


"""simple docstring"""


from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_       							=    logging.get_logger(__name__)

a_       							=    {
    "huggingface/time-series-transformer-tourism-monthly": (
        "https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			'time_series_transformer'
		__UpperCamelCase           =			{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		    'num_hidden_layers': 'encoder_layers',
		}







		def __init__(					self      :       Tuple    ,   a__      :       Optional[int] = None    ,   a__      :       Optional[int] = None    ,   a__      :       str = "student_t"    ,   a__      :       str = "nll"    ,   a__      :       int = 1    ,   a__      :       List[int] = [1, 2, 3, 4, 5, 6, 7]    ,   a__      :       Optional[Union[str, bool]] = "mean"    ,   a__      :       int = 0    ,   a__      :       int = 0    ,   a__      :       int = 0    ,   a__      :       int = 0    ,   a__      :       Optional[List[int]] = None    ,   a__      :       Optional[List[int]] = None    ,   a__      :       int = 32    ,   a__      :       int = 32    ,   a__      :       int = 2    ,   a__      :       int = 2    ,   a__      :       int = 2    ,   a__      :       int = 2    ,   a__      :       bool = True    ,   a__      :       str = "gelu"    ,   a__      :       int = 64    ,   a__      :       float = 0.1    ,   a__      :       float = 0.1    ,   a__      :       float = 0.1    ,   a__      :       float = 0.1    ,   a__      :       float = 0.1    ,   a__      :       int = 1_00    ,   a__      :       float = 0.0_2    ,   a__      :       Tuple=True    ,   **a__      :       Optional[Any]    ,   )       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			prediction_length
						_A					  =			context_length or prediction_length
						_A					  =			distribution_output
						_A					  =			loss
						_A					  =			input_size
						_A					  =			num_time_features
						_A					  =			lags_sequence
						_A					  =			scaling
						_A					  =			num_dynamic_real_features
						_A					  =			num_static_real_features
						_A					  =			num_static_categorical_features
						if cardinality and num_static_categorical_features > 0:
										if len(a__		) != num_static_categorical_features:
														raise ValueError(
														    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
										_A					  =			cardinality
						else:
										_A					  =			[0]
						if embedding_dimension and num_static_categorical_features > 0:
										if len(a__		) != num_static_categorical_features:
														raise ValueError(
														    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
										_A					  =			embedding_dimension
						else:
										_A					  =			[min(50    ,   (cat + 1) // 2		) for cat in self.cardinality]
						_A					  =			num_parallel_samples

						# Transformer architecture configuration
						_A					  =			input_size * len(a__		) + self._number_of_features
						_A					  =			d_model
						_A					  =			encoder_attention_heads
						_A					  =			decoder_attention_heads
						_A					  =			encoder_ffn_dim
						_A					  =			decoder_ffn_dim
						_A					  =			encoder_layers
						_A					  =			decoder_layers

						_A					  =			dropout
						_A					  =			attention_dropout
						_A					  =			activation_dropout
						_A					  =			encoder_layerdrop
						_A					  =			decoder_layerdrop

						_A					  =			activation_function
						_A					  =			init_std

						_A					  =			use_cache

						super().__init__(is_encoder_decoder=a__    ,   **a__		)







		@property
		def  a_ (					self      :       Optional[Any]		)       ->						int:





						'''simple docstring'''







						return (
						    sum(self.embedding_dimension		)
						    + self.num_dynamic_real_features
						    + self.num_time_features
						    + self.num_static_real_features
						    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
						)



"""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)