


'''simple docstring'''
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				_snake_case						       =				42
				_snake_case						       =				42


if is_transformers_available() and is_torch_available():
					from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline






'''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()
lowerCAmelCase   : int             =				logging.get_logger(__name__)

lowerCAmelCase   : Dict             =				{
    """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   : Optional[Any]             =				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def 	_A  (       A							,A							,A							,A							,A							)  ->				Tuple:
	for attribute in key.split("."							):
		lowercase						:		Dict   					=		getattr(A							,A							)

	if weight_type is not None:
		lowercase						:		List[str]   					=		getattr(A							,A							).shape
	else:
		lowercase						:		Any   					=		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":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_g":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_v":
		lowercase						:		int   					=		value
	elif weight_type == "bias":
		lowercase						:		Any   					=		value
	elif weight_type == "running_mean":
		lowercase						:		Tuple   					=		value
	elif weight_type == "running_var":
		lowercase						:		Dict   					=		value
	elif weight_type == "num_batches_tracked":
		lowercase						:		Optional[int]   					=		value
	elif weight_type == "inv_freq":
		lowercase						:		List[Any]   					=		value
	else:
		lowercase						:		Dict   					=		value

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




def 	_A  (       A							,A							,A							)  ->				int:
	lowercase						:		Optional[int]   					=		[]
	lowercase						:		Tuple   					=		fairseq_model.state_dict()

	lowercase						:		Optional[Any]   					=		hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		lowercase						:		str   					=		False
		if "conv_layers" in name:
			load_conv_layer(
			    A							,A							,A							,A							,hf_model.config.feat_extract_norm == "group"							,)
			lowercase						:		Dict   					=		True
		else:
			for key, mapped_key in MAPPING.items():
				lowercase						:		List[str]   					=		"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]:
					lowercase						:		List[str]   					=		True
					if "*" in mapped_key:
						lowercase						:		int   					=		name.split(A							)[0].split("."							)[-2]
						lowercase						:		Optional[Any]   					=		mapped_key.replace("*"							,A							)
					if "pos_bias_u" in name:
						lowercase						:		str   					=		None
					elif "pos_bias_v" in name:
						lowercase						:		Optional[int]   					=		None
					elif "weight_g" in name:
						lowercase						:		int   					=		"weight_g"
					elif "weight_v" in name:
						lowercase						:		Dict   					=		"weight_v"
					elif "bias" in name:
						lowercase						:		List[str]   					=		"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						lowercase						:		Optional[int]   					=		"weight"
					elif "running_mean" in name:
						lowercase						:		Optional[int]   					=		"running_mean"
					elif "inv_freq" in name:
						lowercase						:		Dict   					=		"inv_freq"
					elif "running_var" in name:
						lowercase						:		int   					=		"running_var"
					elif "num_batches_tracked" in name:
						lowercase						:		Optional[Any]   					=		"num_batches_tracked"
					else:
						lowercase						:		int   					=		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  (       A							,A							,A							,A							,A							)  ->				List[str]:
	lowercase						:		Tuple   					=		full_name.split("conv_layers."							)[-1]
	lowercase						:		Optional[Any]   					=		name.split("."							)
	lowercase						:		str   					=		int(items[0]							)
	lowercase						:		List[Any]   					=		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.'''							)
			lowercase						:		Union[str, Any]   					=		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.'''							)
			lowercase						:		str   					=		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.'''							)
			lowercase						:		int   					=		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.'''							)
			lowercase						:		str   					=		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  (       A							,A							,A=None							,A=None							,A=True							)  ->				Optional[Any]:
	if config_path is not None:
		lowercase						:		Tuple   					=		WavaVecaConformerConfig.from_pretrained(A							,hidden_act="swish"							)
	else:
		lowercase						:		Any   					=		WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		lowercase						:		str   					=		"rotary"

	if is_finetuned:
		if dict_path:
			lowercase						:		List[str]   					=		Dictionary.load(A							)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			lowercase						:		Optional[int]   					=		target_dict.pad_index
			lowercase						:		Optional[int]   					=		target_dict.bos_index
			lowercase						:		Optional[Any]   					=		target_dict.eos_index
			lowercase						:		str   					=		len(target_dict.symbols							)
			lowercase						:		List[Any]   					=		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							)
			lowercase						:		Any   					=		target_dict.indices

			# fairseq has the <pad> and <s> switched
			lowercase						:		Any   					=		0
			lowercase						:		Tuple   					=		1
			with open(A							,"w"							,encoding="utf-8"							) as vocab_handle:
				json.dump(A							,A							)
			lowercase						:		Tuple   					=		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							,)
			lowercase						:		Dict   					=		True if config.feat_extract_norm == "layer" else False
			lowercase						:		List[str]   					=		WavaVecaFeatureExtractor(
			    feature_size=1							,sampling_rate=1_6_0_0_0							,padding_value=0							,do_normalize=A							,return_attention_mask=A							,)
			lowercase						:		List[str]   					=		WavaVecaProcessor(feature_extractor=A							,tokenizer=A							)
			processor.save_pretrained(A							)

		lowercase						:		Any   					=		WavaVecaConformerForCTC(A							)
	else:
		lowercase						:		str   					=		WavaVecaConformerForPreTraining(A							)

	if is_finetuned:
		lowercase					,		lowercase					,		lowercase						:		List[str]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
	else:
		lowercase						:		List[str]   					=		argparse.Namespace(task="audio_pretraining"							)
		lowercase						:		Union[str, Any]   					=		fairseq.tasks.setup_task(A							)

		lowercase					,		lowercase					,		lowercase						:		Optional[Any]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,task=A							)

	lowercase						:		List[str]   					=		model[0].eval()

	recursively_load_weights(A							,A							,not is_finetuned							)

	hf_wavavec.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : str             =				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   : int             =				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
					)


