'''simple docstring'''






import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase    :     int         =       get_tests_dir('''fixtures/test_sentencepiece.model''')

if is_sentencepiece_available():
   import sentencepiece as sp


_UpperCAmelCase    :     Union[str, Any]         =       5
_UpperCAmelCase    :     List[str]         =       10







@require_sentencepiece
@require_tokenizers
class lowercase_       (		_UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   SpeechaTextTokenizer
   __lowerCAmelCase         =   False
   __lowerCAmelCase         =   True
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Union[str, Any]:
        super().setUp()

        _A    		=  sp.SentencePieceProcessor()
        spm_model.Load(UpperCamelCase__     )
        _A    		=  ['<s>', '<pad>', '</s>', '<unk>']

        vocab += [spm_model.IdToPiece(id_     ) for id_ in range(len(UpperCamelCase__     )     )]
        _A    		=  dict(zip(UpperCamelCase__, range(len(UpperCamelCase__     )     )     )     )

        _A    		=  Path(self.tmpdirname     )
        save_json(UpperCamelCase__, save_dir / VOCAB_FILES_NAMES['vocab_file']     )
        if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
             copyfile(UpperCamelCase__, save_dir / VOCAB_FILES_NAMES['spm_file']     )

        _A    		=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )
        tokenizer.save_pretrained(self.tmpdirname     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			List[str]:
        _A    		=  '<pad>'
        _A    		=  1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__     ), UpperCamelCase__     )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__     ), UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A    		=  list(self.get_tokenizer().get_vocab().keys()     )

        self.assertEqual(vocab_keys[0], '<s>'     )
        self.assertEqual(vocab_keys[1], '<pad>'     )
        self.assertEqual(vocab_keys[-1], 'j'     )
        self.assertEqual(len(UpperCamelCase__     ), 10_01     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			int:
        self.assertEqual(self.get_tokenizer().vocab_size, 10_01     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Tuple:
        _A    		=  SpeechaTextTokenizer.from_pretrained(self.tmpdirname     )

        _A    		=  tokenizer.tokenize('This is a test'     )
        self.assertListEqual(UpperCamelCase__, ['▁This', '▁is', '▁a', '▁t', 'est']     )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(UpperCamelCase__     ), [2_89, 50, 14, 1_74, 3_86], )

        _A    		=  tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
        self.assertListEqual(
            UpperCamelCase__, [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', '.'], )
        _A    		=  tokenizer.convert_tokens_to_ids(UpperCamelCase__     )
        self.assertListEqual(UpperCamelCase__, [12, 25, 88, 59, 28, 23, 11, 4, 6_06, 3_51, 3_51, 3_51, 7, 16, 70, 50, 76, 84, 10, 4, 8]     )

