
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()
lowerCamelCase      =logging.get_logger(__name__)

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
						UpperCamelCase__    :   Any					=				{
						    '''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,
						}

						UpperCamelCase__    :   Union[str, Any]					=				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
						UpperCamelCase__    :   List[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
						UpperCamelCase__    :   List[Any]					=				'''openwebtext_ccnews_stories_books_cased'''

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

						UpperCamelCase__    :   int					=				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__		)
						UpperCamelCase__    :   Optional[int]					=				original_bort._collect_params_with_prefix()

						# Build our config 🤗
						UpperCamelCase__    :   List[str]					=				{
						    '''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__		),
						}

						UpperCamelCase__    :   Any					=				BertConfig.from_dict(UpperCamelCase__		)
						UpperCamelCase__    :   List[Any]					=				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(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__		):
												UpperCamelCase__    :   str					=				hf_param.shape

												UpperCamelCase__    :   Optional[int]					=				to_torch(params[gluon_param]		)
												UpperCamelCase__    :   List[str]					=				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

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

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

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

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

												UpperCamelCase__    :   int					=				check_and_map_params(
												    self_attn.key.weight.data ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_key.weight'''		)
												UpperCamelCase__    :   str					=				check_and_map_params(
												    self_attn.query.bias.data ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_query.bias'''		)
												UpperCamelCase__    :   Optional[Any]					=				check_and_map_params(
												    self_attn.query.weight.data ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_query.weight'''		)
												UpperCamelCase__    :   str					=				check_and_map_params(
												    self_attn.value.bias.data ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_value.bias'''		)
												UpperCamelCase__    :   Dict					=				check_and_map_params(
												    self_attn.value.weight.data ,		f'''encoder.transformer_cells.{i}.attention_cell.proj_value.weight'''		)

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

												UpperCamelCase__    :   List[str]					=				check_and_map_params(
												    self_output.dense.bias ,		f'''encoder.transformer_cells.{i}.proj.bias'''		)
												UpperCamelCase__    :   Optional[int]					=				check_and_map_params(
												    self_output.dense.weight ,		f'''encoder.transformer_cells.{i}.proj.weight'''		)
												UpperCamelCase__    :   Dict					=				check_and_map_params(
												    self_output.LayerNorm.bias ,		f'''encoder.transformer_cells.{i}.layer_norm.beta'''		)
												UpperCamelCase__    :   Tuple					=				check_and_map_params(
												    self_output.LayerNorm.weight ,		f'''encoder.transformer_cells.{i}.layer_norm.gamma'''		)

												# intermediate
												UpperCamelCase__    :   BertIntermediate					=				layer.intermediate

												UpperCamelCase__    :   str					=				check_and_map_params(
												    intermediate.dense.bias ,		f'''encoder.transformer_cells.{i}.ffn.ffn_1.bias'''		)
												UpperCamelCase__    :   Dict					=				check_and_map_params(
												    intermediate.dense.weight ,		f'''encoder.transformer_cells.{i}.ffn.ffn_1.weight'''		)

												# output
												UpperCamelCase__    :   BertOutput					=				layer.output

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

						UpperCamelCase__    :   Optional[int]					=				tokenizer.encode_plus(UpperCamelCase__		)['''input_ids''']

						# Get gluon output
						UpperCamelCase__    :   int					=				mx.nd.array([input_ids]		)
						UpperCamelCase__    :   List[str]					=				original_bort(inputs=UpperCamelCase__ ,		token_types=[]		)

						# Get Transformer output (save and reload model again)
						hf_bort_model.save_pretrained(UpperCamelCase__		)
						UpperCamelCase__    :   Tuple					=				BertModel.from_pretrained(UpperCamelCase__		)
						hf_bort_model.eval()

						UpperCamelCase__    :   str					=				tokenizer.encode_plus(UpperCamelCase__ ,		return_tensors='''pt'''		)
						UpperCamelCase__    :   str					=				hf_bort_model(**UpperCamelCase__		)[0]

						UpperCamelCase__    :   Optional[Any]					=				output_gluon[0].asnumpy()
						UpperCamelCase__    :   Dict					=				output_hf[0].detach().numpy()

						UpperCamelCase__    :   int					=				np.max(np.abs(hf_layer - gluon_layer		)		).item()
						UpperCamelCase__    :   Optional[Any]					=				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__":
				lowerCamelCase      =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."
				)
				lowerCamelCase      =parser.parse_args()
				convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCamelCase      =logging.get_logger(__name__)  # pylint: disable=invalid-name


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(UpperCamelCase__		):
                  return ext

      raise Exception(
          f'''Unable to determine file format from file extension {path}. '''
          f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      UpperCamelCase__    :   Any					=				try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
      UpperCamelCase__    :   Tuple					=				PipelineDataFormat.from_str(
          format=UpperCamelCase__ ,		output_path=args.output ,		input_path=args.input ,		column=args.column if args.column else nlp.default_input_names ,		overwrite=args.overwrite ,		)
      return RunCommand(UpperCamelCase__ ,		UpperCamelCase__		)




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				parser.add_parser('''run'''						,						help='''Run a pipeline through the CLI'''  )
           run_parser.add_argument('''--task'''						,						choices=get_supported_tasks()						,						help='''Task to run'''  )
           run_parser.add_argument('''--input'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file to use for inference'''  )
           run_parser.add_argument('''--output'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file that will be used post to write results.'''  )
           run_parser.add_argument('''--model'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model to instantiate.'''  )
           run_parser.add_argument('''--config'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model\'s config to instantiate.'''  )
           run_parser.add_argument(
               '''--tokenizer'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the tokenizer to use. (default: same as the model name)'''  )
           run_parser.add_argument(
               '''--column'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''						,						)
           run_parser.add_argument(
               '''--format'''						,						type=__SCREAMING_SNAKE_CASE						,						default='''infer'''						,						choices=PipelineDataFormat.SUPPORTED_FORMATS						,						help='''Input format to read from'''						,						)
           run_parser.add_argument(
               '''--device'''						,						type=__SCREAMING_SNAKE_CASE						,						default=-1						,						help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''						,						)
           run_parser.add_argument('''--overwrite'''						,						action='''store_true'''						,						help='''Allow overwriting the output file.'''  )
           run_parser.set_defaults(func=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self._nlp, []

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				nlp(**__SCREAMING_SNAKE_CASE  ) if self._reader.is_multi_columns else nlp(__SCREAMING_SNAKE_CASE  )
                 if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                       outputs.append(__SCREAMING_SNAKE_CASE  )
                 else:
                       outputs += output

        # Saving data
           if self._nlp.binary_output:
                 UpperCamelCase__    :   Union[str, Any]					=				self._reader.save_binary(__SCREAMING_SNAKE_CASE  )
                 logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}'''  )
           else:
                 self._reader.save(__SCREAMING_SNAKE_CASE  )


