



"""simple docstring"""





from typing import List, Optional

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


lowerCamelCase  			=		logging.get_logger(__name__)

lowerCamelCase  			=		{
    """huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}
class  lowercase__  (       SCREAMING_SNAKE_CASE ):







       '''simple docstring'''


       UpperCamelCase		=	'''autoformer'''
       UpperCamelCase		=	{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
           '''num_hidden_layers''': '''encoder_layers''',
       }



       def __init__(				self     :    Any						,							_UpperCAmelCase     :    Optional[int] = None						,							_UpperCAmelCase     :    Optional[int] = None						,							_UpperCAmelCase     :    str = "student_t"						,							_UpperCAmelCase     :    str = "nll"						,							_UpperCAmelCase     :    int = 1						,							_UpperCAmelCase     :    List[int] = [1, 2, 3, 4, 5, 6, 7]						,							_UpperCAmelCase     :    bool = True						,							_UpperCAmelCase     :    int = 0						,							_UpperCAmelCase     :    int = 0						,							_UpperCAmelCase     :    int = 0						,							_UpperCAmelCase     :    int = 0						,							_UpperCAmelCase     :    Optional[List[int]] = None						,							_UpperCAmelCase     :    Optional[List[int]] = None						,							_UpperCAmelCase     :    int = 64						,							_UpperCAmelCase     :    int = 2						,							_UpperCAmelCase     :    int = 2						,							_UpperCAmelCase     :    int = 2						,							_UpperCAmelCase     :    int = 2						,							_UpperCAmelCase     :    int = 32						,							_UpperCAmelCase     :    int = 32						,							_UpperCAmelCase     :    str = "gelu"						,							_UpperCAmelCase     :    float = 0.1						,							_UpperCAmelCase     :    float = 0.1						,							_UpperCAmelCase     :    float = 0.1						,							_UpperCAmelCase     :    float = 0.1						,							_UpperCAmelCase     :    float = 0.1						,							_UpperCAmelCase     :    int = 100						,							_UpperCAmelCase     :    float = 0.02						,							_UpperCAmelCase     :    bool = True						,							_UpperCAmelCase     :    int=True						,							_UpperCAmelCase     :    int = 10						,							_UpperCAmelCase     :    int = 25						,							_UpperCAmelCase     :    int = 3						,							**_UpperCAmelCase     :    Dict						,							)					->		int:


             '''simple docstring'''




             UpperCAmelCase_     					=				prediction_length
             UpperCAmelCase_     					=				context_length if context_length is not None else prediction_length
             UpperCAmelCase_     					=				distribution_output
             UpperCAmelCase_     					=				loss
             UpperCAmelCase_     					=				input_size
             UpperCAmelCase_     					=				num_time_features
             UpperCAmelCase_     					=				lags_sequence
             UpperCAmelCase_     					=				scaling
             UpperCAmelCase_     					=				num_dynamic_real_features
             UpperCAmelCase_     					=				num_static_real_features
             UpperCAmelCase_     					=				num_static_categorical_features
             if cardinality is not None 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`"       )
                   UpperCAmelCase_     					=				cardinality
             else:
                   UpperCAmelCase_     					=				[0]
             if embedding_dimension is not None 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`"       )
                   UpperCAmelCase_     					=				embedding_dimension
             else:
                   UpperCAmelCase_     					=				[min(50						,							(cat + 1) // 2       ) for cat in self.cardinality]
             UpperCAmelCase_     					=				num_parallel_samples

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

             UpperCAmelCase_     					=				dropout
             UpperCAmelCase_     					=				attention_dropout
             UpperCAmelCase_     					=				activation_dropout
             UpperCAmelCase_     					=				encoder_layerdrop
             UpperCAmelCase_     					=				decoder_layerdrop

             UpperCAmelCase_     					=				activation_function
             UpperCAmelCase_     					=				init_std

             UpperCAmelCase_     					=				use_cache

             # Autoformer
             UpperCAmelCase_     					=				label_length
             UpperCAmelCase_     					=				moving_average
             UpperCAmelCase_     					=				autocorrelation_factor

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







       @property
       def 	lowercase__							(				self     :    List[Any]       )					->		Union[str, Any]:


             '''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 os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse('''0.8.3'''):
       raise Exception('''requires gluonnlp == 0.8.3''')

if version.parse(mx.__version__) != version.parse('''1.5.0'''):
       raise Exception('''requires mxnet == 1.5.0''')

logging.set_verbosity_info()
UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	'''The Nymphenburg Palace is a beautiful palace in Munich!'''

