





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							int   		=		"marian"
   a						:							int   		=		["past_key_values"]
   a						:							Optional[Any]   		=		{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




   def __init__(    self,		__magic_name__=58101,		__magic_name__=None,		__magic_name__=1024,		__magic_name__=12,		__magic_name__=4096,		__magic_name__=16,		__magic_name__=12,		__magic_name__=4096,		__magic_name__=16,		__magic_name__=0.0,		__magic_name__=0.0,		__magic_name__=True,		__magic_name__=True,		__magic_name__="gelu",		__magic_name__=1024,		__magic_name__=0.1,		__magic_name__=0.0,		__magic_name__=0.0,		__magic_name__=0.02,		__magic_name__=58100,		__magic_name__=False,		__magic_name__=58100,		__magic_name__=0,		__magic_name__=0,		__magic_name__=True,		**__magic_name__,		) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				int          =      vocab_size
       UpperCamelCase__							:				Dict          =      decoder_vocab_size or vocab_size
       UpperCamelCase__							:				str          =      max_position_embeddings
       UpperCamelCase__							:				Optional[int]          =      d_model
       UpperCamelCase__							:				int          =      encoder_ffn_dim
       UpperCamelCase__							:				List[Any]          =      encoder_layers
       UpperCamelCase__							:				List[str]          =      encoder_attention_heads
       UpperCamelCase__							:				Optional[Any]          =      decoder_ffn_dim
       UpperCamelCase__							:				str          =      decoder_layers
       UpperCamelCase__							:				List[str]          =      decoder_attention_heads
       UpperCamelCase__							:				Optional[Any]          =      dropout
       UpperCamelCase__							:				Optional[Any]          =      attention_dropout
       UpperCamelCase__							:				Tuple          =      activation_dropout
       UpperCamelCase__							:				Any          =      activation_function
       UpperCamelCase__							:				int          =      init_std
       UpperCamelCase__							:				Dict          =      encoder_layerdrop
       UpperCamelCase__							:				Any          =      decoder_layerdrop
       UpperCamelCase__							:				Optional[int]          =      use_cache
       UpperCamelCase__							:				Union[str, Any]          =      encoder_layers
       UpperCamelCase__							:				Union[str, Any]          =      scale_embedding  # scale factor will be sqrt(d_model) if True
       UpperCamelCase__							:				List[Any]          =      share_encoder_decoder_embeddings
       super().__init__(
           pad_token_id=__magic_name__,		eos_token_id=__magic_name__,		is_encoder_decoder=__magic_name__,		decoder_start_token_id=__magic_name__,		forced_eos_token_id=__magic_name__,		**__magic_name__,		)






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:				int          =      OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]							)

           if self.use_past:
               UpperCamelCase__							:				List[Any]          =      {0: '''batch'''}
               UpperCamelCase__							:				str          =      {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
           else:
               UpperCamelCase__							:				Union[str, Any]          =      {0: '''batch''', 1: '''decoder_sequence'''}
               UpperCamelCase__							:				Optional[int]          =      {0: '''batch''', 1: '''decoder_sequence'''}

           if self.use_past:
               self.fill_with_past_key_values_(__magic_name__,		direction='''inputs'''							)
       elif self.task == "causal-lm":
           # TODO: figure this case out.
           UpperCamelCase__							:				str          =      OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
               ]							)
           if self.use_past:
               UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      self.num_layers
               for i in range(__magic_name__							):
                   UpperCamelCase__							:				Dict          =      {0: '''batch''', 2: '''past_sequence + sequence'''}
                   UpperCamelCase__							:				Dict          =      {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
           UpperCamelCase__							:				str          =      OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                   ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                   ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
               ]							)

       return common_inputs




   @property
   # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:				int          =      super().outputs
       else:
           UpperCamelCase__							:				str          =      super(__magic_name__,		self							).outputs
           if self.use_past:
               UpperCamelCase__   ,UpperCamelCase__							:				Dict          =      self.num_layers
               for i in range(__magic_name__							):
                   UpperCamelCase__							:				Any          =      {0: '''batch''', 2: '''past_sequence + sequence'''}
                   UpperCamelCase__							:				Optional[Any]          =      {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = -1,		__magic_name__ = -1,		__magic_name__ = False,		__magic_name__ = None,		) ->      Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				List[str]          =      self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							)

