






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     : int   	=      logging.get_logger(__name__)

_a     : Any   	=      {
    '''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     : str   	=      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''projector''',
    '''classifier''',
]


def 					snake_case__				(				UpperCAmelCase				:	Optional[int]							):
    lowerCAmelCase__ :int   				=					{}
    with open(_UpperCamelCase     ,      "r"							) as file:
        for line_number, line in enumerate(_UpperCamelCase							):
            lowerCAmelCase__ :Optional[Any]   				=					line.strip()
            if line:
                lowerCAmelCase__ :Optional[Any]   				=					line.split()
                lowerCAmelCase__ :Any   				=					line_number
                lowerCAmelCase__ :Optional[int]   				=					words[0]
                lowerCAmelCase__ :int   				=					value
    return result


def 					snake_case__				(				UpperCAmelCase				:	int     ,      UpperCAmelCase				:	List[Any]     ,      UpperCAmelCase				:	int     ,      UpperCAmelCase				:	Any     ,      UpperCAmelCase				:	Tuple							):







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

    lowerCAmelCase__ :int   				=					None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(_UpperCamelCase							):
            lowerCAmelCase__ :Tuple   				=					PARAM_MAPPING[full_name.split("."							)[-1]]
            lowerCAmelCase__ :str   				=					"param"

    if weight_type is not None and weight_type != "param":
        lowerCAmelCase__ :Any   				=					getattr(_UpperCamelCase     ,      _UpperCamelCase							).shape
    elif weight_type is not None and weight_type == "param":
        lowerCAmelCase__ :Union[str, Any]   				=					hf_pointer
        for attribute in hf_param_name.split("."							):
            lowerCAmelCase__ :Tuple   				=					getattr(_UpperCamelCase     ,      _UpperCamelCase							)
        lowerCAmelCase__ :str   				=					shape_pointer.shape

        # let's reduce dimension
        lowerCAmelCase__ :Dict   				=					value[0]
    else:
        lowerCAmelCase__ :Tuple   				=					hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
            F''' {value.shape} for {full_name}'''							)

    if weight_type == "weight":
        lowerCAmelCase__ :str   				=					value
    elif weight_type == "weight_g":
        lowerCAmelCase__ :Tuple   				=					value
    elif weight_type == "weight_v":
        lowerCAmelCase__ :List[str]   				=					value
    elif weight_type == "bias":
        lowerCAmelCase__ :Any   				=					value
    elif weight_type == "param":
        for attribute in hf_param_name.split("."							):
            lowerCAmelCase__ :Union[str, Any]   				=					getattr(_UpperCamelCase     ,      _UpperCamelCase							)
        lowerCAmelCase__ :List[str]   				=					value
    else:
        lowerCAmelCase__ :int   				=					value

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


def 					snake_case__				(				UpperCAmelCase				:	Any     ,      UpperCAmelCase				:	List[str]     ,      UpperCAmelCase				:	Optional[int]     ,      UpperCAmelCase				:	Any     ,      UpperCAmelCase				:	List[str]							):
    lowerCAmelCase__ :str   				=					None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(_UpperCamelCase							):
            lowerCAmelCase__ :List[str]   				=					PARAM_MAPPING[full_name.split("."							)[-1]]
            lowerCAmelCase__ :Any   				=					"param"

    if weight_type is not None and weight_type != "param":
        lowerCAmelCase__ :int   				=					".".join([key, weight_type]							)
    elif weight_type is not None and weight_type == "param":
        lowerCAmelCase__ :Union[str, Any]   				=					".".join([key, hf_param_name]							)
    else:
        lowerCAmelCase__ :Union[str, Any]   				=					key

    lowerCAmelCase__ :Optional[Any]   				=					value if "lm_head" in full_key else value[0]


_a     : Optional[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__				(				UpperCAmelCase				:	Any     ,      UpperCAmelCase				:	Optional[Any]     ,      UpperCAmelCase				:	List[str]=None     ,      UpperCAmelCase				:	Optional[Any]=None							):
    lowerCAmelCase__ :Optional[Any]   				=					False
    for key, mapped_key in MAPPING.items():
        lowerCAmelCase__ :Any   				=					"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]:
            lowerCAmelCase__ :Any   				=					True
            if "*" in mapped_key:
                lowerCAmelCase__ :Dict   				=					name.split(_UpperCamelCase							)[0].split("."							)[-2]
                lowerCAmelCase__ :List[str]   				=					mapped_key.replace("*"     ,      _UpperCamelCase							)
            if "weight_g" in name:
                lowerCAmelCase__ :Tuple   				=					"weight_g"
            elif "weight_v" in name:
                lowerCAmelCase__ :Tuple   				=					"weight_v"
            elif "bias" in name:
                lowerCAmelCase__ :int   				=					"bias"
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                lowerCAmelCase__ :Optional[int]   				=					"weight"
            else:
                lowerCAmelCase__ :Tuple   				=					None
            if hf_dict is not None:
                rename_dict(_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase							)
            else:
                set_recursively(_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase							)
            return is_used
    return is_used


def 					snake_case__				(				UpperCAmelCase				:	Any     ,      UpperCAmelCase				:	str     ,      UpperCAmelCase				:	Optional[int]							):
    lowerCAmelCase__ :str   				=					[]
    lowerCAmelCase__ :List[str]   				=					fairseq_model.state_dict()

    lowerCAmelCase__ :List[Any]   				=					hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        lowerCAmelCase__ :List[str]   				=					False
        if "conv_layers" in name:
            load_conv_layer(
                _UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase     ,      hf_model.config.feat_extract_norm == "group"     ,      )
            lowerCAmelCase__ :Optional[Any]   				=					True
        else:
            lowerCAmelCase__ :Dict   				=					load_wavaveca_layer(_UpperCamelCase     ,      _UpperCamelCase     ,      _UpperCamelCase							)
        if not is_used:
            unused_weights.append(_UpperCamelCase							)

