






"""simple docstring"""



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


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


if is_vision_available():
						import PIL







class    lowercase__			(							UpperCAmelCase_   ):
			_UpperCAmelCase		:int   			= ["""pixel_values"""]


			def __init__(      self     :  Union[str, Any]   ,       snake_case__     :  Any = True   ,       snake_case__     :  Any = None   ,       snake_case__     :  List[Any] = PILImageResampling.BICUBIC   ,       snake_case__     :  str = True   ,       snake_case__     :  Any = None   ,       snake_case__     :  Optional[Any] = True   ,       snake_case__     :  int = 1 / 255   ,       snake_case__     :  Dict = True   ,       snake_case__     :  List[Any] = None   ,       snake_case__     :  int = None   ,       snake_case__     :  Any = True   ,       **snake_case__     :  int   ,       ):
										super().__init__(**__lowercase					)
										lowerCamelCase_       :							List[Any]									=size if size is not None else {'''shortest_edge''': 224}
										lowerCamelCase_       :							List[str]									=get_size_dict(__lowercase   ,       default_to_square=__lowercase					)
										lowerCamelCase_       :							Optional[Any]									=crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
										lowerCamelCase_       :							str									=get_size_dict(__lowercase   ,       default_to_square=__lowercase   ,       param_name="crop_size"					)

										lowerCamelCase_       :							List[str]									=do_resize
										lowerCamelCase_       :							Union[str, Any]									=size
										lowerCamelCase_       :							Dict									=resample
										lowerCamelCase_       :							Optional[Any]									=do_center_crop
										lowerCamelCase_       :							Dict									=crop_size
										lowerCamelCase_       :							List[str]									=do_rescale
										lowerCamelCase_       :							Any									=rescale_factor
										lowerCamelCase_       :							Any									=do_normalize
										lowerCamelCase_       :							int									=image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										lowerCamelCase_       :							Union[str, Any]									=image_std if image_std is not None else OPENAI_CLIP_STD
										lowerCamelCase_       :							Any									=do_convert_rgb


			def 	UpperCAmelCase__						(      self     :  Optional[int]   ,       snake_case__     :  Optional[Any]   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  str = PILImageResampling.BICUBIC   ,       snake_case__     :  Any = None   ,       **snake_case__     :  Tuple   ,       ):
										lowerCamelCase_       :							List[Any]									=get_size_dict(__lowercase   ,       default_to_square=__lowercase					)
										if "shortest_edge" not in size:
																	raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""					)
										lowerCamelCase_       :							List[Any]									=get_resize_output_image_size(__lowercase   ,       size=size["shortest_edge"]   ,       default_to_square=__lowercase					)
										return resize(__lowercase   ,       size=__lowercase   ,       resample=__lowercase   ,       data_format=__lowercase   ,       **__lowercase					)


			def 	UpperCAmelCase__						(      self     :  Union[str, Any]   ,       snake_case__     :  Dict   ,       snake_case__     :  str   ,       snake_case__     :  Optional[int] = None   ,       **snake_case__     :  Tuple   ,       ):
										lowerCamelCase_       :							Optional[int]									=get_size_dict(__lowercase					)
										if "height" not in size or "width" not in size:
																	raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""					)
										return center_crop(__lowercase   ,       size=(size["height"], size["width"])   ,       data_format=__lowercase   ,       **__lowercase					)


			def 	UpperCAmelCase__						(      self     :  int   ,       snake_case__     :  Optional[int]   ,       snake_case__     :  List[str]   ,       snake_case__     :  Any = None   ,       **snake_case__     :  int   ,       ):
										return rescale(__lowercase   ,       scale=__lowercase   ,       data_format=__lowercase   ,       **__lowercase					)


			def 	UpperCAmelCase__						(      self     :  List[str]   ,       snake_case__     :  str   ,       snake_case__     :  str   ,       snake_case__     :  str   ,       snake_case__     :  List[Any] = None   ,       **snake_case__     :  Dict   ,       ):
										return normalize(__lowercase   ,       mean=__lowercase   ,       std=__lowercase   ,       data_format=__lowercase   ,       **__lowercase					)


