



import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case       :	Tuple            =get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
							from transformers.models.mbart.modeling_mbart import shift_tokens_right

__snake_case       :	Dict            =2_5_0_0_0_4
__snake_case       :	Union[str, Any]            =2_5_0_0_2_0



@require_sentencepiece
@require_tokenizers
class  lowerCamelCase__    (							lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=MBartaaTokenizer
		snake_case_			=MBartaaTokenizerFast
		snake_case_			=True
		snake_case_			=True



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						super().setUp()

						# We have a SentencePiece fixture for testing
						lowerCAmelCase__  :       List[Any]				=  MBartaaTokenizer(__lowerCamelCase	,src_lang='''en_XX'''	,tgt_lang='''ro_RO'''	,keep_accents=__lowerCamelCase				)
						tokenizer.save_pretrained(self.tmpdirname				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  '''<s>'''
						lowerCAmelCase__  :       Optional[int]				=  0

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



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  list(self.get_tokenizer().get_vocab().keys()				)

						self.assertEqual(vocab_keys[0]	,'''<s>'''				)
						self.assertEqual(vocab_keys[1]	,'''<pad>'''				)
						self.assertEqual(vocab_keys[-1]	,'''<mask>'''				)
						self.assertEqual(len(__lowerCamelCase				)	,10_54				)



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

						"""simple docstring"""






						self.assertEqual(self.get_tokenizer().vocab_size	,10_54				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  MBartaaTokenizer(__lowerCamelCase	,src_lang='''en_XX'''	,tgt_lang='''ro_RO'''	,keep_accents=__lowerCamelCase				)

						lowerCAmelCase__  :       str				=  tokenizer.tokenize('''This is a test'''				)
						self.assertListEqual(__lowerCamelCase	,['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(__lowerCamelCase				)	,[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]	,)

						lowerCAmelCase__  :       Any				=  tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
						self.assertListEqual(
						    __lowerCamelCase	,[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''', '''.''']	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.convert_tokens_to_ids(__lowerCamelCase				)
						self.assertListEqual(
						    __lowerCamelCase	,[
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
						    ]	,)

						lowerCAmelCase__  :       Any				=  tokenizer.convert_ids_to_tokens(__lowerCamelCase				)
						self.assertListEqual(
						    __lowerCamelCase	,[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>''', '''.''']	,)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  {'''input_ids''': [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 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, 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], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1]], '''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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=__lowerCamelCase	,model_name='''facebook/mbart-large-50'''	,revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''	,)



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

						"""simple docstring"""






						if not self.test_slow_tokenizer:
										# as we don't have a slow version, we can't compare the outputs between slow and fast versions
										return

						lowerCAmelCase__  :       int				=  (self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
														lowerCAmelCase__  :       int				=  self.rust_tokenizer_class.from_pretrained(__lowerCamelCase	,**__lowerCamelCase				)
														lowerCAmelCase__  :       int				=  self.tokenizer_class.from_pretrained(__lowerCamelCase	,**__lowerCamelCase				)

														lowerCAmelCase__  :       List[Any]				=  tempfile.mkdtemp()

														lowerCAmelCase__  :       int				=  tokenizer_r.save_pretrained(__lowerCamelCase				)
														lowerCAmelCase__  :       Dict				=  tokenizer_p.save_pretrained(__lowerCamelCase				)

														# Checks it save with the same files + the tokenizer.json file for the fast one
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)
														lowerCAmelCase__  :       Any				=  tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f				)
														self.assertSequenceEqual(__lowerCamelCase	,__lowerCamelCase				)

														# Checks everything loads correctly in the same way
														lowerCAmelCase__  :       Dict				=  tokenizer_r.from_pretrained(__lowerCamelCase				)
														lowerCAmelCase__  :       List[str]				=  tokenizer_p.from_pretrained(__lowerCamelCase				)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowerCamelCase	,__lowerCamelCase				)				)
																		# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																		# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

														shutil.rmtree(__lowerCamelCase				)

														# Save tokenizer rust, legacy_format=True
														lowerCAmelCase__  :       Any				=  tempfile.mkdtemp()

														lowerCAmelCase__  :       Optional[int]				=  tokenizer_r.save_pretrained(__lowerCamelCase	,legacy_format=__lowerCamelCase				)
														lowerCAmelCase__  :       Optional[Any]				=  tokenizer_p.save_pretrained(__lowerCamelCase				)

