


"""simple docstring"""






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCamelCase     =  logging.get_logger(__name__)

_lowerCamelCase     =  {
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}


def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Tuple						,					lowerCamelCase_  :      int						,					lowerCamelCase_  :      Any						,					lowerCamelCase_  :      Dict  ):
				for attribute in key.split("."  ):
								SCREAMING_SNAKE_CASE__     							=				getattr(lowerCamelCase_						,					lowerCamelCase_  )

				if weight_type is not None:
								SCREAMING_SNAKE_CASE__     							=				getattr(lowerCamelCase_						,					lowerCamelCase_  ).shape
				else:
								SCREAMING_SNAKE_CASE__     							=				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__     							=				value
				elif weight_type == "weight_g":
								SCREAMING_SNAKE_CASE__     							=				value
				elif weight_type == "weight_v":
								SCREAMING_SNAKE_CASE__     							=				value
				elif weight_type == "bias":
								SCREAMING_SNAKE_CASE__     							=				value
				else:
								SCREAMING_SNAKE_CASE__     							=				value

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


def __lowercase     (      lowerCamelCase_  :      Union[str, Any]						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      List[str]  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				fairseq_model.state_dict()

				SCREAMING_SNAKE_CASE__     							=				hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								SCREAMING_SNAKE_CASE__     							=				False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					hf_model.config.feat_extract_norm == "group"						,					)
												SCREAMING_SNAKE_CASE__     							=				True
								else:
												for key, mapped_key in MAPPING.items():
																SCREAMING_SNAKE_CASE__     							=				"sew." + 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]:
																				SCREAMING_SNAKE_CASE__     							=				True
																				if "*" in mapped_key:
																								SCREAMING_SNAKE_CASE__     							=				name.split(lowerCamelCase_  )[0].split("."  )[-2]
																								SCREAMING_SNAKE_CASE__     							=				mapped_key.replace("*"						,					lowerCamelCase_  )
																				if "weight_g" in name:
																								SCREAMING_SNAKE_CASE__     							=				"weight_g"
																				elif "weight_v" in name:
																								SCREAMING_SNAKE_CASE__     							=				"weight_v"
																				elif "weight" in name:
																								SCREAMING_SNAKE_CASE__     							=				"weight"
																				elif "bias" in name:
																								SCREAMING_SNAKE_CASE__     							=				"bias"
																				else:
																								SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      Dict						,					lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      Dict  ):
				SCREAMING_SNAKE_CASE__     							=				full_name.split("conv_layers."  )[-1]
				SCREAMING_SNAKE_CASE__     							=				name.split("."  )
				SCREAMING_SNAKE_CASE__     							=				int(items[0]  )
				SCREAMING_SNAKE_CASE__     							=				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__     							=				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__     							=				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__     							=				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__     							=				value
												logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
				else:
								unused_weights.append(lowerCamelCase_  )


def __lowercase     (      lowerCamelCase_  :      Optional[Any]						,					lowerCamelCase_  :      List[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				SEWConfig()
				if is_finetuned:
								SCREAMING_SNAKE_CASE__     							=				model.wav_encoder.wav_model.cfg
				else:
								SCREAMING_SNAKE_CASE__     							=				model.cfg

				SCREAMING_SNAKE_CASE__     							=				fs_config.conv_bias
				SCREAMING_SNAKE_CASE__     							=				eval(fs_config.conv_feature_layers  )
				SCREAMING_SNAKE_CASE__     							=				[x[0] for x in conv_layers]
				SCREAMING_SNAKE_CASE__     							=				[x[1] for x in conv_layers]
				SCREAMING_SNAKE_CASE__     							=				[x[2] for x in conv_layers]
				SCREAMING_SNAKE_CASE__     							=				"gelu"
				SCREAMING_SNAKE_CASE__     							=				"layer" if fs_config.extractor_mode == "layer_norm" else "group"
				SCREAMING_SNAKE_CASE__     							=				0.0
				SCREAMING_SNAKE_CASE__     							=				fs_config.activation_fn.name
				SCREAMING_SNAKE_CASE__     							=				fs_config.encoder_embed_dim
				SCREAMING_SNAKE_CASE__     							=				0.02
				SCREAMING_SNAKE_CASE__     							=				fs_config.encoder_ffn_embed_dim
				SCREAMING_SNAKE_CASE__     							=				1e-5
				SCREAMING_SNAKE_CASE__     							=				fs_config.encoder_layerdrop
				SCREAMING_SNAKE_CASE__     							=				fs_config.encoder_attention_heads
				SCREAMING_SNAKE_CASE__     							=				fs_config.conv_pos_groups
				SCREAMING_SNAKE_CASE__     							=				fs_config.conv_pos
				SCREAMING_SNAKE_CASE__     							=				len(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				fs_config.encoder_layers
				SCREAMING_SNAKE_CASE__     							=				fs_config.squeeze_factor

