




'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    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.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
A     =[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def 						snake_case_							(_a	:							int    ,   _a	:							Optional[int]    ,   _a	:							int    ,   _a	:							List[Any]    ,   _a	:							Tuple     ):
    for attribute in key.split('''.'''     ):
        UpperCAmelCase			       =		getattr(_a    ,   _a     )

    if weight_type is not None:
        UpperCAmelCase			       =		getattr(_a    ,   _a     ).shape
    else:
        UpperCAmelCase			       =		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":
        UpperCAmelCase			       =		value
    elif weight_type == "weight_g":
        UpperCAmelCase			       =		value
    elif weight_type == "weight_v":
        UpperCAmelCase			       =		value
    elif weight_type == "bias":
        UpperCAmelCase			       =		value
    elif weight_type == "running_mean":
        UpperCAmelCase			       =		value
    elif weight_type == "running_var":
        UpperCAmelCase			       =		value
    elif weight_type == "num_batches_tracked":
        UpperCAmelCase			       =		value
    elif weight_type == "inv_freq":
        UpperCAmelCase			       =		value
    else:
        UpperCAmelCase			       =		value

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





def 						snake_case_							(_a	:							List[Any]    ,   _a	:							int    ,   _a	:							Dict     ):
    UpperCAmelCase			       =		[]
    UpperCAmelCase			       =		fairseq_model.state_dict()

    UpperCAmelCase			       =		hf_model.wavaveca_conformer.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCAmelCase			       =		False
        if "conv_layers" in name:
            load_conv_layer(
                _a    ,   _a    ,   _a    ,   _a    ,   hf_model.config.feat_extract_norm == '''group'''    ,   )
            UpperCAmelCase			       =		True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCAmelCase			       =		'''wav2vec2_conformer.''' + 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]:
                    UpperCAmelCase			       =		True
                    if "*" in mapped_key:
                        UpperCAmelCase			       =		name.split(_a     )[0].split('''.'''     )[-2]
                        UpperCAmelCase			       =		mapped_key.replace('''*'''    ,   _a     )
                    if "pos_bias_u" in name:
                        UpperCAmelCase			       =		None
                    elif "pos_bias_v" in name:
                        UpperCAmelCase			       =		None
                    elif "weight_g" in name:
                        UpperCAmelCase			       =		'''weight_g'''
                    elif "weight_v" in name:
                        UpperCAmelCase			       =		'''weight_v'''
                    elif "bias" in name:
                        UpperCAmelCase			       =		'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        UpperCAmelCase			       =		'''weight'''
                    elif "running_mean" in name:
                        UpperCAmelCase			       =		'''running_mean'''
                    elif "inv_freq" in name:
                        UpperCAmelCase			       =		'''inv_freq'''
                    elif "running_var" in name:
                        UpperCAmelCase			       =		'''running_var'''
                    elif "num_batches_tracked" in name:
                        UpperCAmelCase			       =		'''num_batches_tracked'''
                    else:
                        UpperCAmelCase			       =		None
                    set_recursively(_a    ,   _a    ,   _a    ,   _a    ,   _a     )
                continue
        if not is_used:
            unused_weights.append(_a     )

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





def 						snake_case_							(_a	:							Any    ,   _a	:							int    ,   _a	:							str    ,   _a	:							Any    ,   _a	:							Dict     ):
    UpperCAmelCase			       =		full_name.split('''conv_layers.'''     )[-1]
    UpperCAmelCase			       =		name.split('''.'''     )
    UpperCAmelCase			       =		int(items[0]     )
    UpperCAmelCase			       =		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."     )
            UpperCAmelCase			       =		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."     )
            UpperCAmelCase			       =		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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."     )
            UpperCAmelCase			       =		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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."     )
            UpperCAmelCase			       =		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 						snake_case_							(_a	:							List[Any]    ,   _a	:							int    ,   _a	:							Any=None    ,   _a	:							List[str]=None    ,   _a	:							List[Any]=True     ):
    if config_path is not None:
        UpperCAmelCase			       =		WavaVecaConformerConfig.from_pretrained(_a    ,   hidden_act='''swish'''     )
    else:
        UpperCAmelCase			       =		WavaVecaConformerConfig()

    if "rope" in checkpoint_path:
        UpperCAmelCase			       =		'''rotary'''

    if is_finetuned:
        if dict_path:
            UpperCAmelCase			       =		Dictionary.load(_a     )

