




'''simple docstring'''





import os
import unittest

from transformers import ElectraTokenizerFast
from transformers.models.electra.tokenization_electra import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    ElectraTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english

@require_tokenizers
class      _a       (		__lowerCAmelCase      ,      unittest.TestCase							):
     SCREAMING_SNAKE_CASE_							:					Optional[int]					       =		ElectraTokenizer
     SCREAMING_SNAKE_CASE_							:					List[Any]					       =		ElectraTokenizerFast
     SCREAMING_SNAKE_CASE_							:					List[str]					       =		True
     SCREAMING_SNAKE_CASE_							:					Union[str, Any]					       =		True
     SCREAMING_SNAKE_CASE_							:					int					       =		filter_non_english






     def        _lowercase			(				self					)							->    str:
          super().setUp()

          _snake_case							=   [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          _snake_case							=   os.path.join(self.tmpdirname      ,VOCAB_FILES_NAMES["vocab_file"]					)
          with open(self.vocab_file      ,"w"      ,encoding="utf-8"					) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens]					)					)






     def        _lowercase			(				self      ,_SCREAMING_SNAKE_CASE					)							->    List[str]:
          _snake_case							=   "UNwant\u00E9d,running"
          _snake_case							=   "unwanted, running"
          return input_text, output_text






     def        _lowercase			(				self					)							->    Any:
          _snake_case							=   self.tokenizer_class(self.vocab_file					)

          _snake_case							=   tokenizer.tokenize("UNwant\u00E9d,running"					)
          self.assertListEqual(a_      ,["un", "##want", "##ed", ",", "runn", "##ing"]					)
          self.assertListEqual(tokenizer.convert_tokens_to_ids(a_					)      ,[9, 6, 7, 12, 10, 11]					)






     def        _lowercase			(				self					)							->    Dict:
          if not self.test_rust_tokenizer:
               return

          _snake_case							=   self.get_tokenizer()
          _snake_case							=   self.get_rust_tokenizer()

          _snake_case							=   "UNwant\u00E9d,running"

          _snake_case							=   tokenizer.tokenize(a_					)
          _snake_case							=   rust_tokenizer.tokenize(a_					)
          self.assertListEqual(a_      ,a_					)

          _snake_case							=   tokenizer.encode(a_      ,add_special_tokens=a_					)
          _snake_case							=   rust_tokenizer.encode(a_      ,add_special_tokens=a_					)
          self.assertListEqual(a_      ,a_					)

          _snake_case							=   self.get_rust_tokenizer()
          _snake_case							=   tokenizer.encode(a_					)
          _snake_case							=   rust_tokenizer.encode(a_					)
          self.assertListEqual(a_      ,a_					)

          # With lower casing
          _snake_case							=   self.get_tokenizer(do_lower_case=a_					)
          _snake_case							=   self.get_rust_tokenizer(do_lower_case=a_					)

          _snake_case							=   "UNwant\u00E9d,running"

          _snake_case							=   tokenizer.tokenize(a_					)
          _snake_case							=   rust_tokenizer.tokenize(a_					)
          self.assertListEqual(a_      ,a_					)

          _snake_case							=   tokenizer.encode(a_      ,add_special_tokens=a_					)
          _snake_case							=   rust_tokenizer.encode(a_      ,add_special_tokens=a_					)
          self.assertListEqual(a_      ,a_					)

          _snake_case							=   self.get_rust_tokenizer()
          _snake_case							=   tokenizer.encode(a_					)
          _snake_case							=   rust_tokenizer.encode(a_					)
          self.assertListEqual(a_      ,a_					)






     def        _lowercase			(				self					)							->    int:
          _snake_case							=   BasicTokenizer()

          self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"					)      ,["ah", "\u535A", "\u63A8", "zz"]					)






     def        _lowercase			(				self					)							->    List[Any]:
          _snake_case							=   BasicTokenizer(do_lower_case=a_					)

          self.assertListEqual(
              tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "					)      ,["hello", "!", "how", "are", "you", "?"]					)
          self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)      ,["hello"]					)






     def        _lowercase			(				self					)							->    Optional[int]:
          _snake_case							=   BasicTokenizer(do_lower_case=a_      ,strip_accents=a_					)

