






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      :       str				  =   logging.get_logger(__name__)

__a      :       Union[str, Any]				  =   "The Nymphenburg Palace is a beautiful palace in Munich!"



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			str    )						->      str:





    """simple docstring"""

    __A         =	{
        """attention_cell""": """multi_head""",
        """num_layers""": 4,
        """units""": 1_0_2_4,
        """hidden_size""": 7_6_8,
        """max_length""": 5_1_2,
        """num_heads""": 8,
        """scaled""": True,
        """dropout""": 0.1,
        """use_residual""": True,
        """embed_size""": 1_0_2_4,
        """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=__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
    __A         =	"""openwebtext_ccnews_stories_books_cased"""

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

    __A         =	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    )
    __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(__lowercase    ),
    }

    __A         =	BertConfig.from_dict(__lowercase    )
    __A         =	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   :			Optional[int]    ) -> 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   :			Dict			,				__lowercase   :			Dict    ):
        __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(__lowercase    )["""input_ids"""]

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

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

    __A         =	tokenizer.encode_plus(__lowercase			,				return_tensors="""pt"""    )
    __A         =	hf_bort_model(**__lowercase    )[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(__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__":
  __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[int]				  =   parser.parse_args()
  convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)












from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


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

__a      :       List[str]				  =   {
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				"gpt_neo"
     SCREAMING_SNAKE_CASE   						=				["past_key_values"]
     SCREAMING_SNAKE_CASE   						=				{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




     def __init__(				self       :			Optional[Any] ,	UpperCamelCase_       :			List[Any]=50_257 ,	UpperCamelCase_       :			Union[str, Any]=2_048 ,	UpperCamelCase_       :			List[str]=2_048 ,	UpperCamelCase_       :			Optional[int]=24 ,	UpperCamelCase_       :			Any=[[["global", "local"], 12]] ,	UpperCamelCase_       :			Tuple=16 ,	UpperCamelCase_       :			List[Any]=None ,	UpperCamelCase_       :			Optional[Any]=256 ,	UpperCamelCase_       :			List[str]="gelu_new" ,	UpperCamelCase_       :			str=0.0 ,	UpperCamelCase_       :			Optional[Any]=0.0 ,	UpperCamelCase_       :			str=0.0 ,	UpperCamelCase_       :			List[Any]=0.1 ,	UpperCamelCase_       :			str=1e-5 ,	UpperCamelCase_       :			Optional[Any]=0.02 ,	UpperCamelCase_       :			Union[str, Any]=True ,	UpperCamelCase_       :			Union[str, Any]=50_256 ,	UpperCamelCase_       :			Dict=50_256 ,	**UpperCamelCase_       :			Union[str, Any] ,	):


         """simple docstring"""



         __A         =	vocab_size
         __A         =	max_position_embeddings
         __A         =	hidden_size
         __A         =	num_layers
         __A         =	num_heads
         __A         =	intermediate_size
         __A         =	window_size
         __A         =	activation_function
         __A         =	resid_dropout
         __A         =	embed_dropout
         __A         =	attention_dropout
         __A         =	classifier_dropout
         __A         =	layer_norm_epsilon
         __A         =	initializer_range
         __A         =	use_cache

         __A         =	bos_token_id
         __A         =	eos_token_id

         __A         =	attention_types
         __A         =	self.expand_attention_types_params(UpperCamelCase_				)

         if len(self.attention_layers				) != self.num_layers:
             raise ValueError(
                 """Configuration for convolutional module is incorrect. """
                 """It is required that `len(config.attention_layers)` == `config.num_layers` """
                 F"but is `len(config.attention_layers) = {len(self.attention_layers				)}`, "
                 F"`config.num_layers = {self.num_layers}`. "
                 """`config.attention_layers` is prepared using `config.attention_types`. """
                 """Please verify the value of `config.attention_types` argument."""				)

         super().__init__(bos_token_id=UpperCamelCase_ ,	eos_token_id=UpperCamelCase_ ,	**UpperCamelCase_				)