														# Checks it save with the same files
														self.assertSequenceEqual(__lowerCamelCase	,__lowerCamelCase				)

														# Checks everything loads correctly in the same way
														lowerCAmelCase__  :       Optional[int]				=  tokenizer_r.from_pretrained(__lowerCamelCase				)
														lowerCAmelCase__  :       str				=  tokenizer_p.from_pretrained(__lowerCamelCase				)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowerCamelCase	,__lowerCamelCase				)				)

														shutil.rmtree(__lowerCamelCase				)

														# Save tokenizer rust, legacy_format=False
														lowerCAmelCase__  :       Optional[int]				=  tempfile.mkdtemp()

														lowerCAmelCase__  :       Union[str, Any]				=  tokenizer_r.save_pretrained(__lowerCamelCase	,legacy_format=__lowerCamelCase				)
														lowerCAmelCase__  :       int				=  tokenizer_p.save_pretrained(__lowerCamelCase				)

														# Checks it saved the tokenizer.json file
														self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files				)				)

														# Checks everything loads correctly in the same way
														lowerCAmelCase__  :       Dict				=  tokenizer_r.from_pretrained(__lowerCamelCase				)
														lowerCAmelCase__  :       Dict				=  tokenizer_p.from_pretrained(__lowerCamelCase				)

														# Check special tokens are set accordingly on Rust and Python
														for key in tokenizer_pp.special_tokens_map:
																		self.assertTrue(hasattr(__lowerCamelCase	,__lowerCamelCase				)				)

														shutil.rmtree(__lowerCamelCase				)



@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		snake_case_			="""facebook/mbart-large-50-one-to-many-mmt"""
		snake_case_			=[
		    """ UN Chief Says There Is No Military Solution in Syria""",
		    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
		]
		snake_case_			=[
		    """Şeful ONU declară că nu există o soluţie militară în Siria""",
		    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
		    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
		    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
		]
		snake_case_			=[EN_CODE, 8274, 12_7873, 2_5916, 7, 8622, 2071, 438, 6_7485, 53, 18_7895, 23, 5_1712, 2]



		@classmethod
		def 	lowerCAmelCase__   (cls				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       MBartaaTokenizer				=  MBartaaTokenizer.from_pretrained(
						    cls.checkpoint_name	,src_lang='''en_XX'''	,tgt_lang='''ro_RO'''				)
						lowerCAmelCase__  :       str				=  1
						return cls



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

						"""simple docstring"""






						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']	,25_00_01				)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']	,25_00_04				)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']	,25_00_20				)
						self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']	,25_00_38				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
						self.assertListEqual(self.expected_src_tokens	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						self.assertIn(__lowerCamelCase	,self.tokenizer.all_special_ids				)
						lowerCAmelCase__  :       Union[str, Any]				=  [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
						lowerCAmelCase__  :       List[Any]				=  self.tokenizer.decode(__lowerCamelCase	,skip_special_tokens=__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  self.tokenizer.decode(generated_ids[1:]	,skip_special_tokens=__lowerCamelCase				)
						self.assertEqual(__lowerCamelCase	,__lowerCamelCase				)
						self.assertNotIn(self.tokenizer.eos_token	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  ['''this is gunna be a long sentence ''' * 20]
						assert isinstance(src_text[0]	,__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  10
						lowerCAmelCase__  :       List[str]				=  self.tokenizer(__lowerCamelCase	,max_length=__lowerCamelCase	,truncation=__lowerCamelCase				).input_ids[0]
						self.assertEqual(ids[0]	,__lowerCamelCase				)
						self.assertEqual(ids[-1]	,2				)
						self.assertEqual(len(__lowerCamelCase				)	,__lowerCamelCase				)



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

						"""simple docstring"""






						self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']				)	,[25_00_53, 25_00_01]				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  tempfile.mkdtemp()
						lowerCAmelCase__  :       Optional[Any]				=  self.tokenizer.fairseq_tokens_to_ids
						self.tokenizer.save_pretrained(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  MBartaaTokenizer.from_pretrained(__lowerCamelCase				)
						self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,__lowerCamelCase				)



		@require_torch
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  self.tokenizer(self.src_text	,text_target=self.tgt_text	,padding=__lowerCamelCase	,return_tensors='''pt'''				)
						lowerCAmelCase__  :       Any				=  shift_tokens_right(batch['''labels''']	,self.tokenizer.pad_token_id				)

						# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
						assert batch.input_ids[1][0] == EN_CODE
						assert batch.input_ids[1][-1] == 2
						assert batch.labels[1][0] == RO_CODE
						assert batch.labels[1][-1] == 2
						assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]