				# take care of any params that are overridden by the Wav2VecCtc model
				if is_finetuned:
								SCREAMING_SNAKE_CASE__     							=				model.cfg
								SCREAMING_SNAKE_CASE__     							=				fs_config.final_dropout
								SCREAMING_SNAKE_CASE__     							=				fs_config.layerdrop
				SCREAMING_SNAKE_CASE__     							=				fs_config.activation_dropout
				SCREAMING_SNAKE_CASE__     							=				fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
				SCREAMING_SNAKE_CASE__     							=				fs_config.attention_dropout
				SCREAMING_SNAKE_CASE__     							=				fs_config.dropout_input
				SCREAMING_SNAKE_CASE__     							=				fs_config.dropout
				SCREAMING_SNAKE_CASE__     							=				fs_config.mask_channel_length
				SCREAMING_SNAKE_CASE__     							=				fs_config.mask_channel_prob
				SCREAMING_SNAKE_CASE__     							=				fs_config.mask_length
				SCREAMING_SNAKE_CASE__     							=				fs_config.mask_prob

				SCREAMING_SNAKE_CASE__     							=				"Wav2Vec2FeatureExtractor"
				SCREAMING_SNAKE_CASE__     							=				"Wav2Vec2CTCTokenizer"

				return config


@torch.no_grad()
def __lowercase     (      lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      Any						,					lowerCamelCase_  :      List[str]=None						,					lowerCamelCase_  :      str=None						,					lowerCamelCase_  :      Union[str, Any]=True  ):

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

				if config_path is not None:
								SCREAMING_SNAKE_CASE__     							=				SEWConfig.from_pretrained(lowerCamelCase_  )
				else:
								SCREAMING_SNAKE_CASE__     							=				convert_config(model[0]						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				model[0].eval()

				SCREAMING_SNAKE_CASE__     							=				True if config.feat_extract_norm == "layer" else False
				SCREAMING_SNAKE_CASE__     							=				WavaVecaFeatureExtractor(
				    feature_size=1						,					sampling_rate=16000						,					padding_value=0						,					do_normalize=lowerCamelCase_						,					return_attention_mask=lowerCamelCase_						,					)

				if is_finetuned:
								if dict_path:
												SCREAMING_SNAKE_CASE__     							=				Dictionary.load(lowerCamelCase_  )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												SCREAMING_SNAKE_CASE__     							=				target_dict.pad_index
												SCREAMING_SNAKE_CASE__     							=				target_dict.bos_index
												SCREAMING_SNAKE_CASE__     							=				target_dict.pad_index
												SCREAMING_SNAKE_CASE__     							=				target_dict.bos_index
												SCREAMING_SNAKE_CASE__     							=				target_dict.eos_index
												SCREAMING_SNAKE_CASE__     							=				len(target_dict.symbols  )
												SCREAMING_SNAKE_CASE__     							=				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__     							=				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__     							=				WavaVecaProcessor(feature_extractor=lowerCamelCase_						,					tokenizer=lowerCamelCase_  )
												processor.save_pretrained(lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				SEWForCTC(lowerCamelCase_  )
				else:
								SCREAMING_SNAKE_CASE__     							=				SEWModel(lowerCamelCase_  )
								feature_extractor.save_pretrained(lowerCamelCase_  )

				recursively_load_weights(lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  )

				hf_model.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(
	    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
	)
	_lowerCamelCase     =  parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)




"""simple docstring"""






import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCamelCase     =  0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_lowerCamelCase     =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				WATERMARK_BITS
								SCREAMING_SNAKE_CASE__     							=				WatermarkEncoder()

								self.encoder.set_watermark("bits"       ,		self.watermark     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# can't encode images that are smaller than 256
								if images.shape[-1] < 256:
												return images

								SCREAMING_SNAKE_CASE__     							=				(255 * (images / 2 + 0.5)).cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()

								SCREAMING_SNAKE_CASE__     							=				[self.encoder.encode(UpperCAmelCase__       ,		"dwtDct"     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				torch.from_numpy(np.array(UpperCAmelCase__     )     ).permute(0       ,		3       ,		1       ,		2     )

								SCREAMING_SNAKE_CASE__     							=				torch.clamp(2 * (images / 255 - 0.5)       ,		min=-1.0       ,		max=1.0     )
								return images
