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__		:       Any       						=       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:       Optional[Any]       						=       "The Nymphenburg Palace is a beautiful palace in Munich!"







def      _a					(   lowercase__	:      str  ,	lowercase__	:      str			):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__					:					List[Any]      =		{
         '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,
     }

     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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
     SCREAMING_SNAKE_CASE__					:					str      =		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
     SCREAMING_SNAKE_CASE__					:					Dict      =		'openwebtext_ccnews_stories_books_cased'

     # Specify download folder to Gluonnlp's vocab
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		os.path.join(get_home_dir()  ,	'models'			)
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		_load_vocab(lowercase__  ,	lowercase__  ,	lowercase__  ,	cls=lowercase__			)

     SCREAMING_SNAKE_CASE__					:					Any      =		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__			)
     SCREAMING_SNAKE_CASE__					:					int      =		original_bort._collect_params_with_prefix()

     # Build our config 🤗
     SCREAMING_SNAKE_CASE__					:					List[str]      =		{
         '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__			),
     }

     SCREAMING_SNAKE_CASE__					:					List[Any]      =		BertConfig.from_dict(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Any      =		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__	:      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__	:      Tuple  ,	lowercase__	:      Union[str, Any]			):
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		hf_param.shape

          SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		to_torch(params[gluon_param]			)
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		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

     SCREAMING_SNAKE_CASE__					:					Tuple      =		check_and_map_params(
         hf_bort_model.bert.embeddings.word_embeddings.weight  ,	'word_embed.0.weight'			)
     SCREAMING_SNAKE_CASE__					:					List[str]      =		check_and_map_params(
         hf_bort_model.bert.embeddings.position_embeddings.weight  ,	'encoder.position_weight'			)
     SCREAMING_SNAKE_CASE__					:					Dict      =		check_and_map_params(
         hf_bort_model.bert.embeddings.LayerNorm.bias  ,	'encoder.layer_norm.beta'			)
     SCREAMING_SNAKE_CASE__					:					str      =		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)
     SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.zeros_like(
         hf_bort_model.bert.embeddings.token_type_embeddings.weight.data			)

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

          # self attention
          SCREAMING_SNAKE_CASE__					:					BertSelfAttention      =		layer.attention.self

          SCREAMING_SNAKE_CASE__					:					List[Any]      =		check_and_map_params(
              self_attn.key.bias.data  ,	f'''encoder.transformer_cells.{i}.attention_cell.proj_key.bias'''			)

          SCREAMING_SNAKE_CASE__					:					Tuple      =		check_and_map_params(
              self_attn.key.weight.data  ,	f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''			)
          SCREAMING_SNAKE_CASE__					:					str      =		check_and_map_params(
              self_attn.query.bias.data  ,	f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''			)
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		check_and_map_params(
              self_attn.query.weight.data  ,	f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''			)
          SCREAMING_SNAKE_CASE__					:					List[str]      =		check_and_map_params(
              self_attn.value.bias.data  ,	f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''			)
          SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		check_and_map_params(
              self_attn.value.weight.data  ,	f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''			)

          # self attention output
          SCREAMING_SNAKE_CASE__					:					BertSelfOutput      =		layer.attention.output

          SCREAMING_SNAKE_CASE__					:					List[str]      =		check_and_map_params(
              self_output.dense.bias  ,	f'''encoder.transformer_cells.{i}.proj.bias'''			)
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		check_and_map_params(
              self_output.dense.weight  ,	f'''encoder.transformer_cells.{i}.proj.weight'''			)
          SCREAMING_SNAKE_CASE__					:					Tuple      =		check_and_map_params(
              self_output.LayerNorm.bias  ,	f'''encoder.transformer_cells.{i}.layer_norm.beta'''			)
          SCREAMING_SNAKE_CASE__					:					Tuple      =		check_and_map_params(
              self_output.LayerNorm.weight  ,	f'''encoder.transformer_cells.{i}.layer_norm.gamma'''			)

