




import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin




@require_phonemizer
class       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      List[Any]		 =     WavaVecaPhonemeCTCTokenizer
     a		:      str		 =     False







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          super().setUp()

          a     =				(
              "<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
              "ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
              "ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
              "oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
              "pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
              "yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
              "əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ "
              "ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
              "ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
              "uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
              "ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
              "ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
              "ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
          ).split(" "			)
          a     =				dict(zip(A  ,		range(len(A			)			)			)			)

          a     =				{"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

          a     =				os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["vocab_file"]			)
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8"			) as fp:
               fp.write(json.dumps(A			) + "\n"			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A=False  ,		A=20  ,		A=5			)  ->   Tuple[str, list]:



          '''simple docstring'''



          a     =				[(i, tokenizer.decode([i]  ,		clean_up_tokenization_spaces=A			)) for i in range(len(A			)			)]
          a     =				list(filter(lambda A			: [t[0]] == tokenizer.encode(t[1]  ,		do_phonemize=A			)  ,		A			)			)
          if max_length is not None and len(A			) > max_length:
               a     =				toks[:max_length]
          if min_length is not None and len(A			) < min_length and len(A			) > 0:
               while len(A			) < min_length:
                    a     =				toks + toks
        # toks_str = [t[1] for t in toks]
          a     =				[t[0] for t in toks]

          # Ensure consistency
          a     =				tokenizer.decode(A  ,		clean_up_tokenization_spaces=A			)
          if " " not in output_txt and len(A			) > 1:
               a     =				(
                   tokenizer.decode([toks_ids[0]]  ,		clean_up_tokenization_spaces=A			)
                   + " "
                   + tokenizer.decode(toks_ids[1:]  ,		clean_up_tokenization_spaces=A			)
               )
          if with_prefix_space:
               a     =				" " + output_txt
          a     =				tokenizer.encode(A  ,		add_special_tokens=A			)
          return output_txt, output_ids







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



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"			)

          # check adding a single token
          tokenizer.add_tokens("xxx"			)
          a     =				tokenizer("m xxx ɪ"  ,		do_phonemize=A			).input_ids
          self.assertEqual(A  ,		[13, 392, 17]			)  # xxx should be last token

          tokenizer.add_tokens(["aaa", "bbb", "ccc"]			)
          a     =				tokenizer("m aaa ɪ ccc"  ,		do_phonemize=A			).input_ids
          self.assertEqual(A  ,		[13, 393, 17, 395]			)  # aaa and ccc should be after xxx and 2 after aaa

          a     =				tokenizer("maɪ c"  ,		do_phonemize=A			).input_ids
          self.assertEqual(A  ,		[3, 200]			)  # mai should be <unk> (=3)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"			)

          a     =				"Hello how are you"
          a     =				tokenizer.phonemize(A  ,		phonemizer_lang="en-us"			)
          self.assertEqual(A  ,		"h ə l oʊ h aʊ ɑːɹ j uː"			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"			)

          a     =				"Hello how are you"
          a     =				tokenizer.phonemize(A  ,		phonemizer_lang="en-us"			)
          self.assertEqual(tokenizer(A			).input_ids  ,		tokenizer(A  ,		do_phonemize=A			).input_ids			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"			)
          a     =				"Hello how are you"
          a     =				tokenizer.phonemize(A  ,		phonemizer_lang="en-us"			)

          a     =				tokenizer.decode(tokenizer(A			).input_ids			)

          self.assertEqual(A  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"			)

          a     =				[
              [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
              [24, 22, 5, 24, 22, 5, 77],
          ]
          a     =				tokenizer.decode(sample_ids[0]			)
          a     =				tokenizer.batch_decode(A			)
          self.assertEqual(A  ,		batch_tokens[0]			)
          self.assertEqual(A  ,		["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft"  ,		word_delimiter_token="|"			)
          tokenizer.add_tokens("|"			)

          a     =				"Hello how are you"
          a     =				tokenizer.phonemize(A  ,		phonemizer_lang="en-us"			)
          self.assertEqual(A  ,		"h ə l oʊ | h aʊ | ɑːɹ | j uː |"			)







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft"  ,		word_delimiter_token="|"			)
          tokenizer.add_tokens("|"			)

          a     =				"Hello how are you"
          a     =				tokenizer.phonemize(A  ,		phonemizer_lang="en-us"			)
          self.assertEqual(tokenizer(A			).input_ids  ,		tokenizer(A  ,		do_phonemize=A			).input_ids			)







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



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft"  ,		word_delimiter_token="|"			)
          tokenizer.add_tokens("|"			)

