

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()
a_     =			logging.get_logger(__name__)

a_     =			{
    "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__       (    _a							,	_a							,	_a							,	_a							,	_a):
	for attribute in key.split("."):
		SCREAMING_SNAKE_CASE      :					List[str]    =				getattr(__lowerCamelCase							,	__lowerCamelCase)

	if weight_type is not None:
		SCREAMING_SNAKE_CASE      :					str    =				getattr(__lowerCamelCase							,	__lowerCamelCase).shape
	else:
		SCREAMING_SNAKE_CASE      :					Tuple    =				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":
		SCREAMING_SNAKE_CASE      :					int    =				value
	elif weight_type == "weight_g":
		SCREAMING_SNAKE_CASE      :					Any    =				value
	elif weight_type == "weight_v":
		SCREAMING_SNAKE_CASE      :					Optional[int]    =				value
	elif weight_type == "bias":
		SCREAMING_SNAKE_CASE      :					Dict    =				value
	else:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				value

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

def        lowerCamelCase__       (    _a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					int    =				[]
	SCREAMING_SNAKE_CASE      :					Tuple    =				fairseq_model.state_dict()

	SCREAMING_SNAKE_CASE      :					Tuple    =				hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		SCREAMING_SNAKE_CASE      :					str    =				False
		if "conv_layers" in name:
			load_conv_layer(
			    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	hf_model.config.feat_extract_norm == "group"							,	)
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				True
		else:
			for key, mapped_key in MAPPING.items():
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				'''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):
					SCREAMING_SNAKE_CASE      :					List[str]    =				True
					if "*" in mapped_key:
						SCREAMING_SNAKE_CASE      :					Any    =				name.split(__lowerCamelCase)[0].split(".")[-2]
						SCREAMING_SNAKE_CASE      :					Tuple    =				mapped_key.replace("*"							,	__lowerCamelCase)
					if "weight_g" in name:
						SCREAMING_SNAKE_CASE      :					int    =				'''weight_g'''
					elif "weight_v" in name:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				'''weight_v'''
					elif "weight" in name:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				'''weight'''
					elif "bias" in name:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				'''bias'''
					else:
						SCREAMING_SNAKE_CASE      :					List[Any]    =				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        lowerCamelCase__       (    _a							,	_a							,	_a							,	_a							,	_a):
	SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				full_name.split("conv_layers.")[-1]
	SCREAMING_SNAKE_CASE      :					Dict    =				name.split(".")
	SCREAMING_SNAKE_CASE      :					List[Any]    =				int(items[0])
	SCREAMING_SNAKE_CASE      :					str    =				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."
			)
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				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."
			)
			SCREAMING_SNAKE_CASE      :					List[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."
			)
			SCREAMING_SNAKE_CASE      :					Any    =				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."
			)
			SCREAMING_SNAKE_CASE      :					Any    =				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        lowerCamelCase__       (    _a							,	_a							,	_a=None							,	_a=None							,	_a=True):
	if config_path is not None:
		SCREAMING_SNAKE_CASE      :					Dict    =				HubertConfig.from_pretrained(__lowerCamelCase)
	else:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				HubertConfig()

	if is_finetuned:
		if dict_path:
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				Dictionary.load(__lowerCamelCase)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			SCREAMING_SNAKE_CASE      :					Tuple    =				target_dict.pad_index
			SCREAMING_SNAKE_CASE      :					Tuple    =				target_dict.bos_index
			SCREAMING_SNAKE_CASE      :					int    =				target_dict.eos_index
			SCREAMING_SNAKE_CASE      :					List[Any]    =				len(target_dict.symbols)
			SCREAMING_SNAKE_CASE      :					Tuple    =				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)
			with open(__lowerCamelCase							,	"w"							,	encoding="utf-8") as vocab_handle:
				json.dump(target_dict.indices							,	__lowerCamelCase)
			SCREAMING_SNAKE_CASE      :					List[Any]    =				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							,	)
			SCREAMING_SNAKE_CASE      :					Any    =				True if config.feat_extract_norm == '''layer''' else False
			SCREAMING_SNAKE_CASE      :					Optional[Any]    =				WavaVecaFeatureExtractor(
			    feature_size=1							,	sampling_rate=16000							,	padding_value=0							,	do_normalize=__lowerCamelCase							,	return_attention_mask=__lowerCamelCase							,	)
			SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				WavaVecaProcessor(feature_extractor=__lowerCamelCase							,	tokenizer=__lowerCamelCase)
			processor.save_pretrained(__lowerCamelCase)

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

	if is_finetuned:
		SCREAMING_SNAKE_CASE      :					Tuple    =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,	arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
	else:
		SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

	SCREAMING_SNAKE_CASE      :					List[Any]    =				model[0].eval()

	recursively_load_weights(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase)

	hf_wavavec.save_pretrained(__lowerCamelCase)


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_hubert_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)




"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_lowercase : Any      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  Any,  *lowerCamelCase							:  str,  **lowerCamelCase							:  Optional[Any]     )->					None:
								warnings.warn(
								    '''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use OwlViTImageProcessor instead.''',  lowerCamelCase,  )
								super().__init__(*lowerCamelCase,  **lowerCamelCase     )




