




'''simple docstring'''





import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase_		:     Dict          =		get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
@require_tokenizers
class   _a							(		__lowerCAmelCase   ,   unittest.TestCase				):
     SCREAMING_SNAKE_CASE_				:       int       							=		ReformerTokenizer
     SCREAMING_SNAKE_CASE_				:       List[Any]       							=		ReformerTokenizerFast
     SCREAMING_SNAKE_CASE_				:       int       							=		True
     SCREAMING_SNAKE_CASE_				:       Any       							=		False
     SCREAMING_SNAKE_CASE_				:       Optional[int]       							=		True






     def 							_lowercase      (					self       )				->     Dict:
          super().setUp()

          _snake_case				= ReformerTokenizer(_SCREAMING_SNAKE_CASE   ,keep_accents=_SCREAMING_SNAKE_CASE       )
          tokenizer.save_pretrained(self.tmpdirname       )






     def 							_lowercase      (					self       )				->     str:
          _snake_case				= "<s>"
          _snake_case				= 1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_SCREAMING_SNAKE_CASE       )   ,_SCREAMING_SNAKE_CASE       )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_SCREAMING_SNAKE_CASE       )   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     Union[str, Any]:
          _snake_case				= list(self.get_tokenizer().get_vocab().keys()       )

          self.assertEqual(vocab_keys[0]   ,"<unk>"       )
          self.assertEqual(vocab_keys[1]   ,"<s>"       )
          self.assertEqual(vocab_keys[-1]   ,"j"       )
          self.assertEqual(len(_SCREAMING_SNAKE_CASE       )   ,1_000       )






     def 							_lowercase      (					self       )				->     Any:
          self.assertEqual(self.get_tokenizer().vocab_size   ,1_000       )






     def 							_lowercase      (					self       )				->     Optional[Any]:
          if not self.test_rust_tokenizer:
               return

          _snake_case				= self.get_tokenizer()
          _snake_case				= self.get_rust_tokenizer()

          _snake_case				= "I was born in 92000, and this is falsé."

          _snake_case				= tokenizer.tokenize(_SCREAMING_SNAKE_CASE       )
          _snake_case				= rust_tokenizer.tokenize(_SCREAMING_SNAKE_CASE       )
          self.assertListEqual(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

          _snake_case				= tokenizer.encode(_SCREAMING_SNAKE_CASE   ,add_special_tokens=_SCREAMING_SNAKE_CASE       )
          _snake_case				= rust_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,add_special_tokens=_SCREAMING_SNAKE_CASE       )
          self.assertListEqual(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )

          _snake_case				= self.get_rust_tokenizer()
          _snake_case				= tokenizer.encode(_SCREAMING_SNAKE_CASE       )
          _snake_case				= rust_tokenizer.encode(_SCREAMING_SNAKE_CASE       )
          self.assertListEqual(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE=15       )				->     Any:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""       ):
                    _snake_case				= self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE   ,**_SCREAMING_SNAKE_CASE       )

                    # Simple input
                    _snake_case				= "This is a simple input"
                    _snake_case				= ["This is a simple input 1", "This is a simple input 2"]
                    _snake_case				= ("This is a simple input", "This is a pair")
                    _snake_case				= [
                        ("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(_SCREAMING_SNAKE_CASE   ,tokenizer_r.encode   ,_SCREAMING_SNAKE_CASE   ,max_length=_SCREAMING_SNAKE_CASE   ,padding="max_length"       )

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

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

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

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

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






     def 							_lowercase      (					self       )				->     Tuple:
          pass






     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				= ReformerTokenizer(_SCREAMING_SNAKE_CASE   ,keep_accents=_SCREAMING_SNAKE_CASE       )

          _snake_case				= tokenizer.tokenize("This is a test"       )
          self.assertListEqual(_SCREAMING_SNAKE_CASE   ,["▁This", "▁is", "▁a", "▁t", "est"]       )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE       )   ,[285, 46, 10, 170, 382]   ,)

          _snake_case				= tokenizer.tokenize("I was born in 92000, and this is falsé."       )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE   ,[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "9",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "é",
                  ".",
              ]   ,)
          _snake_case				= tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE       )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE   ,[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]   ,)

          _snake_case				= tokenizer.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE       )
          self.assertListEqual(
              _SCREAMING_SNAKE_CASE   ,[
                  SPIECE_UNDERLINE + "I",
                  SPIECE_UNDERLINE + "was",
                  SPIECE_UNDERLINE + "b",
                  "or",
                  "n",
                  SPIECE_UNDERLINE + "in",
                  SPIECE_UNDERLINE + "",
                  "<unk>",
                  "2",
                  "0",
                  "0",
                  "0",
                  ",",
                  SPIECE_UNDERLINE + "and",
                  SPIECE_UNDERLINE + "this",
                  SPIECE_UNDERLINE + "is",
                  SPIECE_UNDERLINE + "f",
                  "al",
                  "s",
                  "<unk>",
                  ".",
              ]   ,)