		@require_torch
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.tokenizer(
						    self.src_text	,text_target=self.tgt_text	,padding=__lowerCamelCase	,truncation=__lowerCamelCase	,max_length=len(self.expected_src_tokens				)	,return_tensors='''pt'''	,)
						lowerCAmelCase__  :       Union[str, Any]				=  shift_tokens_right(batch['''labels''']	,self.tokenizer.pad_token_id				)

						self.assertIsInstance(__lowerCamelCase	,__lowerCamelCase				)

						self.assertEqual((2, 14)	,batch.input_ids.shape				)
						self.assertEqual((2, 14)	,batch.attention_mask.shape				)
						lowerCAmelCase__  :       str				=  batch.input_ids.tolist()[0]
						self.assertListEqual(self.expected_src_tokens	,__lowerCamelCase				)
						self.assertEqual(2	,batch.decoder_input_ids[0, 0]				)  # decoder_start_token_id
						# Test that special tokens are reset
						self.assertEqual(self.tokenizer.prefix_tokens	,[EN_CODE]				)
						self.assertEqual(self.tokenizer.suffix_tokens	,[self.tokenizer.eos_token_id]				)



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

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.tokenizer(self.src_text	,padding=__lowerCamelCase	,truncation=__lowerCamelCase	,max_length=3	,return_tensors='''pt'''				)
						lowerCAmelCase__  :       int				=  self.tokenizer(
						    text_target=self.tgt_text	,padding=__lowerCamelCase	,truncation=__lowerCamelCase	,max_length=10	,return_tensors='''pt'''				)
						lowerCAmelCase__  :       int				=  targets['''input_ids''']
						lowerCAmelCase__  :       List[str]				=  shift_tokens_right(__lowerCamelCase	,self.tokenizer.pad_token_id				)

						self.assertEqual(batch.input_ids.shape[1]	,3				)
						self.assertEqual(batch.decoder_input_ids.shape[1]	,10				)



		@require_torch
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  self.tokenizer._build_translation_inputs(
						    '''A test'''	,return_tensors='''pt'''	,src_lang='''en_XX'''	,tgt_lang='''ar_AR'''				)

						self.assertEqual(
						    nested_simplify(__lowerCamelCase				)	,{
						        # en_XX, A, test, EOS
						        '''input_ids''': [[25_00_04, 62, 30_34, 2]],
						        '''attention_mask''': [[1, 1, 1, 1]],
						        # ar_AR
						        '''forced_bos_token_id''': 25_00_01,
						    }	,)






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__snake_case       :	Dict            =logging.get_logger(__name__)

__snake_case       :	Union[str, Any]            ={
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
__snake_case       :	Tuple            =[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	int ,lowerCamelCase_	:	int ,lowerCamelCase_	:	Optional[Any] ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				for attribute in key.split('''.'''):
								if is_finetuned:
												if attribute in ["quantizer", "project_q", "project_hid"]:
																# those layers are only relevant for pretraining and should be dropped
																return

												if attribute == "ctc_proj":
																# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
																lowerCAmelCase__  :       str				=  '''lm_head'''

								lowerCAmelCase__  :       List[str]				=  getattr(lowerCamelCase_ ,lowerCamelCase_)

				if weight_type is not None:
								lowerCAmelCase__  :       Union[str, Any]				=  getattr(lowerCamelCase_ ,lowerCamelCase_).shape
				else:
								lowerCAmelCase__  :       Union[str, Any]				=  hf_pointer.shape

				assert hf_shape == value.shape, (
				    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
				    f""" {value.shape} for {full_name}"""
				)

				if weight_type == "weight":
								lowerCAmelCase__  :       str				=  value
				elif weight_type == "weight_g":
								lowerCAmelCase__  :       Any				=  value
				elif weight_type == "weight_v":
								lowerCAmelCase__  :       Tuple				=  value
				elif weight_type == "bias":
								lowerCAmelCase__  :       int				=  value
				else:
								lowerCAmelCase__  :       Tuple				=  value

				logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""")






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  []
				lowerCAmelCase__  :       Optional[Any]				=  fairseq_model.state_dict()

				lowerCAmelCase__  :       str				=  hf_model.unispeech.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCAmelCase__  :       Union[str, Any]				=  False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,hf_model.config.feat_extract_norm == '''group''' ,)
												lowerCAmelCase__  :       Tuple				=  True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCAmelCase__  :       List[str]				=  '''unispeech.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																if key in name or key.split('''w2v_model.''')[-1] == name.split('''.''')[0]:
																				lowerCAmelCase__  :       Dict				=  True
																				if "*" in mapped_key:
																								lowerCAmelCase__  :       Tuple				=  name.split(lowerCamelCase_)[0].split('''.''')[-2]
																								lowerCAmelCase__  :       str				=  mapped_key.replace('''*''' ,lowerCamelCase_)
																				if "weight_g" in name:
																								lowerCAmelCase__  :       List[str]				=  '''weight_g'''
																				elif "weight_v" in name:
																								lowerCAmelCase__  :       Tuple				=  '''weight_v'''
																				elif "bias" in name:
																								lowerCAmelCase__  :       Any				=  '''bias'''
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowerCAmelCase__  :       Union[str, Any]				=  '''weight'''
																				else:
																								lowerCAmelCase__  :       Dict				=  None
																				set_recursively(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase_)

				logger.warning(f"""Unused weights: {unused_weights}""")






