




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()
__UpperCamelCase   :			int          = logging.get_logger(__name__)

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





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	a									=				{
	    """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,
	}

	a									=				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
	a									=				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
	a									=				"""openwebtext_ccnews_stories_books_cased"""

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

	a									=				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       )
	a									=				original_bort._collect_params_with_prefix()

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

	a									=				BertConfig.from_dict(__lowerCamelCase       )
	a									=				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       ):
		a									=				hf_param.shape

		a									=				to_torch(params[gluon_param]       )
		a									=				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

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

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

		# self attention
		a									=				layer.attention.self

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

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

		# self attention output
		a									=				layer.attention.output

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

		# intermediate
		a									=				layer.intermediate

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

		# output
		a									=				layer.output

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

	a									=				tokenizer.encode_plus(__lowerCamelCase       )["""input_ids"""]

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

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

	a									=				tokenizer.encode_plus(__lowerCamelCase				,     return_tensors="""pt"""       )
	a									=				hf_bort_model(**__lowerCamelCase       )[0]

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

	a									=				np.max(np.abs(hf_layer - gluon_layer       )       ).item()
	a									=				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__":
		__UpperCamelCase   :			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."
		)
		__UpperCamelCase   :			str          = 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_torch_available


__UpperCamelCase   :			Optional[int]          = {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase   :			int          = [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase   :			List[str]          = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

