

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(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	for attribute in key.split('.'	):
		snake_case_      		= getattr(lowercase__			, lowercase__	)

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

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





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	snake_case_      		= fairseq_model.state_dict()

	snake_case_      		= hf_model.wavaveca_conformer.feature_extractor

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

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





def a(lowercase__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= full_name.split('conv_layers.'	)[-1]
	snake_case_      		= name.split('.'	)
	snake_case_      		= int(items[0]	)
	snake_case_      		= 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."""	)
			snake_case_      		= 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."""	)
			snake_case_      		= 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."""	)
			snake_case_      		= 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."""	)
			snake_case_      		= value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
	else:
		unused_weights.append(lowercase__	)





@torch.no_grad()
def a(lowercase__			, lowercase__			, lowercase__=None			, lowercase__=None			, lowercase__=True	):





	'''simple docstring'''
	if config_path is not None:
		snake_case_      		= WavaVecaConformerConfig.from_pretrained(lowercase__			, hidden_act='swish'	)
	else:
		snake_case_      		= WavaVecaConformerConfig()

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

	if is_finetuned:
		if dict_path:
			snake_case_      		= Dictionary.load(lowercase__	)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			snake_case_      		= target_dict.pad_index
			snake_case_      		= target_dict.bos_index
			snake_case_      		= target_dict.eos_index
			snake_case_      		= len(target_dict.symbols	)
			snake_case_      		= os.path.join(lowercase__			, 'vocab.json'	)
			if not os.path.isdir(lowercase__	):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase__	)	)
				return
			os.makedirs(lowercase__			, exist_ok=lowercase__	)
			snake_case_      		= target_dict.indices

			# fairseq has the <pad> and <s> switched
			snake_case_      		= 0
			snake_case_      		= 1
			with open(lowercase__			, 'w'			, encoding='utf-8'	) as vocab_handle:
				json.dump(lowercase__			, lowercase__	)
			snake_case_      		= WavaVecaCTCTokenizer(
			    lowercase__			, 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=lowercase__			, )
			snake_case_      		= True if config.feat_extract_norm == 'layer' else False
			snake_case_      		= WavaVecaFeatureExtractor(
			    feature_size=1			, sampling_rate=16000			, padding_value=0			, do_normalize=lowercase__			, return_attention_mask=lowercase__			, )
			snake_case_      		= WavaVecaProcessor(feature_extractor=lowercase__			, tokenizer=lowercase__	)
			processor.save_pretrained(lowercase__	)

		snake_case_      		= WavaVecaConformerForCTC(lowercase__	)
	else:
		snake_case_      		= WavaVecaConformerForPreTraining(lowercase__	)

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

		snake_case_		,       snake_case_		,       snake_case_      		= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			, task=lowercase__	)

	snake_case_      		= model[0].eval()

	recursively_load_weights(lowercase__			, lowercase__			, not is_finetuned	)

	hf_wavavec.save_pretrained(lowercase__	)


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
		)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


A											=     {
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ASTForAudioClassification',
		    'ASTModel',
		    'ASTPreTrainedModel',
		]

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['ASTFeatureExtractor']

if TYPE_CHECKING:
		from .configuration_audio_spectrogram_transformer import (
		    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ASTConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_audio_spectrogram_transformer import (
				    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ASTForAudioClassification,
				    ASTModel,
				    ASTPreTrainedModel,
				)

		try:
				if not is_speech_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
		import sys

		A											=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
