


'''simple docstring'''


import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler







@dataclass
class 							__magic_name__	(       lowerCAmelCase ):

     """simple docstring"""






     lowerCamelCase__       						=				0
     lowerCamelCase__       						=				False
     lowerCamelCase__       						=				3.0







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.assertDictEqual(MockClass().to_kwargs()			,     {}		)
          self.assertDictEqual(MockClass(a=2		).to_kwargs()			,     {"a": 2}		)
          self.assertDictEqual(MockClass(a=2			,     b=lowerCamelCase		).to_kwargs()			,     {"a": 2, "b": True}		)
          self.assertDictEqual(MockClass(a=2			,     c=2.25		).to_kwargs()			,     {"a": 2, "c": 2.25}		)


     @require_cuda
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							GradScalerKwargs(init_scale=1024			,     growth_factor=2		)
          AcceleratorState._reset_state()
          __A	:       Optional[int]      =							Accelerator(mixed_precision="fp16"			,     kwargs_handlers=[scaler_handler]		)
          print(accelerator.use_fpaa		)
          __A	:       Optional[int]      =							accelerator.scaler

          # Check the kwargs have been applied
          self.assertEqual(scaler._init_scale			,     1024.0		)
          self.assertEqual(scaler._growth_factor			,     2.0		)

          # Check the other values are at the default
          self.assertEqual(scaler._backoff_factor			,     0.5		)
          self.assertEqual(scaler._growth_interval			,     2000		)
          self.assertEqual(scaler._enabled			,     lowerCamelCase		)


     @require_multi_gpu
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       List[str]      =							["torchrun", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__		)]
          execute_subprocess_async(lowerCamelCase			,     env=os.environ.copy()		)


if __name__ == "__main__":
      _UpperCamelCase 							=  DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
      _UpperCamelCase 							=  Accelerator(kwargs_handlers=[ddp_scaler])
      _UpperCamelCase 							=  torch.nn.Linear(100, 200)
      _UpperCamelCase 							=  accelerator.prepare(model)

      # Check the values changed in kwargs
      _UpperCamelCase 							=  """"""
      _UpperCamelCase 							=  model.bucket_bytes_cap // (1024 * 1024)
      if observed_bucket_cap_map != 15:
            error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
      if model.find_unused_parameters is not True:
            error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

      # Check the values of the defaults
      if model.dim != 0:
            error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
      if model.broadcast_buffers is not True:
            error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
      if model.gradient_as_bucket_view is not False:
            error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)





'''simple docstring'''


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      _UpperCamelCase 							=  argparse.ArgumentParser(
          description=(
              """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
              """ Distillation"""
          )
      )
      parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
      parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
      parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
      parser.add_argument("""--vocab_transform""", action="""store_true""")
      _UpperCamelCase 							=  parser.parse_args()

      if args.model_type == "roberta":
            _UpperCamelCase 							=  RobertaForMaskedLM.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """roberta"""
      elif args.model_type == "gpt2":
            _UpperCamelCase 							=  GPTaLMHeadModel.from_pretrained(args.model_name)
            _UpperCamelCase 							=  """transformer"""

      _UpperCamelCase 							=  model.state_dict()
      _UpperCamelCase 							=  {}

      # Embeddings #
      if args.model_type == "gpt2":
            for param_name in ["wte.weight", "wpe.weight"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.{param_name}"""]
      else:
            for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.{w}.weight"""
                  _UpperCamelCase 							=  state_dict[param_name]
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  F"""{prefix}.embeddings.LayerNorm.{w}"""
                  _UpperCamelCase 							=  state_dict[param_name]

    # Transformer Blocks #
      _UpperCamelCase 							=  0
      for teacher_idx in [0, 2, 4, 7, 9, 11]:
            if args.model_type == "gpt2":
                  for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                        for w in ["weight", "bias"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
                              ]
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
            else:
                  for layer in [
                      "attention.self.query",
                      "attention.self.key",
                      "attention.self.value",
                      "attention.output.dense",
                      "attention.output.LayerNorm",
                      "intermediate.dense",
                      "output.dense",
                      "output.LayerNorm",
                  ]:
                        for w in ["weight", "bias"]:
                              _UpperCamelCase 							=  state_dict[
                                  F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
                              ]
            std_idx += 1

      # Language Modeling Head ###s
      if args.model_type == "roberta":
            for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{layer}"""]
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        _UpperCamelCase 							=  state_dict[F"""lm_head.dense.{w}"""]
                        _UpperCamelCase 							=  state_dict[F"""lm_head.layer_norm.{w}"""]
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  _UpperCamelCase 							=  state_dict[F"""{prefix}.ln_f.{w}"""]
            _UpperCamelCase 							=  state_dict["""lm_head.weight"""]

      print(F"""N layers selected for distillation: {std_idx}""")
      print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

      print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
      torch.save(compressed_sd, args.dump_checkpoint)

