


"""simple docstring"""






import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
 raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__				:  str						= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				:  Optional[int]						= "Hello world! cécé herlolip"




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     Tuple:
   a   :	Any						=    os.path.split(lowerCAmelCase_				)
   a   :	List[Any]						=    DataaVecTextModel.from_pretrained(
       lowerCAmelCase_       ,   checkpoint_file=lowerCAmelCase_				)
   dataavec.eval()  # disable dropout
   a   :	Optional[int]						=    dataavec.models[0]
   a   :	List[str]						=    dataavec_model.encoder.sentence_encoder
   a   :	Tuple						=    DataaVecTextConfig(
       vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings       ,   hidden_size=dataavec_model.args.encoder_embed_dim       ,   num_hidden_layers=dataavec_model.args.encoder_layers       ,   num_attention_heads=dataavec_model.args.encoder_attention_heads       ,   intermediate_size=dataavec_model.args.encoder_ffn_embed_dim       ,   max_position_embeddings=514       ,   type_vocab_size=1       ,   layer_norm_eps=1e-5       ,   )
   if classification_head:
      a   :	Optional[int]						=    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
   print('Our BERT config:'       ,   lowerCAmelCase_				)

   a   :	Any						=    DataaVecTextForSequenceClassification(lowerCAmelCase_				) if classification_head else DataaVecTextForMaskedLM(lowerCAmelCase_				)
   model.eval()

   # Now let's copy all the weights.
   # Embeddings
   a   :	int						=    dataavec_sent_encoder.embed_tokens.weight
   a   :	str						=    dataavec_sent_encoder.embed_positions.weight
   a   :	Union[str, Any]						=    torch.zeros_like(
       model.dataavec_text.embeddings.token_type_embeddings.weight				)  # just zero them out b/c data2vec doesn't use them.
   a   :	Dict						=    dataavec_sent_encoder.layernorm_embedding.weight
   a   :	Optional[Any]						=    dataavec_sent_encoder.layernorm_embedding.bias

   for i in range(config.num_hidden_layers				):
      # Encoder: start of layer
      a   :	BertLayer						=    model.dataavec_text.encoder.layer[i]
      a   :	TransformerSentenceEncoderLayer						=    dataavec_sent_encoder.layers[i]

      # self attention
      a   :	BertSelfAttention						=    layer.attention.self
      assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
          (config.hidden_size, config.hidden_size)				), (
          "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
          F' {torch.Size((config.hidden_size, config.hidden_size)				)}'
      )
      assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
          (config.hidden_size, config.hidden_size)				), (
          "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
          F' {torch.Size((config.hidden_size, config.hidden_size)				)}'
      )
      assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
          (config.hidden_size, config.hidden_size)				), (
          "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
          F' {torch.Size((config.hidden_size, config.hidden_size)				)}'
      )

      a   :	Dict						=    dataavec_layer.self_attn.q_proj.weight
      a   :	Optional[int]						=    dataavec_layer.self_attn.q_proj.bias
      a   :	List[Any]						=    dataavec_layer.self_attn.k_proj.weight
      a   :	int						=    dataavec_layer.self_attn.k_proj.bias
      a   :	int						=    dataavec_layer.self_attn.v_proj.weight
      a   :	List[str]						=    dataavec_layer.self_attn.v_proj.bias

      # self-attention output
      a   :	BertSelfOutput						=    layer.attention.output
      assert (
          self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
      ), F'Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'
      a   :	Tuple						=    dataavec_layer.self_attn.out_proj.weight
      a   :	int						=    dataavec_layer.self_attn.out_proj.bias
      a   :	Union[str, Any]						=    dataavec_layer.self_attn_layer_norm.weight
      a   :	Optional[int]						=    dataavec_layer.self_attn_layer_norm.bias

      # intermediate
      a   :	BertIntermediate						=    layer.intermediate
      assert (
          intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
      ), F'Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'
      a   :	List[str]						=    dataavec_layer.fca.weight
      a   :	Dict						=    dataavec_layer.fca.bias

      # output
      a   :	BertOutput						=    layer.output
      assert (
          bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
      ), F'Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'
      a   :	List[Any]						=    dataavec_layer.fca.weight
      a   :	Any						=    dataavec_layer.fca.bias
      a   :	Union[str, Any]						=    dataavec_layer.final_layer_norm.weight
      a   :	int						=    dataavec_layer.final_layer_norm.bias
      # end of layer

   if classification_head:
      a   :	Union[str, Any]						=    dataavec.model.classification_heads['''mnli'''].dense.weight
      a   :	str						=    dataavec.model.classification_heads['''mnli'''].dense.bias
      a   :	Union[str, Any]						=    dataavec.model.classification_heads['''mnli'''].out_proj.weight
      a   :	Dict						=    dataavec.model.classification_heads['''mnli'''].out_proj.bias
   else:
      # LM Head
      a   :	Union[str, Any]						=    dataavec_model.encoder.lm_head.dense.weight
      a   :	Tuple						=    dataavec_model.encoder.lm_head.dense.bias
      a   :	Dict						=    dataavec_model.encoder.lm_head.layer_norm.weight
      a   :	Any						=    dataavec_model.encoder.lm_head.layer_norm.bias
      a   :	List[Any]						=    dataavec_model.encoder.lm_head.weight
      a   :	Union[str, Any]						=    dataavec_model.encoder.lm_head.bias

