


'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCAmelCase					       =							get_tests_dir('''fixtures/test_sentencepiece.model''')


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

__UpperCAmelCase					       =							250_004
__UpperCAmelCase					       =							250_020




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





							'''simple docstring'''






							lowercase__    :     Any					=   MBartTokenizer
							lowercase__    :     str					=   MBartTokenizerFast
							lowercase__    :     List[Any]					=   True
							lowercase__    :     List[Any]					=   True







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														super().setUp()

														# We have a SentencePiece fixture for testing
														lowerCAmelCase__      =			MBartTokenizer(lowerCamelCase_      ,					keep_accents=lowerCamelCase_     )
														tokenizer.save_pretrained(self.tmpdirname     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Any:
														lowerCAmelCase__      =			MBartTokenizer(lowerCamelCase_      ,					keep_accents=lowerCamelCase_     )

														lowerCAmelCase__      =			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__      =			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__      =			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]
														        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
														    ]      ,					)

														lowerCAmelCase__      =			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>''',
														        '''.''',
														    ]      ,					)







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														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__      =			(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart''', {})
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
																												lowerCAmelCase__      =			self.rust_tokenizer_class.from_pretrained(lowerCamelCase_      ,					**lowerCamelCase_     )
																												lowerCAmelCase__      =			self.tokenizer_class.from_pretrained(lowerCamelCase_      ,					**lowerCamelCase_     )

																												lowerCAmelCase__      =			tempfile.mkdtemp()

																												lowerCAmelCase__      =			tokenizer_r.save_pretrained(lowerCamelCase_     )
																												lowerCAmelCase__      =			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__      =			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__      =			tokenizer_r.from_pretrained(lowerCamelCase_     )
																												lowerCAmelCase__      =			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__      =			tempfile.mkdtemp()

																												lowerCAmelCase__      =			tokenizer_r.save_pretrained(lowerCamelCase_      ,					legacy_format=lowerCamelCase_     )
																												lowerCAmelCase__      =			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__      =			tokenizer_r.from_pretrained(lowerCamelCase_     )
																												lowerCAmelCase__      =			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__      =			tempfile.mkdtemp()

																												lowerCAmelCase__      =			tokenizer_r.save_pretrained(lowerCamelCase_      ,					legacy_format=lowerCamelCase_     )
																												lowerCAmelCase__      =			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__      =			tokenizer_r.from_pretrained(lowerCamelCase_     )
																												lowerCAmelCase__      =			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 	a__       (							unittest.TestCase						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "facebook/mbart-large-en-ro"
							lowercase__    :     Tuple					=   [
							    " 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.",
							]
							lowercase__    :     Optional[int]					=   [
							    "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.",
							]
							lowercase__    :     Optional[int]					=   [8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2, EN_CODE]







							@classmethod
							def 						__SCREAMING_SNAKE_CASE							(	cls     ) ->							List[str]:
														lowerCAmelCase__      =			MBartTokenizer.from_pretrained(
														    cls.checkpoint_name      ,					src_lang='''en_XX'''      ,					tgt_lang='''ro_RO'''     )
														lowerCAmelCase__      =			1
														return cls







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														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     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														lowerCAmelCase__      =			self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
														self.assertListEqual(self.expected_src_tokens      ,					lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														self.assertIn(lowerCamelCase_      ,					self.tokenizer.all_special_ids     )
														lowerCAmelCase__      =			[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
														lowerCAmelCase__      =			self.tokenizer.decode(lowerCamelCase_      ,					skip_special_tokens=lowerCamelCase_     )
														lowerCAmelCase__      =			self.tokenizer.decode(generated_ids[1:]      ,					skip_special_tokens=lowerCamelCase_     )
														self.assertEqual(lowerCamelCase_      ,					lowerCamelCase_     )
														self.assertNotIn(self.tokenizer.eos_token      ,					lowerCamelCase_     )







