
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__lowerCAmelCase    :	Union[str, Any]						=						logging.get_logger(__name__)

__lowerCAmelCase    :	int						=						{
    "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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  List[Any]:






    for attribute in key.split('''.'''		):
        __lowercase						:	Optional[int]					=				getattr(_snake_case		,    _snake_case		)

    if weight_type is not None:
        __lowercase						:	Dict					=				getattr(_snake_case		,    _snake_case		).shape
    else:
        __lowercase						:	int					=				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":
        __lowercase						:	int					=				value
    elif weight_type == "weight_g":
        __lowercase						:	Optional[Any]					=				value
    elif weight_type == "weight_v":
        __lowercase						:	str					=				value
    elif weight_type == "bias":
        __lowercase						:	Union[str, Any]					=				value
    else:
        __lowercase						:	List[str]					=				value

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


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  Optional[int]:
    __lowercase						:	List[Any]					=				[]
    __lowercase						:	Any					=				fairseq_model.state_dict()

    __lowercase						:	Optional[Any]					=				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __lowercase						:	Any					=				False
        if "conv_layers" in name:
            load_conv_layer(
                _snake_case		,    _snake_case		,    _snake_case		,    _snake_case		,    hf_model.config.feat_extract_norm == '''group'''		,    )
            __lowercase						:	int					=				True
        else:
            for key, mapped_key in MAPPING.items():
                __lowercase						:	Dict					=				'''hubert.''' + 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] and not is_finetuned):
                    __lowercase						:	Any					=				True
                    if "*" in mapped_key:
                        __lowercase						:	Union[str, Any]					=				name.split(_snake_case		)[0].split('''.'''		)[-2]
                        __lowercase						:	Any					=				mapped_key.replace('''*'''		,    _snake_case		)
                    if "weight_g" in name:
                        __lowercase						:	Union[str, Any]					=				'''weight_g'''
                    elif "weight_v" in name:
                        __lowercase						:	Optional[int]					=				'''weight_v'''
                    elif "weight" in name:
                        __lowercase						:	Tuple					=				'''weight'''
                    elif "bias" in name:
                        __lowercase						:	Union[str, Any]					=				'''bias'''
                    else:
                        __lowercase						:	List[Any]					=				None
                    set_recursively(_snake_case		,    _snake_case		,    _snake_case		,    _snake_case		,    _snake_case		)
                continue
        if not is_used:
            unused_weights.append(_snake_case		)

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


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  List[Any]:
    __lowercase						:	str					=				full_name.split('''conv_layers.'''		)[-1]
    __lowercase						:	List[Any]					=				name.split('''.'''		)
    __lowercase						:	Tuple					=				int(items[0]		)
    __lowercase						:	Any					=				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.'
            )
            __lowercase						:	str					=				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.'
            )
            __lowercase						:	Dict					=				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."
            )
            __lowercase						:	Optional[int]					=				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.'
            )
            __lowercase						:	str					=				value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
    else:
        unused_weights.append(_snake_case		)


@torch.no_grad()
def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=None		,    __lowerCAmelCase=True		)       ->  List[str]:






    if config_path is not None:
        __lowercase						:	Any					=				HubertConfig.from_pretrained(_snake_case		)
    else:
        __lowercase						:	Any					=				HubertConfig()

    if is_finetuned:
        if dict_path:
            __lowercase						:	str					=				Dictionary.load(_snake_case		)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __lowercase						:	Any					=				target_dict.pad_index
            __lowercase						:	Optional[Any]					=				target_dict.bos_index
            __lowercase						:	Optional[int]					=				target_dict.eos_index
            __lowercase						:	List[str]					=				len(target_dict.symbols		)
            __lowercase						:	Optional[int]					=				os.path.join(_snake_case		,    '''vocab.json'''		)
            if not os.path.isdir(_snake_case		):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(_snake_case		)		)
                return
            os.makedirs(_snake_case		,    exist_ok=_snake_case		)
            with open(_snake_case		,    '''w'''		,    encoding='''utf-8'''		) as vocab_handle:
                json.dump(target_dict.indices		,    _snake_case		)
            __lowercase						:	str					=				WavaVecaCTCTokenizer(
                _snake_case		,    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=_snake_case		,    )
            __lowercase						:	List[str]					=				True if config.feat_extract_norm == '''layer''' else False
            __lowercase						:	List[Any]					=				WavaVecaFeatureExtractor(
                feature_size=1		,    sampling_rate=16_000		,    padding_value=0		,    do_normalize=_snake_case		,    return_attention_mask=_snake_case		,    )
            __lowercase						:	Optional[Any]					=				WavaVecaProcessor(feature_extractor=_snake_case		,    tokenizer=_snake_case		)
            processor.save_pretrained(_snake_case		)

        __lowercase						:	Union[str, Any]					=				HubertForCTC(_snake_case		)
    else:
        __lowercase						:	List[Any]					=				HubertModel(_snake_case		)

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

    __lowercase						:	int					=				model[0].eval()

    recursively_load_weights(_snake_case		,    _snake_case		,    _snake_case		)

    hf_wavavec.save_pretrained(_snake_case		)


if __name__ == "__main__":
 __lowerCAmelCase    :	Union[str, 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"
 )
 __lowerCAmelCase    :	Dict						=						parser.parse_args()
 convert_hubert_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






			__a        =[False] * len(_snake_case	)
			__a        =[-1] * len(_snake_case	)

			def dfs(_snake_case   :  Dict       ,     _snake_case   :  Any	):
						__a        =True
						__a        =c
						for u in graph[v]:
									if not visited[u]:
												dfs(_snake_case       ,     1 - c	)

			for i in range(len(_snake_case	)	):
						if not visited[i]:
									dfs(_snake_case       ,     0	)

			for i in range(len(_snake_case	)	):
						for j in graph[i]:
									if color[i] == color[j]:
												return False

			return True


# Adjacency list of graph
_lowerCAmelCase					:			int  				=			{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}


print(check_bipartite_dfs(graph))