        _A    		=  tokenizer.convert_ids_to_tokens(UpperCamelCase__     )
        self.assertListEqual(
            UpperCamelCase__, [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 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        # fmt: off
        _A    		=  {'input_ids': [[37_91, 7_97, 31, 11, 64, 7_97, 31, 24_29, 4_33, 12, 11_76, 12, 20, 7_86, 9_15, 1_42, 24_13, 2_40, 37, 32_38, 7_97, 31, 11, 35, 93, 9_15, 1_42, 24_13, 2_40, 37, 55_40, 5_67, 12_76, 93, 37, 6_10, 40, 62, 4_55, 6_57, 10_42, 1_23, 7_80, 1_77, 37, 3_09, 2_41, 12_98, 5_14, 20, 2_92, 27_37, 1_14, 24_69, 2_41, 85, 64, 3_02, 5_48, 5_28, 4_23, 4, 5_09, 4_06, 4_23, 37, 6_01, 4, 7_77, 3_02, 5_48, 5_28, 4_23, 2_84, 4, 33_88, 5_11, 4_59, 4, 35_55, 40, 3_21, 3_02, 7_05, 4, 33_88, 5_11, 5_83, 3_26, 5, 5, 5, 62, 33_10, 5_60, 1_77, 26_80, 2_17, 15_08, 32, 31, 8_53, 4_18, 64, 5_83, 5_11, 16_05, 62, 35, 93, 5_60, 1_77, 26_80, 2_17, 15_08, 15_21, 64, 5_83, 5_11, 5_19, 62, 20, 15_15, 7_64, 20, 1_49, 2_61, 56_25, 79_72, 20, 55_40, 5_67, 12_76, 93, 39_25, 16_75, 11, 15, 8_02, 79_72, 5_76, 2_17, 15_08, 11, 35, 93, 12_53, 24_41, 15, 2_89, 6_52, 31, 4_16, 3_21, 38_42, 1_15, 40, 9_11, 8, 4_76, 6_19, 4, 3_80, 1_42, 4_23, 3_35, 2_40, 35, 93, 2_64, 8, 11, 3_35, 5_69, 4_20, 1_63, 5, 2], [2_60, 5_48, 5_28, 4_23, 20, 4_51, 20, 26_81, 11_53, 34_34, 20, 55_40, 37, 5_67, 1_26, 12_53, 24_41, 33_76, 4_49, 2_10, 4_31, 15_63, 1_77, 7_67, 55_40, 11, 12_03, 4_72, 11, 29_53, 6_85, 2_85, 3_64, 7_06, 11_53, 20, 67_99, 20, 28_69, 20, 44_64, 1_26, 40, 24_29, 20, 10_40, 8_66, 26_64, 4_18, 20, 3_18, 20, 17_26, 1_86, 20, 2_65, 5_22, 35, 93, 21_91, 46_34, 20, 10_40, 12, 67_99, 15, 2_28, 23_56, 1_42, 31, 11, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_75, 26_66, 6_84, 15_82, 11_76, 12, 6_27, 1_49, 6_19, 20, 49_02, 5_63, 11, 20, 1_49, 2_61, 34_20, 23_56, 1_74, 1_42, 47_14, 1_31, 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, 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, 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, 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, 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, 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=UpperCamelCase__, model_name='facebook/s2t-small-mustc-en-de-st', revision='a14f04cf0776c02f62a8cb800cf7909e15ea23ad', )







@require_sentencepiece
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "valhalla/s2t_mustc_multilinguial_medium"

   __lowerCAmelCase         =   "C'est trop cool"
   __lowerCAmelCase         =   "Esto es genial"
   @classmethod
   def 	__UpperCAmelCase   (      cls      :			Any     )  ->			List[str]:
        _A    		=  SpeechaTextTokenizer.from_pretrained(cls.checkpoint_name     )
        return cls
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			Optional[int]:
        self.assertEqual(self.tokenizer.lang_code_to_id['pt'], 4     )
        self.assertEqual(self.tokenizer.lang_code_to_id['ru'], 6     )
        self.assertEqual(self.tokenizer.lang_code_to_id['it'], 9     )
        self.assertEqual(self.tokenizer.lang_code_to_id['de'], 11     )
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Optional[int]:
        self.assertEqual(self.tokenizer.vocab_size, 1_00_00     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			int:
        self.assertIn(UpperCamelCase__, self.tokenizer.all_special_ids     )
        _A    		=  [ES_CODE, 4, 16_01, 47, 76_47, 2]
        _A    		=  self.tokenizer.decode(UpperCamelCase__, skip_special_tokens=UpperCamelCase__     )
        _A    		=  self.tokenizer.decode(generated_ids[1:], skip_special_tokens=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, UpperCamelCase__     )
        self.assertNotIn(self.tokenizer.eos_token, UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			str:
        _A    		=  'fr'
        _A    		=  self.tokenizer(self.french_text     ).input_ids
        self.assertEqual(encoded[0], UpperCamelCase__     )
        self.assertEqual(encoded[-1], self.tokenizer.eos_token_id     )

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Any:
        _A    		=  'fr'
        self.assertListEqual(self.tokenizer.prefix_tokens, [FR_CODE]     )

        _A    		=  'es'
        self.assertListEqual(self.tokenizer.prefix_tokens, [ES_CODE]     )










import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
_A					:				List[str]				=			logging.get_logger(__name__)