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







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Tuple:
														self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']     )      ,					[25_00_26, 25_00_01]     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Dict:
														lowerCAmelCase__      =			tempfile.mkdtemp()
														lowerCAmelCase__      =			self.tokenizer.fairseq_tokens_to_ids
														self.tokenizer.save_pretrained(lowerCamelCase_     )
														lowerCAmelCase__      =			MBartTokenizer.from_pretrained(lowerCamelCase_     )
														self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,					lowerCamelCase_     )







							@require_torch
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[int]:
														lowerCAmelCase__      =			self.tokenizer(self.src_text      ,					text_target=self.tgt_text      ,					padding=lowerCamelCase_      ,					return_tensors='''pt'''     )
														lowerCAmelCase__      =			shift_tokens_right(batch['''labels''']      ,					self.tokenizer.pad_token_id     )

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







							@require_torch
							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[Any]:
														lowerCAmelCase__      =			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__      =			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__      =			batch.input_ids.tolist()[0]
														self.assertListEqual(self.expected_src_tokens      ,					lowerCamelCase_     )
														self.assertEqual(2      ,					batch.decoder_input_ids[0, -1]     )  # EOS
														# Test that special tokens are reset
														self.assertEqual(self.tokenizer.prefix_tokens      ,					[]     )
														self.assertEqual(self.tokenizer.suffix_tokens      ,					[self.tokenizer.eos_token_id, EN_CODE]     )







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														lowerCAmelCase__      =			self.tokenizer(self.src_text      ,					padding=lowerCamelCase_      ,					truncation=lowerCamelCase_      ,					max_length=3      ,					return_tensors='''pt'''     )
														lowerCAmelCase__      =			self.tokenizer(
														    text_target=self.tgt_text      ,					padding=lowerCamelCase_      ,					truncation=lowerCamelCase_      ,					max_length=10      ,					return_tensors='''pt'''     )
														lowerCAmelCase__      =			targets['''input_ids''']
														lowerCAmelCase__      =			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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														lowerCAmelCase__      =			self.tokenizer._build_translation_inputs(
														    '''A test'''      ,					return_tensors='''pt'''      ,					src_lang='''en_XX'''      ,					tgt_lang='''ar_AR'''     )

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



'''simple docstring'''






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''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''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__UpperCAmelCase					       =							[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]


def       _snake_case     (  A				,					A				,					A				,					A				,					A )		->					Optional[Any]:
							for attribute in key.split('''.''' ):
														lowerCAmelCase__      =			getattr(A				,					A )

							if weight_type is not None:
														lowerCAmelCase__      =			getattr(A				,					A ).shape
							else:
														lowerCAmelCase__      =			hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    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__      =			value
							elif weight_type == "weight_g":
														lowerCAmelCase__      =			value
							elif weight_type == "weight_v":
														lowerCAmelCase__      =			value
							elif weight_type == "bias":
														lowerCAmelCase__      =			value
							elif weight_type == "running_mean":
														lowerCAmelCase__      =			value
							elif weight_type == "running_var":
														lowerCAmelCase__      =			value
							elif weight_type == "num_batches_tracked":
														lowerCAmelCase__      =			value
							elif weight_type == "inv_freq":
														lowerCAmelCase__      =			value
							else:
														lowerCAmelCase__      =			value

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


def       _snake_case     (  A				,					A				,					A )		->					Any:
							lowerCAmelCase__      =			[]
							lowerCAmelCase__      =			fairseq_model.state_dict()

