


import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


snake_case							:	str					      =				{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

snake_case							:	Optional[int]					      =				logging.get_logger(__name__)







class 	snake_case_       (lowerCamelCase_				):
					UpperCAmelCase__ :   List[Any]   				=      '''mask2former'''
					UpperCAmelCase__ :   Any   				=      ['''swin''']
					UpperCAmelCase__ :   str   				=      {'''hidden_size''': '''hidden_dim'''}
					def __init__(				self       :int      ,__snake_case       :Optional[Dict] = None      ,__snake_case       :int = 2_56      ,__snake_case       :int = 2_56      ,__snake_case       :int = 2_56      ,__snake_case       :int = 10_24      ,__snake_case       :str = "relu"      ,__snake_case       :int = 6      ,__snake_case       :int = 10      ,__snake_case       :int = 8      ,__snake_case       :float = 0.0      ,__snake_case       :int = 20_48      ,__snake_case       :bool = False      ,__snake_case       :bool = False      ,__snake_case       :int = 4      ,__snake_case       :int = 2_55      ,__snake_case       :int = 1_00      ,__snake_case       :float = 0.1      ,__snake_case       :float = 2.0      ,__snake_case       :float = 5.0      ,__snake_case       :float = 5.0      ,__snake_case       :int = 1_25_44      ,__snake_case       :float = 3.0      ,__snake_case       :float = 0.75      ,__snake_case       :float = 0.02      ,__snake_case       :float = 1.0      ,__snake_case       :bool = True      ,__snake_case       :List[int] = [4, 8, 16, 32]      ,__snake_case       :bool = None      ,**__snake_case       :List[Any]      ,) ->   Dict:
												if backbone_config is None:
																			logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'				)
																			a__					=					CONFIG_MAPPING['swin'](
																			    image_size=2_24      ,in_channels=3      ,patch_size=4      ,embed_dim=96      ,depths=[2, 2, 18, 2]      ,num_heads=[3, 6, 12, 24]      ,window_size=7      ,drop_path_rate=0.3      ,use_absolute_embeddings=__snake_case      ,out_features=['stage1', 'stage2', 'stage3', 'stage4']      ,)

												if isinstance(__snake_case      ,__snake_case				):
																			a__					=					backbone_config.pop('model_type'				)
																			a__					=					CONFIG_MAPPING[backbone_model_type]
																			a__					=					config_class.from_dict(__snake_case				)

												# verify that the backbone is supported
												if backbone_config.model_type not in self.backbones_supported:
																			logger.warning_once(
																			    F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
																			    F'Supported model types: {",".join(self.backbones_supported				)}'				)

												a__					=					backbone_config
												a__					=					feature_size
												a__					=					mask_feature_size
												a__					=					hidden_dim
												a__					=					encoder_feedforward_dim
												a__					=					activation_function
												a__					=					encoder_layers
												a__					=					decoder_layers
												a__					=					num_attention_heads
												a__					=					dropout
												a__					=					dim_feedforward
												a__					=					pre_norm
												a__					=					enforce_input_projection
												a__					=					common_stride
												a__					=					ignore_value
												a__					=					num_queries
												a__					=					no_object_weight
												a__					=					class_weight
												a__					=					mask_weight
												a__					=					dice_weight
												a__					=					train_num_points
												a__					=					oversample_ratio
												a__					=					importance_sample_ratio
												a__					=					init_std
												a__					=					init_xavier_std
												a__					=					use_auxiliary_loss
												a__					=					feature_strides
												a__					=					output_auxiliary_logits
												a__					=					decoder_layers

												super().__init__(**__snake_case				)
					@classmethod
					def 			lowerCamelCase__(				cls       :Any      ,__snake_case       :PretrainedConfig      ,**__snake_case       :Dict				) ->   int:
												return cls(
												    backbone_config=__snake_case      ,**__snake_case      ,)



					def 			lowerCamelCase__(				self       :int				) ->   Dict[str, any]:
												a__					=					copy.deepcopy(self.__dict__				)
												a__					=					self.backbone_config.to_dict()
												a__					=					self.__class__.model_type
												return output








import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
snake_case							:	str					      =				logging.get_logger(__name__)

snake_case							:	Optional[Any]					      =				{
    '''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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
snake_case							:	Dict					      =				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Dict       ):
							for attribute in key.split('.'       ):
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       )

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

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






def 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[Any]       ):
							a__					=					[]
							a__					=					fairseq_model.state_dict()

							a__					=					hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														a__					=					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   hf_model.config.feat_extract_norm == 'group' ,   )
																					a__					=					True
														else:
																					for key, mapped_key in MAPPING.items():
																												if key in name or key.split('w2v_model.'       )[-1] == name.split('.'       )[0]:
																																			a__					=					True
																																			if "*" in mapped_key:
																																										a__					=					name.split(__lowerCAmelCase       )[0].split('.'       )[-2]
																																										a__					=					mapped_key.replace('*' ,   __lowerCAmelCase       )
																																			if "weight_g" in name:
																																										a__					=					'weight_g'
																																			elif "weight_v" in name:
																																										a__					=					'weight_v'
																																			elif "bias" in name and "relative_attention_bias" not in name:
																																										a__					=					'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										a__					=					'weight'
																																			else:
																																										a__					=					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							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Optional[int]       ):
							a__					=					full_name.split('conv_layers.'       )[-1]
							a__					=					name.split('.'       )
							a__					=					int(items[0]       )
							a__					=					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.'
																					)
																					a__					=					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.'
																					)
																					a__					=					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."
																					)
																					a__					=					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.'
																					)
																					a__					=					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 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[Any]=None       ):
							# load the pre-trained checkpoints
							a__					=					torch.load(__lowerCAmelCase       )
							a__					=					WavLMConfigOrig(checkpoint['cfg']       )
							a__					=					WavLMOrig(__lowerCAmelCase       )
							model.load_state_dict(checkpoint['model']       )
							model.eval()

							if config_path is not None:
														a__					=					WavLMConfig.from_pretrained(__lowerCAmelCase       )
							else:
														a__					=					WavLMConfig()

							a__					=					WavLMModel(__lowerCAmelCase       )

							recursively_load_weights(__lowerCAmelCase ,   __lowerCAmelCase       )

							hf_wavlm.save_pretrained(__lowerCAmelCase       )


if __name__ == "__main__":
						snake_case							:	Union[str, Any]					      =				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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
						snake_case							:	Union[str, Any]					      =				parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




