "ln"
															__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":
															# Make sure the QKV pointer is nil.
															assert attention_qkv_weight is None, ""

															__lowerCAmelCase							  =			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 3 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
															# Store the tensor as we need the bias as well to interleave QKV and biases.
															__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":
															# 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.
															__lowerCAmelCase							  =			attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
															__lowerCAmelCase							  =			attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
															__lowerCAmelCase							  =			attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

															__lowerCAmelCase							  =			fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , 3 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
															# Split the bias.
															__lowerCAmelCase							  =			out_val[0 * config.hidden_size : 1 * config.hidden_size]
															__lowerCAmelCase							  =			out_val[1 * config.hidden_size : 2 * config.hidden_size]
															__lowerCAmelCase							  =			out_val[2 * config.hidden_size : 3 * config.hidden_size]

															# Store.
															__lowerCAmelCase							  =			q
															__lowerCAmelCase							  =			q_bias
															__lowerCAmelCase							  =			k
															__lowerCAmelCase							  =			k_bias
															__lowerCAmelCase							  =			v
															__lowerCAmelCase							  =			v_bias

															# Clear the stored tensor.
															__lowerCAmelCase							  =			None

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

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

					# The pooler.
					__lowerCAmelCase							  =			lm["pooler"]

					# Store the matrix and the bias.
					__lowerCAmelCase							  =			pooler["dense.weight"]
					__lowerCAmelCase							  =			pooler["dense.bias"]

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

					# The transform matrix.
					__lowerCAmelCase							  =			lm_head["dense.weight"]
					__lowerCAmelCase							  =			lm_head["dense.bias"]

					# The transform LN.
					__lowerCAmelCase							  =			lm_head["layernorm.weight"]
					__lowerCAmelCase							  =			lm_head["layernorm.bias"]

					# For the decoder, we replicate the weights.
					__lowerCAmelCase							  =			word_embeddings
					__lowerCAmelCase							  =			lm_head["bias"]

					# The classifier from Megatron (for MLNI).
					__lowerCAmelCase							  =			model["binary_head"]

					# Store the classifier.
					__lowerCAmelCase							  =			binary_head["weight"]
					__lowerCAmelCase							  =			binary_head["bias"]

					# It should be done!
					return output_state_dict







def       _a (		):
					# Create the argument parser.
					__lowerCAmelCase							  =			argparse.ArgumentParser()
					parser.add_argument("--print-checkpoint-structure" , action="store_true"	)
					parser.add_argument("path_to_checkpoint" , type=SCREAMING_SNAKE_CASE_ , help="Path to the ZIP file containing the checkpoint"	)
					parser.add_argument(
					    "--config_file" , default="" , type=SCREAMING_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(SCREAMING_SNAKE_CASE_ , map_location="cpu"	)
					else:
										__lowerCAmelCase							  =			torch.load(args.path_to_checkpoint , map_location="cpu"	)

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

					# Convert.
					print("Converting"	)
					__lowerCAmelCase							  =			convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

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

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

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


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

if __name__ == "__main__":
					main()

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