							lowerCAmelCase__      =			hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCAmelCase__      =			False
														if "conv_layers" in name:
																					load_conv_layer(
																					    A				,					A				,					A				,					A				,					hf_model.config.feat_extract_norm == '''group'''				,					)
																					lowerCAmelCase__      =			True
														else:
																					for key, mapped_key in MAPPING.items():
																												lowerCAmelCase__      =			'''wav2vec2_conformer.''' + 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__      =			True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__      =			name.split(A )[0].split('''.''' )[-2]
																																										lowerCAmelCase__      =			mapped_key.replace('''*'''				,					A )
																																			if "pos_bias_u" in name:
																																										lowerCAmelCase__      =			None
																																			elif "pos_bias_v" in name:
																																										lowerCAmelCase__      =			None
																																			elif "weight_g" in name:
																																										lowerCAmelCase__      =			'''weight_g'''
																																			elif "weight_v" in name:
																																										lowerCAmelCase__      =			'''weight_v'''
																																			elif "bias" in name:
																																										lowerCAmelCase__      =			'''bias'''
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										lowerCAmelCase__      =			'''weight'''
																																			elif "running_mean" in name:
																																										lowerCAmelCase__      =			'''running_mean'''
																																			elif "inv_freq" in name:
																																										lowerCAmelCase__      =			'''inv_freq'''
																																			elif "running_var" in name:
																																										lowerCAmelCase__      =			'''running_var'''
																																			elif "num_batches_tracked" in name:
																																										lowerCAmelCase__      =			'''num_batches_tracked'''
																																			else:
																																										lowerCAmelCase__      =			None
																																			set_recursively(A				,					A				,					A				,					A				,					A )
																												continue
														if not is_used:
																					unused_weights.append(A )

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


def       _snake_case     (  A				,					A				,					A				,					A				,					A )		->					Tuple:
							lowerCAmelCase__      =			full_name.split('''conv_layers.''' )[-1]
							lowerCAmelCase__      =			name.split('''.''' )
							lowerCAmelCase__      =			int(items[0] )
							lowerCAmelCase__      =			int(items[1] )

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
																					lowerCAmelCase__      =			value
																					logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
																					lowerCAmelCase__      =			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:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
																					lowerCAmelCase__      =			value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"""{full_name} has size {value.shape}, but"""
																												    F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
																					lowerCAmelCase__      =			value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
							else:
														unused_weights.append(A )


@torch.no_grad()
def       _snake_case     (  A				,					A				,					A=None				,					A=None				,					A=True )		->					Optional[int]:
							if config_path is not None:
														lowerCAmelCase__      =			WavaVecaConformerConfig.from_pretrained(A				,					hidden_act='''swish''' )
							else:
														lowerCAmelCase__      =			WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														lowerCAmelCase__      =			'''rotary'''

							if is_finetuned:
														if dict_path:
																					lowerCAmelCase__      =			Dictionary.load(A )

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

																					# fairseq has the <pad> and <s> switched
																					lowerCAmelCase__      =			0
																					lowerCAmelCase__      =			1
																					with open(A				,					'''w'''				,					encoding='''utf-8''' ) as vocab_handle:
																												json.dump(A				,					A )
																					lowerCAmelCase__      =			WavaVecaCTCTokenizer(
																					    A				,					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=A				,					)
																					lowerCAmelCase__      =			True if config.feat_extract_norm == '''layer''' else False
																					lowerCAmelCase__      =			WavaVecaFeatureExtractor(
																					    feature_size=1				,					sampling_rate=16000				,					padding_value=0				,					do_normalize=A				,					return_attention_mask=A				,					)
																					lowerCAmelCase__      =			WavaVecaProcessor(feature_extractor=A				,					tokenizer=A )
																					processor.save_pretrained(A )

														lowerCAmelCase__      =			WavaVecaConformerForCTC(A )
							else:
														lowerCAmelCase__      =			WavaVecaConformerForPreTraining(A )

							if is_finetuned:
														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]				,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
							else:
														lowerCAmelCase__      =			argparse.Namespace(task='''audio_pretraining''' )
														lowerCAmelCase__      =			fairseq.tasks.setup_task(A )

														lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,					task=A )

							lowerCAmelCase__      =			model[0].eval()

							recursively_load_weights(A				,					A				,					not is_finetuned )

							hf_wavavec.save_pretrained(A )


if __name__ == "__main__":
		__UpperCAmelCase					       =							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'''
		)
		__UpperCAmelCase					       =							parser.parse_args()
		convert_wavaveca_conformer_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)