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


def 					snake_case__				(				UpperCAmelCase				:	str     ,      UpperCAmelCase				:	Union[str, Any]     ,      UpperCAmelCase				:	List[str]     ,      UpperCAmelCase				:	str     ,      UpperCAmelCase				:	Tuple							):
    lowerCAmelCase__ :Optional[Any]   				=					full_name.split("conv_layers."							)[-1]
    lowerCAmelCase__ :Optional[int]   				=					name.split("."							)
    lowerCAmelCase__ :Dict   				=					int(items[0]							)
    lowerCAmelCase__ :List[Any]   				=					int(items[1]							)

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
            lowerCAmelCase__ :Optional[int]   				=					value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
            lowerCAmelCase__ :List[str]   				=					value
            logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''							)
            lowerCAmelCase__ :str   				=					value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    F'''{full_name} has size {value.shape}, but'''
                    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''							)
            lowerCAmelCase__ :Union[str, Any]   				=					value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
    else:
        unused_weights.append(_UpperCamelCase							)


@torch.no_grad()
def 					snake_case__				(				UpperCAmelCase				:	Union[str, Any]     ,      UpperCAmelCase				:	str     ,      UpperCAmelCase				:	Optional[int]=None     ,      UpperCAmelCase				:	Tuple=None     ,      UpperCAmelCase				:	str=True     ,      UpperCAmelCase				:	Optional[int]=False							):







    if config_path is not None:
        lowerCAmelCase__ :Any   				=					WavaVecaConfig.from_pretrained(_UpperCamelCase							)
    else:
        lowerCAmelCase__ :Tuple   				=					WavaVecaConfig()

    if is_seq_class:
        lowerCAmelCase__ :int   				=					read_txt_into_dict(_UpperCamelCase							)
        lowerCAmelCase__ :Optional[Any]   				=					idalabel
        lowerCAmelCase__ :Dict   				=					WavaVecaForSequenceClassification(_UpperCamelCase							)
        lowerCAmelCase__ :Any   				=					WavaVecaFeatureExtractor(
            feature_size=1     ,      sampling_rate=1_6_0_0_0     ,      padding_value=0     ,      do_normalize=_UpperCamelCase     ,      return_attention_mask=_UpperCamelCase     ,      )
        feature_extractor.save_pretrained(_UpperCamelCase							)

    elif is_finetuned:
        if dict_path:
            lowerCAmelCase__ :Optional[Any]   				=					Dictionary.load(_UpperCamelCase							)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            lowerCAmelCase__ :List[Any]   				=					target_dict.pad_index
            lowerCAmelCase__ :Dict   				=					target_dict.bos_index
            lowerCAmelCase__ :str   				=					target_dict.eos_index
            lowerCAmelCase__ :Dict   				=					len(target_dict.symbols							)
            lowerCAmelCase__ :Optional[int]   				=					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							)
            lowerCAmelCase__ :Any   				=					target_dict.indices

            # fairseq has the <pad> and <s> switched
            lowerCAmelCase__ :str   				=					0
            lowerCAmelCase__ :List[str]   				=					1
            with open(_UpperCamelCase     ,      "w"     ,      encoding="utf-8"							) as vocab_handle:
                json.dump(_UpperCamelCase     ,      _UpperCamelCase							)
            lowerCAmelCase__ :List[str]   				=					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     ,      )
            lowerCAmelCase__ :str   				=					True if config.feat_extract_norm == "layer" else False
            lowerCAmelCase__ :Any   				=					WavaVecaFeatureExtractor(
                feature_size=1     ,      sampling_rate=1_6_0_0_0     ,      padding_value=0     ,      do_normalize=_UpperCamelCase     ,      return_attention_mask=_UpperCamelCase     ,      )
            lowerCAmelCase__ :Dict   				=					WavaVecaProcessor(feature_extractor=_UpperCamelCase     ,      tokenizer=_UpperCamelCase							)
            processor.save_pretrained(_UpperCamelCase							)

        lowerCAmelCase__ :Tuple   				=					WavaVecaForCTC(_UpperCamelCase							)
    else:
        lowerCAmelCase__ :Dict   				=					WavaVecaForPreTraining(_UpperCamelCase							)

    if is_finetuned or is_seq_class:
        lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__ :Optional[int]   				=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]     ,      arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
    else:
        lowerCAmelCase__ :Union[str, Any]   				=					argparse.Namespace(task="audio_pretraining"							)
        lowerCAmelCase__ :List[Any]   				=					fairseq.tasks.setup_task(_UpperCamelCase							)

        lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__ :Union[str, Any]   				=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,      task=_UpperCamelCase							)

    lowerCAmelCase__ :Tuple   				=					model[0].eval()

    recursively_load_weights(_UpperCamelCase     ,      _UpperCamelCase     ,      not is_finetuned							)

    hf_wavavec.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
 _a     : Optional[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(
     """--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     : Union[str, Any]   	=      parser.parse_args()

 _a     : 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,
 )







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a			:			Optional[Any]												=					{
    '''configuration_llama''': ['''LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LlamaConfig'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Optional[int]												=					['''LlamaTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a			:			Any												=					[
	    '''LlamaForCausalLM''',
	    '''LlamaModel''',
	    '''LlamaPreTrainedModel''',
	    '''LlamaForSequenceClassification''',
	]


if TYPE_CHECKING:
	from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_llama_fast import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
	import sys

	a			:			Dict												=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