          self.assertListEqual(
              tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,["hällo", "!", "how", "are", "you", "?"]					)
          self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)      ,["h\u00E9llo"]					)






     def        _lowercase			(				self					)							->    int:
          _snake_case							=   BasicTokenizer(do_lower_case=a_      ,strip_accents=a_					)

          self.assertListEqual(
              tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,["hallo", "!", "how", "are", "you", "?"]					)
          self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)      ,["hello"]					)






     def        _lowercase			(				self					)							->    Any:
          _snake_case							=   BasicTokenizer(do_lower_case=a_					)

          self.assertListEqual(
              tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,["hallo", "!", "how", "are", "you", "?"]					)
          self.assertListEqual(tokenizer.tokenize("H\u00E9llo"					)      ,["hello"]					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   BasicTokenizer(do_lower_case=a_					)

          self.assertListEqual(
              tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "					)      ,["HeLLo", "!", "how", "Are", "yoU", "?"]					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   BasicTokenizer(do_lower_case=a_      ,strip_accents=a_					)

          self.assertListEqual(
              tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,["HäLLo", "!", "how", "Are", "yoU", "?"]					)






     def        _lowercase			(				self					)							->    Union[str, Any]:
          _snake_case							=   BasicTokenizer(do_lower_case=a_      ,strip_accents=a_					)

          self.assertListEqual(
              tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "					)      ,["HaLLo", "!", "how", "Are", "yoU", "?"]					)






     def        _lowercase			(				self					)							->    Any:
          _snake_case							=   BasicTokenizer(do_lower_case=a_      ,never_split=["[UNK]"]					)

          self.assertListEqual(
              tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"					)      ,["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]					)






     def        _lowercase			(				self					)							->    List[str]:
          _snake_case							=   ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

          _snake_case							=   {}
          for i, token in enumerate(a_					):
               _snake_case							=   i
          _snake_case							=   WordpieceTokenizer(vocab=a_      ,unk_token="[UNK]"					)

          self.assertListEqual(tokenizer.tokenize(""					)      ,[]					)

          self.assertListEqual(tokenizer.tokenize("unwanted running"					)      ,["un", "##want", "##ed", "runn", "##ing"]					)

          self.assertListEqual(tokenizer.tokenize("unwantedX running"					)      ,["[UNK]", "runn", "##ing"]					)






     def        _lowercase			(				self					)							->    List[str]:
          self.assertTrue(_is_whitespace(" "					)					)
          self.assertTrue(_is_whitespace("\t"					)					)
          self.assertTrue(_is_whitespace("\r"					)					)
          self.assertTrue(_is_whitespace("\n"					)					)
          self.assertTrue(_is_whitespace("\u00A0"					)					)

          self.assertFalse(_is_whitespace("A"					)					)
          self.assertFalse(_is_whitespace("-"					)					)






     def        _lowercase			(				self					)							->    int:
          self.assertTrue(_is_control("\u0005"					)					)

          self.assertFalse(_is_control("A"					)					)
          self.assertFalse(_is_control(" "					)					)
          self.assertFalse(_is_control("\t"					)					)
          self.assertFalse(_is_control("\r"					)					)






     def        _lowercase			(				self					)							->    Tuple:
          self.assertTrue(_is_punctuation("-"					)					)
          self.assertTrue(_is_punctuation("$"					)					)
          self.assertTrue(_is_punctuation("`"					)					)
          self.assertTrue(_is_punctuation("."					)					)

          self.assertFalse(_is_punctuation("A"					)					)
          self.assertFalse(_is_punctuation(" "					)					)






     def        _lowercase			(				self					)							->    Optional[Any]:
          _snake_case							=   self.get_tokenizer()
          _snake_case							=   self.get_rust_tokenizer()

          # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
          self.assertListEqual([tokenizer.tokenize(a_					) for t in ["Test", "\xad", "test"]]      ,[["[UNK]"], [], ["[UNK]"]]					)

          self.assertListEqual(
              [rust_tokenizer.tokenize(a_					) for t in ["Test", "\xad", "test"]]      ,[["[UNK]"], [], ["[UNK]"]]					)






     @slow
     def        _lowercase			(				self					)							->    Tuple:
          _snake_case							=   self.tokenizer_class.from_pretrained("google/electra-base-discriminator"					)

