

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

lowerCAmelCase__      		=			'''The Nymphenburg Palace is a beautiful palace in Munich!'''


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				{
     '''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,
 }

 __lowercase													=				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
 __lowercase													=				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
 __lowercase													=				'''openwebtext_ccnews_stories_books_cased'''

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

 __lowercase													=				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__   )
 __lowercase													=				original_bort._collect_params_with_prefix()

 # Build our config 🤗
 __lowercase													=				{
     '''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''': 0.0_2,
     '''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__   ),
 }

 __lowercase													=				BertConfig.from_dict(A__   )
 __lowercase													=				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__   ) -> 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__  ,       A__   ):
  __lowercase													=				hf_param.shape

  __lowercase													=				to_torch(params[gluon_param]   )
  __lowercase													=				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

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

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

  # self attention
  __lowercase													=				layer.attention.self

  __lowercase													=				check_and_map_params(
      self_attn.key.bias.data  ,       F"encoder.transformer_cells.{i}.attention_cell.proj_key.bias"   )

  __lowercase													=				check_and_map_params(
      self_attn.key.weight.data  ,       F"encoder.transformer_cells.{i}.attention_cell.proj_key.weight"   )
  __lowercase													=				check_and_map_params(
      self_attn.query.bias.data  ,       F"encoder.transformer_cells.{i}.attention_cell.proj_query.bias"   )
  __lowercase													=				check_and_map_params(
      self_attn.query.weight.data  ,       F"encoder.transformer_cells.{i}.attention_cell.proj_query.weight"   )
  __lowercase													=				check_and_map_params(
      self_attn.value.bias.data  ,       F"encoder.transformer_cells.{i}.attention_cell.proj_value.bias"   )
  __lowercase													=				check_and_map_params(
      self_attn.value.weight.data  ,       F"encoder.transformer_cells.{i}.attention_cell.proj_value.weight"   )

  # self attention output
  __lowercase													=				layer.attention.output

  __lowercase													=				check_and_map_params(
      self_output.dense.bias  ,       F"encoder.transformer_cells.{i}.proj.bias"   )
  __lowercase													=				check_and_map_params(
      self_output.dense.weight  ,       F"encoder.transformer_cells.{i}.proj.weight"   )
  __lowercase													=				check_and_map_params(
      self_output.LayerNorm.bias  ,       F"encoder.transformer_cells.{i}.layer_norm.beta"   )
  __lowercase													=				check_and_map_params(
      self_output.LayerNorm.weight  ,       F"encoder.transformer_cells.{i}.layer_norm.gamma"   )

  # intermediate
  __lowercase													=				layer.intermediate

  __lowercase													=				check_and_map_params(
      intermediate.dense.bias  ,       F"encoder.transformer_cells.{i}.ffn.ffn_1.bias"   )
  __lowercase													=				check_and_map_params(
      intermediate.dense.weight  ,       F"encoder.transformer_cells.{i}.ffn.ffn_1.weight"   )

  # output
  __lowercase													=				layer.output

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

 __lowercase													=				tokenizer.encode_plus(A__   )['''input_ids''']

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

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

 __lowercase													=				tokenizer.encode_plus(A__  ,       return_tensors='''pt'''   )
 __lowercase													=				hf_bort_model(**A__   )[0]

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

 __lowercase													=				np.max(np.abs(hf_layer - gluon_layer   )   ).item()
 __lowercase													=				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__":
    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)




'''simple docstring'''






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
 import tensorflow as tf

if is_keras_nlp_available():
 from transformers.models.gpta import TFGPTaTokenizer


lowerCAmelCase__      		=			['''gpt2''']
lowerCAmelCase__      		=			'''gpt2'''

if is_tf_available():






 class     lowercase_			(tf.Module   ):




