





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def        __lowerCamelCase (							__a       :Any   ):






		"""simple docstring"""

		if "model" in orig_key:
				A__  				=       orig_key.replace("""model."""   ,					""""""   )
		if "norm1" in orig_key:
				A__  				=       orig_key.replace("""norm1"""   ,					"""attention.output.LayerNorm"""   )
		if "norm2" in orig_key:
				A__  				=       orig_key.replace("""norm2"""   ,					"""output.LayerNorm"""   )
		if "norm" in orig_key:
				A__  				=       orig_key.replace("""norm"""   ,					"""LayerNorm"""   )
		if "transformer" in orig_key:
				A__  				=       orig_key.split("""."""   )[0].split("""_"""   )[-1]
				A__  				=       orig_key.replace(F'transformer_{layer_num}'   ,					F'encoder.layer.{layer_num}'   )
		if "mha.attn" in orig_key:
				A__  				=       orig_key.replace("""mha.attn"""   ,					"""attention.self"""   )
		if "mha" in orig_key:
				A__  				=       orig_key.replace("""mha"""   ,					"""attention"""   )
		if "W_q" in orig_key:
				A__  				=       orig_key.replace("""W_q"""   ,					"""self.query"""   )
		if "W_k" in orig_key:
				A__  				=       orig_key.replace("""W_k"""   ,					"""self.key"""   )
		if "W_v" in orig_key:
				A__  				=       orig_key.replace("""W_v"""   ,					"""self.value"""   )
		if "ff1" in orig_key:
				A__  				=       orig_key.replace("""ff1"""   ,					"""intermediate.dense"""   )
		if "ff2" in orig_key:
				A__  				=       orig_key.replace("""ff2"""   ,					"""output.dense"""   )
		if "ff" in orig_key:
				A__  				=       orig_key.replace("""ff"""   ,					"""output.dense"""   )
		if "mlm_class" in orig_key:
				A__  				=       orig_key.replace("""mlm.mlm_class"""   ,					"""cls.predictions.decoder"""   )
		if "mlm" in orig_key:
				A__  				=       orig_key.replace("""mlm"""   ,					"""cls.predictions.transform"""   )
		if "cls" not in orig_key:
				A__  				=       """yoso.""" + orig_key

		return orig_key







def        __lowerCamelCase (							__a       :Union[str, Any]   ,					__a       :Union[str, Any]   ):






		"""simple docstring"""

		for key in orig_state_dict.copy().keys():
				A__  				=       orig_state_dict.pop(__a   )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						A__  				=       val

		A__  				=       orig_state_dict["""cls.predictions.decoder.bias"""]
		A__  				=       torch.arange(__a   ).expand((1, -1)   ) + 2

		return orig_state_dict







def        __lowerCamelCase (							__a       :Optional[int]   ,					__a       :int   ,					__a       :List[Any]   ):






		"""simple docstring"""

		A__  				=       torch.load(__a   ,					map_location="""cpu"""   )["""model_state_dict"""]
		A__  				=       YosoConfig.from_json_file(__a   )
		A__  				=       YosoForMaskedLM(__a   )

		A__  				=       convert_checkpoint_helper(config.max_position_embeddings   ,					__a   )

		print(model.load_state_dict(__a   )   )
		model.eval()
		model.save_pretrained(__a   )

		print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					A   : Tuple			=				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
					)
					parser.add_argument(
					    '''--config_file''',
					    default=None,
					    type=str,
					    required=True,
					    help='''The json file for YOSO model config.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
					)
					A   : Any			=				parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










from typing import List, Optional, Union

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


A   : Optional[Any]			=				logging.get_logger(__name__)

A   : List[str]			=				{
    '''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   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Any          =   '''time_series_transformer'''
			__lowerCamelCase     :	Dict          =   {
			    '''hidden_size''': '''d_model''',
			    '''num_attention_heads''': '''encoder_attention_heads''',
			    '''num_hidden_layers''': '''encoder_layers''',
			}






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







					"""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(__lowerCAmelCase       ) != 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(__lowerCAmelCase       ) != 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(__lowerCAmelCase       ) + 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=__lowerCAmelCase							,     **__lowerCAmelCase       )







			@property
			def        a_	(      self       :   List[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
					)



