






import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    ):
							"""simple docstring"""







							super().__init__()

							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(
							    speech_model=UpperCAmelCase       ,    speech_processor=UpperCAmelCase       ,    vae=UpperCAmelCase       ,    text_encoder=UpperCAmelCase       ,    tokenizer=UpperCAmelCase       ,    unet=UpperCAmelCase       ,    scheduler=UpperCAmelCase       ,    feature_extractor=UpperCAmelCase       ,    )

	def        UpperCamelCase    (							self       ,    UpperCAmelCase = "auto"    ):
							"""simple docstring"""







							if slice_size == "auto":
													_UpperCAmelCase									=			self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(UpperCAmelCase    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							self.enable_attention_slicing(UpperCAmelCase    )

	@torch.no_grad()
	def __call__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=1_6000       ,    UpperCAmelCase = 512       ,    UpperCAmelCase = 512       ,    UpperCAmelCase = 50       ,    UpperCAmelCase = 7.5       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 1       ,    UpperCAmelCase = 0.0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = "pil"       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 1       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.speech_processor.feature_extractor(
							    UpperCAmelCase       ,    return_tensors='pt'       ,    sampling_rate=UpperCAmelCase    ).input_features.to(self.device    )
							_UpperCAmelCase									=			self.speech_model.generate(UpperCAmelCase       ,    max_length=48_0000    )

							_UpperCAmelCase									=			self.speech_processor.tokenizer.batch_decode(UpperCAmelCase       ,    skip_special_tokens=UpperCAmelCase       ,    normalize=UpperCAmelCase    )[
							    0
							]

							if isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			1
							elif isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
													_UpperCAmelCase									=			len(UpperCAmelCase    )
							else:
													raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase    )}"""    )

							if height % 8 != 0 or width % 8 != 0:
													raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""    )

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(UpperCAmelCase       ,    UpperCAmelCase    ) or callback_steps <= 0)
							):
													raise ValueError(
													    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
													    F""" {type(UpperCAmelCase    )}."""    )

							# get prompt text embeddings
							_UpperCAmelCase									=			self.tokenizer(
							    UpperCAmelCase       ,    padding='max_length'       ,    max_length=self.tokenizer.model_max_length       ,    return_tensors='pt'       ,    )
							_UpperCAmelCase									=			text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
													_UpperCAmelCase									=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
													logger.warning(
													    'The following part of your input was truncated because CLIP can only handle sequences up to'
													    F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""    )
													_UpperCAmelCase									=			text_input_ids[:, : self.tokenizer.model_max_length]
							_UpperCAmelCase									=			self.text_encoder(text_input_ids.to(self.device    )    )[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							_UpperCAmelCase					, _UpperCAmelCase					, _UpperCAmelCase									=			text_embeddings.shape
							_UpperCAmelCase									=			text_embeddings.repeat(1       ,    UpperCAmelCase       ,    1    )
							_UpperCAmelCase									=			text_embeddings.view(bs_embed * num_images_per_prompt       ,    UpperCAmelCase       ,    -1    )

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							_UpperCAmelCase									=			guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
													_UpperCAmelCase									=			42
													if negative_prompt is None:
																			_UpperCAmelCase									=			[''] * batch_size
													elif type(UpperCAmelCase    ) is not type(UpperCAmelCase    ):
																			raise TypeError(
																			    F"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase    )} !="""
																			    F""" {type(UpperCAmelCase    )}."""    )
													elif isinstance(UpperCAmelCase       ,    UpperCAmelCase    ):
																			_UpperCAmelCase									=			[negative_prompt]
													elif batch_size != len(UpperCAmelCase    ):
																			raise ValueError(
																			    F"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase    )}, but `prompt`:"""
																			    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																			    ' the batch size of `prompt`.'    )
													else:
																			_UpperCAmelCase									=			negative_prompt

													_UpperCAmelCase									=			text_input_ids.shape[-1]
													_UpperCAmelCase									=			self.tokenizer(
													    UpperCAmelCase       ,    padding='max_length'       ,    max_length=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    return_tensors='pt'       ,    )
													_UpperCAmelCase									=			self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													_UpperCAmelCase									=			uncond_embeddings.shape[1]
													_UpperCAmelCase									=			uncond_embeddings.repeat(1       ,    UpperCAmelCase       ,    1    )
													_UpperCAmelCase									=			uncond_embeddings.view(batch_size * num_images_per_prompt       ,    UpperCAmelCase       ,    -1    )

													# For classifier free guidance, we need to do two forward passes.
													# Here we concatenate the unconditional and text embeddings into a single batch
													# to avoid doing two forward passes
													_UpperCAmelCase									=			torch.cat([uncond_embeddings, text_embeddings]    )

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							_UpperCAmelCase									=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							_UpperCAmelCase									=			text_embeddings.dtype
							if latents is None:
													if self.device.type == "mps":
																			# randn does not exist on mps
																			_UpperCAmelCase									=			torch.randn(UpperCAmelCase       ,    generator=UpperCAmelCase       ,    device='cpu'       ,    dtype=UpperCAmelCase    ).to(
																			    self.device    )
													else:
																			_UpperCAmelCase									=			torch.randn(UpperCAmelCase       ,    generator=UpperCAmelCase       ,    device=self.device       ,    dtype=UpperCAmelCase    )
							else:
													if latents.shape != latents_shape:
																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
													_UpperCAmelCase									=			latents.to(self.device    )

