




import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
  from test_module.custom_tokenization_fast import CustomTokenizerFast







class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Tuple:
           _A							:     str			=						0

     @slow
     def 					a__	(  self				)						->							Tuple:
           for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
                 _A							:     Tuple			=						AutoTokenizer.from_pretrained(_a				)
                 self.assertIsNotNone(_a				)
                 self.assertIsInstance(_a		,     (BertTokenizer, BertTokenizerFast)				)
                 self.assertGreater(len(_a				)		,     0				)

           for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
                 _A							:     Optional[Any]			=						AutoTokenizer.from_pretrained(_a				)
                 self.assertIsNotNone(_a				)
                 self.assertIsInstance(_a		,     (GPTaTokenizer, GPTaTokenizerFast)				)
                 self.assertGreater(len(_a				)		,     0				)

     def 					a__	(  self				)						->							List[str]:
           _A							:     str			=						AutoTokenizer.from_pretrained(_a				)
           self.assertIsInstance(_a		,     (BertTokenizer, BertTokenizerFast)				)
           self.assertEqual(tokenizer.vocab_size		,     12				)

     def 					a__	(  self				)						->							Tuple:
           _A							:     str			=						AutoTokenizer.from_pretrained(_a				)
           self.assertIsInstance(_a		,     (RobertaTokenizer, RobertaTokenizerFast)				)
           self.assertEqual(tokenizer.vocab_size		,     20				)

     def 					a__	(  self				)						->							str:
           _A							:     int			=						AutoConfig.from_pretrained(_a				)
           self.assertIsInstance(_a		,     _a				)
           # Check that tokenizer_type ≠ model_type
           _A							:     int			=						AutoTokenizer.from_pretrained(_a		,     config=_a				)
           self.assertIsInstance(_a		,     (BertTokenizer, BertTokenizerFast)				)
           self.assertEqual(tokenizer.vocab_size		,     12				)

     def 					a__	(  self				)						->							Dict:
           with tempfile.TemporaryDirectory() as tmp_dir:
                 shutil.copy("""./tests/fixtures/vocab.txt"""		,     os.path.join(_a		,     """vocab.txt"""				)				)

                 _A							:     Dict			=						AutoTokenizer.from_pretrained(_a		,     tokenizer_type="""bert"""		,     use_fast=_a				)
                 self.assertIsInstance(_a		,     _a				)

           with tempfile.TemporaryDirectory() as tmp_dir:
                 shutil.copy("""./tests/fixtures/vocab.json"""		,     os.path.join(_a		,     """vocab.json"""				)				)
                 shutil.copy("""./tests/fixtures/merges.txt"""		,     os.path.join(_a		,     """merges.txt"""				)				)

                 _A							:     str			=						AutoTokenizer.from_pretrained(_a		,     tokenizer_type="""gpt2"""		,     use_fast=_a				)
                 self.assertIsInstance(_a		,     _a				)

     @require_tokenizers
     def 					a__	(  self				)						->							Union[str, Any]:
           with tempfile.TemporaryDirectory() as tmp_dir:
                 shutil.copy("""./tests/fixtures/vocab.txt"""		,     os.path.join(_a		,     """vocab.txt"""				)				)

                 _A							:     int			=						AutoTokenizer.from_pretrained(_a		,     tokenizer_type="""bert"""				)
                 self.assertIsInstance(_a		,     _a				)

           with tempfile.TemporaryDirectory() as tmp_dir:
                 shutil.copy("""./tests/fixtures/vocab.json"""		,     os.path.join(_a		,     """vocab.json"""				)				)
                 shutil.copy("""./tests/fixtures/merges.txt"""		,     os.path.join(_a		,     """merges.txt"""				)				)

                 _A							:     Dict			=						AutoTokenizer.from_pretrained(_a		,     tokenizer_type="""gpt2"""				)
                 self.assertIsInstance(_a		,     _a				)

     def 					a__	(  self				)						->							Tuple:
           with pytest.raises(_a				):
                 AutoTokenizer.from_pretrained("""./"""		,     tokenizer_type="""xxx"""				)