     @cached_property
     def 							_lowercase      (					self       )				->     List[str]:
          return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"       )






     @slow
     def 							_lowercase      (					self       )				->     Optional[int]:
          _snake_case				= "Hello World!"
          _snake_case				= [126, 32, 262, 152, 38, 72, 287]

          self.assertListEqual(_SCREAMING_SNAKE_CASE   ,self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE       )       )






     @slow
     def 							_lowercase      (					self       )				->     List[Any]:
          _snake_case				= (
              "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
              " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
          )
          _snake_case				= [
              108,
              265,
              24,
              111,
              4,
              258,
              156,
              35,
              28,
              275,
              3,
              259,
              297,
              260,
              84,
              4,
              35,
              110,
              44,
              8,
              259,
              91,
              268,
              21,
              11,
              209,
              274,
              109,
              266,
              277,
              117,
              86,
              93,
              315,
              258,
              278,
              258,
              277,
              258,
              0,
              258,
              288,
              258,
              319,
              258,
              0,
              258,
              0,
              258,
              0,
              258,
              0,
              258,
              287,
              258,
              315,
              258,
              289,
              258,
              278,
              99,
              269,
              266,
              262,
              8,
              259,
              241,
              4,
              217,
              230,
              268,
              266,
              55,
              168,
              106,
              75,
              193,
              266,
              223,
              27,
              49,
              26,
              282,
              25,
              264,
              299,
              19,
              26,
              0,
              258,
              277,
              117,
              86,
              93,
              176,
              183,
              270,
              11,
              262,
              42,
              61,
              265,
          ]

          self.assertListEqual(_SCREAMING_SNAKE_CASE   ,self.big_tokenizer.encode(_SCREAMING_SNAKE_CASE       )       )






     @require_torch
     @slow
     def 							_lowercase      (					self       )				->     str:
          import torch

          from transformers import ReformerConfig, ReformerModel

          # Build sequence
          _snake_case				= list(self.big_tokenizer.get_vocab().keys()       )[:10]
          _snake_case				= " ".join(_SCREAMING_SNAKE_CASE       )
          _snake_case				= self.big_tokenizer.encode_plus(_SCREAMING_SNAKE_CASE   ,return_tensors="pt"       )
          _snake_case				= self.big_tokenizer.batch_encode_plus([sequence, sequence]   ,return_tensors="pt"       )

          _snake_case				= ReformerConfig()
          # The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
          _snake_case				= encoded_sequence["input_ids"].shape
          _snake_case				= ReformerModel(_SCREAMING_SNAKE_CASE       )

          # Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
          assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

          with torch.no_grad():
               model(**_SCREAMING_SNAKE_CASE       )
               model(**_SCREAMING_SNAKE_CASE       )






     @slow
     def 							_lowercase      (					self       )				->     List[str]:
          # fmt: off
          _snake_case				= {"input_ids": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # This tokenizer does not know some characters like ")".
          # That is the reason why we use very simple texts here.
          # Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
          _snake_case				= [
              "This is a very simple sentence.",
              "The quick brown fox jumps over the lazy dog.",
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=_SCREAMING_SNAKE_CASE   ,model_name="google/reformer-crime-and-punishment"   ,revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"   ,padding=_SCREAMING_SNAKE_CASE   ,sequences=_SCREAMING_SNAKE_CASE   ,)








'''simple docstring'''





import argparse

import torch

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


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

UpperCamelCase_		:     Tuple          =		[
    ['''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       __a      (  _UpperCamelCase:       List[Any]       )		->					Union[str, Any]:

     """simple docstring"""




     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          _snake_case				= k.replace(_UpperCamelCase   ,		_UpperCamelCase       )

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





def       __a      (  _UpperCamelCase:       Dict       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= [
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          _snake_case				= sd.pop(_UpperCamelCase       )
          _snake_case				= k.replace("layernorm_embedding"   ,		"layer_norm"       )
          assert new_k not in sd
          _snake_case				= v


UpperCamelCase_		:     Union[str, Any]          =		['''START''']





@torch.no_grad()
def       __a      (  _UpperCamelCase:       Any   ,		_UpperCamelCase:       Union[str, Any]   ,		_UpperCamelCase:       Union[str, Any]       )		->					Optional[int]:

     """simple docstring"""




     _snake_case				= torch.load(_UpperCamelCase   ,		map_location="cpu"       )
     _snake_case				= model["model"]
     _snake_case				= BlenderbotConfig.from_json_file(_UpperCamelCase       )
     _snake_case				= BlenderbotForConditionalGeneration(_UpperCamelCase       )
     _snake_case				= m.model.state_dict().keys()
     _snake_case				= []
     _snake_case				= {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          _snake_case				= rename_state_dict_key(_UpperCamelCase       )
          if new_k not in valid_keys:
               failures.append([k, new_k]       )
          else:
               _snake_case				= 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__":
   UpperCamelCase_		:     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'''
   )
   UpperCamelCase_		:     Optional[int]          =		parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