def 					__UpperCamelCase				(						lowercase__    :	str,       lowercase__    :	str			):






     '''simple docstring'''





     __lowercase      		={
         'attention_cell': 'multi_head',
         'num_layers': 4,
         'units': 10_24,
         'hidden_size': 7_68,
         'max_length': 5_12,
         'num_heads': 8,
         'scaled': True,
         'dropout': 0.1,
         'use_residual': True,
         'embed_size': 10_24,
         'embed_dropout': 0.1,
         'word_embed': None,
         'layer_norm_eps': 1E-5,
         'token_type_vocab_size': 2,
     }

     __lowercase      		=bort_4_8_768_1024_hparams

     # Let's construct the original Bort model here
     # Taken from official BERT implementation, see:
     # https://github.com/alexa/bort/blob/master/bort/bort.py
     __lowercase      		=BERTEncoder(
         attention_cell=predefined_args['attention_cell'],       num_layers=predefined_args['num_layers'],       units=predefined_args['units'],       hidden_size=predefined_args['hidden_size'],       max_length=predefined_args['max_length'],       num_heads=predefined_args['num_heads'],       scaled=predefined_args['scaled'],       dropout=predefined_args['dropout'],       output_attention=lowercase__,       output_all_encodings=lowercase__,       use_residual=predefined_args['use_residual'],       activation=predefined_args.get('activation',       'gelu'			),       layer_norm_eps=predefined_args.get('layer_norm_eps',       lowercase__			),       )

     # Vocab information needs to be fetched first
     # It's the same as RoBERTa, so RobertaTokenizer can be used later
     __lowercase      		='openwebtext_ccnews_stories_books_cased'

     # Specify download folder to Gluonnlp's vocab
     __lowercase      		=os.path.join(get_home_dir(),       'models'			)
     __lowercase      		=_load_vocab(lowercase__,       lowercase__,       lowercase__,       cls=lowercase__			)

     __lowercase      		=nlp.model.BERTModel(
         lowercase__,       len(lowercase__			),       units=predefined_args['units'],       embed_size=predefined_args['embed_size'],       embed_dropout=predefined_args['embed_dropout'],       word_embed=predefined_args['word_embed'],       use_pooler=lowercase__,       use_token_type_embed=lowercase__,       token_type_vocab_size=predefined_args['token_type_vocab_size'],       use_classifier=lowercase__,       use_decoder=lowercase__,       )

     original_bort.load_parameters(lowercase__,       cast_dtype=lowercase__,       ignore_extra=lowercase__			)
     __lowercase      		=original_bort._collect_params_with_prefix()

     # Build our config 🤗
     __lowercase      		={
         'architectures': ['BertForMaskedLM'],
         'attention_probs_dropout_prob': predefined_args['dropout'],
         'hidden_act': 'gelu',
         'hidden_dropout_prob': predefined_args['dropout'],
         'hidden_size': predefined_args['embed_size'],
         'initializer_range': 0.02,
         'intermediate_size': predefined_args['hidden_size'],
         'layer_norm_eps': predefined_args['layer_norm_eps'],
         'max_position_embeddings': predefined_args['max_length'],
         'model_type': 'bort',
         'num_attention_heads': predefined_args['num_heads'],
         'num_hidden_layers': predefined_args['num_layers'],
         'pad_token_id': 1,  # 2 = BERT, 1 = RoBERTa
         'type_vocab_size': 1,  # 2 = BERT, 1 = RoBERTa
         'vocab_size': len(lowercase__			),
     }

     __lowercase      		=BertConfig.from_dict(lowercase__			)
     __lowercase      		=BertForMaskedLM(lowercase__			)
     hf_bort_model.eval()

     # Parameter mapping table (Gluonnlp to Transformers)
     # * denotes layer index
     #
     # | Gluon Parameter                                                | Transformers Parameter
     # | -------------------------------------------------------------- | ----------------------
     # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
     # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
     # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
     # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
     # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
     # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
     # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
     # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
     # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
     # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
     # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
     # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
     # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
     # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
     # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
     # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

     # Helper function to convert MXNET Arrays to PyTorch
     def to_torch(lowercase__    :	Optional[Any]			) -> nn.Parameter:
          return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()			)			)

     # Check param shapes and map new HF param back
     def check_and_map_params(lowercase__    :	Optional[Any],       lowercase__    :	Any			):
          __lowercase      		=hf_param.shape

