
'''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()
a__						:				Tuple           =    logging.get_logger(__name__)

a__						:				List[str]           =    {
    "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     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     str:







      """simple docstring"""
      for attribute in key.split('.'		):
            __A    		=     getattr(UpperCAmelCase ,					UpperCAmelCase		)

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

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





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     int:







      """simple docstring"""
      __A    		=     []
      __A    		=     fairseq_model.state_dict()

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

      for name, value in fairseq_dict.items():
            __A    		=     False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					hf_model.config.feat_extract_norm == 'group' ,					)
                  __A    		=     True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A    		=     '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]:
                              __A    		=     True
                              if "*" in mapped_key:
                                    __A    		=     name.split(UpperCAmelCase		)[0].split('.'		)[-2]
                                    if not layer_index.isnumeric():
                                          continue
                                    __A    		=     mapped_key.replace('*' ,					UpperCAmelCase		)
                              if "weight_g" in name:
                                    __A    		=     'weight_g'
                              elif "weight_v" in name:
                                    __A    		=     'weight_v'
                              elif "weight" in name:
                                    __A    		=     'weight'
                              elif "bias" in name:
                                    __A    		=     'bias'
                              else:
                                    __A    		=     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     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     List[str]:







      """simple docstring"""
      __A    		=     full_name.split('conv_layers.'		)[-1]
      __A    		=     name.split('.'		)
      __A    		=     int(items[0]		)
      __A    		=     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.'
                  )
                  __A    		=     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.'
                  )
                  __A    		=     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."
                  )
                  __A    		=     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.'
                  )
                  __A    		=     value
                  logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
      else:
            unused_weights.append(UpperCAmelCase		)





def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase		)->     Union[str, Any]:







      """simple docstring"""
      __A    		=     SEWDConfig()
      if is_finetuned:
            __A    		=     model.wav_encoder.wav_model.cfg
      else:
            __A    		=     model.cfg

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

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

      __A    		=     'Wav2Vec2FeatureExtractor'
      __A    		=     'Wav2Vec2CTCTokenizer'

      return config





@torch.no_grad()
def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase=None ,					UpperCAmelCase=None ,					UpperCAmelCase=True		)->     Dict:







      """simple docstring"""

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

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

      __A    		=     True if config.feat_extract_norm == 'layer' else False
      __A    		=     WavaVecaFeatureExtractor(
          feature_size=1 ,					sampling_rate=1_6_0_0_0 ,					padding_value=0 ,					do_normalize=UpperCAmelCase ,					return_attention_mask=UpperCAmelCase ,					)

      if is_finetuned:
            if dict_path:
                  __A    		=     Dictionary.load(UpperCAmelCase		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  __A    		=     target_dict.pad_index
                  __A    		=     target_dict.bos_index
                  __A    		=     target_dict.pad_index
                  __A    		=     target_dict.bos_index
                  __A    		=     target_dict.eos_index
                  __A    		=     len(target_dict.symbols		)
                  __A    		=     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		)
                  __A    		=     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 ,					)
                  __A    		=     WavaVecaProcessor(feature_extractor=UpperCAmelCase ,					tokenizer=UpperCAmelCase		)
                  processor.save_pretrained(UpperCAmelCase		)

            __A    		=     SEWDForCTC(UpperCAmelCase		)
      else:
            __A    		=     SEWDModel(UpperCAmelCase		)
            feature_extractor.save_pretrained(UpperCAmelCase		)

      recursively_load_weights(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)

      hf_model.save_pretrained(UpperCAmelCase		)


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








'''simple docstring'''





import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE ,       unittest.TestCase):
 UpperCAmelCase__      :      Tuple  			=   PreTrainedTokenizerFast
 UpperCAmelCase__      :      Tuple  			=   False
 UpperCAmelCase__      :      str  			=   True
 UpperCAmelCase__      :      str  			=   'tokenizer_file'






 def 		lowercase_  (     self					:Union[str, Any]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     False  # because we don't have pretrained_vocab_files_map
       super().setUp()
       __A    		=     True

       __A    		=     ['robot-test/dummy-tokenizer-fast', 'robot-test/dummy-tokenizer-wordlevel']
       __A    		=     'SaulLu/dummy-tokenizer-bytelevel-bpe'

       # Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
       __A    		=     [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

       __A    		=     PreTrainedTokenizerFast.from_pretrained(model_paths[0]     )
       tokenizer.save_pretrained(self.tmpdirname     )






 def 		lowercase_  (     self					:Any     )			->     List[str]:







       '''simple docstring'''



       pass






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







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:Tuple     )			->     str:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:List[Any]     )			->     Tuple:







       '''simple docstring'''



       pass






 def 		lowercase_  (     self					:List[Any]     )			->     str:







       '''simple docstring'''



