






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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  List[str]:





						"""simple docstring"""



						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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  str:





						"""simple docstring"""



						_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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  Any:





						"""simple docstring"""



						_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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None ,		__lowerCAmelCase=True	)->  Optional[int]:





						"""simple docstring"""



						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=16_000 ,		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




