




'''simple docstring'''







import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
   from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import DPTImageProcessor


class 			_a  :
       def __init__(	self						:	str   ,				lowercase						:	List[Any]   ,				lowercase						:	Dict=2   ,				lowercase						:	str=32   ,				lowercase						:	Optional[Any]=16   ,				lowercase						:	Optional[Any]=3   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	List[Any]=True   ,				lowercase						:	Optional[int]=32   ,				lowercase						:	Any=4   ,				lowercase						:	str=[0, 1, 2, 3]   ,				lowercase						:	List[Any]=4   ,				lowercase						:	str=37   ,				lowercase						:	Optional[Any]="gelu"   ,				lowercase						:	Tuple=0.1   ,				lowercase						:	Tuple=0.1   ,				lowercase						:	Union[str, Any]=0.02   ,				lowercase						:	int=3   ,				lowercase						:	int=[1, 384, 24, 24]   ,				lowercase						:	str=True   ,				lowercase						:	List[Any]=None   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		parent
           UpperCAmelCase			       =		batch_size
           UpperCAmelCase			       =		image_size
           UpperCAmelCase			       =		patch_size
           UpperCAmelCase			       =		num_channels
           UpperCAmelCase			       =		is_training
           UpperCAmelCase			       =		use_labels
           UpperCAmelCase			       =		hidden_size
           UpperCAmelCase			       =		num_hidden_layers
           UpperCAmelCase			       =		backbone_out_indices
           UpperCAmelCase			       =		num_attention_heads
           UpperCAmelCase			       =		intermediate_size
           UpperCAmelCase			       =		hidden_act
           UpperCAmelCase			       =		hidden_dropout_prob
           UpperCAmelCase			       =		attention_probs_dropout_prob
           UpperCAmelCase			       =		initializer_range
           UpperCAmelCase			       =		num_labels
           UpperCAmelCase			       =		backbone_featmap_shape
           UpperCAmelCase			       =		scope
           UpperCAmelCase			       =		is_hybrid
           # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
           UpperCAmelCase			       =		(image_size // patch_size) ** 2
           UpperCAmelCase			       =		num_patches + 1
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           UpperCAmelCase			       =		None
           if self.use_labels:
               UpperCAmelCase			       =		ids_tensor([self.batch_size, self.image_size, self.image_size]   ,				self.num_labels				)

           UpperCAmelCase			       =		self.get_config()

           return config, pixel_values, labels
       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase			       =		{
               '''global_padding''': '''same''',
               '''layer_type''': '''bottleneck''',
               '''depths''': [3, 4, 9],
               '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
               '''embedding_dynamic_padding''': True,
               '''hidden_sizes''': [96, 192, 384, 768],
               '''num_groups''': 2,
           }

           return DPTConfig(
               image_size=self.image_size   ,				patch_size=self.patch_size   ,				num_channels=self.num_channels   ,				hidden_size=self.hidden_size   ,				num_hidden_layers=self.num_hidden_layers   ,				backbone_out_indices=self.backbone_out_indices   ,				num_attention_heads=self.num_attention_heads   ,				intermediate_size=self.intermediate_size   ,				hidden_act=self.hidden_act   ,				hidden_dropout_prob=self.hidden_dropout_prob   ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,				is_decoder=lowercase   ,				initializer_range=self.initializer_range   ,				is_hybrid=self.is_hybrid   ,				backbone_config=lowercase   ,				backbone_featmap_shape=self.backbone_featmap_shape   ,				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	str   ,				lowercase						:	Optional[Any]   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		DPTModel(config=lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase				)
           self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)				)
       def     A      (	self						:	List[str]   ,				lowercase						:	Tuple   ,				lowercase						:	Optional[int]   ,				lowercase						:	List[str]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_labels
           UpperCAmelCase			       =		DPTForDepthEstimation(lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase				)
           self.parent.assertEqual(result.predicted_depth.shape   ,				(self.batch_size, self.image_size, self.image_size)				)
       def     A      (	self						:	int   ,				lowercase						:	Dict   ,				lowercase						:	Union[str, Any]   ,				lowercase						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.num_labels
           UpperCAmelCase			       =		DPTForSemanticSegmentation(lowercase				)
           model.to(lowercase				)
           model.eval()
           UpperCAmelCase			       =		model(lowercase   ,				labels=lowercase				)
           self.parent.assertEqual(
               result.logits.shape   ,				(self.batch_size, self.num_labels, self.image_size, self.image_size)				)







       def     A      (	self						:	Tuple				):



