

# 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 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=0					)							->   Union[str, Any]:

     """simple docstring"""







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

     """simple docstring"""







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







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     {}

     # old versions did not store training args
     _lowerCAmelCase :       List[Any]	   =     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 :       int	   =     ds_args.tokenizer_type
          _lowerCAmelCase :       Any	   =     ds_args.padded_vocab_size
          _lowerCAmelCase :       Union[str, Any]	   =     ds_args.max_position_embeddings
          _lowerCAmelCase :       Optional[Any]	   =     ds_args.hidden_size
          _lowerCAmelCase :       Dict	   =     ds_args.num_layers
          _lowerCAmelCase :       int	   =     ds_args.num_attention_heads
          _lowerCAmelCase :       List[str]	   =     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 :       str	   =     config.num_attention_heads
     # The hidden_size per head.
     _lowerCAmelCase :       Dict	   =     config.hidden_size // heads
     # Megatron-LM checkpoint version
     if "checkpoint_version" in input_state_dict.keys():
          _lowerCAmelCase :       Optional[Any]	   =     input_state_dict["checkpoint_version"]
     else:
          _lowerCAmelCase :       Tuple	   =     0.0

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

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

     # The position embeddings.
     _lowerCAmelCase :       List[str]	   =     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 :       List[str]	   =     pos_embeddings

     # The token-type embeddings.
     _lowerCAmelCase :       Optional[int]	   =     embeddings["tokentype_embeddings"]["weight"]
     # Store the position embeddings.
     _lowerCAmelCase :       int	   =     tokentype_embeddings

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

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

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

     # Keep track of the attention/query/value tensor.
     _lowerCAmelCase :       Tuple	   =     None

     # Extract the layers.
     for key, val in transformer.items():
          # Match the name.
          _lowerCAmelCase :       Optional[int]	   =     layer_re.match(SCREAMING_SNAKE_CASE					)

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

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

          # The name of the layer.
          _lowerCAmelCase :       List[Any]	   =     f'''bert.encoder.layer.{layer_idx}'''

          # For layernorm(s), simply store the layer norm.
          if op_name.endswith("layernorm"					):
               _lowerCAmelCase :       str	   =     "attention.ln" if op_name.startswith("input"					) else "ln"
               _lowerCAmelCase :       Optional[int]	   =     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 :       List[Any]	   =     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 :       List[str]	   =     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 :       str	   =     attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
               _lowerCAmelCase :       Optional[int]	   =     attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
               _lowerCAmelCase :       Any	   =     attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

               _lowerCAmelCase :       Optional[int]	   =     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				3		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE					)
               # Split the bias.
               _lowerCAmelCase :       str	   =     out_val[0 * config.hidden_size : 1 * config.hidden_size]
               _lowerCAmelCase :       str	   =     out_val[1 * config.hidden_size : 2 * config.hidden_size]
               _lowerCAmelCase :       Tuple	   =     out_val[2 * config.hidden_size : 3 * config.hidden_size]

               # Store.
               _lowerCAmelCase :       List[Any]	   =     q
               _lowerCAmelCase :       List[Any]	   =     q_bias
               _lowerCAmelCase :       Dict	   =     k
               _lowerCAmelCase :       int	   =     k_bias
               _lowerCAmelCase :       List[str]	   =     v
               _lowerCAmelCase :       str	   =     v_bias

               # Clear the stored tensor.
               _lowerCAmelCase :       List[str]	   =     None

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

    # The final layernorm.
     _lowerCAmelCase :       List[str]	   =     transformer["final_layernorm.weight"]
     _lowerCAmelCase :       Any	   =     transformer["final_layernorm.bias"]

     # The pooler.
     _lowerCAmelCase :       Dict	   =     lm["pooler"]

     # Store the matrix and the bias.
     _lowerCAmelCase :       Tuple	   =     pooler["dense.weight"]
     _lowerCAmelCase :       int	   =     pooler["dense.bias"]

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

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

     # The transform LN.
     _lowerCAmelCase :       Optional[Any]	   =     lm_head["layernorm.weight"]
     _lowerCAmelCase :       Optional[int]	   =     lm_head["layernorm.bias"]

     # For the decoder, we replicate the weights.
     _lowerCAmelCase :       str	   =     word_embeddings
     _lowerCAmelCase :       int	   =     lm_head["bias"]

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

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

     # It should be done!
     return output_state_dict







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

     """simple docstring"""







     _lowerCAmelCase :       str	   =     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 :       List[str]	   =     parser.parse_args()