_A					:				List[str]				=			{
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
_A					:				Optional[int]				=			[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]


def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			{}
    with open(lowerCAmelCase_ ,							'''r'''	) as file:
        for line_number, line in enumerate(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			line.strip()
            if line:
                SCREAMING_SNAKE_CASE__											=			line.split()
                SCREAMING_SNAKE_CASE__											=			line_number
                SCREAMING_SNAKE_CASE__											=			words[0]
                SCREAMING_SNAKE_CASE__											=			value
    return result


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    for attribute in key.split('''.'''	):
        SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			PARAM_MAPPING[full_name.split('''.'''	)[-1]]
            SCREAMING_SNAKE_CASE__											=			'''param'''

    if weight_type is not None and weight_type != "param":
        SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	).shape
    elif weight_type is not None and weight_type == "param":
        SCREAMING_SNAKE_CASE__											=			hf_pointer
        for attribute in hf_param_name.split('''.'''	):
            SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			shape_pointer.shape

        # let's reduce dimension
        SCREAMING_SNAKE_CASE__											=			value[0]
    else:
        SCREAMING_SNAKE_CASE__											=			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":
        SCREAMING_SNAKE_CASE__											=			value
    elif weight_type == "weight_g":
        SCREAMING_SNAKE_CASE__											=			value
    elif weight_type == "weight_v":
        SCREAMING_SNAKE_CASE__											=			value
    elif weight_type == "bias":
        SCREAMING_SNAKE_CASE__											=			value
    elif weight_type == "param":
        for attribute in hf_param_name.split('''.'''	):
            SCREAMING_SNAKE_CASE__											=			getattr(lowerCAmelCase_ ,							lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			value
    else:
        SCREAMING_SNAKE_CASE__											=			value

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


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(lowerCAmelCase_	):
            SCREAMING_SNAKE_CASE__											=			PARAM_MAPPING[full_name.split('''.'''	)[-1]]
            SCREAMING_SNAKE_CASE__											=			'''param'''

    if weight_type is not None and weight_type != "param":
        SCREAMING_SNAKE_CASE__											=			'''.'''.join([key, weight_type]	)
    elif weight_type is not None and weight_type == "param":
        SCREAMING_SNAKE_CASE__											=			'''.'''.join([key, hf_param_name]	)
    else:
        SCREAMING_SNAKE_CASE__											=			key

    SCREAMING_SNAKE_CASE__											=			value if '''lm_head''' in full_key else value[0]


_A					:				Union[str, Any]				=			{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None	)				->						Tuple:
    SCREAMING_SNAKE_CASE__											=			False
    for key, mapped_key in MAPPING.items():
        SCREAMING_SNAKE_CASE__											=			'''wav2vec2.''' + 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]:
            SCREAMING_SNAKE_CASE__											=			True
            if "*" in mapped_key:
                SCREAMING_SNAKE_CASE__											=			name.split(lowerCAmelCase_	)[0].split('''.'''	)[-2]
                SCREAMING_SNAKE_CASE__											=			mapped_key.replace('''*''' ,							lowerCAmelCase_	)
            if "weight_g" in name:
                SCREAMING_SNAKE_CASE__											=			'''weight_g'''
            elif "weight_v" in name:
                SCREAMING_SNAKE_CASE__											=			'''weight_v'''
            elif "bias" in name:
                SCREAMING_SNAKE_CASE__											=			'''bias'''
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                SCREAMING_SNAKE_CASE__											=			'''weight'''
            else:
                SCREAMING_SNAKE_CASE__											=			None
            if hf_dict is not None:
                rename_dict(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
            else:
                set_recursively(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
            return is_used
    return is_used


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[int]:
    SCREAMING_SNAKE_CASE__											=			[]
    SCREAMING_SNAKE_CASE__											=			fairseq_model.state_dict()

    SCREAMING_SNAKE_CASE__											=			hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        SCREAMING_SNAKE_CASE__											=			False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							hf_model.config.feat_extract_norm == '''group''' ,							)
            SCREAMING_SNAKE_CASE__											=			True
        else:
            SCREAMING_SNAKE_CASE__											=			load_wavaveca_layer(lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)
        if not is_used:
            unused_weights.append(lowerCAmelCase_	)