           '''simple docstring'''

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






@require_torch
class 			_a  (	__a							,							__a							,							unittest.TestCase ):
       __a				:     Dict   				=					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
       __a				:     Optional[int]   				=					(
           {
               """depth-estimation""": DPTForDepthEstimation,
               """feature-extraction""": DPTModel,
               """image-segmentation""": DPTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       __a				:     Any   				=					False
       __a				:     List[Any]   				=					False
       __a				:     Dict   				=					False
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase			       =		DPTModelTester(self				)
           UpperCAmelCase			       =		ConfigTester(self   ,				config_class=lowercase   ,				has_text_modality=lowercase   ,				hidden_size=37				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       @unittest.skip(reason='''DPT does not use inputs_embeds'''				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           pass
       def     A      (	self						:	int				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		model_class(lowercase				)
               self.assertIsInstance(model.get_input_embeddings()   ,				(nn.Module)				)
               UpperCAmelCase			       =		model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowercase   ,				nn.Linear				)				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

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

               UpperCAmelCase			       =		['''pixel_values''']
               self.assertListEqual(arg_names[:1]   ,				lowercase				)
       def     A      (	self						:	Tuple				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase				)
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*lowercase				)
       def     A      (	self						:	Any				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*lowercase				)
       def     A      (	self						:	List[str]				):



           '''simple docstring'''

           for model_class in self.all_model_classes:
               if model_class.__name__ == "DPTForDepthEstimation":
                   continue

               UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
               UpperCAmelCase			       =		True

               if model_class in get_values(lowercase				):
                   continue

               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.train()
               UpperCAmelCase			       =		self._prepare_for_class(lowercase   ,				lowercase   ,				return_labels=lowercase				)
               UpperCAmelCase			       =		model(**lowercase				).loss
               loss.backward()
       def     A      (	self						:	str				):



           '''simple docstring'''

           for model_class in self.all_model_classes:
               if model_class.__name__ == "DPTForDepthEstimation":
                   continue

               UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
               UpperCAmelCase			       =		False
               UpperCAmelCase			       =		True

               if model_class in get_values(lowercase				) or not model_class.supports_gradient_checkpointing:
                   continue
               UpperCAmelCase			       =		model_class(lowercase				)
               model.to(lowercase				)
               model.gradient_checkpointing_enable()
               model.train()
               UpperCAmelCase			       =		self._prepare_for_class(lowercase   ,				lowercase   ,				return_labels=lowercase				)
               UpperCAmelCase			       =		model(**lowercase				).loss
               loss.backward()
       def     A      (	self						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()

           UpperCAmelCase			       =		_config_zero_init(lowercase				)
           for model_class in self.all_model_classes:
               UpperCAmelCase			       =		model_class(config=lowercase				)
               # Skip the check for the backbone
               UpperCAmelCase			       =		[]
               for name, module in model.named_modules():
                   if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                       UpperCAmelCase			       =		[f"{name}.{key}" for key in module.state_dict().keys()]
                       break

               for name, param in model.named_parameters():
                   if param.requires_grad:
                       if name in backbone_params:
                           continue
                       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"   ,				)
       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
       def     A      (	self						:	int				):



           '''simple docstring'''

           pass
       @slow
       def     A      (	self						:	Any				):



           '''simple docstring'''

           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
               UpperCAmelCase			       =		DPTModel.from_pretrained(lowercase				)
               self.assertIsNotNone(lowercase				)







       def     A      (	self						:	str				):



           '''simple docstring'''

