






"""simple docstring"""
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 _lowerCAmelCase						(    unittest.TestCase       ):







						def 			A      (    self							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    inspect.getfile(accelerate.test_utils							)
										_SCREAMING_SNAKE_CASE   :		str       		=    os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['scripts', 'test_script.py']							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    os.path.sep.join(
										    mod_file.split(os.path.sep							)[:-1] + ['scripts', 'test_distributed_data_loop.py']							)
										_SCREAMING_SNAKE_CASE   :		int       		=    os.path.sep.join(mod_file.split(os.path.sep							)[:-1] + ['scripts', 'test_ops.py']							)







						@require_multi_gpu
						def 			A      (    self							)		->							Optional[Any]:
										print(F"""Found {torch.cuda.device_count()} devices."""							)
										_SCREAMING_SNAKE_CASE   :		str       		=    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", self.test_file_path]
										with patch_environment(omp_num_threads=1							):
														execute_subprocess_async(lowerCAmelCase_   ,				env=os.environ.copy()							)







						@require_multi_gpu
						def 			A      (    self							)		->							Tuple:
										print(F"""Found {torch.cuda.device_count()} devices."""							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    ['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(lowerCAmelCase_   ,				env=os.environ.copy()							)







						@require_multi_gpu
						def 			A      (    self							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__							)]
										with patch_environment(omp_num_threads=1							):
														execute_subprocess_async(lowerCAmelCase_   ,				env=os.environ.copy()							)







						@require_multi_gpu
						def 			A      (    self							)		->							Optional[int]:
										print(F"""Found {torch.cuda.device_count()} devices, using 2 devices only"""							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ['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(lowerCAmelCase_   ,				env=os.environ.copy()							)


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

			lowerCAmelCase__    						=			''

			lowerCAmelCase__    						=			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)."

			lowerCAmelCase__    						=			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."
			lowerCAmelCase__    						=			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)









"""simple docstring"""
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
			lowerCAmelCase__    						=			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')
			lowerCAmelCase__    						=			parser.parse_args()

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

			lowerCAmelCase__    						=			model.state_dict()
			lowerCAmelCase__    						=			{}

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

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

