



"""simple docstring"""







import argparse

import torch

from transformers import (
    SpeechTaConfig,
    SpeechTaFeatureExtractor,
    SpeechTaForSpeechToSpeech,
    SpeechTaForSpeechToText,
    SpeechTaForTextToSpeech,
    SpeechTaProcessor,
    SpeechTaTokenizer,
    logging,
)
from transformers.tokenization_utils import AddedToken


logging.set_verbosity_info()
lowercase__		:				Union[str, Any]          =	logging.get_logger("""transformers.models.speecht5""")

lowercase__		:				Union[str, Any]          =	{
    """speech_encoder_prenet.layer_norm""": """speecht5.encoder.prenet.feature_projection.layer_norm""",
    """speech_encoder_prenet.post_extract_proj""": """speecht5.encoder.prenet.feature_projection.projection""",
    """speech_encoder_prenet.pos_conv.0""": """speecht5.encoder.prenet.pos_conv_embed.conv""",
    """speech_encoder_prenet.mask_emb""": """speecht5.encoder.prenet.masked_spec_embed""",
}
lowercase__		:				Optional[Any]          =	{
    """text_encoder_prenet.encoder_prenet.0""": """speecht5.encoder.prenet.embed_tokens""",
    """text_encoder_prenet.encoder_prenet.1.alpha""": """speecht5.encoder.prenet.encode_positions.alpha""",
}
lowercase__		:				Tuple          =	{
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0""": """speecht5.decoder.prenet.layers.0""",
    """speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0""": """speecht5.decoder.prenet.layers.1""",
    """speech_decoder_prenet.decoder_prenet.0.1""": """speecht5.decoder.prenet.final_layer""",
    """speech_decoder_prenet.decoder_prenet.1.alpha""": """speecht5.decoder.prenet.encode_positions.alpha""",
    """speech_decoder_prenet.spkembs_layer.0""": """speecht5.decoder.prenet.speaker_embeds_layer""",
}
lowercase__		:				Tuple          =	{
    """speech_decoder_postnet.feat_out""": """speech_decoder_postnet.feat_out""",
    """speech_decoder_postnet.prob_out""": """speech_decoder_postnet.prob_out""",
    """speech_decoder_postnet.postnet.postnet.0.0""": """speech_decoder_postnet.layers.0.conv""",
    """speech_decoder_postnet.postnet.postnet.0.1""": """speech_decoder_postnet.layers.0.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.1.0""": """speech_decoder_postnet.layers.1.conv""",
    """speech_decoder_postnet.postnet.postnet.1.1""": """speech_decoder_postnet.layers.1.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.2.0""": """speech_decoder_postnet.layers.2.conv""",
    """speech_decoder_postnet.postnet.postnet.2.1""": """speech_decoder_postnet.layers.2.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.3.0""": """speech_decoder_postnet.layers.3.conv""",
    """speech_decoder_postnet.postnet.postnet.3.1""": """speech_decoder_postnet.layers.3.batch_norm""",
    """speech_decoder_postnet.postnet.postnet.4.0""": """speech_decoder_postnet.layers.4.conv""",
    """speech_decoder_postnet.postnet.postnet.4.1""": """speech_decoder_postnet.layers.4.batch_norm""",
}
lowercase__		:				Dict          =	{
    """text_decoder_prenet.embed_tokens""": """speecht5.decoder.prenet.embed_tokens""",
}
lowercase__		:				Union[str, Any]          =	{
    """text_decoder_postnet.output_projection""": """text_decoder_postnet.lm_head""",
}
lowercase__		:				List[str]          =	{
    """encoder.layers.*.self_attn.k_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj""",
    """encoder.layers.*.self_attn.v_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj""",
    """encoder.layers.*.self_attn.q_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj""",
    """encoder.layers.*.self_attn.out_proj""": """speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj""",
    """encoder.layers.*.self_attn_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.layer_norm""",
    """encoder.layers.*.fc1""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense""",
    """encoder.layers.*.fc2""": """speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense""",
    """encoder.layers.*.final_layer_norm""": """speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """speecht5.encoder.wrapped_encoder.layer_norm""",
    """encoder.pos_emb.pe_k""": """speecht5.encoder.wrapped_encoder.embed_positions.pe_k""",
}
lowercase__		:				Optional[int]          =	{
    """decoder.layers.*.self_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj""",
    """decoder.layers.*.self_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj""",
    """decoder.layers.*.self_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj""",
    """decoder.layers.*.self_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj""",
    """decoder.layers.*.self_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm""",
    """decoder.layers.*.encoder_attn.k_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj""",
    """decoder.layers.*.encoder_attn.v_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj""",
    """decoder.layers.*.encoder_attn.q_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj""",
    """decoder.layers.*.encoder_attn.out_proj""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj""",
    """decoder.layers.*.encoder_attn_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm""",
    """decoder.layers.*.fc1""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense""",
    """decoder.layers.*.fc2""": """speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense""",
    """decoder.layers.*.final_layer_norm""": """speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm""",
}
lowercase__		:				int          =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_TEXT_DECODER_PRENET,
    **MAPPING_TEXT_DECODER_POSTNET,
}
lowercase__		:				Dict          =	{
    **MAPPING_TEXT_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase__		:				Tuple          =	{
    **MAPPING_SPEECH_ENCODER_PRENET,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
    **MAPPING_SPEECH_DECODER_PRENET,
    **MAPPING_SPEECH_DECODER_POSTNET,
}
lowercase__		:				str          =	[]
lowercase__		:				Union[str, Any]          =	[
    """encoder.version""",
    """encoder.layers.*.norm_k.weight""",
    """encoder.layers.*.norm_k.bias""",
    """decoder.version""",
    """decoder.layers.*.norm_k.weight""",
    """decoder.layers.*.norm_k.bias""",
    """decoder.pos_emb.pe_k""",
    """speech_encoder_prenet.embed_positions._float_tensor""",
    """text_decoder_prenet.embed_positions._float_tensor""",
]
lowercase__		:				List[Any]          =	IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """speech_decoder_prenet.*""",
    """speech_decoder_postnet.*""",
]
lowercase__		:				Optional[Any]          =	IGNORE_KEYS + [
    """encoder.proj""",
    """speech_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]
lowercase__		:				Union[str, Any]          =	IGNORE_KEYS + [
    """encoder.proj""",
    """text_encoder_prenet.*""",
    """text_decoder_prenet.*""",
    """text_decoder_postnet.*""",
]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				int						)						->   int:

