'''simple docstring'''




import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
						_lowercase		:				int	       =		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		:				str	       =		parser.parse_args()

						if args.model_type == "roberta":
												_lowercase		:				Optional[Any]	       =		RobertaForMaskedLM.from_pretrained(args.model_name)
												_lowercase		:				int	       =		"roberta"
						elif args.model_type == "gpt2":
												_lowercase		:				List[str]	       =		GPTaLMHeadModel.from_pretrained(args.model_name)
												_lowercase		:				Optional[int]	       =		"transformer"

						_lowercase		:				Tuple	       =		model.state_dict()
						_lowercase		:				Any	       =		{}

						# Embeddings #
						if args.model_type == "gpt2":
												for param_name in ["wte.weight", "wpe.weight"]:
																		_lowercase		:				Optional[int]	       =		state_dict[f"""{prefix}.{param_name}"""]
						else:
												for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																		_lowercase		:				List[Any]	       =		f"""{prefix}.embeddings.{w}.weight"""
																		_lowercase		:				List[str]	       =		state_dict[param_name]
												for w in ["weight", "bias"]:
																		_lowercase		:				Optional[Any]	       =		f"""{prefix}.embeddings.LayerNorm.{w}"""
																		_lowercase		:				Union[str, Any]	       =		state_dict[param_name]

    # Transformer Blocks #
						_lowercase		:				List[str]	       =		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		:				str	       =		state_dict[
																														    f"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
																														]
																		_lowercase		:				List[str]	       =		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		:				Optional[int]	       =		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		:				Any	       =		state_dict[f"""{layer}"""]
												if args.vocab_transform:
																		for w in ["weight", "bias"]:
																								_lowercase		:				Dict	       =		state_dict[f"""lm_head.dense.{w}"""]
																								_lowercase		:				str	       =		state_dict[f"""lm_head.layer_norm.{w}"""]
						elif args.model_type == "gpt2":
												for w in ["weight", "bias"]:
																		_lowercase		:				str	       =		state_dict[f"""{prefix}.ln_f.{w}"""]
												_lowercase		:				List[Any]	       =		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)






'''simple docstring'''




import pytest

import datasets.config
from datasets.utils.info_utils import is_small_dataset




@pytest.mark.parametrize("""dataset_size"""   ,   [None, 400 * 2**20, 600 * 2**20]				)
@pytest.mark.parametrize("""input_in_memory_max_size"""   ,   ["""default""", 0, 100 * 2**20, 900 * 2**20]				)
def 							lowerCamelCase			(   UpperCAmelCase__ :      Union[str, Any]   ,   UpperCAmelCase__ :      List[Any]   ,   UpperCAmelCase__ :      str				)    ->   Any:
							if input_in_memory_max_size != "default":
														monkeypatch.setattr(datasets.config   ,   """IN_MEMORY_MAX_SIZE"""   ,   UpperCAmelCase__				)
							lowercase_      :    List[Any]       		= datasets.config.IN_MEMORY_MAX_SIZE
							if input_in_memory_max_size == "default":
														assert in_memory_max_size == 0
							else:
														assert in_memory_max_size == input_in_memory_max_size
							if dataset_size and in_memory_max_size:
														lowercase_      :    str       		= dataset_size < in_memory_max_size
							else:
														lowercase_      :    List[Any]       		= False
							lowercase_      :    Any       		= is_small_dataset(UpperCAmelCase__				)
							assert result == expected





