
"""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()
SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

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




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       str			)			->   int:






    """simple docstring"""




    UpperCAmelCase          =       {
        'attention_cell': 'multi_head',
        'num_layers': 4,
        'units': 1_024,
        'hidden_size': 768,
        'max_length': 512,
        'num_heads': 8,
        'scaled': True,
        'dropout': 0.1,
        'use_residual': True,
        'embed_size': 1_024,
        'embed_dropout': 0.1,
        'word_embed': None,
        'layer_norm_eps': 1E-5,
        'token_type_vocab_size': 2,
    }

    UpperCAmelCase          =       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
    UpperCAmelCase          =       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=lowerCAmelCase			,					output_all_encodings=lowerCAmelCase			,					use_residual=predefined_args['use_residual']			,					activation=predefined_args.get('activation'			,					'gelu'			)			,					layer_norm_eps=predefined_args.get('layer_norm_eps'			,					lowerCAmelCase			)			,					)

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

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

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

    original_bort.load_parameters(lowerCAmelCase			,					cast_dtype=lowerCAmelCase			,					ignore_extra=lowerCAmelCase			)
    UpperCAmelCase          =       original_bort._collect_params_with_prefix()

    # Build our config 🤗
    UpperCAmelCase          =       {
        '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(lowerCAmelCase			),
    }

    UpperCAmelCase          =       BertConfig.from_dict(lowerCAmelCase			)
    UpperCAmelCase          =       BertForMaskedLM(lowerCAmelCase			)
    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(lowerCAmelCase					:       int			) -> 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(lowerCAmelCase					:       str			,					lowerCAmelCase					:       Tuple			):
        UpperCAmelCase          =       hf_param.shape

        UpperCAmelCase          =       to_torch(params[gluon_param]			)
        UpperCAmelCase          =       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

    UpperCAmelCase          =       check_and_map_params(
        hf_bort_model.bert.embeddings.word_embeddings.weight			,					'word_embed.0.weight'			)
    UpperCAmelCase          =       check_and_map_params(
        hf_bort_model.bert.embeddings.position_embeddings.weight			,					'encoder.position_weight'			)
    UpperCAmelCase          =       check_and_map_params(
        hf_bort_model.bert.embeddings.LayerNorm.bias			,					'encoder.layer_norm.beta'			)
    UpperCAmelCase          =       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)
    UpperCAmelCase          =       torch.zeros_like(
        hf_bort_model.bert.embeddings.token_type_embeddings.weight.data			)

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

        # self attention
        UpperCAmelCase          =       layer.attention.self

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

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

        # self attention output
        UpperCAmelCase          =       layer.attention.output

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

        # intermediate
        UpperCAmelCase          =       layer.intermediate

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

        # output
        UpperCAmelCase          =       layer.output

        UpperCAmelCase          =       check_and_map_params(
            bert_output.dense.bias			,					F"encoder.transformer_cells.{i}.ffn.ffn_2.bias"			)
        UpperCAmelCase          =       check_and_map_params(
            bert_output.dense.weight			,					F"encoder.transformer_cells.{i}.ffn.ffn_2.weight"			)
        UpperCAmelCase          =       check_and_map_params(
            bert_output.LayerNorm.bias			,					F"encoder.transformer_cells.{i}.ffn.layer_norm.beta"			)
        UpperCAmelCase          =       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
    UpperCAmelCase          =       RobertaTokenizer.from_pretrained('roberta-base'			)

    UpperCAmelCase          =       tokenizer.encode_plus(lowerCAmelCase			)['input_ids']

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

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

    UpperCAmelCase          =       tokenizer.encode_plus(lowerCAmelCase			,					return_tensors='pt'			)
    UpperCAmelCase          =       hf_bort_model(**lowerCAmelCase			)[0]

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

    UpperCAmelCase          =       np.max(np.abs(hf_layer - gluon_layer			)			).item()
    UpperCAmelCase          =       np.allclose(lowerCAmelCase			,					lowerCAmelCase			,					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:'			,					lowerCAmelCase			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    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.'''
   )
   SCREAMING_SNAKE_CASE_            =    parser.parse_args()
   convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