       __A    		=     self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                   try:
                         __A    		=     tempfile.mkdtemp()
                         __A    		=     self.rust_tokenizer_class.from_pretrained(_A		,			**_A     )

                         tokenizer.save_pretrained(self.tmpdirname     )
                         super().test_training_new_tokenizer()
                   finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname     )
                         __A    		=     tmpdirname_orig






 def 		lowercase_  (     self					:int     )			->     int:







       '''simple docstring'''



       __A    		=     self.tmpdirname
       # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'     ):
                   try:
                         __A    		=     tempfile.mkdtemp()
                         __A    		=     self.rust_tokenizer_class.from_pretrained(_A		,			**_A     )

                         tokenizer.save_pretrained(self.tmpdirname     )
                         super().test_training_new_tokenizer_with_special_tokens_change()
                   finally:
                         # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
                         # is restored
                         shutil.rmtree(self.tmpdirname     )
                         __A    		=     tmpdirname_orig






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







       '''simple docstring'''



       __A    		=     self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name     )

       __A    		=     ('a' for _ in range(1_000     ))
       __A    		=     tokenizer.train_new_from_iterator(text_iterator=_A		,			length=1_000		,			vocab_size=50     )

       __A    		=     new_tokenizer.encode('a🤗'     )
       self.assertEqual(_A		,			[64, 172, 253, 97, 245]     )


@require_tokenizers
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:Tuple     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     AutoTokenizer.from_pretrained('bert-base-cased'     )
       __A    		=     json.loads(tokenizer._tokenizer.to_str()     )
       __A    		=     len(_A     )

       with tempfile.TemporaryDirectory() as tmp_dir:
             # Hack to save this in the tokenizer_config.json
             __A    		=     ['tokenizer.4.0.0.json']
             tokenizer.save_pretrained(_A     )
             json.dump(_A		,			open(os.path.join(_A		,			'tokenizer.4.0.0.json'     )		,			'w'     )     )

             # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
             __A    		=     AutoTokenizer.from_pretrained(_A     )
             self.assertEqual(len(_A     )		,			len(_A     ) + 1     )
             __A    		=     json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertIn('huggingface'		,			json_tokenizer['model']['vocab']     )

             # Will need to be adjusted if we reach v42 and this test is still here.
             # Should pick the old tokenizer file as the version of Transformers is < 4.0.0
             shutil.move(os.path.join(_A		,			'tokenizer.4.0.0.json'     )		,			os.path.join(_A		,			'tokenizer.42.0.0.json'     )     )
             __A    		=     ['tokenizer.42.0.0.json']
             tokenizer.save_pretrained(_A     )
             __A    		=     AutoTokenizer.from_pretrained(_A     )
             self.assertEqual(len(_A     )		,			len(_A     )     )
             __A    		=     json.loads(new_tokenizer._tokenizer.to_str()     )
             self.assertNotIn('huggingface'		,			json_tokenizer['model']['vocab']     )






 def 		lowercase_  (     self					:List[Any]     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     'hf-internal-testing/test-two-tokenizers'

       # This should pick the new tokenizer file as the version of Transformers is > 4.0.0
       __A    		=     AutoTokenizer.from_pretrained(_A     )
       self.assertEqual(len(_A     )		,			28_997     )
       __A    		=     json.loads(tokenizer._tokenizer.to_str()     )
       self.assertIn('huggingface'		,			json_tokenizer['model']['vocab']     )

       # Testing an older version by monkey-patching the version in the module it's used.
       import transformers as old_transformers

       __A    		=     '3.0.0'
       __A    		=     old_transformers.models.auto.AutoTokenizer.from_pretrained(_A     )
       self.assertEqual(len(_A     )		,			28_996     )
       __A    		=     json.loads(old_tokenizer._tokenizer.to_str()     )
       self.assertNotIn('huggingface'		,			json_tokenizer['model']['vocab']     )


@require_tokenizers
class 	UpperCamelCase__					(      unittest.TestCase):






 def 		lowercase_  (     self					:List[str]     )			->     List[Any]:







       '''simple docstring'''



       __A    		=     PreTrainedTokenizerFast.from_pretrained('robot-test/dummy-tokenizer-wordlevel'     )
       __A    		=     'The Matrix is a 1999 science fiction action film.'

       with concurrent.futures.ThreadPoolExecutor() as executor:
             __A    		=     [executor.submit(self.fetch		,			_A		,			_A     ) for i in range(10     )]
             __A    		=     [future.result() for future in futures]
             self.assertEqual(_A		,			[[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10     )]     )






 def 		lowercase_  (     self					:Optional[int]		,			_A					:str		,			_A					:Any     )			->     Optional[int]:







       '''simple docstring'''



       return tokenizer.encode(_A		,			truncation='longest_first'		,			padding='longest'     )






