





"""simple docstring"""




import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import DinatBackbone, DinatForImageClassification, DinatModel
						from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor




class 					_a		:






				def __init__(					self						:       Dict,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Any=1_3,					lowerCAmelCase__						:       str=6_4,					lowerCAmelCase__						:       Optional[int]=4,					lowerCAmelCase__						:       str=3,					lowerCAmelCase__						:       Optional[Any]=1_6,					lowerCAmelCase__						:       Tuple=[1, 2, 1],					lowerCAmelCase__						:       int=[2, 4, 8],					lowerCAmelCase__						:       Optional[Any]=3,					lowerCAmelCase__						:       str=[[3], [1, 2], [1]],					lowerCAmelCase__						:       Dict=2.0,					lowerCAmelCase__						:       Tuple=True,					lowerCAmelCase__						:       List[Any]=0.0,					lowerCAmelCase__						:       Tuple=0.0,					lowerCAmelCase__						:       Tuple=0.1,					lowerCAmelCase__						:       List[str]="gelu",					lowerCAmelCase__						:       Optional[Any]=True,					lowerCAmelCase__						:       Dict=0.02,					lowerCAmelCase__						:       Optional[Any]=1e-5,					lowerCAmelCase__						:       List[str]=True,					lowerCAmelCase__						:       int=None,					lowerCAmelCase__						:       Any=True,					lowerCAmelCase__						:       Optional[Any]=1_0,					lowerCAmelCase__						:       Any=["stage1", "stage2"],					lowerCAmelCase__						:       Union[str, Any]=[1, 2],					)      -> Any:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       parent
										_UpperCamelCase     :   Optional[int]      =       batch_size
										_UpperCamelCase     :   int      =       image_size
										_UpperCamelCase     :   List[str]      =       patch_size
										_UpperCamelCase     :   Any      =       num_channels
										_UpperCamelCase     :   List[Any]      =       embed_dim
										_UpperCamelCase     :   int      =       depths
										_UpperCamelCase     :   Optional[Any]      =       num_heads
										_UpperCamelCase     :   str      =       kernel_size
										_UpperCamelCase     :   Union[str, Any]      =       dilations
										_UpperCamelCase     :   List[Any]      =       mlp_ratio
										_UpperCamelCase     :   List[str]      =       qkv_bias
										_UpperCamelCase     :   List[Any]      =       hidden_dropout_prob
										_UpperCamelCase     :   List[str]      =       attention_probs_dropout_prob
										_UpperCamelCase     :   Dict      =       drop_path_rate
										_UpperCamelCase     :   int      =       hidden_act
										_UpperCamelCase     :   List[Any]      =       patch_norm
										_UpperCamelCase     :   List[str]      =       layer_norm_eps
										_UpperCamelCase     :   Tuple      =       initializer_range
										_UpperCamelCase     :   Any      =       is_training
										_UpperCamelCase     :   List[Any]      =       scope
										_UpperCamelCase     :   Any      =       use_labels
										_UpperCamelCase     :   Optional[int]      =       num_labels
										_UpperCamelCase     :   List[str]      =       out_features
										_UpperCamelCase     :   str      =       out_indices






				def 							snake_case      (					self						:       Optional[int]				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

										_UpperCamelCase     :   Dict      =       None
										if self.use_labels:
																_UpperCamelCase     :   int      =       ids_tensor([self.batch_size],					self.num_labels				)

										_UpperCamelCase     :   str      =       self.get_config()

										return config, pixel_values, labels






				def 							snake_case      (					self						:       List[Any]				)      -> Union[str, Any]:




										'''simple docstring'''



										return DinatConfig(
										    num_labels=self.num_labels,					image_size=self.image_size,					patch_size=self.patch_size,					num_channels=self.num_channels,					embed_dim=self.embed_dim,					depths=self.depths,					num_heads=self.num_heads,					kernel_size=self.kernel_size,					dilations=self.dilations,					mlp_ratio=self.mlp_ratio,					qkv_bias=self.qkv_bias,					hidden_dropout_prob=self.hidden_dropout_prob,					attention_probs_dropout_prob=self.attention_probs_dropout_prob,					drop_path_rate=self.drop_path_rate,					hidden_act=self.hidden_act,					patch_norm=self.patch_norm,					layer_norm_eps=self.layer_norm_eps,					initializer_range=self.initializer_range,					out_features=self.out_features,					out_indices=self.out_indices,					)






				def 							snake_case      (					self						:       Optional[Any],					lowerCAmelCase__						:       Union[str, Any],					lowerCAmelCase__						:       Dict,					lowerCAmelCase__						:       List[str]				)      -> List[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       DinatModel(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[str]      =       model(lowerCAmelCase__				)

