





from __future__ import annotations

UpperCamelCase__   : List[Any]					  = """#"""







class   _UpperCamelCase  :







     '''simple docstring'''

     def __init__(						self    :  List[Any]		):






      '''simple docstring'''







      UpperCAmelCase_								=     {}





     def        SCREAMING_SNAKE_CASE							(						self    :  Tuple ,					__lowercase    :  str		):






      '''simple docstring'''







      UpperCAmelCase_								=     self._trie
      for char in text:
       if char not in trie:
        UpperCAmelCase_								=     {}
       UpperCAmelCase_								=     trie[char]
      UpperCAmelCase_								=     True





     def        SCREAMING_SNAKE_CASE							(						self    :  Any ,					__lowercase    :  str		):






      '''simple docstring'''







      UpperCAmelCase_								=     self._trie
      for char in prefix:
       if char in trie:
        UpperCAmelCase_								=     trie[char]
       else:
        return []
      return self._elements(__lowercase		)





     def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  dict		):






      '''simple docstring'''







      UpperCAmelCase_								=     []
      for c, v in d.items():
       UpperCAmelCase_								=     [""" """] if c == END else [(c + s) for s in self._elements(__lowercase		)]
       result.extend(__lowercase		)
      return tuple(__lowercase		)


UpperCamelCase__   : Optional[Any]					  = Trie()
UpperCamelCase__   : int					  = ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
    trie.insert_word(word)




def    A_(			A   ):
 UpperCAmelCase_								=     trie.find_word(A   )
 return tuple(string + word for word in suffixes   )




def    A_(			):
 print(autocomplete_using_trie("""de"""   )   )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()









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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase__   : str					  = logging.get_logger(__name__)

UpperCamelCase__   : Any					  = {
    """post_extract_proj""": """feature_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.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_(			A				,      A				,      A				,      A				,      A   ):
	for attribute in key.split("""."""   ):
		UpperCAmelCase_								=     getattr(A				,      A   )

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

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




def    A_(			A				,      A				,      A   ):
	UpperCAmelCase_								=     []
	UpperCAmelCase_								=     fairseq_model.state_dict()

	UpperCAmelCase_								=     hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCAmelCase_								=     False
		if "conv_layers" in name:
			load_conv_layer(
			    A				,      A				,      A				,      A				,      hf_model.config.feat_extract_norm == """group"""				,      )
			UpperCAmelCase_								=     True
		else:
			for key, mapped_key in MAPPING.items():
				UpperCAmelCase_								=     """sew.""" + 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]:
					UpperCAmelCase_								=     True
					if "*" in mapped_key:
						UpperCAmelCase_								=     name.split(A   )[0].split("""."""   )[-2]
						UpperCAmelCase_								=     mapped_key.replace("""*"""				,      A   )
					if "weight_g" in name:
						UpperCAmelCase_								=     """weight_g"""
					elif "weight_v" in name:
						UpperCAmelCase_								=     """weight_v"""
					elif "weight" in name:
						UpperCAmelCase_								=     """weight"""
					elif "bias" in name:
						UpperCAmelCase_								=     """bias"""
					else:
						UpperCAmelCase_								=     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    A_(			A				,      A				,      A				,      A				,      A   ):
	UpperCAmelCase_								=     full_name.split("""conv_layers."""   )[-1]
	UpperCAmelCase_								=     name.split("""."""   )
	UpperCAmelCase_								=     int(items[0]   )
	UpperCAmelCase_								=     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."""
			)
			UpperCAmelCase_								=     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."""
			)
			UpperCAmelCase_								=     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."
			)
			UpperCAmelCase_								=     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."""
			)
			UpperCAmelCase_								=     value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
	else:
		unused_weights.append(A   )




def    A_(			A				,      A   ):
	UpperCAmelCase_								=     SEWConfig()
	if is_finetuned:
		UpperCAmelCase_								=     model.wav_encoder.wav_model.cfg
	else:
		UpperCAmelCase_								=     model.cfg

	UpperCAmelCase_								=     fs_config.conv_bias
	UpperCAmelCase_								=     eval(fs_config.conv_feature_layers   )
	UpperCAmelCase_								=     [x[0] for x in conv_layers]
	UpperCAmelCase_								=     [x[1] for x in conv_layers]
	UpperCAmelCase_								=     [x[2] for x in conv_layers]
	UpperCAmelCase_								=     """gelu"""
	UpperCAmelCase_								=     """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
	UpperCAmelCase_								=     0.0
	UpperCAmelCase_								=     fs_config.activation_fn.name
	UpperCAmelCase_								=     fs_config.encoder_embed_dim
	UpperCAmelCase_								=     0.02
	UpperCAmelCase_								=     fs_config.encoder_ffn_embed_dim
	UpperCAmelCase_								=     1E-5
	UpperCAmelCase_								=     fs_config.encoder_layerdrop
	UpperCAmelCase_								=     fs_config.encoder_attention_heads
	UpperCAmelCase_								=     fs_config.conv_pos_groups
	UpperCAmelCase_								=     fs_config.conv_pos
	UpperCAmelCase_								=     len(A   )
	UpperCAmelCase_								=     fs_config.encoder_layers
	UpperCAmelCase_								=     fs_config.squeeze_factor

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

	UpperCAmelCase_								=     """Wav2Vec2FeatureExtractor"""
	UpperCAmelCase_								=     """Wav2Vec2CTCTokenizer"""

	return config




@torch.no_grad()
def    A_(			A				,      A				,      A=None				,      A=None				,      A=True   ):

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

	if config_path is not None:
		UpperCAmelCase_								=     SEWConfig.from_pretrained(A   )
	else:
		UpperCAmelCase_								=     convert_config(model[0]				,      A   )
	UpperCAmelCase_								=     model[0].eval()

	UpperCAmelCase_								=     True if config.feat_extract_norm == """layer""" else False
	UpperCAmelCase_								=     WavaVecaFeatureExtractor(
	    feature_size=1				,      sampling_rate=16000				,      padding_value=0				,      do_normalize=A				,      return_attention_mask=A				,      )

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

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			UpperCAmelCase_								=     target_dict.pad_index
			UpperCAmelCase_								=     target_dict.bos_index
			UpperCAmelCase_								=     target_dict.pad_index
			UpperCAmelCase_								=     target_dict.bos_index
			UpperCAmelCase_								=     target_dict.eos_index
			UpperCAmelCase_								=     len(target_dict.symbols   )
			UpperCAmelCase_								=     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   )
			with open(A				,      """w"""				,      encoding="""utf-8"""   ) as vocab_handle:
				json.dump(target_dict.indices				,      A   )
			UpperCAmelCase_								=     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				,      )
			UpperCAmelCase_								=     WavaVecaProcessor(feature_extractor=A				,      tokenizer=A   )
			processor.save_pretrained(A   )

		UpperCAmelCase_								=     SEWForCTC(A   )
	else:
		UpperCAmelCase_								=     SEWModel(A   )
		feature_extractor.save_pretrained(A   )

	recursively_load_weights(A				,      A				,      A   )

	hf_model.save_pretrained(A   )


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
				)