   # Let's check that we get the same results.
   a   :	torch.Tensor						=    dataavec.encode(lowerCAmelCase_				).unsqueeze(0				)  # batch of size 1

   a   :	Optional[int]						=    model(lowerCAmelCase_				)[0]
   if classification_head:
      a   :	Any						=    dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(lowerCAmelCase_				)				)
   else:
      a   :	Union[str, Any]						=    dataavec_model(lowerCAmelCase_				)[0]
   print(our_output.shape       ,   their_output.shape				)
   a   :	Tuple						=    torch.max(torch.abs(our_output - their_output				)				).item()
   print(F'max_absolute_diff = {max_absolute_diff}'				)  # ~ 1e-7
   a   :	List[str]						=    torch.allclose(lowerCAmelCase_       ,   lowerCAmelCase_       ,   atol=1e-3				)
   print('Do both models output the same tensors?'       ,   '🔥' if success else '💩'				)
   if not success:
      raise Exception('Something went wRoNg'				)

   pathlib.Path(lowerCAmelCase_				).mkdir(parents=lowerCAmelCase_       ,   exist_ok=lowerCAmelCase_				)
   print(F'Saving model to {pytorch_dump_folder_path}'				)
   model.save_pretrained(lowerCAmelCase_				)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__				:  List[str]						= argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 parser.add_argument(
     "--classification_head", action="store_true", help="Whether to convert a final classification head."
 )
 SCREAMING_SNAKE_CASE__				:  Any						= parser.parse_args()
 convert_dataavec_checkpoint_to_pytorch(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )






import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






@require_tokenizers
class 				_SCREAMING_SNAKE_CASE( unittest.TestCase						):
   UpperCamelCase_					:    int			      = EsmTokenizer
   def _lowercase(     self     )						->Dict:
        super().setUp()
        a	:     Tuple						      =    tempfile.mkdtemp()
        # fmt: off
        a	:     List[str]						      =    ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
        # fmt: on
        a	:     Tuple						      =    os.path.join(self.tmpdirname				,  VOCAB_FILES_NAMES['''vocab_file''']     )
        with open(self.vocab_file				,  '''w'''				,  encoding='''utf-8'''     ) as vocab_writer:
             vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]     )     )
   def _lowercase(     self				,  **lowerCAmelCase_     )						->List[PreTrainedTokenizerBase]:
        return [self.get_tokenizer(**lowerCAmelCase_     )]
   def _lowercase(     self				,  **lowerCAmelCase_     )						->PreTrainedTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname				,  **lowerCAmelCase_     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[int]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     List[Any]						      =    tokenizer.tokenize('''LAGVS'''     )
        self.assertListEqual(lowerCAmelCase_				,  ['''L''', '''A''', '''G''', '''V''', '''S''']     )
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_     )				,  [4, 5, 6, 7, 8]     )
   def _lowercase(     self     )						->Optional[Any]:
        a	:     Optional[int]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     Optional[Any]						      =    '''LAGVS'''
        self.assertListEqual(tokenizer.encode(lowerCAmelCase_     )				,  [0, 4, 5, 6, 7, 8, 2]     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Any						      =    self.tokenizer_class(self.vocab_file     )

        a	:     Optional[int]						      =    ['''LAGVS''', '''WCB''']
        a	:     Optional[Any]						      =    tokenizer(lowerCAmelCase_				,  padding=lowerCAmelCase_     )['''input_ids''']

        self.assertListEqual(lowerCAmelCase_				,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]     )
   def _lowercase(     self     )						->List[Any]:
        a	:     Optional[Any]						      =    self.tokenizer_class(self.vocab_file     )

        a	:     str						      =    ['''LAGVS''', '''WCB''']
        a	:     Dict						      =    tokenizer(lowerCAmelCase_				,  padding=lowerCAmelCase_     )['''input_ids''']

        self.assertListEqual(lowerCAmelCase_				,  [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]     )


   def _lowercase(     self     )						->List[str]:
        a	:     int						      =    self.get_tokenizers(fast=lowerCAmelCase_     )
        for tokenizer in tokenizers:
             with self.subTest(F"""{tokenizer.__class__.__name__}"""     ):
                  a	:     Union[str, Any]						      =    '''<unk>'''
                  a	:     List[Any]						      =    '''<mask>'''

                  a	:     List[Any]						      =    tokenizer.tokenize(lowerCAmelCase_     )
                  a	:     Tuple						      =    tokenizer.tokenize(lowerCAmelCase_     )

                  self.assertEqual(len(lowerCAmelCase_     )				,  1     )
                  self.assertEqual(len(lowerCAmelCase_     )				,  1     )
                  self.assertEqual(token_a[0]				,  lowerCAmelCase_     )
                  self.assertEqual(token_a[0]				,  lowerCAmelCase_     )