										_UpperCamelCase     :   Tuple      =       (config.image_size // config.patch_size) // (2 ** (len(config.depths				) - 1))
										_UpperCamelCase     :   str      =       int(config.embed_dim * 2 ** (len(config.depths				) - 1)				)

										self.parent.assertEqual(
										    result.last_hidden_state.shape,					(self.batch_size, expected_height, expected_width, expected_dim)				)






				def 							snake_case      (					self						:       Any,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       Optional[Any]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       DinatForImageClassification(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__,					labels=lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)

										# test greyscale images
										_UpperCamelCase     :   Any      =       1
										_UpperCamelCase     :   List[str]      =       DinatForImageClassification(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()

										_UpperCamelCase     :   str      =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
										_UpperCamelCase     :   Optional[int]      =       model(lowerCAmelCase__				)
										self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Optional[int],					lowerCAmelCase__						:       Dict				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       DinatBackbone(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[Any]      =       model(lowerCAmelCase__				)

										# verify hidden states
										self.parent.assertEqual(len(result.feature_maps				),					len(config.out_features				)				)
										self.parent.assertListEqual(list(result.feature_maps[0].shape				),					[self.batch_size, model.channels[0], 1_6, 1_6]				)

										# verify channels
										self.parent.assertEqual(len(model.channels				),					len(config.out_features				)				)

										# verify backbone works with out_features=None
										_UpperCamelCase     :   List[Any]      =       None
										_UpperCamelCase     :   Union[str, Any]      =       DinatBackbone(config=lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()
										_UpperCamelCase     :   List[Any]      =       model(lowerCAmelCase__				)

										# verify feature maps
										self.parent.assertEqual(len(result.feature_maps				),					1				)
										self.parent.assertListEqual(list(result.feature_maps[0].shape				),					[self.batch_size, model.channels[-1], 4, 4]				)

										# verify channels
										self.parent.assertEqual(len(model.channels				),					1				)






				def 							snake_case      (					self						:       str				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Tuple      =       self.prepare_config_and_inputs()
										_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       config_and_inputs
										_UpperCamelCase     :   str      =       {'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_natten
@require_torch
class 					_a		( _lowerCAmelCase      ,							_lowerCAmelCase      ,							unittest.TestCase							):
				UpperCamelCase								=       (
				    (
				        DinatModel,
				        DinatForImageClassification,
				        DinatBackbone,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase								=       (
				    {'''feature-extraction''': DinatModel, '''image-classification''': DinatForImageClassification}
				    if is_torch_available()
				    else {}
				)
				UpperCamelCase								=       False

				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       False
				UpperCamelCase								=       False






				def 							snake_case      (					self						:       Union[str, Any]				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       DinatModelTester(self				)
										_UpperCamelCase     :   Optional[Any]      =       ConfigTester(self,					config_class=lowerCAmelCase__,					embed_dim=3_7				)






				def 							snake_case      (					self						:       Optional[int]				)      -> Union[str, Any]:




										'''simple docstring'''



										self.create_and_test_config_common_properties()
										self.config_tester.create_and_test_config_to_json_string()
										self.config_tester.create_and_test_config_to_json_file()
										self.config_tester.create_and_test_config_from_and_save_pretrained()
										self.config_tester.create_and_test_config_with_num_labels()
										self.config_tester.check_config_can_be_init_without_params()
										self.config_tester.check_config_arguments_init()






				def 							snake_case      (					self						:       Optional[int]				)      -> int:




										'''simple docstring'''



										return






				def 							snake_case      (					self						:       Dict				)      -> str:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   int      =       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_backbone(*lowerCAmelCase__				)






				@unittest.skip(reason='''Dinat does not use inputs_embeds'''				)
				def 							snake_case      (					self						:       str				)      -> int:




										'''simple docstring'''



										pass






				@unittest.skip(reason='''Dinat does not use feedforward chunking'''				)
				def 							snake_case      (					self						:       Tuple				)      -> Optional[int]:




										'''simple docstring'''



										pass






				def 							snake_case      (					self						:       Any				)      -> str:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase     :   Optional[Any]      =       model_class(lowerCAmelCase__				)
																self.assertIsInstance(model.get_input_embeddings(),					(nn.Module)				)
																_UpperCamelCase     :   Any      =       model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowerCAmelCase__,					nn.Linear				)				)






				def 							snake_case      (					self						:       Optional[Any]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   str      =       self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase     :   int      =       model_class(lowerCAmelCase__				)
																_UpperCamelCase     :   Any      =       inspect.signature(model.forward				)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCamelCase     :   Optional[int]      =       [*signature.parameters.keys()]

																_UpperCamelCase     :   str      =       ['''pixel_values''']
																self.assertListEqual(arg_names[:1],					lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[str]				)      -> List[Any]:




										'''simple docstring'''



										self.skipTest('''Dinat\'s attention operation is handled entirely by NATTEN.'''				)






				def 							snake_case      (					self						:       str,					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       List[Any],					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[Any]      =       model_class(lowerCAmelCase__				)
										model.to(lowerCAmelCase__				)
										model.eval()

										with torch.no_grad():
																_UpperCamelCase     :   Optional[int]      =       model(**self._prepare_for_class(lowerCAmelCase__,					lowerCAmelCase__				)				)