     @require_tokenizers
     def 					a__	(  self				)						->							List[str]:
           for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
                 _A							:     List[str]			=						tokenizer_class.from_pretrained("""wietsedv/bert-base-dutch-cased"""				)
                 self.assertIsInstance(_a		,     (BertTokenizer, BertTokenizerFast)				)

                 if isinstance(_a		,     _a				):
                       self.assertEqual(tokenizer.basic_tokenizer.do_lower_case		,     _a				)
                 else:
                       self.assertEqual(tokenizer.do_lower_case		,     _a				)

                 self.assertEqual(tokenizer.model_max_length		,     512				)

     @require_tokenizers
     def 					a__	(  self				)						->							Union[str, Any]:
           for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
                 with self.assertRaisesRegex(
                     _a		,     """julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"""		,     ):
                       _A							:     Dict			=						tokenizer_class.from_pretrained("""julien-c/herlolip-not-exists"""				)

     def 					a__	(  self				)						->							int:
           # tests: https://github.com/huggingface/transformers/pull/13251
           # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
           # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
           _A							:     Dict			=						TOKENIZER_MAPPING.values()
           _A							:     Optional[Any]			=						[]

           for slow_tok, fast_tok in tokenizers:
                 if slow_tok is not None:
                       tokenizer_names.append(slow_tok.__name__				)

                 if fast_tok is not None:
                       tokenizer_names.append(fast_tok.__name__				)

           for tokenizer_name in tokenizer_names:
                 # must find the right class
                 tokenizer_class_from_name(_a				)

     @require_tokenizers
     def 					a__	(  self				)						->							str:
           self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""		,     use_fast=_a				)		,     _a				)
           self.assertIsInstance(AutoTokenizer.from_pretrained("""bert-base-cased"""				)		,     _a				)

     @require_tokenizers
     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     Optional[Any]			=						AutoTokenizer.from_pretrained("""distilbert-base-uncased"""		,     do_lower_case=_a				)
           _A							:     Optional[Any]			=						"""Hello, world. How are you?"""
           _A							:     List[Any]			=						tokenizer.tokenize(_a				)
           self.assertEqual("""[UNK]"""		,     tokens[0]				)

           _A							:     str			=						AutoTokenizer.from_pretrained("""microsoft/mpnet-base"""		,     do_lower_case=_a				)
           _A							:     Tuple			=						tokenizer.tokenize(_a				)
           self.assertEqual("""[UNK]"""		,     tokens[0]				)

     @require_tokenizers
     def 					a__	(  self				)						->							Any:
           _A							:     Optional[int]			=						AutoTokenizer.from_pretrained("""robot-test/dummy-tokenizer-fast-with-model-config"""				)
           self.assertEqual(type(_a				)		,     _a				)
           self.assertEqual(tokenizer.model_max_length		,     512				)
           self.assertEqual(tokenizer.vocab_size		,     3_0000				)
           self.assertEqual(tokenizer.unk_token		,     """[UNK]"""				)
           self.assertEqual(tokenizer.padding_side		,     """right"""				)
           self.assertEqual(tokenizer.truncation_side		,     """right"""				)

     def 					a__	(  self				)						->							Tuple:
           _A							:     Optional[int]			=						AutoTokenizer.from_pretrained(_a				)
           self.assertIsInstance(_a		,     (BertTokenizer, BertTokenizerFast)				)
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(_a				)
                 _A							:     List[str]			=						AutoTokenizer.from_pretrained(_a				)

           self.assertIsInstance(_a		,     tokenizer.__class__				)
           self.assertEqual(tokenizera.vocab_size		,     12				)

     def 					a__	(  self				)						->							Dict:
           _A							:     Tuple			=						AutoTokenizer.from_pretrained("""ctrl"""				)
           # There is no fast CTRL so this always gives us a slow tokenizer.
           self.assertIsInstance(_a		,     _a				)

     def 					a__	(  self				)						->							Union[str, Any]:
           # Check we can load the tokenizer config of an online model.
           _A							:     List[str]			=						get_tokenizer_config("""bert-base-cased"""				)
           _A							:     Any			=						config.pop("""_commit_hash"""		,     _a				)
           # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
           self.assertEqual(_a		,     {"""do_lower_case""": False}				)

