

"""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    :Dict			=       logging.get_logger(__name__)

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




def        _lowercase    (			__lowerCAmelCase     ,    __lowerCAmelCase							)				-> Tuple:
      SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		{
          """attention_cell""": """multi_head""",
          """num_layers""": 4,
          """units""": 1024,
          """hidden_size""": 768,
          """max_length""": 512,
          """num_heads""": 8,
          """scaled""": True,
          """dropout""": 0.1,
          """use_residual""": True,
          """embed_size""": 1024,
          """embed_dropout""": 0.1,
          """word_embed""": None,
          """layer_norm_eps""": 1E-5,
          """token_type_vocab_size""": 2,
      }

      SCREAMING_SNAKE_CASE__      :					Tuple											=		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__      :					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=_UpperCamelCase     ,    output_all_encodings=_UpperCamelCase     ,    use_residual=predefined_args["""use_residual"""]     ,    activation=predefined_args.get("""activation"""     ,    """gelu"""							)     ,    layer_norm_eps=predefined_args.get("""layer_norm_eps"""     ,    _UpperCamelCase							)     ,    )

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

      # Specify download folder to Gluonnlp's vocab
      SCREAMING_SNAKE_CASE__      :					int											=		os.path.join(get_home_dir()     ,    """models"""							)
      SCREAMING_SNAKE_CASE__      :					str											=		_load_vocab(_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase     ,    cls=_UpperCamelCase							)

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

      original_bort.load_parameters(_UpperCamelCase     ,    cast_dtype=_UpperCamelCase     ,    ignore_extra=_UpperCamelCase							)
      SCREAMING_SNAKE_CASE__      :					List[Any]											=		original_bort._collect_params_with_prefix()

      # Build our config 🤗
      SCREAMING_SNAKE_CASE__      :					Tuple											=		{
          """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(_UpperCamelCase							),
      }

      SCREAMING_SNAKE_CASE__      :					Dict											=		BertConfig.from_dict(_UpperCamelCase							)
      SCREAMING_SNAKE_CASE__      :					List[str]											=		BertForMaskedLM(_UpperCamelCase							)
      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							):
            SCREAMING_SNAKE_CASE__      :					Any											=		hf_param.shape

            SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		to_torch(params[gluon_param]							)
            SCREAMING_SNAKE_CASE__      :					Dict											=		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__      :					List[Any]											=		check_and_map_params(
          hf_bort_model.bert.embeddings.word_embeddings.weight     ,    """word_embed.0.weight"""							)
      SCREAMING_SNAKE_CASE__      :					Tuple											=		check_and_map_params(
          hf_bort_model.bert.embeddings.position_embeddings.weight     ,    """encoder.position_weight"""							)
      SCREAMING_SNAKE_CASE__      :					str											=		check_and_map_params(
          hf_bort_model.bert.embeddings.LayerNorm.bias     ,    """encoder.layer_norm.beta"""							)
      SCREAMING_SNAKE_CASE__      :					List[Any]											=		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__      :					List[str]											=		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__      :					Optional[int]											=		hf_bort_model.bert.encoder.layer[i]

            # self attention
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		layer.attention.self

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

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

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

            # intermediate
            SCREAMING_SNAKE_CASE__      :					Optional[int]											=		layer.intermediate

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

            # output
            SCREAMING_SNAKE_CASE__      :					List[str]											=		layer.output

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

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

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

      # Get Transformer output (save and reload model again)
      hf_bort_model.save_pretrained(_UpperCamelCase							)
      SCREAMING_SNAKE_CASE__      :					List[Any]											=		BertModel.from_pretrained(_UpperCamelCase							)
      hf_bort_model.eval()

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

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

      SCREAMING_SNAKE_CASE__      :					Any											=		np.max(np.abs(hf_layer - gluon_layer							)							).item()
      SCREAMING_SNAKE_CASE__      :					int											=		np.allclose(_UpperCamelCase     ,    _UpperCamelCase     ,    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:"""     ,    _UpperCamelCase							)


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








"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=None    ,   __a=True    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			config_class
										__lowerCAmelCase				    =			has_text_modality
										__lowerCAmelCase				    =			kwargs
										__lowerCAmelCase				    =			common_properties

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			(
										    ["hidden_size", "num_attention_heads", "num_hidden_layers"]
										    if self.common_properties is None
										    else self.common_properties
										)

										# Add common fields for text models
										if self.has_text_modality:
													common_properties.extend(["vocab_size"]	)

										# Test that config has the common properties as getters
										for prop in common_properties:
													self.parent.assertTrue(hasattr(__a    ,   __a	)    ,   msg=f"`{prop}` does not exist"	)

										# Test that config has the common properties as setter
										for idx, name in enumerate(__a	):
													try:
																setattr(__a    ,   __a    ,   __a	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

        # Test if config class can be called with Config(prop_name=..)
										for idx, name in enumerate(__a	):
													try:
																__lowerCAmelCase				    =			self.config_class(**{name: idx}	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			json.loads(config.to_json_string()	)
										for key, value in self.inputs_dict.items():
													self.parent.assertEqual(obj[key]    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   "config.json"	)
													config_first.to_json_file(__a	)
													__lowerCAmelCase				    =			self.config_class.from_json_file(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										__lowerCAmelCase				    =			"test"
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   __a	)
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a    ,   subfolder=__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict    ,   num_labels=5	)
										self.parent.assertEqual(len(config.idalabel	)    ,   5	)
										self.parent.assertEqual(len(config.labelaid	)    ,   5	)

										__lowerCAmelCase				    =			3
										self.parent.assertEqual(len(config.idalabel	)    ,   3	)
										self.parent.assertEqual(len(config.labelaid	)    ,   3	)

							def 			snake_case				(     self	):
										if self.config_class.is_composition:
													return
										__lowerCAmelCase				    =			self.config_class()
										self.parent.assertIsNotNone(__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			copy.deepcopy(__a	)
										__lowerCAmelCase				    =			self.config_class(**__a	)
										__lowerCAmelCase				    =			[]
										for key, value in config_common_kwargs.items():
													if key == "torch_dtype":
																if not is_torch_available():
																			continue
																else:
																			import torch

																			if config.torch_dtype != torch.floataa:
																						wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)	)
													elif getattr(__a    ,   __a	) != value:
																wrong_values.append((key, getattr(__a    ,   __a	), value)	)

										if len(__a	) > 0:
													__lowerCAmelCase				    =			"\n".join([f"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values]	)
													raise ValueError(f"The following keys were not properly set in the config:\n{errors}"	)



							def 			snake_case				(     self	):
										self.create_and_test_config_common_properties()
										self.create_and_test_config_to_json_string()
										self.create_and_test_config_to_json_file()
										self.create_and_test_config_from_and_save_pretrained()
										self.create_and_test_config_from_and_save_pretrained_subfolder()
										self.create_and_test_config_with_num_labels()
										self.check_config_can_be_init_without_params()
										self.check_config_arguments_init()