										_UpperCamelCase     :   Dict      =       outputs.hidden_states

										_UpperCamelCase     :   List[str]      =       getattr(
										    self.model_tester,					'''expected_num_hidden_layers''',					len(self.model_tester.depths				) + 1				)
										self.assertEqual(len(lowerCAmelCase__				),					lowerCAmelCase__				)

										# Dinat has a different seq_length
										_UpperCamelCase     :   Dict      =       (
										    config.patch_size
										    if isinstance(config.patch_size,					collections.abc.Iterable				)
										    else (config.patch_size, config.patch_size)
										)

										_UpperCamelCase     :   Any      =       image_size[0] // patch_size[0]
										_UpperCamelCase     :   Optional[Any]      =       image_size[1] // patch_size[1]

										self.assertListEqual(
										    list(hidden_states[0].shape[-3:]				),					[height, width, self.model_tester.embed_dim],					)

										if model_class.__name__ != "DinatBackbone":
																_UpperCamelCase     :   Optional[int]      =       outputs.reshaped_hidden_states
																self.assertEqual(len(lowerCAmelCase__				),					lowerCAmelCase__				)

																_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       reshaped_hidden_states[0].shape
																_UpperCamelCase     :   int      =       (
																    reshaped_hidden_states[0].view(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				).permute(0,					2,					3,					1				)
																)
																self.assertListEqual(
																    list(reshaped_hidden_states.shape[-3:]				),					[height, width, self.model_tester.embed_dim],					)






				def 							snake_case      (					self						:       Tuple				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Any      =       self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCamelCase     :   Optional[Any]      =       (
										    self.model_tester.image_size
										    if isinstance(self.model_tester.image_size,					collections.abc.Iterable				)
										    else (self.model_tester.image_size, self.model_tester.image_size)
										)

										for model_class in self.all_model_classes:
																_UpperCamelCase     :   List[Any]      =       True
																self.check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_UpperCamelCase     :   int      =       True

																self.check_hidden_states_output(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






				@slow
				def 							snake_case      (					self						:       List[Any]				)      -> Tuple:




										'''simple docstring'''



										for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase     :   Tuple      =       DinatModel.from_pretrained(lowerCAmelCase__				)
																self.assertIsNotNone(lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> Any:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   List[str]      =       self.model_tester.prepare_config_and_inputs_for_common()

										_UpperCamelCase     :   Any      =       _config_zero_init(lowerCAmelCase__				)
										for model_class in self.all_model_classes:
																_UpperCamelCase     :   str      =       model_class(config=lowerCAmelCase__				)
																for name, param in model.named_parameters():
																						if "embeddings" not in name and param.requires_grad:
																												self.assertIn(
																												    ((param.data.mean() * 1e9).round() / 1e9).item(),					[0.0, 1.0],					msg=f"""Parameter {name} of model {model_class} seems not properly initialized""",					)




@require_natten
@require_vision
@require_torch
class 					_a		( unittest.TestCase							):






				@cached_property
				def 							snake_case      (					self						:       List[Any]				)      -> List[str]:




										'''simple docstring'''



										return AutoImageProcessor.from_pretrained('''shi-labs/dinat-mini-in1k-224'''				) if is_vision_available() else None






				@slow
				def 							snake_case      (					self						:       int				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       DinatForImageClassification.from_pretrained('''shi-labs/dinat-mini-in1k-224'''				).to(lowerCAmelCase__				)
										_UpperCamelCase     :   List[Any]      =       self.default_image_processor

										_UpperCamelCase     :   Union[str, Any]      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
										_UpperCamelCase     :   Any      =       image_processor(images=lowerCAmelCase__,					return_tensors='''pt'''				).to(lowerCAmelCase__				)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase     :   str      =       model(**lowerCAmelCase__				)

										# verify the logits
										_UpperCamelCase     :   Any      =       torch.Size((1, 1_0_0_0)				)
										self.assertEqual(outputs.logits.shape,					lowerCAmelCase__				)
										_UpperCamelCase     :   str      =       torch.tensor([-0.1_545, -0.7_667, 0.4_642]				).to(lowerCAmelCase__				)
										self.assertTrue(torch.allclose(outputs.logits[0, :3],					lowerCAmelCase__,					atol=1e-4				)				)




@require_torch
@require_natten
class 					_a		( unittest.TestCase      ,							_lowerCAmelCase							):
				UpperCamelCase								=       (DinatBackbone,) if is_torch_available() else ()
				UpperCamelCase								=       DinatConfig






				def 							snake_case      (					self						:       Any				)      -> Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase     :   str      =       DinatModelTester(self				)









# 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()

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