   """simple docstring"""




   def __init__(   self     :  List[str]      ,lowercase__     :  Tuple    ):
    super().__init__()
    __lowercase													=				tokenizer
    __lowercase													=				AutoConfig.from_pretrained(lowercase__    )
    __lowercase													=				TFGPTaLMHeadModel.from_config(lowercase__    )






   @tf.function(input_signature=(tf.TensorSpec((None,)      ,tf.string      ,name='''text'''    ),)    )
   def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Dict    ):
    __lowercase													=				self.tokenizer(lowercase__    )
    __lowercase													=				tokenized['''input_ids'''].to_tensor()

    __lowercase													=				tf.cast(input_ids_dense > 0      ,tf.intaa    )
    # input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

    __lowercase													=				self.model(input_ids=lowercase__      ,attention_mask=lowercase__    )['''logits''']

    return outputs






@require_tf
@require_keras_nlp
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   super().setUp()

   __lowercase													=				[GPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
   __lowercase													=				[TFGPTaTokenizer.from_pretrained(lowercase__    ) for checkpoint in TOKENIZER_CHECKPOINTS]
   assert len(self.tokenizers    ) == len(self.tf_tokenizers    )

   __lowercase													=				[
       '''This is a straightforward English test sentence.''',
       '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
       '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
       '''And some much more rare Chinese: 齉 堃 齉堃''',
       '''Je vais aussi écrire en français pour tester les accents''',
       '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
   ]
   __lowercase													=				list(zip(self.test_sentences      ,self.test_sentences[::-1]    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tokenizer, tf_tokenizer in zip(self.tokenizers      ,self.tf_tokenizers    ):
    for test_inputs in self.test_sentences:
     __lowercase													=				tokenizer([test_inputs]      ,return_tensors='''tf'''    )
     __lowercase													=				tf_tokenizer([test_inputs]    )

     for key in python_outputs.keys():
      # convert them to numpy to avoid messing with ragged tensors
      __lowercase													=				python_outputs[key].numpy()
      __lowercase													=				tf_outputs[key].numpy()

      self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape    )    )
      self.assertTrue(tf.reduce_all(tf.cast(lowercase__      ,tf.intaa    ) == tf_outputs_values    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.function(lowercase__    )
    for test_inputs in self.test_sentences:
     __lowercase													=				tf.constant(lowercase__    )
     __lowercase													=				compiled_tokenizer(lowercase__    )
     __lowercase													=				tf_tokenizer(lowercase__    )

     for key in eager_outputs.keys():
      self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				ModelToSave(tokenizer=lowercase__    )
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				model.serving(lowercase__    )  # Build model with some sample inputs
    with TemporaryDirectory() as tempdir:
     __lowercase													=				Path(lowercase__    ) / '''saved.model'''
     tf.saved_model.save(lowercase__      ,lowercase__      ,signatures={'''serving_default''': model.serving}    )
     __lowercase													=				tf.saved_model.load(lowercase__    )
    __lowercase													=				loaded_model.signatures['''serving_default'''](lowercase__    )['''output_0''']
    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
    self.assertTrue(tf.reduce_all(out == loaded_output    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   for tf_tokenizer in self.tf_tokenizers:
    __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
    __lowercase													=				tf_tokenizer(lowercase__    )  # Build model with some sample inputs

    __lowercase													=				tf_tokenizer.get_config()
    __lowercase													=				TFGPTaTokenizer.from_config(lowercase__    )
    __lowercase													=				model_from_config(lowercase__    )

    for key in from_config_output.keys():
     self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   for tf_tokenizer in self.tf_tokenizers:
    # for the test to run
    __lowercase													=				1_2_3_1_2_3

    for max_length in [3, 5, 1_0_2_4]:
     __lowercase													=				tf.convert_to_tensor([self.test_sentences[0]]    )
     __lowercase													=				tf_tokenizer(lowercase__      ,max_length=lowercase__    )

     __lowercase													=				out['''input_ids'''].numpy().shape[1]

     assert out_length == max_length