           # This model does not have a tokenizer_config so we get back an empty dict.
           _A							:     Optional[int]			=						get_tokenizer_config(_a				)
           self.assertDictEqual(_a		,     {}				)

           # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
           _A							:     Union[str, Any]			=						AutoTokenizer.from_pretrained(_a				)
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(_a				)
                 _A							:     List[Any]			=						get_tokenizer_config(_a				)

           # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
           self.assertEqual(config["""tokenizer_class"""]		,     """BertTokenizer"""				)

     def 					a__	(  self				)						->							Optional[int]:
           try:
                 AutoConfig.register("""custom"""		,     _a				)

                 AutoTokenizer.register(_a		,     slow_tokenizer_class=_a				)
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(_a				):
                       AutoTokenizer.register(_a		,     slow_tokenizer_class=_a				)

                 _A							:     List[Any]			=						CustomTokenizer.from_pretrained(_a				)
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       tokenizer.save_pretrained(_a				)

                       _A							:     int			=						AutoTokenizer.from_pretrained(_a				)
                       self.assertIsInstance(_a		,     _a				)

           finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                       del CONFIG_MAPPING._extra_content["custom"]
                 if CustomConfig in TOKENIZER_MAPPING._extra_content:
                       del TOKENIZER_MAPPING._extra_content[CustomConfig]

     @require_tokenizers
     def 					a__	(  self				)						->							str:
           try:
                 AutoConfig.register("""custom"""		,     _a				)

                 # Can register in two steps
                 AutoTokenizer.register(_a		,     slow_tokenizer_class=_a				)
                 self.assertEqual(TOKENIZER_MAPPING[CustomConfig]		,     (CustomTokenizer, None)				)
                 AutoTokenizer.register(_a		,     fast_tokenizer_class=_a				)
                 self.assertEqual(TOKENIZER_MAPPING[CustomConfig]		,     (CustomTokenizer, CustomTokenizerFast)				)

                 del TOKENIZER_MAPPING._extra_content[CustomConfig]
                 # Can register in one step
                 AutoTokenizer.register(
                     _a		,     slow_tokenizer_class=_a		,     fast_tokenizer_class=_a				)
                 self.assertEqual(TOKENIZER_MAPPING[CustomConfig]		,     (CustomTokenizer, CustomTokenizerFast)				)

                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(_a				):
                       AutoTokenizer.register(_a		,     fast_tokenizer_class=_a				)

                 # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
                 # and that model does not have a tokenizer.json
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       _A							:     str			=						BertTokenizerFast.from_pretrained(_a				)
                       bert_tokenizer.save_pretrained(_a				)
                       _A							:     Optional[Any]			=						CustomTokenizerFast.from_pretrained(_a				)

                 with tempfile.TemporaryDirectory() as tmp_dir:
                       tokenizer.save_pretrained(_a				)

                       _A							:     Any			=						AutoTokenizer.from_pretrained(_a				)
                       self.assertIsInstance(_a		,     _a				)

                       _A							:     Dict			=						AutoTokenizer.from_pretrained(_a		,     use_fast=_a				)
                       self.assertIsInstance(_a		,     _a				)

           finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                       del CONFIG_MAPPING._extra_content["custom"]
                 if CustomConfig in TOKENIZER_MAPPING._extra_content:
                       del TOKENIZER_MAPPING._extra_content[CustomConfig]

     def 					a__	(  self				)						->							List[str]:
           # If remote code is not set, we will time out when asking whether to load the model.
           with self.assertRaises(_a				):
                 _A							:     List[Any]			=						AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""				)
           # If remote code is disabled, we can't load this config.
           with self.assertRaises(_a				):
                 _A							:     int			=						AutoTokenizer.from_pretrained(
                     """hf-internal-testing/test_dynamic_tokenizer"""		,     trust_remote_code=_a				)