           UpperCAmelCase       ,     UpperCAmelCase			       =		self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase			       =		'''add'''
           with self.assertRaises(lowercase				):
               UpperCAmelCase			       =		DPTForDepthEstimation(lowercase				)






def 						snake_case_							():
    UpperCAmelCase			       =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
    return image







@require_torch
@require_vision
@slow
class 			_a  (	unittest.TestCase ):
       def     A      (	self						:	Dict				):



           '''simple docstring'''

           UpperCAmelCase			       =		DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''				)
           UpperCAmelCase			       =		DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''				).to(lowercase				)

           UpperCAmelCase			       =		prepare_img()
           UpperCAmelCase			       =		image_processor(images=lowercase   ,				return_tensors='''pt'''				).to(lowercase				)

           # forward pass
           with torch.no_grad():
               UpperCAmelCase			       =		model(**lowercase				)
               UpperCAmelCase			       =		outputs.predicted_depth

           # verify the predicted depth
           UpperCAmelCase			       =		torch.Size((1, 384, 384)				)
           self.assertEqual(predicted_depth.shape   ,				lowercase				)

           UpperCAmelCase			       =		torch.tensor(
               [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]				).to(lowercase				)

           self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100   ,				lowercase   ,				atol=1E-4				)				)








'''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_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import AutoTokenizer, GPTaConfig





def 	a_					( lowerCamelCase	:     int	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Union[str, Any]=0				):
   # Format the message.
   if name is None:
      lowerCAmelCase     =			None
   else:
      lowerCAmelCase     =			'.' * max(0	, spaces - 2				) + '# {:' + str(50 - spaces				) + 's}'
      lowerCAmelCase     =			fmt.format(lowerCamelCase				)

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





def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     List[str]	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     Dict	, lowerCamelCase	:     Tuple				):
   # 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 GPT2.
   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(*lowerCamelCase				)
      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(*lowerCamelCase				)
      lowerCAmelCase     =			param.transpose(0	, 1				).contiguous()
   lowerCAmelCase     =			param.view(*lowerCamelCase				)
   return param





def 	a_					( lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     str				):
   # The converted output model.
   lowerCAmelCase     =			{}

   # old versions did not store training args
   lowerCAmelCase     =			input_state_dict.get('args'	, lowerCamelCase				)
   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.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
      # pprint(config)

   # The number of heads.
   lowerCAmelCase     =			config.n_head
   # The hidden_size per head.
   lowerCAmelCase     =			config.n_embd // config.n_head
   # 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, :]
   lowerCAmelCase     =			word_embeddings

   # The position embeddings.
   lowerCAmelCase     =			embeddings['position_embeddings']['weight']
   # Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
   lowerCAmelCase     =			pos_embeddings.size(0				)
   if n_positions != config.n_positions:
      raise ValueError(
          f'''pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match'''				)
   # Store the position embeddings.
   lowerCAmelCase     =			pos_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': '.attn.c_proj.',
       'self_attention.dense': '.attn.c_proj.',
       'mlp.dense_h_to_4h': '.mlp.c_fc.',
       'mlp.dense_4h_to_h': '.mlp.c_proj.',
   }

   # Extract the layers.
   for key, val in transformer.items():
      # Match the name.
      lowerCAmelCase     =			layer_re.match(lowerCamelCase				)

      # 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'''transformer.h.{layer_idx}'''

      # For layernorm(s), simply store the layer norm.
      if op_name.endswith('layernorm'				):
         lowerCAmelCase     =			'ln_1' if op_name.startswith('input'				) else 'ln_2'
         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":
         # Insert a tensor of 1x1xDxD bias.
         lowerCAmelCase     =			torch.tril(torch.ones((n_positions, n_positions)	, dtype=torch.floataa				)				).view(
             1	, 1	, lowerCamelCase	, lowerCamelCase				)
         lowerCAmelCase     =			causal_mask

