





'''simple docstring'''






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,
)


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

A__				:   Tuple      ='''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
'''
def    UpperCamelCase__      (		lowerCAmelCase							,	lowerCAmelCase							,	lowerCAmelCase=8  ):





      """simple docstring"""


      _lowerCAmelCase							     =							height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      _lowerCAmelCase							     =							width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor





class  UpperCAmelCase     (			_UpperCAmelCase   ):




      def __init__(			self			: str			,			__snake_case			: UNetaDConditionModel			,			__snake_case			: DDPMScheduler			,			__snake_case			: VQModel			,			)				->      Optional[int]:
            super().__init__()

            self.register_modules(
                unet=UpperCAmelCase_			,			scheduler=UpperCAmelCase_			,			movq=UpperCAmelCase_			,			)
            _lowerCAmelCase							     =							2 ** (len(self.movq.config.block_out_channels  ) - 1)




      def 				lowercase__    (			self			: Union[str, Any]			,			__snake_case			: Dict			,			__snake_case			: Union[str, Any]			,			__snake_case			: List[str]			,			__snake_case			: Optional[int]			,			__snake_case			: Optional[int]			,			__snake_case			: Optional[int]  )				->      Optional[Any]:
            if latents is None:
                  _lowerCAmelCase							     =							randn_tensor(UpperCAmelCase_			,			generator=UpperCAmelCase_			,			device=UpperCAmelCase_			,			dtype=UpperCAmelCase_  )
            else:
                  if latents.shape != shape:
                        raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"  )
                  _lowerCAmelCase							     =							latents.to(UpperCAmelCase_  )

            _lowerCAmelCase							     =							latents * scheduler.init_noise_sigma
            return latents




      def 				lowercase__    (			self			: Tuple			,			__snake_case			: Tuple=0  )				->      int:
            if is_accelerate_available():
                  from accelerate import cpu_offload
            else:
                  raise ImportError("""Please install accelerate via `pip install accelerate`"""  )

            _lowerCAmelCase							     =							torch.device(f"cuda:{gpu_id}"  )

            _lowerCAmelCase							     =							[
                self.unet,
                self.movq,
            ]
            for cpu_offloaded_model in models:
                  if cpu_offloaded_model is not None:
                        cpu_offload(UpperCAmelCase_			,			UpperCAmelCase_  )




      def 				lowercase__    (			self			: Optional[int]			,			__snake_case			: List[Any]=0  )				->      Dict:
            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."""  )

            _lowerCAmelCase							     =							torch.device(f"cuda:{gpu_id}"  )

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

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

            # We'll offload the last model manually.
            _lowerCAmelCase							     =							hook




      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 				lowercase__    (			self			: Dict  )				->      Optional[Any]:
            if not hasattr(self.unet			,			"""_hf_hook"""  ):
                  return self.device
            for module in self.unet.modules():
                  if (
                      hasattr(UpperCAmelCase_			,			"""_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(UpperCAmelCase_  )
      def __call__(			self			: Union[str, Any]			,			__snake_case			: Union[torch.FloatTensor, List[torch.FloatTensor]]			,			__snake_case			: Union[torch.FloatTensor, List[torch.FloatTensor]]			,			__snake_case			: 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			,			)				->      Dict:
            _lowerCAmelCase							     =							self._execution_device

            _lowerCAmelCase							     =							guidance_scale > 1.0

            if isinstance(UpperCAmelCase_			,			UpperCAmelCase_  ):
                  _lowerCAmelCase							     =							torch.cat(UpperCAmelCase_			,			dim=0  )
            if isinstance(UpperCAmelCase_			,			UpperCAmelCase_  ):
                  _lowerCAmelCase							     =							torch.cat(UpperCAmelCase_			,			dim=0  )
            if isinstance(UpperCAmelCase_			,			UpperCAmelCase_  ):
                  _lowerCAmelCase							     =							torch.cat(UpperCAmelCase_			,			dim=0  )

            _lowerCAmelCase							     =							image_embeds.shape[0] * num_images_per_prompt

            if do_classifier_free_guidance:
                  _lowerCAmelCase							     =							image_embeds.repeat_interleave(UpperCAmelCase_			,			dim=0  )
                  _lowerCAmelCase							     =							negative_image_embeds.repeat_interleave(UpperCAmelCase_			,			dim=0  )
                  _lowerCAmelCase							     =							hint.repeat_interleave(UpperCAmelCase_			,			dim=0  )

            _lowerCAmelCase							     =							torch.cat([negative_image_embeds, image_embeds]			,			dim=0  ).to(dtype=self.unet.dtype			,			device=UpperCAmelCase_  )
            _lowerCAmelCase							     =							torch.cat([hint, hint]			,			dim=0  ).to(dtype=self.unet.dtype			,			device=UpperCAmelCase_  )
            self.scheduler.set_timesteps(UpperCAmelCase_			,			device=UpperCAmelCase_  )
            _lowerCAmelCase							     =							self.scheduler.timesteps

            _lowerCAmelCase							     =							self.movq.config.latent_channels

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							downscale_height_and_width(UpperCAmelCase_			,			UpperCAmelCase_			,			self.movq_scale_factor  )

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

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

                  _lowerCAmelCase							     =							{"""image_embeds""": image_embeds, """hint""": hint}
                  _lowerCAmelCase							     =							self.unet(
                      sample=UpperCAmelCase_			,			timestep=UpperCAmelCase_			,			encoder_hidden_states=UpperCAmelCase_			,			added_cond_kwargs=UpperCAmelCase_			,			return_dict=UpperCAmelCase_			,			)[0]

