





'''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()
A__							:  Optional[int]    =logging.get_logger(__name__)

A__							:  Optional[int]    ='''The Nymphenburg Palace is a beautiful palace in Munich!'''
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				{
          """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,
      }

      _lowerCAmelCase					=				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
      _lowerCAmelCase					=				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
      _lowerCAmelCase					=				"""openwebtext_ccnews_stories_books_cased"""

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

      _lowerCAmelCase					=				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    )
      _lowerCAmelCase					=				original_bort._collect_params_with_prefix()

      # Build our config 🤗
      _lowerCAmelCase					=				{
          """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    ),
      }

      _lowerCAmelCase					=				BertConfig.from_dict(lowerCAmelCase    )
      _lowerCAmelCase					=				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    ) -> 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				,      lowerCAmelCase    ):
            _lowerCAmelCase					=				hf_param.shape

            _lowerCAmelCase					=				to_torch(params[gluon_param]    )
            _lowerCAmelCase					=				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

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

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

            # self attention
            _lowerCAmelCase					=				layer.attention.self

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

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

            # self attention output
            _lowerCAmelCase					=				layer.attention.output

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

            # intermediate
            _lowerCAmelCase					=				layer.intermediate

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

            # output
            _lowerCAmelCase					=				layer.output

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

      _lowerCAmelCase					=				tokenizer.encode_plus(lowerCAmelCase    )["""input_ids"""]

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

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

      _lowerCAmelCase					=				tokenizer.encode_plus(lowerCAmelCase				,      return_tensors="""pt"""    )
      _lowerCAmelCase					=				hf_bort_model(**lowerCAmelCase    )[0]

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

      _lowerCAmelCase					=				np.max(np.abs(hf_layer - gluon_layer    )    ).item()
      _lowerCAmelCase					=				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__":
      A__							:  Tuple    =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.'''
      )
      A__							:  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_torch_available,
)


A__							:  int    ={
    '''configuration_wav2vec2''': ['''WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Wav2Vec2Config'''],
    '''feature_extraction_wav2vec2''': ['''Wav2Vec2FeatureExtractor'''],
    '''processing_wav2vec2''': ['''Wav2Vec2Processor'''],
    '''tokenization_wav2vec2''': ['''Wav2Vec2CTCTokenizer''', '''Wav2Vec2Tokenizer'''],
}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  str    =[
          '''WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''Wav2Vec2ForAudioFrameClassification''',
          '''Wav2Vec2ForCTC''',
          '''Wav2Vec2ForMaskedLM''',
          '''Wav2Vec2ForPreTraining''',
          '''Wav2Vec2ForSequenceClassification''',
          '''Wav2Vec2ForXVector''',
          '''Wav2Vec2Model''',
          '''Wav2Vec2PreTrainedModel''',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  Union[str, Any]    =[
          '''TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFWav2Vec2ForCTC''',
          '''TFWav2Vec2Model''',
          '''TFWav2Vec2PreTrainedModel''',
          '''TFWav2Vec2ForSequenceClassification''',
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__							:  List[str]    =[
          '''FlaxWav2Vec2ForCTC''',
          '''FlaxWav2Vec2ForPreTraining''',
          '''FlaxWav2Vec2Model''',
          '''FlaxWav2Vec2PreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
      from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
      from .processing_wavaveca import WavaVecaProcessor
      from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_wavaveca import (
                WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                WavaVecaForAudioFrameClassification,
                WavaVecaForCTC,
                WavaVecaForMaskedLM,
                WavaVecaForPreTraining,
                WavaVecaForSequenceClassification,
                WavaVecaForXVector,
                WavaVecaModel,
                WavaVecaPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFWavaVecaForCTC,
                TFWavaVecaForSequenceClassification,
                TFWavaVecaModel,
                TFWavaVecaPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_wavaveca import (
                FlaxWavaVecaForCTC,
                FlaxWavaVecaForPreTraining,
                FlaxWavaVecaModel,
                FlaxWavaVecaPreTrainedModel,
            )


else:
      import sys

      A__							:  Any    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






