
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()
lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "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",
}
lowerCamelCase__	=							[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				str:

		"""simple docstring"""






		for attribute in key.split(""".""" ):
				_UpperCamelCase						    =	getattr(lowerCAmelCase						,  lowerCAmelCase )

		if weight_type is not None:
				_UpperCamelCase						    =	getattr(lowerCAmelCase						,  lowerCAmelCase ).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 )		->				List[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(
						    lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  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(lowerCAmelCase )[0].split(""".""" )[-2]
												_UpperCamelCase						    =	mapped_key.replace("""*"""						,  lowerCAmelCase )
										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(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
								continue
				if not is_used:
						unused_weights.append(lowerCAmelCase )

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


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[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(lowerCAmelCase )


@torch.no_grad()
def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=None						,  lowerCAmelCase=None						,  lowerCAmelCase=True )		->				List[Any]:

		"""simple docstring"""






		if config_path is not None:
				_UpperCamelCase						    =	WavaVecaConformerConfig.from_pretrained(lowerCAmelCase						,  hidden_act="""swish""" )
		else:
				_UpperCamelCase						    =	WavaVecaConformerConfig()

		if "rope" in checkpoint_path:
				_UpperCamelCase						    =	"""rotary"""

		if is_finetuned:
				if dict_path:
						_UpperCamelCase						    =	Dictionary.load(lowerCAmelCase )

						# 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(lowerCAmelCase						,  """vocab.json""" )
						if not os.path.isdir(lowerCAmelCase ):
								logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase ) )
								return
						os.makedirs(lowerCAmelCase						,  exist_ok=lowerCAmelCase )
						_UpperCamelCase						    =	target_dict.indices

						# fairseq has the <pad> and <s> switched
						_UpperCamelCase						    =	0
						_UpperCamelCase						    =	1
						with open(lowerCAmelCase						,  """w"""						,  encoding="""utf-8""" ) as vocab_handle:
								json.dump(lowerCAmelCase						,  lowerCAmelCase )
						_UpperCamelCase						    =	WavaVecaCTCTokenizer(
						    lowerCAmelCase						,  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=lowerCAmelCase						,  )
						_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=lowerCAmelCase						,  return_attention_mask=lowerCAmelCase						,  )
						_UpperCamelCase						    =	WavaVecaProcessor(feature_extractor=lowerCAmelCase						,  tokenizer=lowerCAmelCase )
						processor.save_pretrained(lowerCAmelCase )

				_UpperCamelCase						    =	WavaVecaConformerForCTC(lowerCAmelCase )
		else:
				_UpperCamelCase						    =	WavaVecaConformerForPreTraining(lowerCAmelCase )

		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(lowerCAmelCase )

				_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						,  task=lowerCAmelCase )

		_UpperCamelCase						    =	model[0].eval()

		recursively_load_weights(lowerCAmelCase						,  lowerCAmelCase						,  not is_finetuned )

		hf_wavavec.save_pretrained(lowerCAmelCase )


if __name__ == "__main__":
		lowerCamelCase__	=							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"
		)
		lowerCamelCase__	=							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
		)





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase__	=							logging.get_logger(__name__)

lowerCamelCase__	=							{
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: int	=		"deta"
						UpperCamelCase_							: Union[str, Any]	=		{
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						}

						def __init__(			self       ,							a=None       ,							a=9_00       ,							a=20_48       ,							a=6       ,							a=20_48       ,							a=8       ,							a=6       ,							a=10_24       ,							a=8       ,							a=0.0       ,							a=True       ,							a="relu"       ,							a=2_56       ,							a=0.1       ,							a=0.0       ,							a=0.0       ,							a=0.02       ,							a=1.0       ,							a=True       ,							a=False       ,							a="sine"       ,							a=5       ,							a=4       ,							a=4       ,							a=True       ,							a=3_00       ,							a=True       ,							a=True       ,							a=1       ,							a=5       ,							a=2       ,							a=1       ,							a=1       ,							a=5       ,							a=2       ,							a=0.1       ,							a=0.25       ,							**a       ,							)					->      Dict:


								'''simple docstring'''

								if backbone_config is None:
										logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""				)
										_UpperCamelCase						    =	CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]				)
								else:
										if isinstance(a       ,							a				):
												_UpperCamelCase						    =	backbone_config.pop("""model_type"""				)
												_UpperCamelCase						    =	CONFIG_MAPPING[backbone_model_type]
												_UpperCamelCase						    =	config_class.from_dict(a				)

								_UpperCamelCase						    =	backbone_config
								_UpperCamelCase						    =	num_queries
								_UpperCamelCase						    =	max_position_embeddings
								_UpperCamelCase						    =	d_model
								_UpperCamelCase						    =	encoder_ffn_dim
								_UpperCamelCase						    =	encoder_layers
								_UpperCamelCase						    =	encoder_attention_heads
								_UpperCamelCase						    =	decoder_ffn_dim
								_UpperCamelCase						    =	decoder_layers
								_UpperCamelCase						    =	decoder_attention_heads
								_UpperCamelCase						    =	dropout
								_UpperCamelCase						    =	attention_dropout
								_UpperCamelCase						    =	activation_dropout
								_UpperCamelCase						    =	activation_function
								_UpperCamelCase						    =	init_std
								_UpperCamelCase						    =	init_xavier_std
								_UpperCamelCase						    =	encoder_layerdrop
								_UpperCamelCase						    =	auxiliary_loss
								_UpperCamelCase						    =	position_embedding_type
								# deformable attributes
								_UpperCamelCase						    =	num_feature_levels
								_UpperCamelCase						    =	encoder_n_points
								_UpperCamelCase						    =	decoder_n_points
								_UpperCamelCase						    =	two_stage
								_UpperCamelCase						    =	two_stage_num_proposals
								_UpperCamelCase						    =	with_box_refine
								_UpperCamelCase						    =	assign_first_stage
								if two_stage is True and with_box_refine is False:
										raise ValueError("""If two_stage is True, with_box_refine must be True."""				)
								# Hungarian matcher
								_UpperCamelCase						    =	class_cost
								_UpperCamelCase						    =	bbox_cost
								_UpperCamelCase						    =	giou_cost
								# Loss coefficients
								_UpperCamelCase						    =	mask_loss_coefficient
								_UpperCamelCase						    =	dice_loss_coefficient
								_UpperCamelCase						    =	bbox_loss_coefficient
								_UpperCamelCase						    =	giou_loss_coefficient
								_UpperCamelCase						    =	eos_coefficient
								_UpperCamelCase						    =	focal_alpha
								super().__init__(is_encoder_decoder=a       ,							**a				)

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.encoder_attention_heads

						@property
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return self.d_model







						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.backbone_config.to_dict()
								_UpperCamelCase						    =	self.__class__.model_type
								return output