			def 	UpperCAmelCase__						(      self     :  Tuple   ,       snake_case__     :  Union[str, Any]   ,       snake_case__     :  Optional[Any] = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  Optional[Any] = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  Optional[Any] = None   ,       snake_case__     :  List[str] = None   ,       snake_case__     :  Union[str, Any] = None   ,       snake_case__     :  List[str] = None   ,       snake_case__     :  Dict = None   ,       snake_case__     :  Optional[int] = None   ,       snake_case__     :  List[str] = None   ,       snake_case__     :  List[Any] = None   ,       snake_case__     :  int = ChannelDimension.FIRST   ,       **snake_case__     :  Union[str, Any]   ,       ):
										lowerCamelCase_       :							int									=do_resize if do_resize is not None else self.do_resize
										lowerCamelCase_       :							Dict									=size if size is not None else self.size
										lowerCamelCase_       :							Union[str, Any]									=get_size_dict(__lowercase   ,       param_name="size"   ,       default_to_square=__lowercase					)
										lowerCamelCase_       :							Optional[int]									=resample if resample is not None else self.resample
										lowerCamelCase_       :							Dict									=do_center_crop if do_center_crop is not None else self.do_center_crop
										lowerCamelCase_       :							List[Any]									=crop_size if crop_size is not None else self.crop_size
										lowerCamelCase_       :							Optional[Any]									=get_size_dict(__lowercase   ,       param_name="crop_size"   ,       default_to_square=__lowercase					)
										lowerCamelCase_       :							Union[str, Any]									=do_rescale if do_rescale is not None else self.do_rescale
										lowerCamelCase_       :							List[str]									=rescale_factor if rescale_factor is not None else self.rescale_factor
										lowerCamelCase_       :							Optional[Any]									=do_normalize if do_normalize is not None else self.do_normalize
										lowerCamelCase_       :							Union[str, Any]									=image_mean if image_mean is not None else self.image_mean
										lowerCamelCase_       :							List[Any]									=image_std if image_std is not None else self.image_std
										lowerCamelCase_       :							Optional[Any]									=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										lowerCamelCase_       :							List[str]									=make_list_of_images(__lowercase					)

										if not valid_images(__lowercase					):
																	raise ValueError(
																	    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																	    "torch.Tensor, tf.Tensor or jax.ndarray."					)

										if do_resize and size is None:
																	raise ValueError("Size must be specified if do_resize is True."					)

										if do_center_crop and crop_size is None:
																	raise ValueError("Crop size must be specified if do_center_crop is True."					)

										if do_rescale and rescale_factor is None:
																	raise ValueError("Rescale factor must be specified if do_rescale is True."					)

										if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError("Image mean and std must be specified if do_normalize is True."					)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																	lowerCamelCase_       :							Optional[Any]									=[convert_to_rgb(__lowercase					) for image in images]

										# All transformations expect numpy arrays.
										lowerCamelCase_       :							List[Any]									=[to_numpy_array(__lowercase					) for image in images]

										if do_resize:
																	lowerCamelCase_       :							Optional[Any]									=[self.resize(image=__lowercase   ,       size=__lowercase   ,       resample=__lowercase					) for image in images]

										if do_center_crop:
																	lowerCamelCase_       :							List[Any]									=[self.center_crop(image=__lowercase   ,       size=__lowercase					) for image in images]

										if do_rescale:
																	lowerCamelCase_       :							List[Any]									=[self.rescale(image=__lowercase   ,       scale=__lowercase					) for image in images]

										if do_normalize:
																	lowerCamelCase_       :							Optional[Any]									=[self.normalize(image=__lowercase   ,       mean=__lowercase   ,       std=__lowercase					) for image in images]

										lowerCamelCase_       :							Dict									=[to_channel_dimension_format(__lowercase   ,       __lowercase					) for image in images]

										lowerCamelCase_       :							Dict									={'''pixel_values''': images}
										return BatchFeature(data=__lowercase   ,       tensor_type=__lowercase					)




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()
__lowercase							 =					logging.get_logger(__name__)

__lowercase							 =					{
    '''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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    for attribute in key.split('''.'''	):
        __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		[]
    __UpperCamelCase     :int			  =		fairseq_model.state_dict()

    __UpperCamelCase     :List[Any]			  =		hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		full_name.split('''conv_layers.'''	)[-1]
    __UpperCamelCase     :Optional[int]			  =		name.split('''.'''	)
    __UpperCamelCase     :str			  =		int(items[0]	)
    __UpperCamelCase     :List[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."""
            )
            __UpperCamelCase     :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."""
            )
            __UpperCamelCase     :Any			  =		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."
            )
            __UpperCamelCase     :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."""
            )
            __UpperCamelCase     :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(SCREAMING_SNAKE_CASE	)



@torch.no_grad()
def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=None  ,			SCREAMING_SNAKE_CASE=True	):
    '''simple docstring'''







    if config_path is not None:
        __UpperCamelCase     :Tuple			  =		HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :Optional[int]			  =		HubertConfig()

    if is_finetuned:
        if dict_path:
            __UpperCamelCase     :Optional[int]			  =		Dictionary.load(SCREAMING_SNAKE_CASE	)

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

        __UpperCamelCase     :List[str]			  =		HubertForCTC(SCREAMING_SNAKE_CASE	)
    else:
        __UpperCamelCase     :str			  =		HubertModel(SCREAMING_SNAKE_CASE	)

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

    __UpperCamelCase     :Dict			  =		model[0].eval()

    recursively_load_weights(SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	)

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    __lowercase							 =					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'''
    )
    __lowercase							 =					parser.parse_args()
    convert_hubert_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
    )





