
'''simple docstring'''



# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

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

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_=0  )   ->   List[str]:




   """simple docstring"""
   # Format the message.
   if name is None:
      _SCREAMING_SNAKE_CASE          = None
   else:
      _SCREAMING_SNAKE_CASE          = """.""" * max(0			,					spaces - 2  ) + """# {:""" + str(50 - spaces  ) + """s}"""
      _SCREAMING_SNAKE_CASE          = fmt.format(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       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Tuple:




   """simple docstring"""
   # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
   # for compatibility with later versions of NVIDIA Megatron-LM.
   # The inverse operation is performed inside Megatron-LM to read checkpoints:
   # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
   # If param is the weight tensor of the self-attention block, the returned tensor
   # will have to be transposed one more time to be read by HuggingFace BERT.
   _SCREAMING_SNAKE_CASE          = param.size()
   if checkpoint_version == 1.0:
      # version 1.0 stores [num_heads * hidden_size * num_splits, :]
      _SCREAMING_SNAKE_CASE          = (num_heads, hidden_size, num_splits) + input_shape[1:]
      _SCREAMING_SNAKE_CASE          = param.view(*SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE          = param.transpose(0			,					2  )
      _SCREAMING_SNAKE_CASE          = param.transpose(1			,					2  ).contiguous()
   elif checkpoint_version >= 2.0:
      # other versions store [num_heads * num_splits * hidden_size, :]
      _SCREAMING_SNAKE_CASE          = (num_heads, num_splits, hidden_size) + input_shape[1:]
      _SCREAMING_SNAKE_CASE          = param.view(*SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE          = param.transpose(0			,					1  ).contiguous()
   _SCREAMING_SNAKE_CASE          = param.view(*SCREAMING_SNAKE_CASE_  )
   return param
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   int:




   """simple docstring"""
   # The converted output model.
   _SCREAMING_SNAKE_CASE          = {}

   # old versions did not store training args
   _SCREAMING_SNAKE_CASE          = 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))

      _SCREAMING_SNAKE_CASE          = ds_args.tokenizer_type
      _SCREAMING_SNAKE_CASE          = ds_args.padded_vocab_size
      _SCREAMING_SNAKE_CASE          = ds_args.max_position_embeddings
      _SCREAMING_SNAKE_CASE          = ds_args.hidden_size
      _SCREAMING_SNAKE_CASE          = ds_args.num_layers
      _SCREAMING_SNAKE_CASE          = ds_args.num_attention_heads
      _SCREAMING_SNAKE_CASE          = ds_args.ffn_hidden_size if """ffn_hidden_size""" in ds_args else 4 * ds_args.hidden_size
      # pprint(config)

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

   # The model.
   _SCREAMING_SNAKE_CASE          = input_state_dict["""model"""]
   # The language model.
   _SCREAMING_SNAKE_CASE          = model["""language_model"""]
   # The embeddings.
   _SCREAMING_SNAKE_CASE          = lm["""embedding"""]

   # The word embeddings.
   _SCREAMING_SNAKE_CASE          = embeddings["""word_embeddings"""]["""weight"""]
   # Truncate the embedding table to vocab_size rows.
   _SCREAMING_SNAKE_CASE          = word_embeddings[: config.vocab_size, :]
   # Store the word embeddings.
   _SCREAMING_SNAKE_CASE          = word_embeddings

   # The position embeddings.
   _SCREAMING_SNAKE_CASE          = embeddings["""position_embeddings"""]["""weight"""]
   assert pos_embeddings.size(0  ) == config.max_position_embeddings and pos_embeddings.size(1  ) == config.hidden_size
   # Store the position embeddings.
   _SCREAMING_SNAKE_CASE          = pos_embeddings

   # The token-type embeddings.
   _SCREAMING_SNAKE_CASE          = embeddings["""tokentype_embeddings"""]["""weight"""]
   # Store the position embeddings.
   _SCREAMING_SNAKE_CASE          = tokentype_embeddings

   # The transformer.
   _SCREAMING_SNAKE_CASE          = lm["""transformer"""] if """transformer""" in lm.keys() else lm["""encoder"""]

   # The regex to extract layer names.
   _SCREAMING_SNAKE_CASE          = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"""  )

