



"""simple docstring"""






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()
_lowercase : str      						=						logging.get_logger(__name__)

_lowercase : 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",
}
_lowercase : Dict      						=						[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def      snake_case__   (		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    Dict	):
			"""simple docstring"""
			for attribute in key.split('''.'''	):
						lowerCamelCase__		:		Optional[Any]			=getattr(__lowerCamelCase							,		__lowerCamelCase	)

			if weight_type is not None:
						lowerCamelCase__		:		List[Any]			=getattr(__lowerCamelCase							,		__lowerCamelCase	).shape
			else:
						lowerCamelCase__		:		int			=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__		:		Optional[int]			=value
			elif weight_type == "weight_g":
						lowerCamelCase__		:		List[Any]			=value
			elif weight_type == "weight_v":
						lowerCamelCase__		:		Optional[Any]			=value
			elif weight_type == "bias":
						lowerCamelCase__		:		str			=value
			else:
						lowerCamelCase__		:		str			=value

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






def      snake_case__   (		__lowerCamelCase				:    Any							,		__lowerCamelCase				:    Optional[Any]	):
			"""simple docstring"""
			lowerCamelCase__		:		List[str]			=[]
			lowerCamelCase__		:		List[str]			=fairseq_model.state_dict()

			lowerCamelCase__		:		Any			=hf_model.feature_extractor

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

			if config_path is not None:
						lowerCamelCase__		:		Any			=WavLMConfig.from_pretrained(__lowerCamelCase	)
			else:
						lowerCamelCase__		:		Optional[Any]			=WavLMConfig()

			lowerCamelCase__		:		str			=WavLMModel(__lowerCamelCase	)

			recursively_load_weights(__lowerCamelCase							,		__lowerCamelCase	)

			hf_wavlm.save_pretrained(__lowerCamelCase	)


if __name__ == "__main__":
		_lowercase : Dict      						=						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")
		_lowercase : Union[str, Any]      						=						parser.parse_args()
		convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)









"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_lowercase : Any      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  Any,  *lowerCamelCase							:  str,  **lowerCamelCase							:  Optional[Any]     )->					None:
								warnings.warn(
								    '''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use OwlViTImageProcessor instead.''',  lowerCamelCase,  )
								super().__init__(*lowerCamelCase,  **lowerCamelCase     )




