





'''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     (	lowercase__      ,	unittest.TestCase    ):
 __lowercase       		=    OpenAIGPTTokenizer
 __lowercase       		=    OpenAIGPTTokenizerFast
 __lowercase       		=    True
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     A__    =      [
         '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>',
     ]
     A__    =      dict(zip(_a						,	range(len(_a							)							)							)							)
     A__    =      ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

     A__    =      os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]							)
     A__    =      os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["merges_file"]							)
     with open(self.vocab_file						,	"w"							) as fp:
         fp.write(json.dumps(_a							)							)
     with open(self.merges_file						,	"w"							) as fp:
         fp.write("\n".join(_a							)							)
 def 					UpperCAmelCase_							(    self				:int						,	lowercase_				:int							)->							str:
     return "lower newer", "lower newer"
 def 					UpperCAmelCase_							(    self				:Any							)->							Optional[int]:
     A__    =      OpenAIGPTTokenizer(self.vocab_file						,	self.merges_file							)

     A__    =      'lower'
     A__    =      ['low', 'er</w>']
     A__    =      tokenizer.tokenize(_a							)
     self.assertListEqual(_a						,	_a							)

     A__    =      tokens + ['<unk>']
     A__    =      [14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(_a							)						,	_a							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:Dict=15							)->							List[Any]:






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
             A__    =      self.rust_tokenizer_class.from_pretrained(_a						,	**_a							)

             # Simple input
             A__    =      'This is a simple input'
             A__    =      ['This is a simple input 1', 'This is a simple input 2']
             A__    =      ('This is a simple input', 'This is a pair')
             A__    =      [
                 ('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 					UpperCAmelCase_							(    self				:Any							)->							str:
     pass






@require_ftfy
@require_spacy
@require_tokenizers
class        UpperCAmelCase     (	lowercase__    ):
 pass






'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     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    (							_lowerCamelCase : int	):
    if k == "embeddings.weight":
        return "shared.weight"

    for parlai_name, hf_name in PATTERNS:
        A__    =      k.replace(_lowerCamelCase						,	_lowerCamelCase	)

    if k.startswith("encoder"	):
        A__    =      k.replace(".attn"						,	".self_attn"	)
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"final_layer_norm"	)
    elif k.startswith("decoder"	):
        A__    =      k.replace("norm1"						,	"self_attn_layer_norm"	)
        A__    =      k.replace("norm2"						,	"encoder_attn_layer_norm"	)
        A__    =      k.replace("norm3"						,	"final_layer_norm"	)
    return k





def 						UpperCamelCase    (							_lowerCamelCase : int	):
    A__    =      [
        "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__    =      sd.pop(_lowerCamelCase	)
        A__    =      k.replace("layernorm_embedding"						,	"layer_norm"	)
        assert new_k not in sd
        A__    =      v


__lowerCAmelCase							:     Optional[int]		     =["START"]





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

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


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



