



import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class 			_snake_case     (   unittest.TestCase):




				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			inspect.getfile(accelerate.test_utils  )
							lowercase__   					=			os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ["scripts", "test_script.py"]  )
							lowercase__   					=			os.path.sep.join(
							    mod_file.split(os.path.sep  )[:-1] + ["scripts", "test_distributed_data_loop.py"]  )
							lowercase__   					=			os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ["scripts", "test_ops.py"]  )




				@require_multi_gpu
				def 							A__ (  self   :  Tuple  ):
							print(F'''Found {torch.cuda.device_count()} devices.'''  )
							lowercase__   					=			["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
							with patch_environment(omp_num_threads=1  ):
										execute_subprocess_async(__lowercase,   env=os.environ.copy()  )




				@require_multi_gpu
				def 							A__ (  self   :  List[str]  ):
							print(F'''Found {torch.cuda.device_count()} devices.'''  )
							lowercase__   					=			["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
							print(F'''Command: {cmd}'''  )
							with patch_environment(omp_num_threads=1  ):
										execute_subprocess_async(__lowercase,   env=os.environ.copy()  )




				@require_multi_gpu
				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__  )]
							with patch_environment(omp_num_threads=1  ):
										execute_subprocess_async(__lowercase,   env=os.environ.copy()  )




				@require_multi_gpu
				def 							A__ (  self   :  int  ):
							print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''  )
							lowercase__   					=			["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
							with patch_environment(omp_num_threads=1,   cuda_visible_devices="0,1"  ):
										execute_subprocess_async(__lowercase,   env=os.environ.copy()  )


if __name__ == "__main__":
							lowercase_						=       Accelerator()
							lowercase_						=       (accelerator.state.process_index + 2, 10)
							lowercase_						=       torch.randint(0, 10, shape).to(accelerator.device)

							lowercase_						=       """"""

							lowercase_						=       accelerator.pad_across_processes(tensor)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
														error_msg += "Padding was not done with the right value (0)."

							lowercase_						=       accelerator.pad_across_processes(tensor, pad_first=True)
							if tensora.shape[0] != accelerator.state.num_processes + 1:
														error_msg += F"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
							lowercase_						=       accelerator.state.num_processes - accelerator.state.process_index - 1
							if not torch.equal(tensora[index:], tensor):
														error_msg += "Tensors have different values."
							if not torch.all(tensora[:index] == 0):
														error_msg += "Padding was not done with the right value (0)."

							# 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)









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase_						=       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""")
							lowercase_						=       parser.parse_args()

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

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

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

    # Transformer Blocks #
							lowercase_						=       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"]:
																																			lowercase_						=       state_dict[
																																			    F'{prefix}.h.{teacher_idx}.{layer}.{w}'
																																			]
																					lowercase_						=       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"]:
																																			lowercase_						=       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"]:
																					lowercase_						=       state_dict[F'{layer}']
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase_						=       state_dict[F'lm_head.dense.{w}']
																												lowercase_						=       state_dict[F'lm_head.layer_norm.{w}']
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase_						=       state_dict[F'{prefix}.ln_f.{w}']
														lowercase_						=       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)