           _A							:     Dict			=						AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""		,     trust_remote_code=_a				)
           self.assertTrue(tokenizer.special_attribute_present				)
           # Test tokenizer can be reloaded.
           with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer.save_pretrained(_a				)
                 _A							:     int			=						AutoTokenizer.from_pretrained(_a		,     trust_remote_code=_a				)
           self.assertTrue(reloaded_tokenizer.special_attribute_present				)

           if is_tokenizers_available():
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizerFast"""				)
                 self.assertEqual(reloaded_tokenizer.__class__.__name__		,     """NewTokenizerFast"""				)

                 # Test we can also load the slow version
                 _A							:     str			=						AutoTokenizer.from_pretrained(
                     """hf-internal-testing/test_dynamic_tokenizer"""		,     trust_remote_code=_a		,     use_fast=_a				)
                 self.assertTrue(tokenizer.special_attribute_present				)
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizer"""				)
                 # Test tokenizer can be reloaded.
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       tokenizer.save_pretrained(_a				)
                       _A							:     List[Any]			=						AutoTokenizer.from_pretrained(_a		,     trust_remote_code=_a		,     use_fast=_a				)
                 self.assertEqual(reloaded_tokenizer.__class__.__name__		,     """NewTokenizer"""				)
                 self.assertTrue(reloaded_tokenizer.special_attribute_present				)
           else:
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizer"""				)
                 self.assertEqual(reloaded_tokenizer.__class__.__name__		,     """NewTokenizer"""				)

     @require_tokenizers
     def 					a__	(  self				)						->							int:







           class 						lowercase							(   UpperCamelCase__		):
                _a							 =						False







           class 						lowercase							(   UpperCamelCase__		):
                _a							 =						NewTokenizer
                _a							 =						False

           try:
                 AutoConfig.register("""custom"""		,     _a				)
                 AutoTokenizer.register(_a		,     slow_tokenizer_class=_a				)
                 AutoTokenizer.register(_a		,     fast_tokenizer_class=_a				)
                 # If remote code is not set, the default is to use local
                 _A							:     int			=						AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""				)
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizerFast"""				)
                 self.assertFalse(tokenizer.special_attribute_present				)
                 _A							:     Optional[Any]			=						AutoTokenizer.from_pretrained("""hf-internal-testing/test_dynamic_tokenizer"""		,     use_fast=_a				)
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizer"""				)
                 self.assertFalse(tokenizer.special_attribute_present				)

                 # If remote code is disabled, we load the local one.
                 _A							:     Union[str, Any]			=						AutoTokenizer.from_pretrained(
                     """hf-internal-testing/test_dynamic_tokenizer"""		,     trust_remote_code=_a				)
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizerFast"""				)
                 self.assertFalse(tokenizer.special_attribute_present				)
                 _A							:     Union[str, Any]			=						AutoTokenizer.from_pretrained(
                     """hf-internal-testing/test_dynamic_tokenizer"""		,     trust_remote_code=_a		,     use_fast=_a				)
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizer"""				)
                 self.assertFalse(tokenizer.special_attribute_present				)

                 # If remote is enabled, we load from the Hub
                 _A							:     str			=						AutoTokenizer.from_pretrained(
                     """hf-internal-testing/test_dynamic_tokenizer"""		,     trust_remote_code=_a				)
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizerFast"""				)
                 self.assertTrue(tokenizer.special_attribute_present				)
                 _A							:     List[Any]			=						AutoTokenizer.from_pretrained(
                     """hf-internal-testing/test_dynamic_tokenizer"""		,     trust_remote_code=_a		,     use_fast=_a				)
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizer"""				)
                 self.assertTrue(tokenizer.special_attribute_present				)

           finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                       del CONFIG_MAPPING._extra_content["custom"]
                 if CustomConfig in TOKENIZER_MAPPING._extra_content:
                       del TOKENIZER_MAPPING._extra_content[CustomConfig]

     def 					a__	(  self				)						->							List[Any]:
           _A							:     List[Any]			=						AutoTokenizer.from_pretrained(
               """hf-internal-testing/test_dynamic_tokenizer_legacy"""		,     trust_remote_code=_a				)
           self.assertTrue(tokenizer.special_attribute_present				)
           if is_tokenizers_available():
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizerFast"""				)

