
'''simple docstring'''





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase    					=   {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = GPTaTokenizer




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Dict="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=False,    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     List[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Dict =    add_prefix_space
																snake_case       :     List[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    add_prefix_space




				def 	__snake_case							(							self	:				Optional[Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     str =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     List[Any] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				"Conversation"      ):
										snake_case       :     int =    []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

										if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
																snake_case       :     str =    input_ids[-self.model_max_length :]
										return input_ids



'''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()
UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   "The Nymphenburg Palace is a beautiful palace in Munich!"

def      A (       A_							:					str    ,							A_							:					str       ):
						snake_case       :     str =    {
						    '''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,
						}

						snake_case       :     int =    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
						snake_case       :     int =    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=A_    ,							output_all_encodings=A_    ,							use_residual=predefined_args['''use_residual''']    ,							activation=predefined_args.get('''activation'''    ,							'''gelu'''       )    ,							layer_norm_eps=predefined_args.get('''layer_norm_eps'''    ,							A_       )    ,							)

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

						# Specify download folder to Gluonnlp's vocab
						snake_case       :     Optional[Any] =    os.path.join(get_home_dir()    ,							'''models'''       )
						snake_case       :     Dict =    _load_vocab(A_    ,							A_    ,							A_    ,							cls=A_       )

						snake_case       :     Tuple =    nlp.model.BERTModel(
						    A_    ,							len(A_       )    ,							units=predefined_args['''units''']    ,							embed_size=predefined_args['''embed_size''']    ,							embed_dropout=predefined_args['''embed_dropout''']    ,							word_embed=predefined_args['''word_embed''']    ,							use_pooler=A_    ,							use_token_type_embed=A_    ,							token_type_vocab_size=predefined_args['''token_type_vocab_size''']    ,							use_classifier=A_    ,							use_decoder=A_    ,							)

						original_bort.load_parameters(A_    ,							cast_dtype=A_    ,							ignore_extra=A_       )
						snake_case       :     Union[str, Any] =    original_bort._collect_params_with_prefix()

						# Build our config 🤗
						snake_case       :     Optional[int] =    {
						    '''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(A_       ),
						}

						snake_case       :     Optional[int] =    BertConfig.from_dict(A_       )
						snake_case       :     int =    BertForMaskedLM(A_       )
						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(A_							:					List[str]       ) -> 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(A_							:					Tuple    ,							A_							:					Dict       ):
												snake_case       :     Tuple =    hf_param.shape

												snake_case       :     List[Any] =    to_torch(params[gluon_param]       )
												snake_case       :     int =    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

						snake_case       :     List[str] =    check_and_map_params(
						    hf_bort_model.bert.embeddings.word_embeddings.weight    ,							'''word_embed.0.weight'''       )
						snake_case       :     Optional[Any] =    check_and_map_params(
						    hf_bort_model.bert.embeddings.position_embeddings.weight    ,							'''encoder.position_weight'''       )
						snake_case       :     Dict =    check_and_map_params(
						    hf_bort_model.bert.embeddings.LayerNorm.bias    ,							'''encoder.layer_norm.beta'''       )
						snake_case       :     Tuple =    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)
						snake_case       :     str =    torch.zeros_like(
						    hf_bort_model.bert.embeddings.token_type_embeddings.weight.data       )

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

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

												snake_case       :     Optional[int] =    check_and_map_params(
												    self_attn.key.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_key.bias"""       )

												snake_case       :     Any =    check_and_map_params(
												    self_attn.key.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_key.weight"""       )
												snake_case       :     Tuple =    check_and_map_params(
												    self_attn.query.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_query.bias"""       )
												snake_case       :     Dict =    check_and_map_params(
												    self_attn.query.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_query.weight"""       )
												snake_case       :     int =    check_and_map_params(
												    self_attn.value.bias.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_value.bias"""       )
												snake_case       :     int =    check_and_map_params(
												    self_attn.value.weight.data    ,							F"""encoder.transformer_cells.{i}.attention_cell.proj_value.weight"""       )

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

												snake_case       :     Optional[Any] =    check_and_map_params(
												    self_output.dense.bias    ,							F"""encoder.transformer_cells.{i}.proj.bias"""       )
												snake_case       :     Tuple =    check_and_map_params(
												    self_output.dense.weight    ,							F"""encoder.transformer_cells.{i}.proj.weight"""       )
												snake_case       :     int =    check_and_map_params(
												    self_output.LayerNorm.bias    ,							F"""encoder.transformer_cells.{i}.layer_norm.beta"""       )
												snake_case       :     Optional[int] =    check_and_map_params(
												    self_output.LayerNorm.weight    ,							F"""encoder.transformer_cells.{i}.layer_norm.gamma"""       )

												# intermediate
												snake_case       :     BertIntermediate =    layer.intermediate

												snake_case       :     Optional[Any] =    check_and_map_params(
												    intermediate.dense.bias    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_1.bias"""       )
												snake_case       :     Optional[int] =    check_and_map_params(
												    intermediate.dense.weight    ,							F"""encoder.transformer_cells.{i}.ffn.ffn_1.weight"""       )

												# output
												snake_case       :     BertOutput =    layer.output

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

						snake_case       :     Any =    tokenizer.encode_plus(A_       )['''input_ids''']

						# Get gluon output
						snake_case       :     Dict =    mx.nd.array([input_ids]       )
						snake_case       :     Optional[int] =    original_bort(inputs=A_    ,							token_types=[]       )

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(A_       )
						snake_case       :     Optional[int] =    BertModel.from_pretrained(A_       )
						hf_bort_model.eval()

						snake_case       :     Any =    tokenizer.encode_plus(A_    ,							return_tensors='''pt'''       )
						snake_case       :     Any =    hf_bort_model(**A_       )[0]

						snake_case       :     Optional[Any] =    output_gluon[0].asnumpy()
						snake_case       :     Union[str, Any] =    output_hf[0].detach().numpy()

						snake_case       :     Tuple =    np.max(np.abs(hf_layer - gluon_layer       )       ).item()
						snake_case       :     Tuple =    np.allclose(A_    ,							A_    ,							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:'''    ,							A_       )


if __name__ == "__main__":
						UpperCAmelCase    					=   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    					=   parser.parse_args()
						convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)

