


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

snake_case							:	Optional[Any]					      =				'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    List[Any]=8       ):
       a__					=					height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       a__					=					width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor







class 	snake_case_       (lowerCamelCase_				):
     def __init__(				self       :List[str]      ,__snake_case       :UNetaDConditionModel      ,__snake_case       :DDPMScheduler      ,__snake_case       :VQModel      ,) ->   Any:
            super().__init__()

            self.register_modules(
                unet=__snake_case      ,scheduler=__snake_case      ,movq=__snake_case      ,)
            a__					=					2 ** (len(self.movq.config.block_out_channels				) - 1)
     def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :Any      ,__snake_case       :Optional[Any]      ,__snake_case       :Any      ,__snake_case       :str      ,__snake_case       :int      ,__snake_case       :Any				) ->   Optional[Any]:
            if latents is None:
                   a__					=					randn_tensor(__snake_case      ,generator=__snake_case      ,device=__snake_case      ,dtype=__snake_case				)
            else:
                   if latents.shape != shape:
                          raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'				)
                   a__					=					latents.to(__snake_case				)

            a__					=					latents * scheduler.init_noise_sigma
            return latents
     def 			lowerCamelCase__(				self       :Tuple      ,__snake_case       :int=0				) ->   int:
            if is_accelerate_available():
                   from accelerate import cpu_offload
            else:
                   raise ImportError('Please install accelerate via `pip install accelerate`'				)

            a__					=					torch.device(F'cuda:{gpu_id}'				)

            a__					=					[
                self.unet,
                self.movq,
            ]
            for cpu_offloaded_model in models:
                   if cpu_offloaded_model is not None:
                          cpu_offload(__snake_case      ,__snake_case				)
     def 			lowerCamelCase__(				self       :Any      ,__snake_case       :Optional[Any]=0				) ->   int:
            if is_accelerate_available() and is_accelerate_version('>='      ,'0.17.0.dev0'				):
                   from accelerate import cpu_offload_with_hook
            else:
                   raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'				)

            a__					=					torch.device(F'cuda:{gpu_id}'				)

            if self.device.type != "cpu":
                   self.to('cpu'      ,silence_dtype_warnings=__snake_case				)
                   torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

            a__					=					None
            for cpu_offloaded_model in [self.unet, self.movq]:
                   a__				, a__					=					cpu_offload_with_hook(__snake_case      ,__snake_case      ,prev_module_hook=__snake_case				)

            # We'll offload the last model manually.
            a__					=					hook
     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   int:
            if not hasattr(self.unet      ,'_hf_hook'				):
                   return self.device
            for module in self.unet.modules():
                   if (
                       hasattr(__snake_case      ,'_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()
     @replace_example_docstring(__snake_case				)
     def __call__(				self       :Union[str, Any]      ,__snake_case       :Union[torch.FloatTensor, List[torch.FloatTensor]]      ,__snake_case       :Union[torch.FloatTensor, List[torch.FloatTensor]]      ,__snake_case       :int = 5_12      ,__snake_case       :int = 5_12      ,__snake_case       :int = 1_00      ,__snake_case       :float = 4.0      ,__snake_case       :int = 1      ,__snake_case       :Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,__snake_case       :Optional[torch.FloatTensor] = None      ,__snake_case       :Optional[str] = "pil"      ,__snake_case       :bool = True      ,) ->   Optional[int]:
            a__					=					self._execution_device

            a__					=					guidance_scale > 1.0

            if isinstance(__snake_case      ,__snake_case				):
                   a__					=					torch.cat(__snake_case      ,dim=0				)
            a__					=					image_embeds.shape[0] * num_images_per_prompt
            if isinstance(__snake_case      ,__snake_case				):
                   a__					=					torch.cat(__snake_case      ,dim=0				)

            if do_classifier_free_guidance:
                   a__					=					image_embeds.repeat_interleave(__snake_case      ,dim=0				)
                   a__					=					negative_image_embeds.repeat_interleave(__snake_case      ,dim=0				)

            a__					=					torch.cat([negative_image_embeds, image_embeds]      ,dim=0				).to(dtype=self.unet.dtype      ,device=__snake_case				)

            self.scheduler.set_timesteps(__snake_case      ,device=__snake_case				)
            a__					=					self.scheduler.timesteps

            a__					=					self.unet.config.in_channels

            a__				, a__					=					downscale_height_and_width(__snake_case      ,__snake_case      ,self.movq_scale_factor				)

            # create initial latent
            a__					=					self.prepare_latents(
                (batch_size, num_channels_latents, height, width)      ,image_embeds.dtype      ,__snake_case      ,__snake_case      ,__snake_case      ,self.scheduler      ,)

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

                   a__					=					{'image_embeds': image_embeds}
                   a__					=					self.unet(
                       sample=__snake_case      ,timestep=__snake_case      ,encoder_hidden_states=__snake_case      ,added_cond_kwargs=__snake_case      ,return_dict=__snake_case      ,)[0]

                   if do_classifier_free_guidance:
                          a__				, a__					=					noise_pred.split(latents.shape[1]      ,dim=1				)
                          a__				, a__					=					noise_pred.chunk(2				)
                          a__				, a__					=					variance_pred.chunk(2				)
                          a__					=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                          a__					=					torch.cat([noise_pred, variance_pred_text]      ,dim=1				)

                   if not (
                       hasattr(self.scheduler.config      ,'variance_type'				)
                       and self.scheduler.config.variance_type in ["learned", "learned_range"]
                   ):
                          a__				, a__					=					noise_pred.split(latents.shape[1]      ,dim=1				)

                   # compute the previous noisy sample x_t -> x_t-1
                   a__					=					self.scheduler.step(
                       __snake_case      ,__snake_case      ,__snake_case      ,generator=__snake_case      ,)[0]
            # post-processing
            a__					=					self.movq.decode(__snake_case      ,force_not_quantize=__snake_case				)['sample']

            if output_type not in ["pt", "np", "pil"]:
                   raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'				)

            if output_type in ["np", "pil"]:
                   a__					=					image * 0.5 + 0.5
                   a__					=					image.clamp(0      ,1				)
                   a__					=					image.cpu().permute(0      ,2      ,3      ,1				).float().numpy()

            if output_type == "pil":
                   a__					=					self.numpy_to_pil(__snake_case				)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=__snake_case				)








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)