  """simple docstring"""




  for attribute in key.split('.'						):
    lowerCAmelCase_   :				Optional[Any]								= getattr(lowerCAmelCase__    ,   lowerCAmelCase__						)

  if weight_type is not None:
    lowerCAmelCase_   :				Optional[int]								= getattr(lowerCAmelCase__    ,   lowerCAmelCase__						).shape
  else:
    lowerCAmelCase_   :				Tuple								= hf_pointer.shape

  if hf_shape != value.shape:
    raise ValueError(
        f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
        f" {value.shape} for {full_name}"						)

  if weight_type == "weight":
    lowerCAmelCase_   :				str								= value
  elif weight_type == "weight_g":
    lowerCAmelCase_   :				Tuple								= value
  elif weight_type == "weight_v":
    lowerCAmelCase_   :				Union[str, Any]								= value
  elif weight_type == "bias":
    lowerCAmelCase_   :				Union[str, Any]								= value
  elif weight_type == "running_mean":
    lowerCAmelCase_   :				List[Any]								= value
  elif weight_type == "running_var":
    lowerCAmelCase_   :				Optional[Any]								= value
  elif weight_type == "num_batches_tracked":
    lowerCAmelCase_   :				Dict								= value
  else:
    lowerCAmelCase_   :				List[Any]								= value

  logger.info(f"{key + ('.' + weight_type if weight_type is not None else '')} was initialized from {full_name}."						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Optional[Any]						)						->   int:

  """simple docstring"""




  for key in ignore_keys:
    if key.endswith('.*'						):
      if name.startswith(key[:-1]						):
        return True
    elif ".*." in key:
      lowerCAmelCase_						,lowerCAmelCase_   :				Optional[Any]								= key.split('.*.'						)
      if prefix in name and suffix in name:
        return True
    elif key in name:
      return True
  return False

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				str						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= []

  if task == "s2t":
    lowerCAmelCase_   :				int								= hf_model.speechta.encoder.prenet.feature_encoder
    lowerCAmelCase_   :				Optional[Any]								= MAPPING_S2T
    lowerCAmelCase_   :				Optional[Any]								= IGNORE_KEYS_S2T
  elif task == "t2s":
    lowerCAmelCase_   :				Any								= None
    lowerCAmelCase_   :				Dict								= MAPPING_T2S
    lowerCAmelCase_   :				str								= IGNORE_KEYS_T2S
  elif task == "s2s":
    lowerCAmelCase_   :				Union[str, Any]								= hf_model.speechta.encoder.prenet.feature_encoder
    lowerCAmelCase_   :				Tuple								= MAPPING_S2S
    lowerCAmelCase_   :				Any								= IGNORE_KEYS_S2S
  else:
    raise ValueError(f"Unsupported task: {task}"						)

  for name, value in fairseq_dict.items():
    if should_ignore(lowerCAmelCase__    ,   lowerCAmelCase__						):
      logger.info(f"{name} was ignored"						)
      continue

    lowerCAmelCase_   :				Optional[Any]								= False
    if "conv_layers" in name:
      load_conv_layer(
          lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   hf_model.config.feat_extract_norm == 'group'    ,   )
      lowerCAmelCase_   :				List[Any]								= True
    else:
      for key, mapped_key in MAPPING.items():
        # mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key

        if "*" in key:
          lowerCAmelCase_						,lowerCAmelCase_   :				Optional[Any]								= key.split('.*.'						)
          if prefix in name and suffix in name:
            lowerCAmelCase_   :				Optional[int]								= suffix

                # if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
        if key in name:
          lowerCAmelCase_   :				Tuple								= True
          if "*" in mapped_key:
            lowerCAmelCase_   :				List[Any]								= name.split(lowerCAmelCase__						)[0].split('.'						)[-2]
            lowerCAmelCase_   :				Dict								= mapped_key.replace('*'    ,   lowerCAmelCase__						)
          if "weight_g" in name:
            lowerCAmelCase_   :				Optional[int]								= 'weight_g'
          elif "weight_v" in name:
            lowerCAmelCase_   :				Any								= 'weight_v'
          elif "bias" in name:
            lowerCAmelCase_   :				List[Any]								= 'bias'
          elif "weight" in name:
            lowerCAmelCase_   :				Optional[Any]								= 'weight'
          elif "running_mean" in name:
            lowerCAmelCase_   :				Optional[Any]								= 'running_mean'
          elif "running_var" in name:
            lowerCAmelCase_   :				Optional[Any]								= 'running_var'
          elif "num_batches_tracked" in name:
            lowerCAmelCase_   :				Union[str, Any]								= 'num_batches_tracked'
          else:
            lowerCAmelCase_   :				Tuple								= None
          set_recursively(lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)
        continue
    if not is_used:
      unused_weights.append(lowerCAmelCase__						)

  logger.warning(f"Unused weights: {unused_weights}"						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]    ,   lowerCAmelCase__      :				Union[str, Any]    ,   lowerCAmelCase__      :				Optional[int]    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				Tuple						)						->   List[Any]:

  """simple docstring"""




  lowerCAmelCase_   :				List[str]								= full_name.split('conv_layers.'						)[-1]
  lowerCAmelCase_   :				Optional[Any]								= name.split('.'						)
  lowerCAmelCase_   :				str								= int(items[0]						)
  lowerCAmelCase_   :				Any								= int(items[1]						)

  if type_id == 0:
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
        raise ValueError(
            f"{full_name} has size {value.shape}, but"
            f" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."						)
      lowerCAmelCase_   :				Optional[int]								= value
      logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
        raise ValueError(
            f"{full_name} has size {value.shape}, but"
            f" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."						)
      lowerCAmelCase_   :				List[Any]								= value
      logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
  elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
    if "bias" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
        raise ValueError(
            f"{full_name} has size {value.shape}, but"
            f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."						)
      lowerCAmelCase_   :				List[str]								= value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
    elif "weight" in name:
      if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
        raise ValueError(
            f"{full_name} has size {value.shape}, but"
            f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."						)
      lowerCAmelCase_   :				List[str]								= value
      logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
  else:
    unused_weights.append(lowerCAmelCase__						)

@torch.no_grad()
def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				Dict    ,   lowerCAmelCase__      :				int    ,   lowerCAmelCase__      :				Optional[int]=None    ,   lowerCAmelCase__      :				Any=None    ,   lowerCAmelCase__      :				Tuple=None    ,   )						->   List[Any]:

  """simple docstring"""




  if config_path is not None:
    lowerCAmelCase_   :				int								= SpeechTaConfig.from_pretrained(lowerCAmelCase__						)
  else:
    lowerCAmelCase_   :				int								= SpeechTaConfig()

  if task == "s2t":
    lowerCAmelCase_   :				Dict								= config.max_text_positions
    lowerCAmelCase_   :				Dict								= SpeechTaForSpeechToText(lowerCAmelCase__						)
  elif task == "t2s":
    lowerCAmelCase_   :				Any								= 1876
    lowerCAmelCase_   :				int								= 600
    lowerCAmelCase_   :				Dict								= config.max_speech_positions
    lowerCAmelCase_   :				Any								= SpeechTaForTextToSpeech(lowerCAmelCase__						)
  elif task == "s2s":
    lowerCAmelCase_   :				Dict								= 1876
    lowerCAmelCase_   :				str								= config.max_speech_positions
    lowerCAmelCase_   :				str								= SpeechTaForSpeechToSpeech(lowerCAmelCase__						)
  else:
    raise ValueError(f"Unknown task name: {task}"						)

  if vocab_path:
    lowerCAmelCase_   :				Union[str, Any]								= SpeechTaTokenizer(lowerCAmelCase__    ,   model_max_length=config.max_text_positions						)

    # Mask token behaves like a normal word, i.e. include the space before it
    lowerCAmelCase_   :				Any								= AddedToken('<mask>'    ,   lstrip=lowerCAmelCase__    ,   rstrip=lowerCAmelCase__						)
    lowerCAmelCase_   :				Dict								= mask_token
    tokenizer.add_special_tokens({'mask_token': mask_token}						)
    tokenizer.add_tokens(['<ctc_blank>']						)

  lowerCAmelCase_   :				str								= SpeechTaFeatureExtractor()
  lowerCAmelCase_   :				Optional[int]								= SpeechTaProcessor(tokenizer=lowerCAmelCase__    ,   feature_extractor=lowerCAmelCase__						)
  processor.save_pretrained(lowerCAmelCase__						)

  lowerCAmelCase_   :				List[Any]								= torch.load(lowerCAmelCase__						)
  recursively_load_weights(fairseq_checkpoint['model']    ,   lowerCAmelCase__    ,   lowerCAmelCase__						)

  model.save_pretrained(lowerCAmelCase__						)

  if repo_id:
    print('Pushing to the hub...'						)
    processor.push_to_hub(lowerCAmelCase__						)
    model.push_to_hub(lowerCAmelCase__						)


if __name__ == "__main__":
     lowercase__		:				str          =	argparse.ArgumentParser()
     parser.add_argument(
         """--task""",
         default="""s2t""",
         type=str,
         help="""Type of the SpeechT5 model you'd like to convert. Should be one of 's2t', 't2s', 's2s'.""",
     )
     parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to fairseq checkpoint""")
     parser.add_argument("""--vocab_path""", default=None, type=str, help="""Path to SentencePiece model""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     parser.add_argument(
         """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
     )

     lowercase__		:				int          =	parser.parse_args()
     convert_speechta_checkpoint(
         args.task,
         args.checkpoint_path,
         args.pytorch_dump_folder_path,
         args.config_path,
         args.vocab_path,
         args.push_to_hub,
     )










