





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()
_UpperCAmelCase							:			Any        =							logging.get_logger(__name__)

_UpperCAmelCase							:			Optional[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""",
    """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""",
}
_UpperCAmelCase							:			Optional[int]        =							[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Dict:
    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
                lowerCamelCase__       : Tuple   					=						'lm_head'

        lowerCamelCase__       : Dict   					=						getattr(__UpperCamelCase		,      __UpperCamelCase					)

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

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


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[Any]:
    lowerCamelCase__       : int   					=						[]
    lowerCamelCase__       : List[str]   					=						fairseq_model.state_dict()

    lowerCamelCase__       : Union[str, Any]   					=						hf_model.unispeech.feature_extractor

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

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


def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	Tuple:
    lowerCamelCase__       : Union[str, Any]   					=						full_name.split('conv_layers.'					)[-1]
    lowerCamelCase__       : Optional[int]   					=						name.split('.'					)
    lowerCamelCase__       : Dict   					=						int(items[0]					)
    lowerCamelCase__       : int   					=						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."""
            )
            lowerCamelCase__       : Dict   					=						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."""
            )
            lowerCamelCase__       : Tuple   					=						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."
            )
            lowerCamelCase__       : str   					=						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."""
            )
            lowerCamelCase__       : int   					=						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 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=None		,      _UpperCAmelCase=None		,      _UpperCAmelCase=True					)     ->	Optional[Any]:
    if config_path is not None:
        lowerCamelCase__       : Optional[Any]   					=						UniSpeechConfig.from_pretrained(__UpperCamelCase					)
    else:
        lowerCamelCase__       : Tuple   					=						UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            lowerCamelCase__       : List[Any]   					=						Dictionary.load_from_json(__UpperCamelCase					)

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

            # fairseq has the <pad> and <s> switched
            lowerCamelCase__       : Tuple   					=						42
            lowerCamelCase__       : Tuple   					=						43
            with open(__UpperCamelCase		,      'w'		,      encoding='utf-8'					) as vocab_handle:
                json.dump(__UpperCamelCase		,      __UpperCamelCase					)
            lowerCamelCase__       : str   					=						WavaVecaPhonemeCTCTokenizer(
                __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__       : Dict   					=						True if config.feat_extract_norm == 'layer' else False
            lowerCamelCase__       : Optional[int]   					=						WavaVecaFeatureExtractor(
                feature_size=1		,      sampling_rate=1_6000		,      padding_value=0		,      do_normalize=__UpperCamelCase		,      return_attention_mask=__UpperCamelCase		,      )
            lowerCamelCase__       : List[str]   					=						WavaVecaProcessor(feature_extractor=__UpperCamelCase		,      tokenizer=__UpperCamelCase					)
            processor.save_pretrained(__UpperCamelCase					)

        lowerCamelCase__       : List[Any]   					=						UniSpeechForCTC(__UpperCamelCase					)
    else:
        lowerCamelCase__       : Optional[int]   					=						UniSpeechForPreTraining(__UpperCamelCase					)

    if is_finetuned:
        lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Tuple   					=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]		,      arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					), 'w2v_path': checkpoint_path}					)
    else:
        lowerCamelCase__					,				lowerCamelCase__					,				lowerCamelCase__       : Optional[int]   					=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

    lowerCamelCase__       : Any   					=						model[0].eval()

    recursively_load_weights(__UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase					)

    hf_unispeech.save_pretrained(__UpperCamelCase					)


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










import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


class   __lowerCAmelCase  (		UpperCAmelCase_   ):
					"""simple docstring"""






					def __init__(  self       :							Dict					,       *_snake_case       :							int					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


								warnings.warn(
								    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
								    ' use CLIPImageProcessor instead.'					,       _snake_case					,       )
								super().__init__(*_snake_case					,       **_snake_case       )