         # Insert a "dummy" tensor for masked_bias.
         lowerCAmelCase     =			torch.tensor(-1e4	, dtype=torch.floataa				)
         lowerCAmelCase     =			masked_bias

         lowerCAmelCase     =			fix_query_key_value_ordering(lowerCamelCase	, lowerCamelCase	, 3	, lowerCamelCase	, lowerCamelCase				)
         # Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
         lowerCAmelCase     =			out_val.transpose(0	, 1				).contiguous()
         # Store.
         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":
         lowerCAmelCase     =			fix_query_key_value_ordering(lowerCamelCase	, lowerCamelCase	, 3	, lowerCamelCase	, lowerCamelCase				)
         # Store. No change of shape.
         lowerCAmelCase     =			out_val

      # Transpose the weights.
      elif weight_or_bias == "weight":
         lowerCAmelCase     =			megatron_to_transformers[op_name]
         lowerCAmelCase     =			val.transpose(0	, 1				)

      # Copy the bias.
      elif weight_or_bias == "bias":
         lowerCAmelCase     =			megatron_to_transformers[op_name]
         lowerCAmelCase     =			val

    # DEBUG.
   assert config.n_layer == layer_idx + 1

   # The final layernorm.
   lowerCAmelCase     =			transformer['final_layernorm.weight']
   lowerCAmelCase     =			transformer['final_layernorm.bias']

   # For LM head, transformers' wants the matrix to weight embeddings.
   lowerCAmelCase     =			word_embeddings

   # 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=lowerCamelCase	, help='Path to the checkpoint file (.zip archive or direct .pt file)'	, )
   parser.add_argument(
       '--config_file'	, default=''	, type=lowerCamelCase	, 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(lowerCamelCase	, map_location='cpu'				)
   else:
      lowerCAmelCase     =			torch.load(args.path_to_checkpoint	, map_location='cpu'				)

   lowerCAmelCase     =			input_state_dict.get('args'	, lowerCamelCase				)

   # Read the config, or default to the model released by NVIDIA.
   if args.config_file == "":
      if ds_args is not None:
         if ds_args.bias_gelu_fusion:
            lowerCAmelCase     =			'gelu_fast'
         elif ds_args.openai_gelu:
            lowerCAmelCase     =			'gelu_new'
         else:
            lowerCAmelCase     =			'gelu'
      else:
         # in the very early days this used to be "gelu_new"
         lowerCAmelCase     =			'gelu_new'

      # Spell out all parameters in case the defaults change.
      lowerCAmelCase     =			GPTaConfig(
          vocab_size=50257	, n_positions=1024	, n_embd=1024	, n_layer=24	, n_head=16	, n_inner=4096	, activation_function=lowerCamelCase	, resid_pdrop=0.1	, embd_pdrop=0.1	, attn_pdrop=0.1	, layer_norm_epsilon=1e-5	, initializer_range=0.02	, summary_type='cls_index'	, summary_use_proj=lowerCamelCase	, summary_activation=lowerCamelCase	, summary_proj_to_labels=lowerCamelCase	, summary_first_dropout=0.1	, scale_attn_weights=lowerCamelCase	, use_cache=lowerCamelCase	, bos_token_id=50256	, eos_token_id=50256	, )
   else:
      lowerCAmelCase     =			GPTaConfig.from_json_file(args.config_file				)

   lowerCAmelCase     =			['GPT2LMHeadModel']

   # Convert.
   print('Converting'				)
   lowerCAmelCase     =			convert_megatron_checkpoint(lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)

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

   # Add tokenizer class info to config
   # see https://github.com/huggingface/transformers/issues/13906)
   if ds_args is not None:
      lowerCAmelCase     =			ds_args.tokenizer_type
      if tokenizer_type == "GPT2BPETokenizer":
         lowerCAmelCase     =			'gpt2'
      elif tokenizer_type == "PretrainedFromHF":
         lowerCAmelCase     =			ds_args.tokenizer_name_or_path
      else:
         raise ValueError(f'''Unrecognized tokenizer_type {tokenizer_type}'''				)
   else:
      lowerCAmelCase     =			'gpt2'

   lowerCAmelCase     =			AutoTokenizer.from_pretrained(lowerCamelCase				)
   lowerCAmelCase     =			type(lowerCamelCase				).__name__
   lowerCAmelCase     =			tokenizer_class

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

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

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


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

if __name__ == "__main__":
       main()

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