          # fmt: off
          a     =				[
              [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
              [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
          ]
          # fmt: on

          # decode with word_del_token filter
          a     =				tokenizer.decode(sample_ids[0]			)
          a     =				tokenizer.batch_decode(A			)
          self.assertEqual(A  ,		batch_tokens[0]			)
          self.assertEqual(A  ,		["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"]			)

          # decode with no word_del_token filter
          a     =				tokenizer.decode(sample_ids[0]  ,		filter_word_delimiter_token=A			)
          a     =				tokenizer.batch_decode(A  ,		filter_word_delimiter_token=A			)
          self.assertEqual(A  ,		batch_tokens[0]			)
          self.assertEqual(A  ,		["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"]			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft"  ,		word_delimiter_token="|"			)
          tokenizer.add_tokens("|"			)

          a     =				"Hello how are you"
          a     =				tokenizer.phonemize(A  ,		phonemizer_lang="en-us"			)

          a     =				tokenizer.decode(tokenizer(A			).input_ids  ,		filter_word_delimiter_token=A			)

          self.assertEqual(A  ,		A			)







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



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft"  ,		word_delimiter_token="|"			)
          tokenizer.add_tokens("|"			)

          a     =				"Hello how are you"
          a     =				tokenizer.phonemize(A  ,		phonemizer_lang="en-us"			)

          a     =				tokenizer.decode(tokenizer(A			).input_ids  ,		filter_word_delimiter_token=A			)

          self.assertEqual(" ".join([p.strip() for p in phonemes.split(" |"			)]			).strip()  ,		A			)







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



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained(
              "facebook/wav2vec2-lv-60-espeak-cv-ft"  ,		word_delimiter_token=A			)
          a     =				"Hello how are you"

          a     =				tokenizer(A  ,		phonemizer_lang="en-us"			).input_ids
          a     =				tokenizer(A  ,		phonemizer_lang="fr-fr"			).input_ids

          self.assertNotEqual(A  ,		A			)

          a     =				tokenizer.decode(A			)
          a     =				tokenizer.decode(A			)

          self.assertEqual(A  ,		"h ə l oʊ h aʊ ɑːɹ j uː"			)
          self.assertEqual(A  ,		"ɛ l o h aʊ a ʁ j u"			)







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



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"			)
          a     =				"Hello how Are you"
          a     =				"hello how are you"

          a     =				tokenizer(A			).input_ids
          a     =				tokenizer(A			).input_ids

          self.assertEqual(A  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				self.tokenizer_class.from_pretrained("facebook/wav2vec2-lv-60-espeak-cv-ft"			)
          tokenizer.add_tokens(["!", "?"]			)
          tokenizer.add_special_tokens({"cls_token": "$$$"}			)

          # fmt: off
          a     =				[
              [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
              [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
          ]
          # fmt: on

          a     =				tokenizer.batch_decode(A			)
          self.assertEqual(A  ,		["k s ɾ ɾ l ɭʲ!?!? $$$", "j ð s j ð s oːɹ $$$"]			)







     @staticmethod
     def       lowerCAmelCase_    (     A  ,		A			)  ->   int:



          '''simple docstring'''



          a     =				[d[key] for d in offsets]
          return retrieved_list







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.get_tokenizer(word_delimiter_token="|"			)
          tokenizer.add_tokens("|"			)

          # fmt: off
          # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
          a     =				[11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
          # fmt: on

          a     =				tokenizer.decode(A  ,		output_char_offsets=A  ,		filter_word_delimiter_token=A			)
          # check Wav2Vec2CTCTokenizerOutput keys for char
          self.assertEqual(len(outputs.keys()			)  ,		2			)
          self.assertTrue("text" in outputs			)
          self.assertTrue("char_offsets" in outputs			)
          self.assertTrue(isinstance(A  ,		A			)			)

          # check that order of chars is correct and identical for both outputs
          self.assertEqual(" ".join(self.get_from_offsets(outputs["char_offsets"]  ,		"char"			)			)  ,		outputs.text			)
          self.assertListEqual(
              self.get_from_offsets(outputs["char_offsets"]  ,		"char"			)  ,		["k", "s", "ɾ", "ɾ", "|", "ɾ", "l", "|", "ɭʲ"]			)