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Dict ,lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	Tuple ,lowerCamelCase_	:	Optional[int]):





				'''simple docstring'''

				lowerCAmelCase__  :       Tuple				=  full_name.split('''conv_layers.''')[-1]
				lowerCAmelCase__  :       Tuple				=  name.split('''.''')
				lowerCAmelCase__  :       Any				=  int(items[0])
				lowerCAmelCase__  :       int				=  int(items[1])

				if type_id == 0:
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
												)
												lowerCAmelCase__  :       List[Any]				=  value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
												)
												lowerCAmelCase__  :       List[Any]				=  value
												logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
				elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
								if "bias" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
												    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
												    " found."
												)
												lowerCAmelCase__  :       Optional[int]				=  value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
								elif "weight" in name:
												assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
												    f"""{full_name} has size {value.shape}, but"""
												    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
												)
												lowerCAmelCase__  :       str				=  value
												logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
				else:
								unused_weights.append(lowerCamelCase_)






@torch.no_grad()
def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[str] ,lowerCamelCase_	:	Union[str, Any] ,lowerCamelCase_	:	str=None ,lowerCamelCase_	:	List[Any]=None ,lowerCamelCase_	:	List[str]=True):





				'''simple docstring'''

				if config_path is not None:
								lowerCAmelCase__  :       Optional[Any]				=  UniSpeechConfig.from_pretrained(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[str]				=  UniSpeechConfig()

				if is_finetuned:
								if dict_path:
												lowerCAmelCase__  :       Dict				=  Dictionary.load_from_json(lowerCamelCase_)

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												lowerCAmelCase__  :       Any				=  target_dict.pad_index
												lowerCAmelCase__  :       str				=  target_dict.bos_index
												lowerCAmelCase__  :       Any				=  target_dict.eos_index
												lowerCAmelCase__  :       List[Any]				=  len(target_dict.symbols)
												lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(lowerCamelCase_ ,'''vocab.json''')
												if not os.path.isdir(lowerCamelCase_):
																logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(lowerCamelCase_))
																return
												os.makedirs(lowerCamelCase_ ,exist_ok=lowerCamelCase_)
												lowerCAmelCase__  :       str				=  target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowerCAmelCase__  :       Optional[int]				=  42
												lowerCAmelCase__  :       List[Any]				=  43
												with open(lowerCamelCase_ ,'''w''' ,encoding='''utf-8''') as vocab_handle:
																json.dump(lowerCamelCase_ ,lowerCamelCase_)
												lowerCAmelCase__  :       Dict				=  WavaVecaPhonemeCTCTokenizer(
												    lowerCamelCase_ ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token='''|''' ,do_lower_case=lowerCamelCase_ ,)
												lowerCAmelCase__  :       Optional[Any]				=  True if config.feat_extract_norm == '''layer''' else False
												lowerCAmelCase__  :       Tuple				=  WavaVecaFeatureExtractor(
												    feature_size=1 ,sampling_rate=16000 ,padding_value=0 ,do_normalize=lowerCamelCase_ ,return_attention_mask=lowerCamelCase_ ,)
												lowerCAmelCase__  :       Any				=  WavaVecaProcessor(feature_extractor=lowerCamelCase_ ,tokenizer=lowerCamelCase_)
												processor.save_pretrained(lowerCamelCase_)

								lowerCAmelCase__  :       Union[str, Any]				=  UniSpeechForCTC(lowerCamelCase_)
				else:
								lowerCAmelCase__  :       List[Any]				=  UniSpeechForPreTraining(lowerCamelCase_)

				if is_finetuned:
								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  fairseq.checkpoint_utils.load_model_ensemble_and_task(
								    [checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''')[:-1]), '''w2v_path''': checkpoint_path})
				else:
								lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

				lowerCAmelCase__  :       Dict				=  model[0].eval()

				recursively_load_weights(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_)

				hf_unispeech.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
							__snake_case       :	Optional[Any]            =argparse.ArgumentParser()
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
							parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
							parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
							parser.add_argument(
							    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
							)
							__snake_case       :	List[Any]            =parser.parse_args()
							convert_unispeech_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)