          # intermediate
          SCREAMING_SNAKE_CASE__					:					BertIntermediate      =		layer.intermediate

          SCREAMING_SNAKE_CASE__					:					Any      =		check_and_map_params(
              intermediate.dense.bias  ,	f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''			)
          SCREAMING_SNAKE_CASE__					:					List[Any]      =		check_and_map_params(
              intermediate.dense.weight  ,	f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''			)

          # output
          SCREAMING_SNAKE_CASE__					:					BertOutput      =		layer.output

          SCREAMING_SNAKE_CASE__					:					List[Any]      =		check_and_map_params(
              bert_output.dense.bias  ,	f'''encoder.transformer_cells.{i}.ffn.ffn_2.bias'''			)
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		check_and_map_params(
              bert_output.dense.weight  ,	f'''encoder.transformer_cells.{i}.ffn.ffn_2.weight'''			)
          SCREAMING_SNAKE_CASE__					:					Tuple      =		check_and_map_params(
              bert_output.LayerNorm.bias  ,	f'''encoder.transformer_cells.{i}.ffn.layer_norm.beta'''			)
          SCREAMING_SNAKE_CASE__					:					Optional[int]      =		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
     SCREAMING_SNAKE_CASE__					:					Tuple      =		RobertaTokenizer.from_pretrained('roberta-base'			)

     SCREAMING_SNAKE_CASE__					:					Any      =		tokenizer.encode_plus(lowercase__			)['input_ids']

     # Get gluon output
     SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		mx.nd.array([input_ids]			)
     SCREAMING_SNAKE_CASE__					:					Any      =		original_bort(inputs=lowercase__  ,	token_types=[]			)

     # Get Transformer output (save and reload model again)
     hf_bort_model.save_pretrained(lowercase__			)
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		BertModel.from_pretrained(lowercase__			)
     hf_bort_model.eval()

     SCREAMING_SNAKE_CASE__					:					Dict      =		tokenizer.encode_plus(lowercase__  ,	return_tensors='pt'			)
     SCREAMING_SNAKE_CASE__					:					Any      =		hf_bort_model(**lowercase__			)[0]

     SCREAMING_SNAKE_CASE__					:					Tuple      =		output_gluon[0].asnumpy()
     SCREAMING_SNAKE_CASE__					:					Optional[int]      =		output_hf[0].detach().numpy()

     SCREAMING_SNAKE_CASE__					:					List[Any]      =		np.max(np.abs(hf_layer - gluon_layer			)			).item()
     SCREAMING_SNAKE_CASE__					:					str      =		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__":
      SCREAMING_SNAKE_CASE__		:       List[Any]       						=       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__		:       List[Any]       						=       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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase									=     {
    '''configuration_whisper''': ['''WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WhisperConfig''', '''WhisperOnnxConfig'''],
    '''feature_extraction_whisper''': ['''WhisperFeatureExtractor'''],
    '''processing_whisper''': ['''WhisperProcessor'''],
    '''tokenization_whisper''': ['''WhisperTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     ['''WhisperTokenizerFast''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''WhisperForConditionalGeneration''',
          '''WhisperModel''',
          '''WhisperPreTrainedModel''',
          '''WhisperForAudioClassification''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWhisperForConditionalGeneration''',
          '''TFWhisperModel''',
          '''TFWhisperPreTrainedModel''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase									=     [
          '''FlaxWhisperForConditionalGeneration''',
          '''FlaxWhisperModel''',
          '''FlaxWhisperPreTrainedModel''',
          '''FlaxWhisperForAudioClassification''',
      ]


if TYPE_CHECKING:
      from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
      from .feature_extraction_whisper import WhisperFeatureExtractor
      from .processing_whisper import WhisperProcessor
      from .tokenization_whisper import WhisperTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_whisper_fast import WhisperTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_whisper import (
                WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
                WhisperForAudioClassification,
                WhisperForConditionalGeneration,
                WhisperModel,
                WhisperPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_whisper import (
                TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWhisperForConditionalGeneration,
                TFWhisperModel,
                TFWhisperPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_whisper import (
                FlaxWhisperForAudioClassification,
                FlaxWhisperForConditionalGeneration,
                FlaxWhisperModel,
                FlaxWhisperPreTrainedModel,
            )

else:
      import sys

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