       # Generate decoder inputs
       UpperCamelCase__							:				int          =      seq_length if not self.use_past else 1
       UpperCamelCase__							:				List[str]          =      self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							)
       UpperCamelCase__							:				Dict          =      {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase__							:				Any          =      dict(**__magic_name__,		**__magic_name__							)

       if self.use_past:
           if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
           else:
               import torch
           UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      common_inputs['''input_ids'''].shape
           UpperCamelCase__							:				Tuple          =      common_inputs['''decoder_input_ids'''].shape[1]
           UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      self.num_attention_heads
           UpperCamelCase__							:				Dict          =      (
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           UpperCamelCase__							:				Optional[Any]          =      decoder_seq_length + 3
           UpperCamelCase__							:				Union[str, Any]          =      (
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )

           UpperCamelCase__							:				Any          =      torch.cat(
               [common_inputs['''decoder_attention_mask'''], torch.ones(__magic_name__,		__magic_name__							)],		dim=1							)

           UpperCamelCase__							:				Optional[int]          =      []
           # If the number of encoder and decoder layers are present in the model configuration, both are considered
           UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      self.num_layers
           UpperCamelCase__							:				Dict          =      min(__magic_name__,		__magic_name__							)
           UpperCamelCase__							:				List[str]          =      max(__magic_name__,		__magic_name__							) - min_num_layers
           UpperCamelCase__							:				Tuple          =      '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

           for _ in range(__magic_name__							):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                       torch.zeros(__magic_name__							),
                   )							)
           # TODO: test this.
           UpperCamelCase__							:				Optional[Any]          =      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
           for _ in range(__magic_name__,		__magic_name__							):
               common_inputs["past_key_values"].append((torch.zeros(__magic_name__							), torch.zeros(__magic_name__							))							)
       return common_inputs




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = -1,		__magic_name__ = -1,		__magic_name__ = False,		__magic_name__ = None,		) ->      Mapping[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      self._generate_dummy_inputs_for_encoder_and_decoder(
           __magic_name__,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							)

       if self.use_past:
           if not is_torch_available():
               raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
           else:
               import torch
           UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      common_inputs['''input_ids'''].shape
           # Not using the same length for past_key_values
           UpperCamelCase__							:				str          =      seqlen + 2
           UpperCamelCase__   ,UpperCamelCase__							:				Any          =      self.num_layers
           UpperCamelCase__   ,UpperCamelCase__							:				Optional[int]          =      self.num_attention_heads
           UpperCamelCase__							:				Optional[Any]          =      (
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )

           UpperCamelCase__							:				str          =      common_inputs['''attention_mask'''].dtype
           UpperCamelCase__							:				str          =      torch.cat(
               [common_inputs['''attention_mask'''], torch.ones(__magic_name__,		__magic_name__,		dtype=__magic_name__							)],		dim=1							)
           UpperCamelCase__							:				int          =      [
               (torch.zeros(__magic_name__							), torch.zeros(__magic_name__							)) for _ in range(__magic_name__							)
           ]
       return common_inputs




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = -1,		__magic_name__ = -1,		__magic_name__ = False,		__magic_name__ = None,		) ->      Mapping[str, Any]:






       """simple docstring"""
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCamelCase__							:				Tuple          =      compute_effective_axis_dimension(
           __magic_name__,		fixed_dimension=OnnxConfig.default_fixed_batch,		num_token_to_add=0							)

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase__							:				Tuple          =      tokenizer.num_special_tokens_to_add(__magic_name__							)
       UpperCamelCase__							:				Tuple          =      compute_effective_axis_dimension(
           __magic_name__,		fixed_dimension=OnnxConfig.default_fixed_sequence,		num_token_to_add=__magic_name__							)

