


"""simple docstring"""


# Copyright 2021 The HuggingFace Team. 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


a_       							=    "Create a default config file for Accelerate with only a few flags set."






def      a__ (				__lowercase="no"     ,			__lowercase = default_json_config_file     ,			__lowercase = False   )	->	Any:
				_A					  =			Path(__lowercase   )
				path.parent.mkdir(parents=__lowercase     ,			exist_ok=__lowercase   )
				if path.exists():
								print(
								    f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""   )
								return False
				_A					  =			mixed_precision.lower()
				if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
								raise ValueError(
								    f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""   )
				_A					  =			{
				    "compute_environment": "LOCAL_MACHINE",
				    "mixed_precision": mixed_precision,
				}
				if torch.cuda.is_available():
								_A					  =			torch.cuda.device_count()
								_A					  =			num_gpus
								_A					  =			False
								if num_gpus > 1:
												_A					  =			"MULTI_GPU"
								else:
												_A					  =			"NO"
				elif is_xpu_available() and use_xpu:
								_A					  =			torch.xpu.device_count()
								_A					  =			num_xpus
								_A					  =			False
								if num_xpus > 1:
												_A					  =			"MULTI_XPU"
								else:
												_A					  =			"NO"
				elif is_npu_available():
								_A					  =			torch.npu.device_count()
								_A					  =			num_npus
								_A					  =			False
								if num_npus > 1:
												_A					  =			"MULTI_NPU"
								else:
												_A					  =			"NO"
				else:
								_A					  =			0
								_A					  =			True
								_A					  =			1
								_A					  =			"NO"
				_A					  =			ClusterConfig(**__lowercase   )
				config.to_json_file(__lowercase   )
				return path






def      a__ (				__lowercase     ,			__lowercase   )	->	Dict:
				_A					  =			parser.add_parser("default"     ,			parents=__lowercase     ,			help=__lowercase     ,			formatter_class=__lowercase   )
				parser.add_argument(
				    "--config_file"     ,			default=__lowercase     ,			help=(
				        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
				        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
				        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
				        "with 'huggingface'."
				    )     ,			dest="save_location"     ,			)

				parser.add_argument(
				    "--mixed_precision"     ,			choices=["no", "fp16", "bf16"]     ,			type=__lowercase     ,			help="Whether or not to use mixed precision training. "
				    "Choose between FP16 and BF16 (bfloat16) training. "
				    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."     ,			default="no"     ,			)
				parser.set_defaults(func=__lowercase   )
				return parser






def      a__ (				__lowercase   )	->	Any:
				_A					  =			write_basic_config(args.mixed_precision     ,			args.save_location   )
				if config_file:
								print(f"""accelerate configuration saved at {config_file}"""   )



"""simple docstring"""


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

		a_       							=    model.state_dict()
		a_       							=    {}

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

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