          _snake_case							=   tokenizer.encode("sequence builders"      ,add_special_tokens=a_					)
          _snake_case							=   tokenizer.encode("multi-sequence build"      ,add_special_tokens=a_					)

          _snake_case							=   tokenizer.build_inputs_with_special_tokens(a_					)
          _snake_case							=   tokenizer.build_inputs_with_special_tokens(a_      ,a_					)

          assert encoded_sentence == [101] + text + [102]
          assert encoded_pair == [101] + text + [102] + text_a + [102]






     def        _lowercase			(				self					)							->    Optional[Any]:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
                    _snake_case							=   self.rust_tokenizer_class.from_pretrained(a_      ,**a_					)

                    _snake_case							=   f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
                    _snake_case							=   tokenizer_r.encode_plus(
                        a_      ,return_attention_mask=a_      ,return_token_type_ids=a_      ,return_offsets_mapping=a_      ,add_special_tokens=a_      ,)

                    _snake_case							=   tokenizer_r.do_lower_case if hasattr(a_      ,"do_lower_case"					) else False
                    _snake_case							=   (
                        [
                            ((0, 0), tokenizer_r.cls_token),
                            ((0, 1), "A"),
                            ((1, 2), ","),
                            ((3, 5), "na"),
                            ((5, 6), "##ï"),
                            ((6, 8), "##ve"),
                            ((9, 15), tokenizer_r.mask_token),
                            ((16, 21), "Allen"),
                            ((21, 23), "##NL"),
                            ((23, 24), "##P"),
                            ((25, 33), "sentence"),
                            ((33, 34), "."),
                            ((0, 0), tokenizer_r.sep_token),
                        ]
                        if not do_lower_case
                        else [
                            ((0, 0), tokenizer_r.cls_token),
                            ((0, 1), "a"),
                            ((1, 2), ","),
                            ((3, 8), "naive"),
                            ((9, 15), tokenizer_r.mask_token),
                            ((16, 21), "allen"),
                            ((21, 23), "##nl"),
                            ((23, 24), "##p"),
                            ((25, 33), "sentence"),
                            ((33, 34), "."),
                            ((0, 0), tokenizer_r.sep_token),
                        ]
                    )

                    self.assertEqual(
                        [e[1] for e in expected_results]      ,tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]					)					)
                    self.assertEqual([e[0] for e in expected_results]      ,tokens["offset_mapping"]					)






     def        _lowercase			(				self					)							->    Any:
          _snake_case							=   ["的", "人", "有"]
          _snake_case							=   "".join(a_					)
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
                    _snake_case							=   True
                    _snake_case							=   self.tokenizer_class.from_pretrained(a_      ,**a_					)
                    _snake_case							=   self.rust_tokenizer_class.from_pretrained(a_      ,**a_					)

                    _snake_case							=   tokenizer_p.encode(a_      ,add_special_tokens=a_					)
                    _snake_case							=   tokenizer_r.encode(a_      ,add_special_tokens=a_					)

                    _snake_case							=   tokenizer_r.convert_ids_to_tokens(a_					)
                    _snake_case							=   tokenizer_p.convert_ids_to_tokens(a_					)

                    # it is expected that each Chinese character is not preceded by "##"
                    self.assertListEqual(a_      ,a_					)
                    self.assertListEqual(a_      ,a_					)

                    _snake_case							=   False
                    _snake_case							=   self.rust_tokenizer_class.from_pretrained(a_      ,**a_					)
                    _snake_case							=   self.tokenizer_class.from_pretrained(a_      ,**a_					)

                    _snake_case							=   tokenizer_r.encode(a_      ,add_special_tokens=a_					)
                    _snake_case							=   tokenizer_p.encode(a_      ,add_special_tokens=a_					)

                    _snake_case							=   tokenizer_r.convert_ids_to_tokens(a_					)
                    _snake_case							=   tokenizer_p.convert_ids_to_tokens(a_					)

                    # it is expected that only the first Chinese character is not preceded by "##".
                    _snake_case							=   [
                        f"""##{token}""" if idx != 0 else token for idx, token in enumerate(a_					)
                    ]
                    self.assertListEqual(a_      ,a_					)
                    self.assertListEqual(a_      ,a_					)








