




"""simple docstring"""




import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[str]		 =     RobertaTokenizer
     a		:      Optional[int]		 =     RobertaTokenizerFast
     a		:      str		 =     True
     a		:      List[str]		 =     {"""cls_token""": """<s>"""}







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          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",
              "\u0120",
              "\u0120l",
              "\u0120n",
              "\u0120lo",
              "\u0120low",
              "er",
              "\u0120lowest",
              "\u0120newer",
              "\u0120wider",
              "<unk>",
          ]
          a     =				dict(zip(A  ,		range(len(A			)			)			)			)
          a     =				["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
          a     =				{"unk_token": "<unk>"}

          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"  ,		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       lowerCAmelCase_    (     self  ,		**A			)  ->   int:



          '''simple docstring'''



          kwargs.update(self.special_tokens_map			)
          return self.tokenizer_class.from_pretrained(self.tmpdirname  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   int:



          '''simple docstring'''



          kwargs.update(self.special_tokens_map			)
          return RobertaTokenizerFast.from_pretrained(self.tmpdirname  ,		**A			)







     def       lowerCAmelCase_    (     self  ,		A			)  ->   Any:



          '''simple docstring'''



          a     =				"lower newer"
          a     =				"lower newer"
          return input_text, output_text







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				self.tokenizer_class(self.vocab_file  ,		self.merges_file  ,		**self.special_tokens_map			)
          a     =				"lower newer"
          a     =				["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
          a     =				tokenizer.tokenize(A			)  # , add_prefix_space=True)
          self.assertListEqual(A  ,		A			)

          a     =				tokens + [tokenizer.unk_token]
          a     =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				self.get_tokenizer()

          self.assertListEqual(tokenizer.encode("Hello world!"  ,		add_special_tokens=A			)  ,		[0, 31414, 232, 328, 2]			)
          self.assertListEqual(
              tokenizer.encode("Hello world! cécé herlolip 418"  ,		add_special_tokens=A			)  ,		[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]  ,		)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("roberta-base"			)

          a     =				tokenizer.encode("sequence builders"  ,		add_special_tokens=A			)
          a     =				tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A			)

          a     =				tokenizer.encode(
              "sequence builders"  ,		add_special_tokens=A  ,		add_prefix_space=A			)
          a     =				tokenizer.encode(
              "sequence builders"  ,		"multi-sequence build"  ,		add_special_tokens=A  ,		add_prefix_space=A			)

          a     =				tokenizer.build_inputs_with_special_tokens(A			)
          a     =				tokenizer.build_inputs_with_special_tokens(A  ,		A			)

          assert encoded_sentence == encoded_text_from_decode
          assert encoded_pair == encoded_pair_from_decode







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.get_tokenizer()

          a     =				"Encode this sequence."
          a     =				tokenizer.byte_encoder[" ".encode("utf-8"			)[0]]

          # Testing encoder arguments
          a     =				tokenizer.encode(A  ,		add_special_tokens=A  ,		add_prefix_space=A			)
          a     =				tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
          self.assertNotEqual(A  ,		A			)

          a     =				tokenizer.encode(A  ,		add_special_tokens=A  ,		add_prefix_space=A			)
          a     =				tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
          self.assertEqual(A  ,		A			)

          tokenizer.add_special_tokens({"bos_token": "<s>"}			)
          a     =				tokenizer.encode(A  ,		add_special_tokens=A			)
          a     =				tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
          self.assertNotEqual(A  ,		A			)

          # Testing spaces after special tokens
          a     =				"<mask>"
          tokenizer.add_special_tokens(
              {"mask_token": AddedToken(A  ,		lstrip=A  ,		rstrip=A			)}			)  # mask token has a left space
          a     =				tokenizer.convert_tokens_to_ids(A			)

          a     =				"Encode <mask> sequence"
          a     =				"Encode <mask>sequence"

          a     =				tokenizer.encode(A			)
          a     =				encoded.index(A			)
          a     =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
          self.assertEqual(A  ,		A			)

          a     =				tokenizer.encode(A			)
          a     =				encoded.index(A			)
          a     =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
          self.assertNotEqual(A  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          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			)
                    a     =				self.tokenizer_class.from_pretrained(A  ,		**A			)
                    a     =				"A, <mask> AllenNLP sentence."
                    a     =				tokenizer_r.encode_plus(A  ,		add_special_tokens=A  ,		return_token_type_ids=A			)
                    a     =				tokenizer_p.encode_plus(A  ,		add_special_tokens=A  ,		return_token_type_ids=A			)

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r["token_type_ids"]			)  ,		sum(tokens_p["token_type_ids"]			)			)

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r["attention_mask"]			) / len(tokens_r["attention_mask"]			)  ,		sum(tokens_p["attention_mask"]			) / len(tokens_p["attention_mask"]			)  ,		)

                    a     =				tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]			)
                    a     =				tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]			)