   # The simple map of names for "automated" rules.
   _SCREAMING_SNAKE_CASE          = {
       """attention.dense""": """.attention.output.dense.""",
       """self_attention.dense""": """.attention.output.dense.""",
       """mlp.dense_h_to_4h""": """.intermediate.dense.""",
       """mlp.dense_4h_to_h""": """.output.dense.""",
   }

   # Keep track of the attention/query/value tensor.
   _SCREAMING_SNAKE_CASE          = None

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

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

      # The index of the layer.
      _SCREAMING_SNAKE_CASE          = int(m.group(1  )  )
      # The name of the operation.
      _SCREAMING_SNAKE_CASE          = m.group(2  )
      # Is it a weight or a bias?
      _SCREAMING_SNAKE_CASE          = m.group(3  )

      # The name of the layer.
      _SCREAMING_SNAKE_CASE          = F"bert.encoder.layer.{layer_idx}"

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

      # Transpose the QKV matrix.
      elif (
          op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
      ) and weight_or_bias == "weight":
         # Make sure the QKV pointer is nil.
         assert attention_qkv_weight is None, ""

         _SCREAMING_SNAKE_CASE          = fix_query_key_value_ordering(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.
         _SCREAMING_SNAKE_CASE          = out_val

      # Transpose the bias.
      elif (
          op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
      ) and weight_or_bias == "bias":
         # Make sure we read the weight tensor.
         assert attention_qkv_weight is not None, ""

         # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
         _SCREAMING_SNAKE_CASE          = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
         _SCREAMING_SNAKE_CASE          = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
         _SCREAMING_SNAKE_CASE          = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

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

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

         # Clear the stored tensor.
         _SCREAMING_SNAKE_CASE          = None

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

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

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

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

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

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

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

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

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

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

   # It should be done!
   return output_state_dict
def       lowerCAmelCase_		( )   ->   Optional[Any]:




   """simple docstring"""
   # Create the argument parser.
   _SCREAMING_SNAKE_CASE          = argparse.ArgumentParser()
   parser.add_argument("""--print-checkpoint-structure"""			,					action="""store_true"""  )
   parser.add_argument("""path_to_checkpoint"""			,					type=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."""			,					)
   _SCREAMING_SNAKE_CASE          = parser.parse_args()

   # Extract the basename.
   _SCREAMING_SNAKE_CASE          = os.path.dirname(args.path_to_checkpoint  )

   # Load the model.
   # the .zip is very optional, let's keep it for backward compatibility
   print(F"Extracting PyTorch state dictionary from \"{args.path_to_checkpoint}\""  )
   if args.path_to_checkpoint.endswith(""".zip"""  ):
      with zipfile.ZipFile(args.path_to_checkpoint			,					"""r"""  ) as checkpoint:
         with checkpoint.open("""release/mp_rank_00/model_optim_rng.pt"""  ) as pytorch_dict:
            _SCREAMING_SNAKE_CASE          = torch.load(SCREAMING_SNAKE_CASE_			,					map_location="""cpu"""  )
   else:
      _SCREAMING_SNAKE_CASE          = torch.load(args.path_to_checkpoint			,					map_location="""cpu"""  )

   if args.config_file == "":
      # Default config of megatron-bert 345m
      _SCREAMING_SNAKE_CASE          = MegatronBertConfig()

      # different megatron-bert-*-345m models have different vocab sizes, so override the default
      # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
      _SCREAMING_SNAKE_CASE          = input_state_dict["""model"""]["""lm_head"""]["""bias"""].numel()
   else:
      _SCREAMING_SNAKE_CASE          = MegatronBertConfig.from_json_file(args.config_file  )

   # Convert.
   print("""Converting"""  )
   _SCREAMING_SNAKE_CASE          = convert_megatron_checkpoint(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.
   _SCREAMING_SNAKE_CASE          = 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()

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








import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ViTImageProcessor

class 				_UpperCamelCase (    unittest.TestCase     ):






