"""simple docstring"""




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
     raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
_a   :	Optional[Any] 					=     logging.get_logger(__name__)

_a   :	Tuple 					=     'Hello world! cécé herlolip'





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: str    ,_lowerCamelCase		: bool			) ->   Any:
  _lowerCAmelCase		: Tuple						    =  FairseqRobertaModel.from_pretrained(_lowerCamelCase			)
  roberta.eval()  # disable dropout
  _lowerCAmelCase		: Tuple						    =  roberta.model.encoder.sentence_encoder
  _lowerCAmelCase		: List[str]						    =  XLMRobertaConfig(
      vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings    ,hidden_size=roberta.cfg.model.encoder_embed_dim    ,num_hidden_layers=roberta.cfg.model.encoder_layers    ,num_attention_heads=roberta.cfg.model.encoder_attention_heads    ,intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim    ,max_position_embeddings=514    ,type_vocab_size=1    ,layer_norm_eps=1e-5    ,)
  if classification_head:
    _lowerCAmelCase		: Tuple						    =  roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]

  print("""Our RoBERTa config:"""    ,_lowerCamelCase			)

  _lowerCAmelCase		: Optional[Any]						    =  XLMRobertaXLForSequenceClassification(_lowerCamelCase			) if classification_head else XLMRobertaXLForMaskedLM(_lowerCamelCase			)
  model.eval()

  # Now let's copy all the weights.
  # Embeddings
  _lowerCAmelCase		: List[str]						    =  roberta_sent_encoder.embed_tokens.weight
  _lowerCAmelCase		: Optional[int]						    =  roberta_sent_encoder.embed_positions.weight
  _lowerCAmelCase		: str						    =  torch.zeros_like(
      model.roberta.embeddings.token_type_embeddings.weight			)  # just zero them out b/c RoBERTa doesn't use them.

  _lowerCAmelCase		: List[Any]						    =  roberta_sent_encoder.layer_norm.weight
  _lowerCAmelCase		: Union[str, Any]						    =  roberta_sent_encoder.layer_norm.bias

  for i in range(config.num_hidden_layers			):
    # Encoder: start of layer
    _lowerCAmelCase		: BertLayer						    =  model.roberta.encoder.layer[i]
    _lowerCAmelCase		: TransformerSentenceEncoderLayer						    =  roberta_sent_encoder.layers[i]

    _lowerCAmelCase		: RobertaAttention						    =  layer.attention
    _lowerCAmelCase		: str						    =  roberta_layer.self_attn_layer_norm.weight
    _lowerCAmelCase		: Union[str, Any]						    =  roberta_layer.self_attn_layer_norm.bias

    # self attention
    _lowerCAmelCase		: BertSelfAttention						    =  layer.attention.self
    assert (
        roberta_layer.self_attn.k_proj.weight.data.shape
        == roberta_layer.self_attn.q_proj.weight.data.shape
        == roberta_layer.self_attn.v_proj.weight.data.shape
        == torch.Size((config.hidden_size, config.hidden_size)			)
    )

    _lowerCAmelCase		: List[str]						    =  roberta_layer.self_attn.q_proj.weight
    _lowerCAmelCase		: List[str]						    =  roberta_layer.self_attn.q_proj.bias
    _lowerCAmelCase		: Union[str, Any]						    =  roberta_layer.self_attn.k_proj.weight
    _lowerCAmelCase		: Union[str, Any]						    =  roberta_layer.self_attn.k_proj.bias
    _lowerCAmelCase		: Optional[int]						    =  roberta_layer.self_attn.v_proj.weight
    _lowerCAmelCase		: int						    =  roberta_layer.self_attn.v_proj.bias

    # self-attention output
    _lowerCAmelCase		: BertSelfOutput						    =  layer.attention.output
    assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
    _lowerCAmelCase		: Any						    =  roberta_layer.self_attn.out_proj.weight
    _lowerCAmelCase		: str						    =  roberta_layer.self_attn.out_proj.bias

    # this one is final layer norm
    _lowerCAmelCase		: Any						    =  roberta_layer.final_layer_norm.weight
    _lowerCAmelCase		: str						    =  roberta_layer.final_layer_norm.bias

    # intermediate
    _lowerCAmelCase		: BertIntermediate						    =  layer.intermediate
    assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
    _lowerCAmelCase		: Optional[int]						    =  roberta_layer.fca.weight
    _lowerCAmelCase		: Any						    =  roberta_layer.fca.bias

