



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()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)

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


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Dict:
			A__	  =     {
			    '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,
			}

			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=_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
			A__	  =     'openwebtext_ccnews_stories_books_cased'

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

			A__	  =     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       )
			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(_A       ),
			}

			A__	  =     BertConfig.from_dict(_A       )
			A__	  =     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(__UpperCamelCase       ) -> 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(__UpperCamelCase			,						__UpperCamelCase       ):
						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(_A       )['input_ids']

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

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

			A__	  =     tokenizer.encode_plus(_A			,						return_tensors='pt'       )
			A__	  =     hf_bort_model(**_A       )[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(_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__":
							SCREAMING_SNAKE_CASE__   			=   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.'''
							)
							SCREAMING_SNAKE_CASE__   			=   parser.parse_args()
							convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		"""T5Config"""
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """mt5"""
  _SCREAMING_SNAKE_CASE     					=      MTaConfig


