'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
__a:			int	       =       logging.get_logger(__name__)

__a:			Optional[int]	       =       [
    ["""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       __UpperCamelCase	(       UpperCAmelCase					):
 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  lowercase__    :   Any						= k.replace(UpperCAmelCase		,					UpperCAmelCase					)

 if k.startswith('''encoder'''					):
  lowercase__    :   Any						= k.replace('''.attn'''		,					'''.self_attn'''					)
  lowercase__    :   str						= k.replace('''norm1'''		,					'''self_attn_layer_norm'''					)
  lowercase__    :   List[Any]						= k.replace('''norm2'''		,					'''final_layer_norm'''					)
 elif k.startswith('''decoder'''					):
  lowercase__    :   Dict						= k.replace('''norm1'''		,					'''self_attn_layer_norm'''					)
  lowercase__    :   Dict						= k.replace('''norm2'''		,					'''encoder_attn_layer_norm'''					)
  lowercase__    :   Optional[Any]						= k.replace('''norm3'''		,					'''final_layer_norm'''					)
 return k
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Union[str, 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:
  lowercase__    :   List[str]						= sd.pop(UpperCAmelCase					)
  lowercase__    :   Optional[int]						= k.replace('''layernorm_embedding'''		,					'''layer_norm'''					)
  assert new_k not in sd
  lowercase__    :   Dict						= v


__a:			List[str]	       =       ["""START"""]
@torch.no_grad()
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Union[str, Any]						= torch.load(UpperCAmelCase		,					map_location='''cpu'''					)
 lowercase__    :   Union[str, Any]						= model['''model''']
 lowercase__    :   List[Any]						= BlenderbotConfig.from_json_file(UpperCAmelCase					)
 lowercase__    :   List[Any]						= BlenderbotForConditionalGeneration(UpperCAmelCase					)
 lowercase__    :   Any						= m.model.state_dict().keys()
 lowercase__    :   Any						= []
 lowercase__    :   str						= {}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

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


if __name__ == "__main__":
  __a:			List[Any]	       =       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"""
  )
  __a:			Any	       =       parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


'''simple docstring'''

import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   OpenAIGPTTokenizer
       SCREAMING_SNAKE_CASE 		=   OpenAIGPTTokenizerFast
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   False

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__    :   Optional[Any]						= [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        lowercase__    :   Optional[int]						= dict(zip(__lowerCAmelCase     ,       range(len(__lowerCAmelCase			)			)			)			)
        lowercase__    :   Union[str, Any]						= ['''#version: 0.2''', '''l o''', '''lo w''', '''e r</w>''', '''''']

        lowercase__    :   Optional[int]						= os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''vocab_file''']			)
        lowercase__    :   Optional[int]						= os.path.join(self.tmpdirname     ,       VOCAB_FILES_NAMES['''merges_file''']			)
        with open(self.vocab_file     ,       '''w'''			) as fp:
         fp.write(json.dumps(__lowerCAmelCase			)			)
        with open(self.merges_file     ,       '''w'''			) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase			)			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Dict:
        return "lower newer", "lower newer"

       def _lowerCAmelCase(						self			)  ->  List[Any]:
        lowercase__    :   int						= OpenAIGPTTokenizer(self.vocab_file     ,       self.merges_file			)

        lowercase__    :   List[Any]						= '''lower'''
        lowercase__    :   Any						= ['''low''', '''er</w>''']
        lowercase__    :   Union[str, Any]						= tokenizer.tokenize(__lowerCAmelCase			)
        self.assertListEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__    :   Tuple						= tokens + ['''<unk>''']
        lowercase__    :   Optional[Any]						= [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase=15			)  ->  Optional[int]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
          lowercase__    :   List[str]						= self.rust_tokenizer_class.from_pretrained(__lowerCAmelCase     ,       **__lowerCAmelCase			)

          # Simple input
          lowercase__    :   List[str]						= '''This is a simple input'''
          lowercase__    :   Any						= ['''This is a simple input 1''', '''This is a simple input 2''']
          lowercase__    :   str						= ('''This is a simple input''', '''This is a pair''')
          lowercase__    :   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(__lowerCAmelCase     ,       tokenizer_r.encode     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''			)

          # Simple input
          self.assertRaises(__lowerCAmelCase     ,       tokenizer_r.encode_plus     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''			)

          # Simple input
          self.assertRaises(
              __lowerCAmelCase     ,       tokenizer_r.batch_encode_plus     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''     ,       )

          # Pair input
          self.assertRaises(__lowerCAmelCase     ,       tokenizer_r.encode     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''			)

          # Pair input
          self.assertRaises(__lowerCAmelCase     ,       tokenizer_r.encode_plus     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''			)

          # Pair input
          self.assertRaises(
              __lowerCAmelCase     ,       tokenizer_r.batch_encode_plus     ,       __lowerCAmelCase     ,       max_length=__lowerCAmelCase     ,       padding='''max_length'''     ,       )


       def _lowerCAmelCase(						self			)  ->  Tuple:
        pass



@require_ftfy
@require_spacy
@require_tokenizers
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       pass