          # check that offsets are actually correct for char
          # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
          # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
          self.assertListEqual(
              self.get_from_offsets(outputs["char_offsets"]  ,		"start_offset"			)  ,		[0, 1, 4, 7, 9, 11, 12, 15, 16]			)
          self.assertListEqual(
              self.get_from_offsets(outputs["char_offsets"]  ,		"end_offset"			)  ,		[1, 4, 6, 9, 10, 12, 15, 16, 17]			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''simple docstring'''



          a     =				self.get_tokenizer(word_delimiter_token="|"			)

          def check_list_tuples_equal(A  ,		A			):
               self.assertTrue(isinstance(A  ,		A			)			)
               self.assertTrue(isinstance(outputs_list[0]  ,		A			)			)

               # transform list to ModelOutput
               a     =				WavaVecaPhonemeCTCTokenizerOutput(
                   {k: [d[k] for d in outputs_list] for k in outputs_list[0]}			)

               self.assertListEqual(outputs_batch["text"]  ,		outputs_batch_a["text"]			)

               def recursive_check(A  ,		A			):
                    if isinstance(A  ,		A			):
                         [recursive_check(A  ,		A			) for la, la in zip(A  ,		A			)]
                    self.assertEqual(A  ,		A			)

               if "char_offsets" in outputs_batch:
                    recursive_check(outputs_batch["char_offsets"]  ,		outputs_batch_a["char_offsets"]			)

        # fmt: off
          a     =				[
              [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
              [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
          ]
          # fmt: on

          # We assume that `decode` works as expected. All we will check now is
          # the output type is correct and the output is identical to `decode`

          # char
          a     =				tokenizer.batch_decode(A  ,		output_char_offsets=A			)
          a     =				[tokenizer.decode(A  ,		output_char_offsets=A			) for ids in sample_ids]
          check_list_tuples_equal(A  ,		A			)







     @unittest.skip("Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"			)
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          pass







     @unittest.skip("Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"			)
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          pass







     @unittest.skip("encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"			)
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          pass







     @unittest.skip("Wav2Vec2PhonemeModel has no max model length => no testing"			)
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.get_tokenizers(do_lower_case=A			)
          for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
                    a     =				tokenizer.vocab_size
                    a     =				len(A			)

                    self.assertNotEqual(A  ,		0			)

                    # We usually have added tokens from the start in tests because our vocab fixtures are
                    # smaller than the original vocabs - let's not assert this
                    # self.assertEqual(vocab_size, all_size)

                    a     =				["aaaaa bbbbbb", "cccccccccdddddddd"]
                    a     =				tokenizer.add_tokens(A			)
                    a     =				tokenizer.vocab_size
                    a     =				len(A			)

                    self.assertNotEqual(A  ,		0			)
                    self.assertEqual(A  ,		A			)
                    self.assertEqual(A  ,		len(A			)			)
                    self.assertEqual(A  ,		all_size + len(A			)			)

                    a     =				tokenizer.encode("aaaaa bbbbbb low cccccccccdddddddd l"  ,		add_special_tokens=A			)

                    self.assertGreaterEqual(len(A			)  ,		4			)
                    self.assertGreater(tokens[0]  ,		tokenizer.vocab_size - 1			)
                    self.assertGreater(tokens[-3]  ,		tokenizer.vocab_size - 1			)

                    a     =				{"eos_token": ">>>>|||<||<<|<<", "pad_token": "<<<<<|||>|>>>>|>"}
                    a     =				tokenizer.add_special_tokens(A			)
                    a     =				tokenizer.vocab_size
                    a     =				len(A			)

                    self.assertNotEqual(A  ,		0			)
                    self.assertEqual(A  ,		A			)
                    self.assertEqual(A  ,		len(A			)			)
                    self.assertEqual(A  ,		all_size_a + len(A			)			)

                    a     =				tokenizer.encode(
                        ">>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"  ,		add_special_tokens=A			)

                    self.assertGreaterEqual(len(A			)  ,		6			)
                    self.assertGreater(tokens[0]  ,		tokenizer.vocab_size - 1			)
                    self.assertGreater(tokens[0]  ,		tokens[1]			)
                    self.assertGreater(tokens[-3]  ,		tokenizer.vocab_size - 1			)
                    self.assertGreater(tokens[-3]  ,		tokens[-4]			)
                    self.assertEqual(tokens[0]  ,		tokenizer.eos_token_id			)
                    self.assertEqual(tokens[-3]  ,		tokenizer.pad_token_id			)







     @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."			)
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          pass







     @unittest.skip("The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."			)
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				self.get_tokenizers(fast=A  ,		do_lower_case=A			)
          for tokenizer in tokenizers:
               with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
                    a     =				["ð", "ɪ", "s", "ɪ", "z", "ɐ", "t", "ɛ", "k", "s", "t"]
                    a     =				tokenizer.convert_tokens_to_string(A			)

                    self.assertIsInstance(output["text"]  ,		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)