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

            # fairseq has the <pad> and <s> switched
            UpperCAmelCase			       =		0
            UpperCAmelCase			       =		1
            with open(_a    ,   '''w'''    ,   encoding='''utf-8'''     ) as vocab_handle:
                json.dump(_a    ,   _a     )
            UpperCAmelCase			       =		WavaVecaCTCTokenizer(
                _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    ,   )
            UpperCAmelCase			       =		True if config.feat_extract_norm == '''layer''' else False
            UpperCAmelCase			       =		WavaVecaFeatureExtractor(
                feature_size=1    ,   sampling_rate=1_6_0_0_0    ,   padding_value=0    ,   do_normalize=_a    ,   return_attention_mask=_a    ,   )
            UpperCAmelCase			       =		WavaVecaProcessor(feature_extractor=_a    ,   tokenizer=_a     )
            processor.save_pretrained(_a     )

        UpperCAmelCase			       =		WavaVecaConformerForCTC(_a     )
    else:
        UpperCAmelCase			       =		WavaVecaConformerForPreTraining(_a     )

    if is_finetuned:
        UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]    ,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
    else:
        UpperCAmelCase			       =		argparse.Namespace(task='''audio_pretraining'''     )
        UpperCAmelCase			       =		fairseq.tasks.setup_task(_a     )

        UpperCAmelCase       ,     UpperCAmelCase       ,     UpperCAmelCase			       =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,   task=_a     )

    UpperCAmelCase			       =		model[0].eval()

    recursively_load_weights(_a    ,   _a    ,   not is_finetuned     )

    hf_wavavec.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_wavaveca_conformer_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )










'''simple docstring'''







from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging


if is_torch_available():
   import torch


A     =logging.get_logger(__name__)


class 			_a  (	__a ):
       __a				:     str   				=					["""pixel_values"""]
       def __init__(	self						:	Optional[int]   ,				lowercase						:	bool = True   ,				lowercase						:	Optional[Dict[str, int]] = None   ,				lowercase						:	PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase						:	bool = True   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	bool = True   ,				lowercase						:	Union[int, float] = 1 / 255   ,				lowercase						:	bool = True   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				**lowercase						:	Union[str, Any]   ,				):



           '''simple docstring'''

           super().__init__(**lowercase				)
           UpperCAmelCase			       =		size if size is not None else {'''shortest_edge''': 256}
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				default_to_square=lowercase				)
           UpperCAmelCase			       =		crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				param_name='''crop_size'''				)
           UpperCAmelCase			       =		do_resize
           UpperCAmelCase			       =		size
           UpperCAmelCase			       =		resample
           UpperCAmelCase			       =		do_center_crop
           UpperCAmelCase			       =		crop_size
           UpperCAmelCase			       =		do_rescale
           UpperCAmelCase			       =		rescale_factor
           UpperCAmelCase			       =		do_normalize
           UpperCAmelCase			       =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCAmelCase			       =		image_std if image_std is not None else IMAGENET_STANDARD_STD
       def     A      (	self						:	Union[str, Any]   ,				lowercase						:	np.ndarray   ,				lowercase						:	Dict[str, int]   ,				lowercase						:	PILImageResampling = PILImageResampling.BICUBIC   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	Any   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		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()}"				)
           UpperCAmelCase			       =		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     A      (	self						:	Union[str, Any]   ,				lowercase						:	np.ndarray   ,				lowercase						:	Dict[str, int]   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	int   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		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` and `width`. Got {size.keys()}"				)
           return center_crop(lowercase   ,				size=(size['''height'''], size['''width'''])   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Tuple   ,				lowercase						:	np.ndarray   ,				lowercase						:	float   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           return rescale(lowercase   ,				scale=lowercase   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	np.ndarray   ,				lowercase						:	Union[float, List[float]]   ,				lowercase						:	Union[float, List[float]]   ,				lowercase						:	Optional[Union[str, ChannelDimension]] = None   ,				**lowercase						:	Union[str, Any]   ,				):