"""simple docstring"""







from typing import List, Optional, Union

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


lowercase__		:				Union[str, Any]          =	logging.get_logger(__name__)

lowercase__		:				List[str]          =	{
    """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       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """informer"""
      _SCREAMING_SNAKE_CASE  				=     {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
          """num_hidden_layers""": """encoder_layers""",
      }



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      str = "student_t"    ,			SCREAMING_SNAKE_CASE_				:      str = "nll"    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			SCREAMING_SNAKE_CASE_				:      List[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, bool]] = "mean"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      int = 6_4    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      str = "gelu"    ,			SCREAMING_SNAKE_CASE_				:      float = 0.05    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      int = 1_0_0    ,			SCREAMING_SNAKE_CASE_				:      float = 0.02    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      str = "prob"    ,			SCREAMING_SNAKE_CASE_				:      int = 5    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			**SCREAMING_SNAKE_CASE_				:      int    ,			):
        # time series specific configuration
        lowerCAmelCase_   :				Dict								= prediction_length
        lowerCAmelCase_   :				List[str]								= context_length or prediction_length
        lowerCAmelCase_   :				List[Any]								= distribution_output
        lowerCAmelCase_   :				int								= loss
        lowerCAmelCase_   :				Optional[int]								= input_size
        lowerCAmelCase_   :				Tuple								= num_time_features
        lowerCAmelCase_   :				List[str]								= lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
        lowerCAmelCase_   :				int								= scaling
        lowerCAmelCase_   :				List[Any]								= num_dynamic_real_features
        lowerCAmelCase_   :				Union[str, Any]								= num_static_real_features
        lowerCAmelCase_   :				Optional[int]								= num_static_categorical_features

        # set cardinality
        if cardinality and num_static_categorical_features > 0:
          if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
            raise ValueError(
                'The cardinality should be a list of the same length as `num_static_categorical_features`'       )
          lowerCAmelCase_   :				str								= cardinality
        else:
          lowerCAmelCase_   :				Any								= [0]

        # set embedding_dimension
        if embedding_dimension and num_static_categorical_features > 0:
          if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
            raise ValueError(
                'The embedding dimension should be a list of the same length as `num_static_categorical_features`'       )
          lowerCAmelCase_   :				Optional[int]								= embedding_dimension
        else:
          lowerCAmelCase_   :				Union[str, Any]								= [min(5_0    ,			(cat + 1) // 2       ) for cat in self.cardinality]

        lowerCAmelCase_   :				Optional[int]								= num_parallel_samples

        # Transformer architecture configuration
        lowerCAmelCase_   :				Any								= input_size * len(self.lags_sequence       ) + self._number_of_features
        lowerCAmelCase_   :				Any								= d_model
        lowerCAmelCase_   :				Union[str, Any]								= encoder_attention_heads
        lowerCAmelCase_   :				Optional[Any]								= decoder_attention_heads
        lowerCAmelCase_   :				Any								= encoder_ffn_dim
        lowerCAmelCase_   :				List[str]								= decoder_ffn_dim
        lowerCAmelCase_   :				Optional[Any]								= encoder_layers
        lowerCAmelCase_   :				Tuple								= decoder_layers

        lowerCAmelCase_   :				Optional[int]								= dropout
        lowerCAmelCase_   :				Dict								= attention_dropout
        lowerCAmelCase_   :				int								= activation_dropout
        lowerCAmelCase_   :				Dict								= encoder_layerdrop
        lowerCAmelCase_   :				str								= decoder_layerdrop

        lowerCAmelCase_   :				Union[str, Any]								= activation_function
        lowerCAmelCase_   :				Union[str, Any]								= init_std

        lowerCAmelCase_   :				Union[str, Any]								= use_cache

        # Informer
        lowerCAmelCase_   :				Optional[int]								= attention_type
        lowerCAmelCase_   :				Any								= sampling_factor
        lowerCAmelCase_   :				int								= distil

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





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        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
        )





