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()
UpperCAmelCase				 =   logging.get_logger(__name__)

UpperCAmelCase				 =   """Hello world! cécé herlolip"""







def 						UpperCAmelCase							(		lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_							)						->						Tuple:
     a      ,				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=5_1_4    ,   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__":
  UpperCAmelCase				 =   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."""
  )
  UpperCAmelCase				 =   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_     )