'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase_		:    Optional[int]									=		logging.get_logger(__name__)

UpperCamelCase_		:    Dict									=		{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}





def 			__a			(     _UpperCamelCase:					Optional[Any]      ,		_UpperCamelCase:					Any      ,		_UpperCamelCase:					str      ,		_UpperCamelCase:					List[Any]      ,		_UpperCamelCase:					Optional[int]					)		->				List[Any]:

     """simple docstring"""




     for attribute in key.split("."					):
          _snake_case							=   getattr(_UpperCamelCase      ,		_UpperCamelCase					)

     if weight_type is not None:
          _snake_case							=   getattr(_UpperCamelCase      ,		_UpperCamelCase					).shape
     else:
          _snake_case							=   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":
          _snake_case							=   value
     elif weight_type == "weight_g":
          _snake_case							=   value
     elif weight_type == "weight_v":
          _snake_case							=   value
     elif weight_type == "bias":
          _snake_case							=   value
     else:
          _snake_case							=   value

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





def 			__a			(     _UpperCamelCase:					Tuple      ,		_UpperCamelCase:					List[str]      ,		_UpperCamelCase:					Optional[int]					)		->				Dict:

     """simple docstring"""




     _snake_case							=   []
     _snake_case							=   fairseq_model.state_dict()

     _snake_case							=   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          _snake_case							=   False
          if "conv_layers" in name:
               load_conv_layer(
                   _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		hf_model.config.feat_extract_norm == "group"      ,		)
               _snake_case							=   True
          else:
               for key, mapped_key in MAPPING.items():
                    _snake_case							=   "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

                    if key in name or key.split("w2v_model."					)[-1] == name.split("."					)[0]:
                         _snake_case							=   True
                         if "*" in mapped_key:
                              _snake_case							=   name.split(_UpperCamelCase					)[0].split("."					)[-2]
                              if not layer_index.isnumeric():
                                   continue
                              _snake_case							=   mapped_key.replace("*"      ,		_UpperCamelCase					)
                         if "weight_g" in name:
                              _snake_case							=   "weight_g"
                         elif "weight_v" in name:
                              _snake_case							=   "weight_v"
                         elif "weight" in name:
                              _snake_case							=   "weight"
                         elif "bias" in name:
                              _snake_case							=   "bias"
                         else:
                              _snake_case							=   None
                         set_recursively(_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase					)
                    continue
          if not is_used:
               unused_weights.append(_UpperCamelCase					)

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





def 			__a			(     _UpperCamelCase:					List[str]      ,		_UpperCamelCase:					Any      ,		_UpperCamelCase:					Tuple      ,		_UpperCamelCase:					Union[str, Any]      ,		_UpperCamelCase:					Tuple					)		->				Optional[Any]:

     """simple docstring"""




     _snake_case							=   full_name.split("conv_layers."					)[-1]
     _snake_case							=   name.split("."					)
     _snake_case							=   int(items[0]					)
     _snake_case							=   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."""
               )
               _snake_case							=   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."""
               )
               _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:
               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."
               )
               _snake_case							=   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."""
               )
               _snake_case							=   value
               logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
     else:
          unused_weights.append(_UpperCamelCase					)





def 			__a			(     _UpperCamelCase:					List[Any]      ,		_UpperCamelCase:					str					)		->				Dict:

     """simple docstring"""




     _snake_case							=   SEWDConfig()
     if is_finetuned:
          _snake_case							=   model.wav_encoder.wav_model.cfg
     else:
          _snake_case							=   model.cfg

     _snake_case							=   fs_config.conv_bias
     _snake_case							=   eval(fs_config.conv_feature_layers					)
     _snake_case							=   [x[0] for x in conv_layers]
     _snake_case							=   [x[1] for x in conv_layers]
     _snake_case							=   [x[2] for x in conv_layers]
     _snake_case							=   "gelu"
     _snake_case							=   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
     _snake_case							=   0.0
     _snake_case							=   fs_config.activation_fn.name
     _snake_case							=   fs_config.encoder_embed_dim
     _snake_case							=   0.02
     _snake_case							=   fs_config.encoder_ffn_embed_dim
     _snake_case							=   1E-5
     _snake_case							=   fs_config.encoder_layerdrop
     _snake_case							=   fs_config.encoder_attention_heads
     _snake_case							=   fs_config.conv_pos_groups
     _snake_case							=   fs_config.conv_pos
     _snake_case							=   len(_UpperCamelCase					)
     _snake_case							=   fs_config.encoder_layers
     _snake_case							=   fs_config.squeeze_factor
     # DeBERTa-specific parameters:
     _snake_case							=   fs_config.max_position_embeddings
     _snake_case							=   fs_config.position_buckets
     _snake_case							=   fs_config.share_att_key
     _snake_case							=   fs_config.relative_attention
     _snake_case							=   fs_config.position_biased_input
     _snake_case							=   tuple(fs_config.pos_att_type.split("|"					)					)
     _snake_case							=   fs_config.norm_rel_ebd