     @staticmethod
     def 		lowerCAmelCase_     (				UpperCamelCase_       :			str				):


         """simple docstring"""



         __A         =	[]
         for item in attention_types:
             for _ in range(item[1]				):
                 attentions.extend(item[0]				)
         return attentions



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Any			,				__lowercase   :			int			,				__lowercase   :			int    )						->      Union[str, Any]:





    """simple docstring"""

    import torch

    __A         =	input.size()
    __A         =	len(__lowercase    )
    __A         =	shape[dimension]

    __A         =	torch.arange(0			,				__lowercase			,				__lowercase    )
    __A         =	torch.div(sizedim - size			,				__lowercase			,				rounding_mode="""floor"""    ) + 1
    __A         =	torch.arange(__lowercase    ) + low_indices[:min_length][:, None]

    __A         =	[slice(__lowercase    )] * rank
    __A         =	indices
    __A         =	input[s]

    __A         =	list(range(0			,				rank + 1    )    )
    perm.append(perm.pop(dimension + 1    )    )

    return sliced.permute(__lowercase    )



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Dict			,				__lowercase   :			Any    )						->      Union[str, Any]:





    """simple docstring"""

    import torch

    __A         =	torch.arange(1			,				__lowercase    )
    __A         =	torch.remainder(__lowercase			,				__lowercase    )
    __A         =	remainders == 0
    __A         =	candidates[divisor_indices]
    __A         =	torch.max(__lowercase    )
    return largest_divisor, torch.div(__lowercase			,				__lowercase			,				rounding_mode="""floor"""    )






class       __lowercase      (	lowercase_  ):







     '''simple docstring'''




     @property
     def 		lowerCAmelCase_     (				self       :			Dict				):


         """simple docstring"""



         __A         =	OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}				)
         if self.use_past:
             self.fill_with_past_key_values_(UpperCamelCase_ ,	direction="""inputs"""				)
             __A         =	{0: """batch""", 1: """past_sequence + sequence"""}
         else:
             __A         =	{0: """batch""", 1: """sequence"""}

         return common_inputs




     @property
     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         return self._config.num_heads




     def 		lowerCAmelCase_     (				self       :			Union[str, Any] ,	UpperCamelCase_       :			PreTrainedTokenizer ,	UpperCamelCase_       :			int = -1 ,	UpperCamelCase_       :			int = -1 ,	UpperCamelCase_       :			bool = False ,	UpperCamelCase_       :			Optional[TensorType] = None ,	):


         """simple docstring"""



         __A         =	super(UpperCamelCase_ ,	self				).generate_dummy_inputs(
             UpperCamelCase_ ,	batch_size=UpperCamelCase_ ,	seq_length=UpperCamelCase_ ,	is_pair=UpperCamelCase_ ,	framework=UpperCamelCase_				)

         # We need to order the input in the way they appears in the forward()
         __A         =	OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}				)

         # Need to add the past_keys
         if self.use_past:
             if not is_torch_available():
                 raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
             else:
                 import torch

                 __A    ,	__A         =	common_inputs["""input_ids"""].shape
                 # Not using the same length for past_key_values
                 __A         =	seqlen + 2
                 __A         =	(
                     batch,
                     self.num_attention_heads,
                     past_key_values_length,
                     self._config.hidden_size // self.num_attention_heads,
                 )
                 __A         =	[
                     (torch.zeros(UpperCamelCase_				), torch.zeros(UpperCamelCase_				)) for _ in range(self.num_layers				)
                 ]

         __A         =	common_inputs["""attention_mask"""]
         if self.use_past:
             __A         =	ordered_inputs["""attention_mask"""].dtype
             __A         =	torch.cat(
                 [ordered_inputs["""attention_mask"""], torch.ones(UpperCamelCase_ ,	UpperCamelCase_ ,	dtype=UpperCamelCase_				)] ,	dim=1				)

         return ordered_inputs





     @property
     def 		lowerCAmelCase_     (				self       :			Tuple				):


         """simple docstring"""



         return 13