    # output
    _lowerCAmelCase		: BertOutput						    =  layer.output
    assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
    _lowerCAmelCase		: Optional[Any]						    =  roberta_layer.fca.weight
    _lowerCAmelCase		: Optional[Any]						    =  roberta_layer.fca.bias
    # end of layer

  if classification_head:
    _lowerCAmelCase		: Dict						    =  roberta.model.classification_heads["""mnli"""].dense.weight
    _lowerCAmelCase		: List[Any]						    =  roberta.model.classification_heads["""mnli"""].dense.bias
    _lowerCAmelCase		: Optional[int]						    =  roberta.model.classification_heads["""mnli"""].out_proj.weight
    _lowerCAmelCase		: Optional[int]						    =  roberta.model.classification_heads["""mnli"""].out_proj.bias
  else:
    # LM Head
    _lowerCAmelCase		: Union[str, Any]						    =  roberta.model.encoder.lm_head.dense.weight
    _lowerCAmelCase		: Any						    =  roberta.model.encoder.lm_head.dense.bias
    _lowerCAmelCase		: Union[str, Any]						    =  roberta.model.encoder.lm_head.layer_norm.weight
    _lowerCAmelCase		: Dict						    =  roberta.model.encoder.lm_head.layer_norm.bias
    _lowerCAmelCase		: Any						    =  roberta.model.encoder.lm_head.weight
    _lowerCAmelCase		: Union[str, Any]						    =  roberta.model.encoder.lm_head.bias

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

  _lowerCAmelCase		: Union[str, Any]						    =  model(_lowerCamelCase			)[0]
  if classification_head:
    _lowerCAmelCase		: List[Any]						    =  roberta.model.classification_heads["""mnli"""](roberta.extract_features(_lowerCamelCase			)			)
  else:
    _lowerCAmelCase		: Tuple						    =  roberta.model(_lowerCamelCase			)[0]
  print(our_output.shape    ,their_output.shape			)
  _lowerCAmelCase		: Dict						    =  torch.max(torch.abs(our_output - their_output			)			).item()
  print(f"max_absolute_diff = {max_absolute_diff}"			)  # ~ 1e-7
  _lowerCAmelCase		: List[Any]						    =  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__":
     _a   :	Tuple 					=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--roberta_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.'
     )
     _a   :	Any 					=     parser.parse_args()
     convert_xlm_roberta_xl_checkpoint_to_pytorch(
         args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
     )





"""simple docstring"""




import json
import os
import re
import unittest

from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizer
     _UpperCamelCase							:    Dict	      =	CodeGenTokenizerFast
     _UpperCamelCase							:    Tuple	      =	True
     _UpperCamelCase							:    List[Any]	      =	{"add_prefix_space": True}
     _UpperCamelCase							:    str	      =	False






     def 					__A						(	self						):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       _lowerCAmelCase		: Union[str, Any]						    =  [
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
           """<|endoftext|>""",
       ]
       _lowerCAmelCase		: Optional[int]						    =  dict(zip(a__       ,  range(len(a__						)						)						)						)
       _lowerCAmelCase		: str						    =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       _lowerCAmelCase		: Any						    =  {"""unk_token""": """<unk>"""}

       _lowerCAmelCase		: List[str]						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       _lowerCAmelCase		: List[Any]						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""merges_file"""]						)
       with open(self.vocab_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         fp.write(json.dumps(a__						) + """\n"""						)
       with open(self.merges_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         fp.write("""\n""".join(a__						)						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return CodeGenTokenizerFast.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: str						    =  """lower newer"""
       _lowerCAmelCase		: Tuple						    =  """lower newer"""
       return input_text, output_text






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  CodeGenTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map						)
       _lowerCAmelCase		: int						    =  """lower newer"""
       _lowerCAmelCase		: List[str]						    =  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       self.assertListEqual(a__       ,  a__						)

       _lowerCAmelCase		: Tuple						    =  tokens + [tokenizer.unk_token]
       _lowerCAmelCase		: List[str]						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self						):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase		: Optional[int]						    =  self.get_tokenizer()
       _lowerCAmelCase		: Optional[int]						    =  self.get_rust_tokenizer(add_prefix_space=a__						)

       _lowerCAmelCase		: Any						    =  """lower newer"""

       # Testing tokenization
       _lowerCAmelCase		: Any						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: int						    =  rust_tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids without special tokens
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer.encode(a__       ,  add_special_tokens=a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Dict						    =  rust_tokenizer.encode(a__       ,  add_special_tokens=a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids with special tokens
       _lowerCAmelCase		: int						    =  self.get_rust_tokenizer(add_prefix_space=a__						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.encode(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Any						    =  rust_tokenizer.encode(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing the unknown token
       _lowerCAmelCase		: List[str]						    =  tokens + [rust_tokenizer.unk_token]
       _lowerCAmelCase		: Dict						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self       ,  *a__       ,  **a__						):
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both CodeGen and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass






     def 					__A						(	self       ,  a__=15						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
           _lowerCAmelCase		: List[Any]						    =  self.rust_tokenizer_class.from_pretrained(a__       ,  **a__						)

