
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()
lowercase  :					List[Any]						    =							logging.get_logger(__name__)

lowercase  :					List[str]						    =							"""The Nymphenburg Palace is a beautiful palace in Munich!"""







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     int       	=      {
           """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,
       }

       lowercase							:     List[Any]       	=      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							:     Union[str, Any]       	=      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=SCREAMING_SNAKE_CASE__					,	output_all_encodings=SCREAMING_SNAKE_CASE__					,	use_residual=predefined_args["""use_residual"""]					,	activation=predefined_args.get("""activation"""					,	"""gelu"""				)					,	layer_norm_eps=predefined_args.get("""layer_norm_eps"""					,	SCREAMING_SNAKE_CASE__				)					,	)

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

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

       lowercase							:     int       	=      nlp.model.BERTModel(
           SCREAMING_SNAKE_CASE__					,	len(SCREAMING_SNAKE_CASE__				)					,	units=predefined_args["""units"""]					,	embed_size=predefined_args["""embed_size"""]					,	embed_dropout=predefined_args["""embed_dropout"""]					,	word_embed=predefined_args["""word_embed"""]					,	use_pooler=SCREAMING_SNAKE_CASE__					,	use_token_type_embed=SCREAMING_SNAKE_CASE__					,	token_type_vocab_size=predefined_args["""token_type_vocab_size"""]					,	use_classifier=SCREAMING_SNAKE_CASE__					,	use_decoder=SCREAMING_SNAKE_CASE__					,	)

       original_bort.load_parameters(SCREAMING_SNAKE_CASE__					,	cast_dtype=SCREAMING_SNAKE_CASE__					,	ignore_extra=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[Any]       	=      original_bort._collect_params_with_prefix()

       # Build our config 🤗
       lowercase							:     Union[str, Any]       	=      {
           """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(SCREAMING_SNAKE_CASE__				),
       }

       lowercase							:     Dict       	=      BertConfig.from_dict(SCREAMING_SNAKE_CASE__				)
       lowercase							:     Any       	=      BertForMaskedLM(SCREAMING_SNAKE_CASE__				)
       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(SCREAMING_SNAKE_CASE__				) -> 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(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     Tuple       	=      hf_param.shape

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

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

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

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

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

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

              lowercase							:     List[Any]       	=      check_and_map_params(
                  self_output.dense.bias					,	f"encoder.transformer_cells.{i}.proj.bias"				)
              lowercase							:     Optional[Any]       	=      check_and_map_params(
                  self_output.dense.weight					,	f"encoder.transformer_cells.{i}.proj.weight"				)
              lowercase							:     Union[str, Any]       	=      check_and_map_params(
                  self_output.LayerNorm.bias					,	f"encoder.transformer_cells.{i}.layer_norm.beta"				)
              lowercase							:     List[str]       	=      check_and_map_params(
                  self_output.LayerNorm.weight					,	f"encoder.transformer_cells.{i}.layer_norm.gamma"				)

              # intermediate
              lowercase							:     BertIntermediate       	=      layer.intermediate

              lowercase							:     Dict       	=      check_and_map_params(
                  intermediate.dense.bias					,	f"encoder.transformer_cells.{i}.ffn.ffn_1.bias"				)
              lowercase							:     str       	=      check_and_map_params(
                  intermediate.dense.weight					,	f"encoder.transformer_cells.{i}.ffn.ffn_1.weight"				)

              # output
              lowercase							:     BertOutput       	=      layer.output

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

       lowercase							:     int       	=      tokenizer.encode_plus(SCREAMING_SNAKE_CASE__				)["""input_ids"""]

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

       # Get Transformer output (save and reload model again)
       hf_bort_model.save_pretrained(SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      BertModel.from_pretrained(SCREAMING_SNAKE_CASE__				)
       hf_bort_model.eval()

       lowercase							:     Union[str, Any]       	=      tokenizer.encode_plus(SCREAMING_SNAKE_CASE__					,	return_tensors="""pt"""				)
       lowercase							:     List[str]       	=      hf_bort_model(**SCREAMING_SNAKE_CASE__				)[0]

       lowercase							:     str       	=      output_gluon[0].asnumpy()
       lowercase							:     Optional[int]       	=      output_hf[0].detach().numpy()

       lowercase							:     Optional[Any]       	=      np.max(np.abs(hf_layer - gluon_layer				)				).item()
       lowercase							:     List[str]       	=      np.allclose(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	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:"""					,	SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  lowercase  :					Optional[int]						    =							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."""
  )
  lowercase  :					Union[str, Any]						    =							parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  :					str						    =							{
    """configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
    """convert_funnel_original_tf_checkpoint_to_pytorch""": [],
    """tokenization_funnel""": ["""FunnelTokenizer"""],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Tuple						    =							["""FunnelTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					List[str]						    =							[
      """FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FunnelBaseModel""",
      """FunnelForMaskedLM""",
      """FunnelForMultipleChoice""",
      """FunnelForPreTraining""",
      """FunnelForQuestionAnswering""",
      """FunnelForSequenceClassification""",
      """FunnelForTokenClassification""",
      """FunnelModel""",
      """FunnelPreTrainedModel""",
      """load_tf_weights_in_funnel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Optional[Any]						    =							[
      """TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFFunnelBaseModel""",
      """TFFunnelForMaskedLM""",
      """TFFunnelForMultipleChoice""",
      """TFFunnelForPreTraining""",
      """TFFunnelForQuestionAnswering""",
      """TFFunnelForSequenceClassification""",
      """TFFunnelForTokenClassification""",
      """TFFunnelModel""",
      """TFFunnelPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
  from .tokenization_funnel import FunnelTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_funnel_fast import FunnelTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_funnel import (
        FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        FunnelBaseModel,
        FunnelForMaskedLM,
        FunnelForMultipleChoice,
        FunnelForPreTraining,
        FunnelForQuestionAnswering,
        FunnelForSequenceClassification,
        FunnelForTokenClassification,
        FunnelModel,
        FunnelPreTrainedModel,
        load_tf_weights_in_funnel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_funnel import (
        TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFunnelBaseModel,
        TFFunnelForMaskedLM,
        TFFunnelForMultipleChoice,
        TFFunnelForPreTraining,
        TFFunnelForQuestionAnswering,
        TFFunnelForSequenceClassification,
        TFFunnelForTokenClassification,
        TFFunnelModel,
        TFFunnelPreTrainedModel,
    )

else:
  import sys

  lowercase  :					Any						    =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