     # Extract the basename.
     _lowerCAmelCase :       Union[str, Any]	   =     os.path.dirname(args.path_to_checkpoint					)

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

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

     # Convert.
     print("Converting"					)
     _lowerCAmelCase :       Dict	   =     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 :       Union[str, Any]	   =     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 ConvNextImageProcessor





class A__     (			unittest.TestCase			):



 """simple docstring"""







 def __init__(		self			:						Optional[Any]  ,    A_			:						Union[str, Any]  ,    A_			:						List[Any]=7  ,    A_			:						Optional[Any]=3  ,    A_			:						Optional[int]=1_8  ,    A_			:						Dict=3_0  ,    A_			:						Dict=4_0_0  ,    A_			:						List[Any]=True  ,    A_			:						int=None  ,    A_			:						List[Any]=0.875  ,    A_			:						List[str]=True  ,    A_			:						List[Any]=[0.5, 0.5, 0.5]  ,    A_			:						Optional[Any]=[0.5, 0.5, 0.5]  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       List[str]	   =     size if size is not None else {"shortest_edge": 2_0}
      _lowerCAmelCase :       Union[str, Any]	   =     parent
      _lowerCAmelCase :       int	   =     batch_size
      _lowerCAmelCase :       Any	   =     num_channels
      _lowerCAmelCase :       str	   =     image_size
      _lowerCAmelCase :       List[Any]	   =     min_resolution
      _lowerCAmelCase :       Tuple	   =     max_resolution
      _lowerCAmelCase :       Any	   =     do_resize
      _lowerCAmelCase :       List[str]	   =     size
      _lowerCAmelCase :       List[str]	   =     crop_pct
      _lowerCAmelCase :       Tuple	   =     do_normalize
      _lowerCAmelCase :       int	   =     image_mean
      _lowerCAmelCase :       Dict	   =     image_std


 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "crop_pct": self.crop_pct,
      }





@require_torch
@require_vision
class A__     (			A	,		unittest.TestCase			):



 """simple docstring"""







 _lowercase  :       int  			=    ConvNextImageProcessor if is_vision_available() else None

 def  __magic_name__							(		self			:						Dict							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     ConvNextImageProcessingTester(self							)

 @property
 def  __magic_name__							(		self			:						str							):






      '''simple docstring'''




      return self.image_processor_tester.prepare_image_processor_dict()

 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       int	   =     self.image_processing_class(**self.image_processor_dict							)
      self.assertTrue(hasattr(A_  ,    "do_resize"							)							)
      self.assertTrue(hasattr(A_  ,    "size"							)							)
      self.assertTrue(hasattr(A_  ,    "crop_pct"							)							)
      self.assertTrue(hasattr(A_  ,    "do_normalize"							)							)
      self.assertTrue(hasattr(A_  ,    "image_mean"							)							)
      self.assertTrue(hasattr(A_  ,    "image_std"							)							)

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     self.image_processing_class.from_dict(self.image_processor_dict							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 2_0}							)

      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class.from_dict(self.image_processor_dict  ,    size=4_2							)
      self.assertEqual(image_processor.size  ,    {"shortest_edge": 4_2}							)

 def  __magic_name__							(		self			:						List[str]							):






      '''simple docstring'''




      pass

 def  __magic_name__							(		self			:						Tuple							):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[int]	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PIL images
      _lowerCAmelCase :       Tuple	   =     prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    Image.Image							)

      # Test not batched input
      _lowerCAmelCase :       List[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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Any	   =     image_processing(A_  ,    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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

 def  __magic_name__							(		self			:						Optional[int]							):






      '''simple docstring'''




      _lowerCAmelCase :       Dict	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random numpy tensors
      _lowerCAmelCase :       str	   =     prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_  ,    numpify=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    np.ndarray							)

      # Test not batched input
      _lowerCAmelCase :       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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Optional[Any]	   =     image_processing(A_  ,    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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )


 def  __magic_name__							(		self			:						List[Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     self.image_processing_class(**self.image_processor_dict							)
      # create random PyTorch tensors
      _lowerCAmelCase :       List[str]	   =     prepare_image_inputs(self.image_processor_tester  ,    equal_resolution=A_  ,    torchify=A_							)
      for image in image_inputs:
           self.assertIsInstance(A_  ,    torch.Tensor							)

      # Test not batched input
      _lowerCAmelCase :       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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )

      # Test batched
      _lowerCAmelCase :       Union[str, Any]	   =     image_processing(A_  ,    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["shortest_edge"],
              self.image_processor_tester.size["shortest_edge"],
          )  ,    )