           '''simple docstring'''

           return normalize(lowercase   ,				mean=lowercase   ,				std=lowercase   ,				data_format=lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[int]   ,				lowercase						:	ImageInput   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	PILImageResampling = None   ,				lowercase						:	bool = None   ,				lowercase						:	Dict[str, int] = None   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Optional[float] = None   ,				lowercase						:	Optional[bool] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[float, List[float]]] = None   ,				lowercase						:	Optional[Union[str, TensorType]] = None   ,				lowercase						:	Union[str, ChannelDimension] = ChannelDimension.FIRST   ,				**lowercase						:	Dict   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		do_resize if do_resize is not None else self.do_resize
           UpperCAmelCase			       =		size if size is not None else self.size
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				default_to_square=lowercase				)
           UpperCAmelCase			       =		resample if resample is not None else self.resample
           UpperCAmelCase			       =		do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCAmelCase			       =		crop_size if crop_size is not None else self.crop_size
           UpperCAmelCase			       =		get_size_dict(lowercase   ,				param_name='''crop_size'''				)
           UpperCAmelCase			       =		do_rescale if do_rescale is not None else self.do_rescale
           UpperCAmelCase			       =		rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCAmelCase			       =		do_normalize if do_normalize is not None else self.do_normalize
           UpperCAmelCase			       =		image_mean if image_mean is not None else self.image_mean
           UpperCAmelCase			       =		image_std if image_std is not None else self.image_std

           UpperCAmelCase			       =		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.'''				)

           # All transformations expect numpy arrays.
           UpperCAmelCase			       =		[to_numpy_array(lowercase				) for image in images]

           if do_resize:
               UpperCAmelCase			       =		[self.resize(image=lowercase   ,				size=lowercase   ,				resample=lowercase				) for image in images]

           if do_center_crop:
               UpperCAmelCase			       =		[self.center_crop(image=lowercase   ,				size=lowercase				) for image in images]

           if do_rescale:
               UpperCAmelCase			       =		[self.rescale(image=lowercase   ,				scale=lowercase				) for image in images]

           if do_normalize:
               UpperCAmelCase			       =		[self.normalize(image=lowercase   ,				mean=lowercase   ,				std=lowercase				) for image in images]

           UpperCAmelCase			       =		[to_channel_dimension_format(lowercase   ,				lowercase				) for image in images]

           UpperCAmelCase			       =		{'''pixel_values''': images}
           return BatchFeature(data=lowercase   ,				tensor_type=lowercase				)







       def     A      (	self						:	Tuple   ,				lowercase						:	str   ,				lowercase						:	List[Tuple] = None				):



           '''simple docstring'''

           UpperCAmelCase			       =		outputs.logits

           # Resize logits and compute semantic segmentation maps
           if target_sizes is not None:
               if len(lowercase				) != len(lowercase				):
                   raise ValueError(
                       '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

               if is_torch_tensor(lowercase				):
                   UpperCAmelCase			       =		target_sizes.numpy()

               UpperCAmelCase			       =		[]

               for idx in range(len(lowercase				)				):
                   UpperCAmelCase			       =		torch.nn.functional.interpolate(
                       logits[idx].unsqueeze(dim=0				)   ,				size=target_sizes[idx]   ,				mode='''bilinear'''   ,				align_corners=lowercase				)
                   UpperCAmelCase			       =		resized_logits[0].argmax(dim=0				)
                   semantic_segmentation.append(lowercase				)
           else:
               UpperCAmelCase			       =		logits.argmax(dim=1				)
               UpperCAmelCase			       =		[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

           return semantic_segmentation