                 # Test we can also load the slow version
                 _A							:     str			=						AutoTokenizer.from_pretrained(
                     """hf-internal-testing/test_dynamic_tokenizer_legacy"""		,     trust_remote_code=_a		,     use_fast=_a				)
                 self.assertTrue(tokenizer.special_attribute_present				)
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizer"""				)
           else:
                 self.assertEqual(tokenizer.__class__.__name__		,     """NewTokenizer"""				)

     def 					a__	(  self				)						->							Tuple:
           with self.assertRaisesRegex(
               _a		,     """bert-base is not a local folder and is not a valid model identifier"""				):
                 _A							:     List[str]			=						AutoTokenizer.from_pretrained("""bert-base"""				)

     def 					a__	(  self				)						->							str:
           with self.assertRaisesRegex(
               _a		,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
                 _A							:     Union[str, Any]			=						AutoTokenizer.from_pretrained(_a		,     revision="""aaaaaa"""				)



     def 					a__	(  self				)						->							str:
           # Make sure we have cached the tokenizer.
           _A							:     Any			=						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)
           with RequestCounter() as counter:
                 _A							:     Optional[int]			=						AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-bert"""				)
                 self.assertEqual(counter.get_request_count		,     0				)
                 self.assertEqual(counter.head_request_count		,     1				)
                 self.assertEqual(counter.other_request_count		,     0				)







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case										=       logging.get_logger(__name__)

_snake_case										=       [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]







def 				lowerCAmelCase_	(     snake_case_     ):
      if k == "embeddings.weight":
            return "shared.weight"

      for parlai_name, hf_name in PATTERNS:
            _A							:     str			=						k.replace(snake_case_,snake_case_     )

      if k.startswith("""encoder"""     ):
            _A							:     Optional[Any]			=						k.replace(""".attn""",""".self_attn"""     )
            _A							:     Dict			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Optional[Any]			=						k.replace("""norm2""","""final_layer_norm"""     )
      elif k.startswith("""decoder"""     ):
            _A							:     str			=						k.replace("""norm1""","""self_attn_layer_norm"""     )
            _A							:     Any			=						k.replace("""norm2""","""encoder_attn_layer_norm"""     )
            _A							:     Optional[int]			=						k.replace("""norm3""","""final_layer_norm"""     )
      return k







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     List[Any]			=						[
          """model.encoder.layernorm_embedding.weight""",
          """model.encoder.layernorm_embedding.bias""",
          """model.decoder.layernorm_embedding.weight""",
          """model.decoder.layernorm_embedding.bias""",
      ]
      for k in keys:
            _A							:     str			=						sd.pop(snake_case_     )
            _A							:     Optional[int]			=						k.replace("""layernorm_embedding""","""layer_norm"""     )
            assert new_k not in sd
            _A							:     Optional[int]			=						v


_snake_case										=       ["START"]







@torch.no_grad()
def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_     ):
      _A							:     Tuple			=						torch.load(snake_case_,map_location="""cpu"""     )
      _A							:     List[Any]			=						model["""model"""]
      _A							:     Optional[Any]			=						BlenderbotConfig.from_json_file(snake_case_     )
      _A							:     List[str]			=						BlenderbotForConditionalGeneration(snake_case_     )
      _A							:     Tuple			=						m.model.state_dict().keys()
      _A							:     Any			=						[]
      _A							:     Dict			=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            _A							:     Optional[int]			=						rename_state_dict_key(snake_case_     )
            if new_k not in valid_keys:
                  failures.append([k, new_k]     )
            else:
                  _A							:     Dict			=						v
      if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
            rename_layernorm_keys(snake_case_     )
      m.model.load_state_dict(snake_case_,strict=snake_case_     )
      m.half()
      m.save_pretrained(snake_case_     )


if __name__ == "__main__":
  _snake_case										=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
  parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
  parser.add_argument(
      "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
  )
  _snake_case										=       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