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase__							:				Union[str, Any]          =      [''' '''.join([tokenizer.unk_token]							) * seq_length] * batch_size
       UpperCamelCase__							:				Union[str, Any]          =      dict(tokenizer(__magic_name__,		return_tensors=__magic_name__							)							)
       return common_inputs




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = -1,		__magic_name__ = -1,		__magic_name__ = False,		__magic_name__ = None,		) ->      Mapping[str, Any]:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:				Dict          =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               __magic_name__,		batch_size=__magic_name__,		seq_length=__magic_name__,		is_pair=__magic_name__,		framework=__magic_name__							)

       else:
           UpperCamelCase__							:				int          =      self._generate_dummy_inputs_for_causal_lm(
               __magic_name__,		batch_size=__magic_name__,		seq_length=__magic_name__,		is_pair=__magic_name__,		framework=__magic_name__							)

       return common_inputs




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							) ->      int:






       """simple docstring"""
       if self.task in ["default", "seq2seq-lm"]:
           UpperCamelCase__							:				int          =      super()._flatten_past_key_values_(__magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							)
       else:
           UpperCamelCase__							:				str          =      super(__magic_name__,		self							)._flatten_past_key_values_(
               __magic_name__,		__magic_name__,		__magic_name__,		__magic_name__							)




   @property
   def       UpperCamelCase__  (    self							) ->      float:






       """simple docstring"""
       return 1E-4








from typing import List, Optional, Union

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


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    '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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[int]   		=		"informer"
   a						:							Tuple   		=		{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }




   def __init__(    self,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = "student_t",		__magic_name__ = "nll",		__magic_name__ = 1,		__magic_name__ = None,		__magic_name__ = "mean",		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = 64,		__magic_name__ = 32,		__magic_name__ = 32,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = True,		__magic_name__ = "gelu",		__magic_name__ = 0.05,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 100,		__magic_name__ = 0.02,		__magic_name__=True,		__magic_name__ = "prob",		__magic_name__ = 5,		__magic_name__ = True,		**__magic_name__,		) ->      Optional[int]:






       """simple docstring"""
       # time series specific configuration
       UpperCamelCase__							:				List[Any]          =      prediction_length
       UpperCamelCase__							:				Any          =      context_length or prediction_length
       UpperCamelCase__							:				Optional[int]          =      distribution_output
       UpperCamelCase__							:				Union[str, Any]          =      loss
       UpperCamelCase__							:				Optional[Any]          =      input_size
       UpperCamelCase__							:				Dict          =      num_time_features
       UpperCamelCase__							:				Dict          =      lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
       UpperCamelCase__							:				Optional[int]          =      scaling
       UpperCamelCase__							:				Any          =      num_dynamic_real_features
       UpperCamelCase__							:				Optional[int]          =      num_static_real_features
       UpperCamelCase__							:				Optional[Any]          =      num_static_categorical_features

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

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

       UpperCamelCase__							:				Optional[Any]          =      num_parallel_samples

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

       UpperCamelCase__							:				Optional[Any]          =      dropout
       UpperCamelCase__							:				List[Any]          =      attention_dropout
       UpperCamelCase__							:				Any          =      activation_dropout
       UpperCamelCase__							:				Optional[int]          =      encoder_layerdrop
       UpperCamelCase__							:				Union[str, Any]          =      decoder_layerdrop

       UpperCamelCase__							:				Tuple          =      activation_function
       UpperCamelCase__							:				List[str]          =      init_std

       UpperCamelCase__							:				int          =      use_cache

       # Informer
       UpperCamelCase__							:				Optional[int]          =      attention_type
       UpperCamelCase__							:				Optional[int]          =      sampling_factor
       UpperCamelCase__							:				Dict          =      distil

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




   @property
   def       UpperCamelCase__  (    self							) ->      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
       )