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p["input_ids"]  ,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)
                    self.assertSequenceEqual(tokens_r["input_ids"]  ,		[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]			)

                    self.assertSequenceEqual(
                        A  ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]			)
                    self.assertSequenceEqual(
                        A  ,		["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          for trim_offsets, add_prefix_space in itertools.product([True, False]  ,		repeat=2			):
               a     =				self.rust_tokenizer_class.from_pretrained(
                   self.tmpdirname  ,		use_fast=A  ,		add_prefix_space=A  ,		trim_offsets=A			)

               a     =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
               a     =				json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

               self.assertEqual(pre_tokenizer_state["add_prefix_space"]  ,		A			)

               self.assertEqual(post_processor_state["add_prefix_space"]  ,		A			)
               self.assertEqual(post_processor_state["trim_offsets"]  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                    a     =				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                    a     =				F'''{text_of_1_token} {text_of_1_token}'''

                    a     =				self.rust_tokenizer_class.from_pretrained(
                        A  ,		use_fast=A  ,		add_prefix_space=A  ,		trim_offsets=A			)
                    a     =				tokenizer_r(A  ,		return_offsets_mapping=A  ,		add_special_tokens=A			)
                    self.assertEqual(encoding.offset_mapping[0]  ,		(0, len(A			))			)
                    self.assertEqual(
                        encoding.offset_mapping[1]  ,		(len(A			) + 1, len(A			) + 1 + len(A			))  ,		)

                    a     =				self.rust_tokenizer_class.from_pretrained(
                        A  ,		use_fast=A  ,		add_prefix_space=A  ,		trim_offsets=A			)
                    a     =				tokenizer_r(A  ,		return_offsets_mapping=A  ,		add_special_tokens=A			)
                    self.assertEqual(encoding.offset_mapping[0]  ,		(0, len(A			))			)
                    self.assertEqual(
                        encoding.offset_mapping[1]  ,		(len(A			) + 1, len(A			) + 1 + len(A			))  ,		)

                    a     =				self.rust_tokenizer_class.from_pretrained(
                        A  ,		use_fast=A  ,		add_prefix_space=A  ,		trim_offsets=A			)
                    a     =				tokenizer_r(A  ,		return_offsets_mapping=A  ,		add_special_tokens=A			)
                    self.assertEqual(encoding.offset_mapping[0]  ,		(0, len(A			))			)
                    self.assertEqual(
                        encoding.offset_mapping[1]  ,		(len(A			), len(A			) + 1 + len(A			))  ,		)

                    a     =				self.rust_tokenizer_class.from_pretrained(
                        A  ,		use_fast=A  ,		add_prefix_space=A  ,		trim_offsets=A			)
                    a     =				tokenizer_r(A  ,		return_offsets_mapping=A  ,		add_special_tokens=A			)
                    self.assertEqual(encoding.offset_mapping[0]  ,		(0, len(A			))			)
                    self.assertEqual(
                        encoding.offset_mapping[1]  ,		(len(A			), len(A			) + 1 + len(A			))  ,		)

                    a     =				F''' {text}'''

                    # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                    #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                    # )
                    # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                    # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                    # self.assertEqual(
                    #     encoding.offset_mapping[1],
                    #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                    # )

                    a     =				self.rust_tokenizer_class.from_pretrained(
                        A  ,		use_fast=A  ,		add_prefix_space=A  ,		trim_offsets=A			)
                    a     =				tokenizer_r(A  ,		return_offsets_mapping=A  ,		add_special_tokens=A			)
                    self.assertEqual(encoding.offset_mapping[0]  ,		(1, 1 + len(A			))			)
                    self.assertEqual(
                        encoding.offset_mapping[1]  ,		(1 + len(A			) + 1, 1 + len(A			) + 1 + len(A			))  ,		)

                    a     =				self.rust_tokenizer_class.from_pretrained(
                        A  ,		use_fast=A  ,		add_prefix_space=A  ,		trim_offsets=A			)
                    a     =				tokenizer_r(A  ,		return_offsets_mapping=A  ,		add_special_tokens=A			)
                    self.assertEqual(encoding.offset_mapping[0]  ,		(0, 1 + len(A			))			)
                    self.assertEqual(
                        encoding.offset_mapping[1]  ,		(1 + len(A			), 1 + len(A			) + 1 + len(A			))  ,		)

                    a     =				self.rust_tokenizer_class.from_pretrained(
                        A  ,		use_fast=A  ,		add_prefix_space=A  ,		trim_offsets=A			)
                    a     =				tokenizer_r(A  ,		return_offsets_mapping=A  ,		add_special_tokens=A			)
                    self.assertEqual(encoding.offset_mapping[0]  ,		(0, 1 + len(A			))			)
                    self.assertEqual(
                        encoding.offset_mapping[1]  ,		(1 + len(A			), 1 + len(A			) + 1 + len(A			))  ,		)







import argparse

import torch

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


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

lowercase__				:      Dict	   = [
    ["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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          a     =				k.replace(__UpperCamelCase		, __UpperCamelCase)

     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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     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(__UpperCamelCase)
          a     =				k.replace("layernorm_embedding"		, "layer_norm")
          assert new_k not in sd
          a     =				v


lowercase__				:      Optional[Any]	   = ["START"]

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				torch.load(__UpperCamelCase		, map_location="cpu")
     a     =				model["model"]
     a     =				BlenderbotConfig.from_json_file(__UpperCamelCase)
     a     =				BlenderbotForConditionalGeneration(__UpperCamelCase)
     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(__UpperCamelCase)
          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(__UpperCamelCase)
     m.model.load_state_dict(__UpperCamelCase		, strict=__UpperCamelCase)
     m.half()
     m.save_pretrained(__UpperCamelCase)


if __name__ == "__main__":
   lowercase__				:      int	   = 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"
   )
   lowercase__				:      str	   = parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

