"""simple docstring"""







import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

						SCREAMING_SNAKE_CASE									=						model.state_dict()
						SCREAMING_SNAKE_CASE									=						{}

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

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







from typing import Any


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	if not input_list:
		return []
	_lowercase :    List[str]							=							[input_list.count(__UpperCAmelCase			) for value in input_list]
	_lowercase :    Optional[int]							=							max(__UpperCAmelCase			)  # Gets the maximum count in the input list.
	# Gets values of modes
	return sorted({input_list[i] for i, value in enumerate(__UpperCAmelCase			) if value == y}			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



