
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_A						      =				logging.get_logger(__name__)

_A						      =				{
    "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",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
_A						      =				[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__				,	A__	)							->							Union[str, Any]:
    """simple docstring"""

    for attribute in key.split("."	):
        if is_finetuned:
            if attribute in ["quantizer", "project_q", "project_hid"]:
                # those layers are only relevant for pretraining and should be dropped
                return

            if attribute == "ctc_proj":
                # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                snake_case				  =      "lm_head"

        snake_case				  =      getattr(A__				,	A__	)

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

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							Tuple:
    """simple docstring"""

    snake_case				  =      []
    snake_case				  =      fairseq_model.state_dict()

    snake_case				  =      hf_model.unispeech.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case				  =      False
        if "conv_layers" in name:
            load_conv_layer(
                A__				,	A__				,	A__				,	A__				,	hf_model.config.feat_extract_norm == "group"				,	)
            snake_case				  =      True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case				  =      "unispeech." + 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]:
                    snake_case				  =      True
                    if "*" in mapped_key:
                        snake_case				  =      name.split(A__	)[0].split("."	)[-2]
                        snake_case				  =      mapped_key.replace("*"				,	A__	)
                    if "weight_g" in name:
                        snake_case				  =      "weight_g"
                    elif "weight_v" in name:
                        snake_case				  =      "weight_v"
                    elif "bias" in name:
                        snake_case				  =      "bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        snake_case				  =      "weight"
                    else:
                        snake_case				  =      None
                    set_recursively(A__				,	A__				,	A__				,	A__				,	A__				,	A__	)
                continue
        if not is_used:
            unused_weights.append(A__	)

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



def 		lowercase_    (     A__				,	A__				,	A__				,	A__				,	A__	)							->							Dict:
    """simple docstring"""

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



@torch.no_grad()
def 		lowercase_    (     A__				,	A__				,	A__=None				,	A__=None				,	A__=True	)							->							List[Any]:
    """simple docstring"""

    if config_path is not None:
        snake_case				  =      UniSpeechConfig.from_pretrained(A__	)
    else:
        snake_case				  =      UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            snake_case				  =      Dictionary.load_from_json(A__	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            snake_case				  =      target_dict.pad_index
            snake_case				  =      target_dict.bos_index
            snake_case				  =      target_dict.eos_index
            snake_case				  =      len(target_dict.symbols	)
            snake_case				  =      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__	)
            snake_case				  =      target_dict.indices

            # fairseq has the <pad> and <s> switched
            snake_case				  =      42
            snake_case				  =      43
            with open(A__				,	"w"				,	encoding="utf-8"	) as vocab_handle:
                json.dump(A__				,	A__	)
            snake_case				  =      WavaVecaPhonemeCTCTokenizer(
                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__				,	)
            snake_case				  =      True if config.feat_extract_norm == "layer" else False
            snake_case				  =      WavaVecaFeatureExtractor(
                feature_size=1				,	sampling_rate=1_6000				,	padding_value=0				,	do_normalize=A__				,	return_attention_mask=A__				,	)
            snake_case				  =      WavaVecaProcessor(feature_extractor=A__				,	tokenizer=A__	)
            processor.save_pretrained(A__	)

        snake_case				  =      UniSpeechForCTC(A__	)
    else:
        snake_case				  =      UniSpeechForPreTraining(A__	)

    if is_finetuned:
        snake_case  ,							snake_case  ,							snake_case				  =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]				,	arg_overrides={"data": "/".join(dict_path.split("/"	)[:-1]	), "w2v_path": checkpoint_path}	)
    else:
        snake_case  ,							snake_case  ,							snake_case				  =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

    snake_case				  =      model[0].eval()

    recursively_load_weights(A__				,	A__				,	A__	)

    hf_unispeech.save_pretrained(A__	)


if __name__ == "__main__":
   _A						      =				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"
   )
   _A						      =				parser.parse_args()
   convert_unispeech_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )



_A						      =				0  # The first color of the flag.
_A						      =				1  # The second color of the flag.
_A						      =				2  # The third color of the flag.
_A						      =				(red, white, blue)



def 		lowercase_    (     A__	)							->							list:
    """simple docstring"""

    if not sequence:
        return []
    if len(A__	) == 1:
        return list(A__	)
    snake_case				  =      0
    snake_case				  =      len(A__	) - 1
    snake_case				  =      0
    while mid <= high:
        if sequence[mid] == colors[0]:
            snake_case  ,							snake_case				  =      sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            snake_case  ,							snake_case				  =      sequence[high], sequence[mid]
            high -= 1
        else:
            snake_case				  =      F'The elements inside the sequence must contains only {colors} values'
            raise ValueError(A__	)
    return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _A						      =				input("Enter numbers separated by commas:\n").strip()
   _A						      =				[int(item.strip()) for item in user_input.split(",")]
   print(f"{dutch_national_flag_sort(unsorted)}")