			'''simple docstring'''

			def __init__(				self       :				Optional[int]  ,		snake_case_       :				Optional[Any]  ,		snake_case_       :				List[Any]=7  ,		snake_case_       :				int=3  ,		snake_case_       :				Tuple=18  ,		snake_case_       :				List[Any]=30  ,		snake_case_       :				Tuple=400  ,		snake_case_       :				Dict=True  ,		snake_case_       :				List[str]=None  ,		snake_case_       :				Any=True  ,		snake_case_       :				str=[0.5, 0.5, 0.5]  ,		snake_case_       :				Optional[Any]=[0.5, 0.5, 0.5]  ,		):
							UpperCamelCase_:							int		=  size if size is not None else {"""height""": 18, """width""": 18}
							UpperCamelCase_:							Any		=  parent
							UpperCamelCase_:							List[str]		=  batch_size
							UpperCamelCase_:							Union[str, Any]		=  num_channels
							UpperCamelCase_:							str		=  image_size
							UpperCamelCase_:							Optional[int]		=  min_resolution
							UpperCamelCase_:							Optional[Any]		=  max_resolution
							UpperCamelCase_:							Dict		=  do_resize
							UpperCamelCase_:							Union[str, Any]		=  size
							UpperCamelCase_:							Optional[Any]		=  do_normalize
							UpperCamelCase_:							str		=  image_mean
							UpperCamelCase_:							int		=  image_std




			def 					lowerCAmelCase__		(				self       :				Tuple					):
							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							}




@require_torch
@require_vision
class 				_UpperCamelCase (    _A   ,				unittest.TestCase     ):






			'''simple docstring'''

			__UpperCamelCase					:  str    					=       ViTImageProcessor if is_vision_available() else None

			def 					lowerCAmelCase__		(				self       :				Any					):
							UpperCamelCase_:							Tuple		=  ViTImageProcessingTester(self					)

			@property
			def 					lowerCAmelCase__		(				self       :				Union[str, Any]					):
							return self.image_processor_tester.prepare_image_processor_dict()

			def 					lowerCAmelCase__		(				self       :				int					):
							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class(**self.image_processor_dict					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_mean"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""image_std"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_normalize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""do_resize"""					)					)
							self.assertTrue(hasattr(snake_case_  ,		"""size"""					)					)

			def 					lowerCAmelCase__		(				self       :				Optional[Any]					):
							UpperCamelCase_:							Optional[Any]		=  self.image_processing_class.from_dict(self.image_processor_dict					)
							self.assertEqual(image_processor.size  ,		{"""height""": 18, """width""": 18}					)

							UpperCamelCase_:							Union[str, Any]		=  self.image_processing_class.from_dict(self.image_processor_dict  ,		size=42					)
							self.assertEqual(image_processor.size  ,		{"""height""": 42, """width""": 42}					)

			def 					lowerCAmelCase__		(				self       :				int					):
							pass

			def 					lowerCAmelCase__		(				self       :				int					):
							# Initialize image_processing
							UpperCamelCase_:							List[str]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PIL images
							UpperCamelCase_:							List[Any]		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		Image.Image					)

							# Test not batched input
							UpperCamelCase_:							Optional[Any]		=  image_processing(image_inputs[0]  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							List[str]		=  image_processing(snake_case_  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

			def 					lowerCAmelCase__		(				self       :				str					):
							# Initialize image_processing
							UpperCamelCase_:							List[str]		=  self.image_processing_class(**self.image_processor_dict					)
							# create random numpy tensors
							UpperCamelCase_:							Tuple		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		numpify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		np.ndarray					)

							# Test not batched input
							UpperCamelCase_:							Any		=  image_processing(image_inputs[0]  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Dict		=  image_processing(snake_case_  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)




			def 					lowerCAmelCase__		(				self       :				Any					):
							# Initialize image_processing
							UpperCamelCase_:							int		=  self.image_processing_class(**self.image_processor_dict					)
							# create random PyTorch tensors
							UpperCamelCase_:							Dict		=  prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=snake_case_  ,		torchify=snake_case_					)
							for image in image_inputs:
											self.assertIsInstance(snake_case_  ,		torch.Tensor					)

							# Test not batched input
							UpperCamelCase_:							List[str]		=  image_processing(image_inputs[0]  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        1,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)

							# Test batched
							UpperCamelCase_:							Any		=  image_processing(snake_case_  ,		return_tensors="""pt"""					).pixel_values
							self.assertEqual(
							    encoded_images.shape  ,		(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        self.image_processor_tester.size["""height"""],
							        self.image_processor_tester.size["""width"""],
							    )  ,		)