          __lowercase      		=to_torch(params[gluon_param]			)
          __lowercase      		=gluon_param.shape

          assert (
              shape_hf == shape_gluon
          ), F'''The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'''

          return gluon_param

     __lowercase      		=check_and_map_params(
         hf_bort_model.bert.embeddings.word_embeddings.weight,       'word_embed.0.weight'			)
     __lowercase      		=check_and_map_params(
         hf_bort_model.bert.embeddings.position_embeddings.weight,       'encoder.position_weight'			)
     __lowercase      		=check_and_map_params(
         hf_bort_model.bert.embeddings.LayerNorm.bias,       'encoder.layer_norm.beta'			)
     __lowercase      		=check_and_map_params(
         hf_bort_model.bert.embeddings.LayerNorm.weight,       'encoder.layer_norm.gamma'			)

     # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
     __lowercase      		=torch.zeros_like(
         hf_bort_model.bert.embeddings.token_type_embeddings.weight.data			)

     for i in range(hf_bort_config.num_hidden_layers			):
          __lowercase      		=hf_bort_model.bert.encoder.layer[i]

          # self attention
          __lowercase      		=layer.attention.self

          __lowercase      		=check_and_map_params(
              self_attn.key.bias.data,       F'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''			)

          __lowercase      		=check_and_map_params(
              self_attn.key.weight.data,       F'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''			)
          __lowercase      		=check_and_map_params(
              self_attn.query.bias.data,       F'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''			)
          __lowercase      		=check_and_map_params(
              self_attn.query.weight.data,       F'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''			)
          __lowercase      		=check_and_map_params(
              self_attn.value.bias.data,       F'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''			)
          __lowercase      		=check_and_map_params(
              self_attn.value.weight.data,       F'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''			)

          # self attention output
          __lowercase      		=layer.attention.output

          __lowercase      		=check_and_map_params(
              self_output.dense.bias,       F'''encoder.transformer_cells.{i}.proj.bias'''			)
          __lowercase      		=check_and_map_params(
              self_output.dense.weight,       F'''encoder.transformer_cells.{i}.proj.weight'''			)
          __lowercase      		=check_and_map_params(
              self_output.LayerNorm.bias,       F'''encoder.transformer_cells.{i}.layer_norm.beta'''			)
          __lowercase      		=check_and_map_params(
              self_output.LayerNorm.weight,       F'''encoder.transformer_cells.{i}.layer_norm.gamma'''			)

          # intermediate
          __lowercase      		=layer.intermediate

          __lowercase      		=check_and_map_params(
              intermediate.dense.bias,       F'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''			)
          __lowercase      		=check_and_map_params(
              intermediate.dense.weight,       F'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''			)

          # output
          __lowercase      		=layer.output

          __lowercase      		=check_and_map_params(
              bert_output.dense.bias,       F'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''			)
          __lowercase      		=check_and_map_params(
              bert_output.dense.weight,       F'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''			)
          __lowercase      		=check_and_map_params(
              bert_output.LayerNorm.bias,       F'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''			)
          __lowercase      		=check_and_map_params(
              bert_output.LayerNorm.weight,       F'''encoder.transformer_cells.{i}.ffn.layer_norm.gamma'''			)

     # Save space and energy 🎄
     hf_bort_model.half()

     # Compare output of both models
     __lowercase      		=RobertaTokenizer.from_pretrained('roberta-base'			)

     __lowercase      		=tokenizer.encode_plus(lowercase__			)['input_ids']

     # Get gluon output
     __lowercase      		=mx.nd.array([input_ids]			)
     __lowercase      		=original_bort(inputs=lowercase__,       token_types=[]			)

     # Get Transformer output (save and reload model again)
     hf_bort_model.save_pretrained(lowercase__			)
     __lowercase      		=BertModel.from_pretrained(lowercase__			)
     hf_bort_model.eval()

     __lowercase      		=tokenizer.encode_plus(lowercase__,       return_tensors='pt'			)
     __lowercase      		=hf_bort_model(**lowercase__			)[0]

     __lowercase      		=output_gluon[0].asnumpy()
     __lowercase      		=output_hf[0].detach().numpy()

     __lowercase      		=np.max(np.abs(hf_layer - gluon_layer			)			).item()
     __lowercase      		=np.allclose(lowercase__,       lowercase__,       atol=1E-3			)

     if success:
          print('✔️ Both model do output the same tensors'			)
     else:
          print('❌ Both model do **NOT** output the same tensors'			)
          print('Absolute difference is:',       lowercase__			)


if __name__ == "__main__":
       UpperCAmelCase					 =	argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--bort_checkpoint_path''', default=None, type=str, required=True, help='''Path the official Bort params file.'''
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
       )
       UpperCAmelCase					 =	parser.parse_args()
       convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)


