




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()
__A								=       logging.get_logger(__name__)

__A								=       {
    "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      lowerCamelCase_						(			UpperCamelCase__							: Tuple					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Tuple					,	UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Optional[int] )	->			Dict:





				"""simple docstring"""


				for attribute in key.split('.' ):
								__lowerCamelCase									=       getattr(UpperCamelCase__					,	UpperCamelCase__ )

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

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







def      lowerCamelCase_						(			UpperCamelCase__							: Union[str, Any]					,	UpperCamelCase__							: Tuple					,	UpperCamelCase__							: Optional[Any] )	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       []
				__lowerCamelCase									=       fairseq_model.state_dict()

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

				for name, value in fairseq_dict.items():
								__lowerCamelCase									=       False
								if "conv_layers" in name:
												load_conv_layer(
												    UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	hf_model.config.feat_extract_norm == 'group'					,	)
												__lowerCamelCase									=       True
								else:
												for key, mapped_key in MAPPING.items():
																__lowerCamelCase									=       '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]:
																				__lowerCamelCase									=       True
																				if "*" in mapped_key:
																								__lowerCamelCase									=       name.split(UpperCamelCase__ )[0].split('.' )[-2]
																								__lowerCamelCase									=       mapped_key.replace('*'					,	UpperCamelCase__ )
																				if "weight_g" in name:
																								__lowerCamelCase									=       'weight_g'
																				elif "weight_v" in name:
																								__lowerCamelCase									=       'weight_v'
																				elif "weight" in name:
																								__lowerCamelCase									=       'weight'
																				elif "bias" in name:
																								__lowerCamelCase									=       'bias'
																				else:
																								__lowerCamelCase									=       None
																				set_recursively(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ )
																continue
								if not is_used:
												unused_weights.append(UpperCamelCase__ )

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







def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: List[str]					,	UpperCamelCase__							: Any					,	UpperCamelCase__							: str					,	UpperCamelCase__							: Optional[Any] )	->			List[Any]:





				"""simple docstring"""


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







def      lowerCamelCase_						(			UpperCamelCase__							: Optional[Any]					,	UpperCamelCase__							: Dict )	->			Optional[int]:





				"""simple docstring"""


				__lowerCamelCase									=       SEWConfig()
				if is_finetuned:
								__lowerCamelCase									=       model.wav_encoder.wav_model.cfg
				else:
								__lowerCamelCase									=       model.cfg

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

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

				__lowerCamelCase									=       'Wav2Vec2FeatureExtractor'
				__lowerCamelCase									=       'Wav2Vec2CTCTokenizer'

				return config







@torch.no_grad()
def      lowerCamelCase_						(			UpperCamelCase__							: Union[str, Any]					,	UpperCamelCase__							: Dict					,	UpperCamelCase__							: Dict=None					,	UpperCamelCase__							: str=None					,	UpperCamelCase__							: Optional[int]=True )	->			List[Any]:





				"""simple docstring"""


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

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

				__lowerCamelCase									=       True if config.feat_extract_norm == 'layer' else False
				__lowerCamelCase									=       WavaVecaFeatureExtractor(
				    feature_size=1					,	sampling_rate=1_6000					,	padding_value=0					,	do_normalize=UpperCamelCase__					,	return_attention_mask=UpperCamelCase__					,	)

				if is_finetuned:
								if dict_path:
												__lowerCamelCase									=       Dictionary.load(UpperCamelCase__ )

												# important change bos & pad token id since CTC symbol is <pad> and
												# not <s> as in fairseq
												__lowerCamelCase									=       target_dict.pad_index
												__lowerCamelCase									=       target_dict.bos_index
												__lowerCamelCase									=       target_dict.pad_index
												__lowerCamelCase									=       target_dict.bos_index
												__lowerCamelCase									=       target_dict.eos_index
												__lowerCamelCase									=       len(target_dict.symbols )
												__lowerCamelCase									=       os.path.join(UpperCamelCase__					,	'vocab.json' )
												if not os.path.isdir(UpperCamelCase__ ):
																logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(UpperCamelCase__ ) )
																return
												os.makedirs(UpperCamelCase__					,	exist_ok=UpperCamelCase__ )
												with open(UpperCamelCase__					,	'w'					,	encoding='utf-8' ) as vocab_handle:
																json.dump(target_dict.indices					,	UpperCamelCase__ )
												__lowerCamelCase									=       WavaVecaCTCTokenizer(
												    UpperCamelCase__					,	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=UpperCamelCase__					,	)
												__lowerCamelCase									=       WavaVecaProcessor(feature_extractor=UpperCamelCase__					,	tokenizer=UpperCamelCase__ )
												processor.save_pretrained(UpperCamelCase__ )

								__lowerCamelCase									=       SEWForCTC(UpperCamelCase__ )
				else:
								__lowerCamelCase									=       SEWModel(UpperCamelCase__ )
								feature_extractor.save_pretrained(UpperCamelCase__ )

				recursively_load_weights(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__ )

				hf_model.save_pretrained(UpperCamelCase__ )


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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A								=       logging.get_logger(__name__)

__A								=       {
    "unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''lxmert'''
			snake_case_  						=			{}


			def __init__( self     ,		lowerCamelCase__=30_522     ,		lowerCamelCase__=768     ,		lowerCamelCase__=12     ,		lowerCamelCase__=9_500     ,		lowerCamelCase__=1_600     ,		lowerCamelCase__=400     ,		lowerCamelCase__=3_072     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=512     ,		lowerCamelCase__=2     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-12     ,		lowerCamelCase__=9     ,		lowerCamelCase__=5     ,		lowerCamelCase__=5     ,		lowerCamelCase__=2_048     ,		lowerCamelCase__=4     ,		lowerCamelCase__=6.67     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		lowerCamelCase__=True     ,		**lowerCamelCase__     ,		)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       num_qa_labels
							__lowerCamelCase									=       num_object_labels
							__lowerCamelCase									=       num_attr_labels
							__lowerCamelCase									=       l_layers
							__lowerCamelCase									=       x_layers
							__lowerCamelCase									=       r_layers
							__lowerCamelCase									=       visual_feat_dim
							__lowerCamelCase									=       visual_pos_dim
							__lowerCamelCase									=       visual_loss_normalizer
							__lowerCamelCase									=       task_matched
							__lowerCamelCase									=       task_mask_lm
							__lowerCamelCase									=       task_obj_predict
							__lowerCamelCase									=       task_qa
							__lowerCamelCase									=       visual_obj_loss
							__lowerCamelCase									=       visual_attr_loss
							__lowerCamelCase									=       visual_feat_loss
							__lowerCamelCase									=       {'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
							super().__init__(**lowerCamelCase__				)