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          _snake_case							=   model.cfg
          _snake_case							=   fs_config.final_dropout
          _snake_case							=   fs_config.layerdrop
     _snake_case							=   fs_config.activation_dropout
     _snake_case							=   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     _snake_case							=   fs_config.attention_dropout
     _snake_case							=   fs_config.dropout_input
     _snake_case							=   fs_config.dropout
     _snake_case							=   fs_config.mask_channel_length
     _snake_case							=   fs_config.mask_channel_prob
     _snake_case							=   fs_config.mask_length
     _snake_case							=   fs_config.mask_prob

     _snake_case							=   "Wav2Vec2FeatureExtractor"
     _snake_case							=   "Wav2Vec2CTCTokenizer"

     return config





@torch.no_grad()
def 			__a			(     _UpperCamelCase:					Optional[int]      ,		_UpperCamelCase:					Any      ,		_UpperCamelCase:					Any=None      ,		_UpperCamelCase:					Union[str, Any]=None      ,		_UpperCamelCase:					Optional[Any]=True					)		->				int:

     """simple docstring"""




     if is_finetuned:
          _snake_case							,      _snake_case							,      _snake_case							=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]      ,		arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
     else:
          _snake_case							,      _snake_case							,      _snake_case							=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

     if config_path is not None:
          _snake_case							=   SEWDConfig.from_pretrained(_UpperCamelCase					)
     else:
          _snake_case							=   convert_config(model[0]      ,		_UpperCamelCase					)
     _snake_case							=   model[0].eval()

     _snake_case							=   True if config.feat_extract_norm == "layer" else False
     _snake_case							=   WavaVecaFeatureExtractor(
         feature_size=1      ,		sampling_rate=16_000      ,		padding_value=0      ,		do_normalize=_UpperCamelCase      ,		return_attention_mask=_UpperCamelCase      ,		)

     if is_finetuned:
          if dict_path:
               _snake_case							=   Dictionary.load(_UpperCamelCase					)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _snake_case							=   target_dict.pad_index
               _snake_case							=   target_dict.bos_index
               _snake_case							=   target_dict.pad_index
               _snake_case							=   target_dict.bos_index
               _snake_case							=   target_dict.eos_index
               _snake_case							=   len(target_dict.symbols					)
               _snake_case							=   os.path.join(_UpperCamelCase      ,		"vocab.json"					)
               if not os.path.isdir(_UpperCamelCase					):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_UpperCamelCase					)					)
                    return
               os.makedirs(_UpperCamelCase      ,		exist_ok=_UpperCamelCase					)
               with open(_UpperCamelCase      ,		"w"      ,		encoding="utf-8"					) as vocab_handle:
                    json.dump(target_dict.indices      ,		_UpperCamelCase					)
               _snake_case							=   WavaVecaCTCTokenizer(
                   _UpperCamelCase      ,		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=_UpperCamelCase      ,		)
               _snake_case							=   WavaVecaProcessor(feature_extractor=_UpperCamelCase      ,		tokenizer=_UpperCamelCase					)
               processor.save_pretrained(_UpperCamelCase					)

          _snake_case							=   SEWDForCTC(_UpperCamelCase					)
     else:
          _snake_case							=   SEWDModel(_UpperCamelCase					)
          feature_extractor.save_pretrained(_UpperCamelCase					)

     recursively_load_weights(_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase					)

     hf_model.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
   UpperCamelCase_		:    str									=		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(
       '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
   )
   UpperCamelCase_		:    Union[str, Any]									=		parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )


