





'''simple docstring'''

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__lowerCAmelCase							:	Tuple			=logging.get_logger(__name__)

__lowerCAmelCase							:	Union[str, 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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__lowerCAmelCase							:	int			=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]





def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Any						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					Dict       ):
    for attribute in key.split("."       ):
        A__				=			getattr(_lowerCamelCase						,       _lowerCamelCase       )

    if weight_type is not None:
        A__				=			getattr(_lowerCamelCase						,       _lowerCamelCase       ).shape
    else:
        A__				=			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":
        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 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					List[Any]       ):
    A__				=			[]
    A__				=			fairseq_model.state_dict()

    A__				=			hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        A__				=			False
        if "conv_layers" in name:
            load_conv_layer(
                _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       hf_model.config.feat_extract_norm == "group"						,       )
            A__				=			True
        else:
            for key, mapped_key in MAPPING.items():
                A__				=			"unispeech_sat." + 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]:
                    if "layer_norm_for_extract" in name and (".".join(name.split("."       )[:-1]       ) != key):
                        # special case since naming is very similar
                        continue
                    A__				=			True
                    if "*" in mapped_key:
                        A__				=			name.split(_lowerCamelCase       )[0].split("."       )[-2]
                        A__				=			mapped_key.replace("*"						,       _lowerCamelCase       )
                    if "weight_g" in name:
                        A__				=			"weight_g"
                    elif "weight_v" in name:
                        A__				=			"weight_v"
                    elif "bias" in name:
                        A__				=			"bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        A__				=			"weight"
                    else:
                        A__				=			None
                    set_recursively(_lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase						,       _lowerCamelCase       )
                continue
        if not is_used:
            unused_weights.append(_lowerCamelCase       )

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





def 						UpperCamelCase				(							_lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Union[str, Any]						,       _lowerCamelCase					:					Any       ):
    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:
            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."       )
            A__				=			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."       )
            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:
            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[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:
            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[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(_lowerCamelCase       )





@torch.no_grad()
def 						UpperCamelCase				(							_lowerCamelCase					:					Optional[int]						,       _lowerCamelCase					:					Tuple						,       _lowerCamelCase					:					Union[str, Any]=None						,       _lowerCamelCase					:					Optional[Any]=None						,       _lowerCamelCase					:					int=True       ):
    if config_path is not None:
        A__				=			UniSpeechSatConfig.from_pretrained(_lowerCamelCase       )
    else:
        A__				=			UniSpeechSatConfig()

    A__				=			""

    if is_finetuned:
        A__				=			UniSpeechSatForCTC(_lowerCamelCase       )
    else:
        A__				=			UniSpeechSatForPreTraining(_lowerCamelCase       )

    A__,	A__,	A__				=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]						,       arg_overrides={"data": "/".join(dict_path.split("/"       )[:-1]       )}       )
    A__				=			model[0].eval()

    recursively_load_weights(_lowerCamelCase						,       _lowerCamelCase       )

    hf_wavavec.save_pretrained(_lowerCamelCase       )


if __name__ == "__main__":
  __lowerCAmelCase							:	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(
      "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  __lowerCAmelCase							:	str			=parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )










'''simple docstring'''

from __future__ import annotations





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]       ):
    create_state_space_tree(_lowerCamelCase						,       []						,       0						,       [0 for i in range(len(_lowerCamelCase       )       )]       )





def 						UpperCamelCase				(							_lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					list[int | str]						,       _lowerCamelCase					:					int						,       _lowerCamelCase					:					list[int]						,       ):

    if index == len(_lowerCamelCase       ):
        print(_lowerCamelCase       )
        return

    for i in range(len(_lowerCamelCase       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            A__				=			True
            create_state_space_tree(_lowerCamelCase						,       _lowerCamelCase						,       index + 1						,       _lowerCamelCase       )
            current_sequence.pop()
            A__				=			False
__lowerCAmelCase							:	list[int | str]			=[3, 1, 2, 4]
generate_all_permutations(sequence)

__lowerCAmelCase							:	list[int | str]			=["A", "B", "C"]



generate_all_permutations(sequence_a)