           # Simple input
           _lowerCAmelCase		: Dict						    =  """This is a simple input"""
           _lowerCAmelCase		: Optional[int]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]
           _lowerCAmelCase		: Optional[int]						    =  ("""This is a simple input""", """This is a pair""")
           _lowerCAmelCase		: str						    =  [
               ("""This is a simple input 1""", """This is a simple input 2"""),
               ("""This is a simple pair 1""", """This is a simple pair 2"""),
           ]

           # Simple input tests
           self.assertRaises(a__       ,  tokenizer_r.encode       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Simple input
           self.assertRaises(a__       ,  tokenizer_r.encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Simple input
           self.assertRaises(
               a__       ,  tokenizer_r.batch_encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""       ,  )

           # Pair input
           self.assertRaises(a__       ,  tokenizer_r.encode       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Pair input
           self.assertRaises(a__       ,  tokenizer_r.encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Pair input
           self.assertRaises(
               a__       ,  tokenizer_r.batch_encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="""<pad>"""						)

       # Simple input
       _lowerCAmelCase		: Union[str, Any]						    =  """This is a simple input"""
       _lowerCAmelCase		: Dict						    =  ["""This is a simple input looooooooong""", """This is a simple input"""]
       _lowerCAmelCase		: Any						    =  ("""This is a simple input""", """This is a pair""")
       _lowerCAmelCase		: Optional[int]						    =  [
           ("""This is a simple input loooooong""", """This is a simple input"""),
           ("""This is a simple pair loooooong""", """This is a simple pair"""),
       ]

       _lowerCAmelCase		: Optional[int]						    =  tokenizer.pad_token_id

       _lowerCAmelCase		: Any						    =  tokenizer(a__       ,  padding="""max_length"""       ,  max_length=30       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: str						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  tokenizer(*a__       ,  padding="""max_length"""       ,  max_length=60       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: int						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)

       # s
       # test single string max_length padding
       self.assertEqual(out_s["""input_ids"""].shape[-1]       ,  30						)
       self.assertTrue(pad_token_id in out_s["""input_ids"""]						)
       self.assertTrue(0 in out_s["""attention_mask"""]						)

       # s2
       # test automatic padding
       self.assertEqual(out_sa["""input_ids"""].shape[-1]       ,  33						)
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]						)
       self.assertFalse(0 in out_sa["""attention_mask"""][0]						)
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]						)
       self.assertTrue(0 in out_sa["""attention_mask"""][1]						)

       # p
       # test single pair max_length padding
       self.assertEqual(out_p["""input_ids"""].shape[-1]       ,  60						)
       self.assertTrue(pad_token_id in out_p["""input_ids"""]						)
       self.assertTrue(0 in out_p["""attention_mask"""]						)

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa["""input_ids"""].shape[-1]       ,  52						)
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]						)
       self.assertFalse(0 in out_pa["""attention_mask"""][0]						)
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]						)
       self.assertTrue(0 in out_pa["""attention_mask"""][1]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  """$$$"""
       _lowerCAmelCase		: str						    =  CodeGenTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=a__       ,  add_bos_token=a__						)

       _lowerCAmelCase		: Tuple						    =  """This is a simple input"""
       _lowerCAmelCase		: Union[str, Any]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]

       _lowerCAmelCase		: List[str]						    =  tokenizer.bos_token_id

       _lowerCAmelCase		: str						    =  tokenizer(a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer(a__						)

       self.assertEqual(out_s.input_ids[0]       ,  a__						)
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.decode(out_s.input_ids						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.batch_decode(out_sa.input_ids						)

       self.assertEqual(decode_s.split()[0]       ,  a__						)
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)






     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: int						    =  CodeGenTokenizer.from_pretrained("""Salesforce/codegen-350M-mono"""						)

       _lowerCAmelCase		: Optional[int]						    =  """\nif len_a > len_b:\n    result = a\nelse:\n    result = b\n\n\n\n#"""
       _lowerCAmelCase		: List[Any]						    =  """\nif len_a > len_b:      result = a\nelse:      result = b"""

       _lowerCAmelCase		: Tuple						    =  tokenizer.encode(a__						)
       _lowerCAmelCase		: Optional[Any]						    =  ["""^#""", re.escape("""<|endoftext|>"""						), """^'''""", """^\"\"\"""", """\n\n\n"""]
       _lowerCAmelCase		: int						    =  tokenizer.decode(a__       ,  truncate_before_pattern=a__						)
       self.assertEqual(a__       ,  a__						)






     def 					__A						(	self						):
       pass