							# set timesteps
							self.scheduler.set_timesteps(UpperCAmelCase    )

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							_UpperCAmelCase									=			self.scheduler.timesteps.to(self.device    )

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCAmelCase									=			latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							_UpperCAmelCase									=			'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
							_UpperCAmelCase									=			{}
							if accepts_eta:
													_UpperCAmelCase									=			eta

							for i, t in enumerate(self.progress_bar(UpperCAmelCase    )    ):
													# expand the latents if we are doing classifier free guidance
													_UpperCAmelCase									=			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
													_UpperCAmelCase									=			self.scheduler.scale_model_input(UpperCAmelCase       ,    UpperCAmelCase    )

													# predict the noise residual
													_UpperCAmelCase									=			self.unet(UpperCAmelCase       ,    UpperCAmelCase       ,    encoder_hidden_states=UpperCAmelCase    ).sample

													# perform guidance
													if do_classifier_free_guidance:
																			_UpperCAmelCase					, _UpperCAmelCase									=			noise_pred.chunk(2    )
																			_UpperCAmelCase									=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													# compute the previous noisy sample x_t -> x_t-1
													_UpperCAmelCase									=			self.scheduler.step(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase       ,    **UpperCAmelCase    ).prev_sample

													# call the callback, if provided
													if callback is not None and i % callback_steps == 0:
																			callback(UpperCAmelCase       ,    UpperCAmelCase       ,    UpperCAmelCase    )

							_UpperCAmelCase									=			1 / 0.1_82_15 * latents
							_UpperCAmelCase									=			self.vae.decode(UpperCAmelCase    ).sample

							_UpperCAmelCase									=			(image / 2 + 0.5).clamp(0       ,    1    )

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							_UpperCAmelCase									=			image.cpu().permute(0       ,    2       ,    3       ,    1    ).float().numpy()

							if output_type == "pil":
													_UpperCAmelCase									=			self.numpy_to_pil(UpperCAmelCase    )

							if not return_dict:
													return image

							return StableDiffusionPipelineOutput(images=UpperCAmelCase       ,    nsfw_content_detected=UpperCAmelCase    )





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

A:   Tuple								=		{
    "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",
}
A:   List[str]								=		[
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def       _snake_case						(						UpperCamelCase						:		str			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Optional[int]			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any						):
 for attribute in key.split("""."""						):
  UpperCAmelCase	:					Optional[Any]	=	getattr(UpperCamelCase			,		UpperCamelCase						)

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

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

def       _snake_case						(						UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		Optional[Any]						):
 UpperCAmelCase	:					Tuple	=	[]
 UpperCAmelCase	:					Any	=	fairseq_model.state_dict()

 UpperCAmelCase	:					Tuple	=	hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  UpperCAmelCase	:					str	=	False
  if "conv_layers" in name:
   load_conv_layer(
       UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		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	:					Dict	=	True
     if "*" in mapped_key:
      UpperCAmelCase	:					str	=	name.split(UpperCamelCase						)[0].split("""."""						)[-2]
      UpperCAmelCase	:					Tuple	=	mapped_key.replace("""*"""			,		UpperCamelCase						)
     if "weight_g" in name:
      UpperCAmelCase	:					Any	=	"""weight_g"""
     elif "weight_v" in name:
      UpperCAmelCase	:					Optional[Any]	=	"""weight_v"""
     elif "bias" in name and "relative_attention_bias" not in name:
      UpperCAmelCase	:					Union[str, Any]	=	"""bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      UpperCAmelCase	:					str	=	"""weight"""
     else:
      UpperCAmelCase	:					Optional[Any]	=	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       _snake_case						(						UpperCamelCase						:		Optional[Any]			,		UpperCamelCase						:		Dict			,		UpperCamelCase						:		Tuple			,		UpperCamelCase						:		Any			,		UpperCamelCase						:		Any						):
 UpperCAmelCase	:					str	=	full_name.split("""conv_layers."""						)[-1]
 UpperCAmelCase	:					Dict	=	name.split("""."""						)
 UpperCAmelCase	:					List[str]	=	int(items[0]						)
 UpperCAmelCase	:					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."
   )
   UpperCAmelCase	:					Optional[Any]	=	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	:					Tuple	=	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	:					str	=	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	:					Optional[Any]	=	value
   logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
 else:
  unused_weights.append(UpperCamelCase						)

@torch.no_grad()
def       _snake_case						(						UpperCamelCase						:		int			,		UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]=None						):
 # load the pre-trained checkpoints
 UpperCAmelCase	:					List[Any]	=	torch.load(UpperCamelCase						)
 UpperCAmelCase	:					List[str]	=	WavLMConfigOrig(checkpoint["""cfg"""]						)
 UpperCAmelCase	:					Optional[int]	=	WavLMOrig(UpperCamelCase						)
 model.load_state_dict(checkpoint["""model"""]						)
 model.eval()

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

 UpperCAmelCase	:					Any	=	WavLMModel(UpperCamelCase						)

 recursively_load_weights(UpperCamelCase			,		UpperCamelCase						)

 hf_wavlm.save_pretrained(UpperCamelCase						)


if __name__ == "__main__":
      A:   int								=		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")
      A:   Tuple								=		parser.parse_args()
      convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