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


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			full_name.split('''conv_layers.'''	)[-1]
    SCREAMING_SNAKE_CASE__											=			name.split('''.'''	)
    SCREAMING_SNAKE_CASE__											=			int(items[0]	)
    SCREAMING_SNAKE_CASE__											=			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.'''	)
            SCREAMING_SNAKE_CASE__											=			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.'''	)
            SCREAMING_SNAKE_CASE__											=			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.'''	)
            SCREAMING_SNAKE_CASE__											=			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.'''	)
            SCREAMING_SNAKE_CASE__											=			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 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=None ,							lowerCAmelCase_=None ,							lowerCAmelCase_=True ,							lowerCAmelCase_=False	)				->						int:
    if config_path is not None:
        SCREAMING_SNAKE_CASE__											=			WavaVecaConfig.from_pretrained(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavaVecaConfig()

    if is_seq_class:
        SCREAMING_SNAKE_CASE__											=			read_txt_into_dict(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			idalabel
        SCREAMING_SNAKE_CASE__											=			WavaVecaForSequenceClassification(lowerCAmelCase_	)
        SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor(
            feature_size=1 ,							sampling_rate=1_6_0_0_0 ,							padding_value=0 ,							do_normalize=lowerCAmelCase_ ,							return_attention_mask=lowerCAmelCase_ ,							)
        feature_extractor.save_pretrained(lowerCAmelCase_	)

    elif is_finetuned:
        if dict_path:
            SCREAMING_SNAKE_CASE__											=			Dictionary.load(lowerCAmelCase_	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            SCREAMING_SNAKE_CASE__											=			target_dict.pad_index
            SCREAMING_SNAKE_CASE__											=			target_dict.bos_index
            SCREAMING_SNAKE_CASE__											=			target_dict.eos_index
            SCREAMING_SNAKE_CASE__											=			len(target_dict.symbols	)
            SCREAMING_SNAKE_CASE__											=			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_	)
            SCREAMING_SNAKE_CASE__											=			target_dict.indices

            # fairseq has the <pad> and <s> switched
            SCREAMING_SNAKE_CASE__											=			0
            SCREAMING_SNAKE_CASE__											=			1
            with open(lowerCAmelCase_ ,							'''w''' ,							encoding='''utf-8'''	) as vocab_handle:
                json.dump(lowerCAmelCase_ ,							lowerCAmelCase_	)
            SCREAMING_SNAKE_CASE__											=			WavaVecaCTCTokenizer(
                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_ ,							)
            SCREAMING_SNAKE_CASE__											=			True if config.feat_extract_norm == '''layer''' else False
            SCREAMING_SNAKE_CASE__											=			WavaVecaFeatureExtractor(
                feature_size=1 ,							sampling_rate=1_6_0_0_0 ,							padding_value=0 ,							do_normalize=lowerCAmelCase_ ,							return_attention_mask=lowerCAmelCase_ ,							)
            SCREAMING_SNAKE_CASE__											=			WavaVecaProcessor(feature_extractor=lowerCAmelCase_ ,							tokenizer=lowerCAmelCase_	)
            processor.save_pretrained(lowerCAmelCase_	)

        SCREAMING_SNAKE_CASE__											=			WavaVecaForCTC(lowerCAmelCase_	)
    else:
        SCREAMING_SNAKE_CASE__											=			WavaVecaForPreTraining(lowerCAmelCase_	)

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

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,							task=lowerCAmelCase_	)

    SCREAMING_SNAKE_CASE__											=			model[0].eval()

    recursively_load_weights(lowerCAmelCase_ ,							lowerCAmelCase_ ,							not is_finetuned	)

    hf_wavavec.save_pretrained(lowerCAmelCase_	)


if __name__ == "__main__":
  _A					:				int				=			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"""
  )
  parser.add_argument(
      """--is_seq_class""",
      action="""store_true""",
      help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
  )
  _A					:				List[str]				=			parser.parse_args()

  _A					:				List[str]				=			not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )
