





'''simple docstring'''


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_lowerCamelCase :		Any 			=				logging.get_logger(__name__)  # pylint: disable=invalid-name



class 							__UpperCAmelCase     (				_a  ):







		'''simple docstring'''







		def __init__(self  :					Optional[int]					,					_lowerCAmelCase  :					CLIPSegForImageSegmentation					,					_lowerCAmelCase  :					CLIPSegProcessor					,					_lowerCAmelCase  :					AutoencoderKL					,					_lowerCAmelCase  :					CLIPTextModel					,					_lowerCAmelCase  :					CLIPTokenizer					,					_lowerCAmelCase  :					UNetaDConditionModel					,					_lowerCAmelCase  :					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]					,					_lowerCAmelCase  :					StableDiffusionSafetyChecker					,					_lowerCAmelCase  :					CLIPImageProcessor					,					):
			super().__init__()

			if hasattr(scheduler.config					,					"""steps_offset"""					) and scheduler.config.steps_offset != 1:
				A							     =					(
				    F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
				    F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
				    """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
				    """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
				    """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
				    """ file"""
				)
				deprecate("""steps_offset!=1"""					,					"""1.0.0"""					,					__lowerCamelCase					,					standard_warn=__lowerCamelCase					)
				A							     =					dict(scheduler.config					)
				A							     =					1
				A							     =					FrozenDict(__lowerCamelCase					)

			if hasattr(scheduler.config					,					"""skip_prk_steps"""					) and scheduler.config.skip_prk_steps is False:
				A							     =					(
				    F"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
				    """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
				    """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
				    """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
				    """ Hub, it would be very nice if you could open a Pull request for the"""
				    """ `scheduler/scheduler_config.json` file"""
				)
				deprecate("""skip_prk_steps not set"""					,					"""1.0.0"""					,					__lowerCamelCase					,					standard_warn=__lowerCamelCase					)
				A							     =					dict(scheduler.config					)
				A							     =					True
				A							     =					FrozenDict(__lowerCamelCase					)

			if safety_checker is None:
				logger.warning(
				    F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
				    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
				    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
				    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
				    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
				    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""					)

			self.register_modules(
			    segmentation_model=__lowerCamelCase					,					segmentation_processor=__lowerCamelCase					,					vae=__lowerCamelCase					,					text_encoder=__lowerCamelCase					,					tokenizer=__lowerCamelCase					,					unet=__lowerCamelCase					,					scheduler=__lowerCamelCase					,					safety_checker=__lowerCamelCase					,					feature_extractor=__lowerCamelCase					,					)







		def 				A   (self  :					Dict					,					_lowerCAmelCase  :					Optional[Union[str, int]] = "auto"					):
			if slice_size == "auto":
				# half the attention head size is usually a good trade-off between
				# speed and memory
				A							     =					self.unet.config.attention_head_dim // 2
			self.unet.set_attention_slice(__lowerCamelCase					)







		def 				A   (self  :					Union[str, Any]					):
			self.enable_attention_slicing(__lowerCamelCase					)







		def 				A   (self  :					Union[str, Any]					):
			if is_accelerate_available():
				from accelerate import cpu_offload
			else:
				raise ImportError("""Please install accelerate via `pip install accelerate`"""					)

			A							     =					torch.device("""cuda"""					)

			for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
				if cpu_offloaded_model is not None:
					cpu_offload(__lowerCamelCase					,					__lowerCamelCase					)







		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 				A   (self  :					Tuple					):
			if self.device != torch.device("""meta"""					) or not hasattr(self.unet					,					"""_hf_hook"""					):
				return self.device
			for module in self.unet.modules():
				if (
				    hasattr(__lowerCamelCase					,					"""_hf_hook"""					)
				    and hasattr(module._hf_hook					,					"""execution_device"""					)
				    and module._hf_hook.execution_device is not None
				):
					return torch.device(module._hf_hook.execution_device					)
			return self.device







		@torch.no_grad()
		def __call__(self  :					Optional[Any]					,					_lowerCAmelCase  :					Union[str, List[str]]					,					_lowerCAmelCase  :					Union[torch.FloatTensor, PIL.Image.Image]					,					_lowerCAmelCase  :					str					,					_lowerCAmelCase  :					int = 512					,					_lowerCAmelCase  :					int = 512					,					_lowerCAmelCase  :					int = 50					,					_lowerCAmelCase  :					float = 7.5					,					_lowerCAmelCase  :					Optional[Union[str, List[str]]] = None					,					_lowerCAmelCase  :					Optional[int] = 1					,					_lowerCAmelCase  :					float = 0.0					,					_lowerCAmelCase  :					Optional[torch.Generator] = None					,					_lowerCAmelCase  :					Optional[torch.FloatTensor] = None					,					_lowerCAmelCase  :					Optional[str] = "pil"					,					_lowerCAmelCase  :					bool = True					,					_lowerCAmelCase  :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,					_lowerCAmelCase  :					int = 1					,					**_lowerCAmelCase  :					List[Any]					,					):
			A							     =					self.segmentation_processor(
			    text=[text]					,					images=[image]					,					padding="""max_length"""					,					return_tensors="""pt"""					).to(self.device					)
			A							     =					self.segmentation_model(**__lowerCamelCase					)
			A							     =					torch.sigmoid(outputs.logits					).cpu().detach().unsqueeze(-1					).numpy()
			A							     =					self.numpy_to_pil(__lowerCamelCase					)[0].resize(image.size					)

