from typing import List, Optional, Union

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


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Dict       						="""informer"""
				UpperCamelCase__			:   int       						={
				    """hidden_size""": """d_model""",
				    """num_attention_heads""": """encoder_attention_heads""",
				    """num_hidden_layers""": """encoder_layers""",
				}




				def __init__(  self   :  Union[str, Any],   __lowercase   :  Optional[int] = None,   __lowercase   :  Optional[int] = None,   __lowercase   :  str = "student_t",   __lowercase   :  str = "nll",   __lowercase   :  int = 1,   __lowercase   :  List[int] = None,   __lowercase   :  Optional[Union[str, bool]] = "mean",   __lowercase   :  int = 0,   __lowercase   :  int = 0,   __lowercase   :  int = 0,   __lowercase   :  int = 0,   __lowercase   :  Optional[List[int]] = None,   __lowercase   :  Optional[List[int]] = None,   __lowercase   :  int = 64,   __lowercase   :  int = 32,   __lowercase   :  int = 32,   __lowercase   :  int = 2,   __lowercase   :  int = 2,   __lowercase   :  int = 2,   __lowercase   :  int = 2,   __lowercase   :  bool = True,   __lowercase   :  str = "gelu",   __lowercase   :  float = 0.05,   __lowercase   :  float = 0.1,   __lowercase   :  float = 0.1,   __lowercase   :  float = 0.1,   __lowercase   :  float = 0.1,   __lowercase   :  int = 100,   __lowercase   :  float = 0.02,   __lowercase   :  Any=True,   __lowercase   :  str = "prob",   __lowercase   :  int = 5,   __lowercase   :  bool = True,   **__lowercase   :  str,   ):
							# time series specific configuration
							lowercase__   					=			prediction_length
							lowercase__   					=			context_length or prediction_length
							lowercase__   					=			distribution_output
							lowercase__   					=			loss
							lowercase__   					=			input_size
							lowercase__   					=			num_time_features
							lowercase__   					=			lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
							lowercase__   					=			scaling
							lowercase__   					=			num_dynamic_real_features
							lowercase__   					=			num_static_real_features
							lowercase__   					=			num_static_categorical_features

							# set cardinality
							if cardinality and num_static_categorical_features > 0:
										if len(__lowercase  ) != num_static_categorical_features:
													raise ValueError(
													    "The cardinality should be a list of the same length as `num_static_categorical_features`"  )
										lowercase__   					=			cardinality
							else:
										lowercase__   					=			[0]

							# set embedding_dimension
							if embedding_dimension and num_static_categorical_features > 0:
										if len(__lowercase  ) != num_static_categorical_features:
													raise ValueError(
													    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"  )
										lowercase__   					=			embedding_dimension
							else:
										lowercase__   					=			[min(50,   (cat + 1) // 2  ) for cat in self.cardinality]

							lowercase__   					=			num_parallel_samples

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

							lowercase__   					=			dropout
							lowercase__   					=			attention_dropout
							lowercase__   					=			activation_dropout
							lowercase__   					=			encoder_layerdrop
							lowercase__   					=			decoder_layerdrop

							lowercase__   					=			activation_function
							lowercase__   					=			init_std

							lowercase__   					=			use_cache

							# Informer
							lowercase__   					=			attention_type
							lowercase__   					=			sampling_factor
							lowercase__   					=			distil

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




				@property
				def 							A__ (  self   :  Tuple  ):
							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
							)









import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase_						=       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_						=       parser.parse_args()

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

							lowercase_						=       model.state_dict()
							lowercase_						=       {}

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

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