			# Run inpainting pipeline with the generated mask
			A							     =					StableDiffusionInpaintPipeline(
			    vae=self.vae					,					text_encoder=self.text_encoder					,					tokenizer=self.tokenizer					,					unet=self.unet					,					scheduler=self.scheduler					,					safety_checker=self.safety_checker					,					feature_extractor=self.feature_extractor					,					)
			return inpainting_pipeline(
			    prompt=__lowerCamelCase					,					image=__lowerCamelCase					,					mask_image=__lowerCamelCase					,					height=__lowerCamelCase					,					width=__lowerCamelCase					,					num_inference_steps=__lowerCamelCase					,					guidance_scale=__lowerCamelCase					,					negative_prompt=__lowerCamelCase					,					num_images_per_prompt=__lowerCamelCase					,					eta=__lowerCamelCase					,					generator=__lowerCamelCase					,					latents=__lowerCamelCase					,					output_type=__lowerCamelCase					,					return_dict=__lowerCamelCase					,					callback=__lowerCamelCase					,					callback_steps=__lowerCamelCase					,					)









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()
UpperCAmelCase_ :						Optional[Any]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						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''',
}
UpperCAmelCase_ :						int      =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Dict	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Optional[int]					)		->			Dict:






	"""simple docstring"""



	for attribute in key.split("""."""					):
		UpperCamelCase     :Dict          =	getattr(__magic_name__	,			__magic_name__					)

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

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  List[str]					)		->			Optional[Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[]
	UpperCamelCase     :Dict          =	fairseq_model.state_dict()

	UpperCamelCase     :int          =	hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCamelCase     :str          =	False
		if "conv_layers" in name:
			load_conv_layer(
			    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			hf_model.config.feat_extract_norm == """group"""	,			)
			UpperCamelCase     :Union[str, Any]          =	True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("""w2v_model."""					)[-1] == name.split("""."""					)[0]:
					UpperCamelCase     :Optional[int]          =	True
					if "*" in mapped_key:
						UpperCamelCase     :List[Any]          =	name.split(__magic_name__					)[0].split("""."""					)[-2]
						UpperCamelCase     :int          =	mapped_key.replace("""*"""	,			__magic_name__					)
					if "weight_g" in name:
						UpperCamelCase     :List[Any]          =	"""weight_g"""
					elif "weight_v" in name:
						UpperCamelCase     :List[Any]          =	"""weight_v"""
					elif "bias" in name and "relative_attention_bias" not in name:
						UpperCamelCase     :Any          =	"""bias"""
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						UpperCamelCase     :List[str]          =	"""weight"""
					else:
						UpperCamelCase     :Optional[int]          =	None

					set_recursively(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)
				continue
		if not is_used:
			unused_weights.append(__magic_name__					)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  List[str]					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	full_name.split("""conv_layers."""					)[-1]
	UpperCamelCase     :int          =	name.split("""."""					)
	UpperCamelCase     :str          =	int(items[0]					)
	UpperCamelCase     :str          =	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."""
			)
			UpperCamelCase     :Tuple          =	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."""
			)
			UpperCamelCase     :Dict          =	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."
			)
			UpperCamelCase     :Tuple          =	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."""
			)
			UpperCamelCase     :Union[str, Any]          =	value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
	else:
		unused_weights.append(__magic_name__					)

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  str=None					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	torch.load(__magic_name__					)
	UpperCamelCase     :List[Any]          =	WavLMConfigOrig(checkpoint["""cfg"""]					)
	UpperCamelCase     :int          =	WavLMOrig(__magic_name__					)
	model.load_state_dict(checkpoint["""model"""]					)
	model.eval()

	if config_path is not None:
		UpperCamelCase     :List[Any]          =	WavLMConfig.from_pretrained(__magic_name__					)
	else:
		UpperCamelCase     :Any          =	WavLMConfig()

	UpperCamelCase     :Dict          =	WavLMModel(__magic_name__					)

	recursively_load_weights(__magic_name__	,			__magic_name__					)

	hf_wavlm.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	UpperCAmelCase_ :						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''')
	UpperCAmelCase_ :						Optional[int]      =      parser.parse_args()
	convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