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

                  # compute the previous noisy sample x_t -> x_t-1
                  _lowerCAmelCase							     =							self.scheduler.step(
                      UpperCAmelCase_			,			UpperCAmelCase_			,			UpperCAmelCase_			,			generator=UpperCAmelCase_			,			)[0]
            # post-processing
            _lowerCAmelCase							     =							self.movq.decode(UpperCAmelCase_			,			force_not_quantize=UpperCAmelCase_  )["""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"]:
                  _lowerCAmelCase							     =							image * 0.5 + 0.5
                  _lowerCAmelCase							     =							image.clamp(0			,			1  )
                  _lowerCAmelCase							     =							image.cpu().permute(0			,			2			,			3			,			1  ).float().numpy()

            if output_type == "pil":
                  _lowerCAmelCase							     =							self.numpy_to_pil(UpperCAmelCase_  )

            if not return_dict:
                  return (image,)

            return ImagePipelineOutput(images=UpperCAmelCase_  )








import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
__snake_case								=  logging.get_logger(__name__)

__snake_case								=  {
    """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_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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
__snake_case								=  [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					for attribute in key.split('.'		):
										SCREAMING_SNAKE_CASE__													= getattr(UpperCamelCase_	,	UpperCamelCase_		)

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

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






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= []
					SCREAMING_SNAKE_CASE__													= fairseq_model.state_dict()

					SCREAMING_SNAKE_CASE__													= hf_model.feature_extractor
					SCREAMING_SNAKE_CASE__													= hf_model.adapter

					for name, value in fairseq_dict.items():
										SCREAMING_SNAKE_CASE__													= False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	hf_model.config.feat_extract_norm == 'group'	,	)
															SCREAMING_SNAKE_CASE__													= True
										elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']		):
															load_adapter(UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)
															SCREAMING_SNAKE_CASE__													= True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split('w2v_model.'		)[-1] == name.split('.'		)[0]:
																									SCREAMING_SNAKE_CASE__													= True
																									if "*" in mapped_key:
																														SCREAMING_SNAKE_CASE__													= name.split(UpperCamelCase_		)[0].split('.'		)[-2]
																														SCREAMING_SNAKE_CASE__													= mapped_key.replace('*'	,	UpperCamelCase_		)
																									if "weight_g" in name:
																														SCREAMING_SNAKE_CASE__													= 'weight_g'
																									elif "weight_v" in name:
																														SCREAMING_SNAKE_CASE__													= 'weight_v'
																									elif "bias" in name:
																														SCREAMING_SNAKE_CASE__													= 'bias'
																									elif "weight" in name:
																														SCREAMING_SNAKE_CASE__													= 'weight'
																									else:
																														SCREAMING_SNAKE_CASE__													= 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 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		List[str]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= full_name.split('conv_layers.'		)[-1]
					SCREAMING_SNAKE_CASE__													= name.split('.'		)
					SCREAMING_SNAKE_CASE__													= int(items[0]		)
					SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= 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."
															)
															SCREAMING_SNAKE_CASE__													= 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.'
															)
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'		)
					else:
										unused_weights.append(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= full_name.split('adaptor.'		)[-1]
					SCREAMING_SNAKE_CASE__													= name.split('.'		)

					if items[1].isdigit():
										SCREAMING_SNAKE_CASE__													= int(items[1]		)
					else:
										SCREAMING_SNAKE_CASE__													= None

					if "adaptor" not in full_name:
										if "proj_ln" in full_name:
															# has to be layer norm
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.bias.data.shape
																				), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'
																				SCREAMING_SNAKE_CASE__													= value
																				logger.info(F'Adapter proj layer norm bias was initialized from {full_name}.'		)
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.weight.data.shape
																				), F'{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'
																				SCREAMING_SNAKE_CASE__													= value
										else:
															# has to be projection layer
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj.bias.data.shape
																				), F'{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'
																				SCREAMING_SNAKE_CASE__													= value
																				logger.info(F'Adapter proj layer bias was initialized from {full_name}.'		)
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj.weight.data.shape
																				), F'{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'
																				SCREAMING_SNAKE_CASE__													= value
																				logger.info(F'Adapter proj layer weight was initialized from {full_name}.'		)
					elif isinstance(UpperCamelCase_	,	UpperCamelCase_		):
										if "bias" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.bias.data.shape
															), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'		)
										elif "weight" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.weight.data.shape
															), F'{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'
															SCREAMING_SNAKE_CASE__													= value
															logger.info(F'Adapter layer {layer_id} bias was initialized from {full_name}.'		)
					else:
										unused_weights.append(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_		)		->		Any:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= emb.weight.shape
					SCREAMING_SNAKE_CASE__													= nn.Linear(UpperCamelCase_	,	UpperCamelCase_	,	bias=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= emb.weight.data
					return lin_layer






@torch.no_grad()
def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	)		->		Tuple:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= WavaVecaConfig.from_pretrained(
					    UpperCamelCase_	,	add_adapter=UpperCamelCase_	,	adapter_stride=UpperCamelCase_	,	adapter_kernel_size=UpperCamelCase_	,	use_auth_token=UpperCamelCase_	,	output_hidden_size=UpperCamelCase_	,	)
					SCREAMING_SNAKE_CASE__													= MBartConfig.from_pretrained(UpperCamelCase_		)

					# load model
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]	,	arg_overrides={
					        'config_yaml': config_yaml_path,
					        'data': '/'.join(dict_path.split('/'		)[:-1]		),
					        'w2v_path': checkpoint_path,
					        'load_pretrained_decoder_from': None,
					    }	,	)
					SCREAMING_SNAKE_CASE__													= model[0].eval()

					# load feature extractor
					SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCamelCase_	,	use_auth_token=UpperCamelCase_		)

					# set weights for wav2vec2 encoder
					SCREAMING_SNAKE_CASE__													= WavaVecaModel(UpperCamelCase_		)

					recursively_load_weights_wavaveca(model.encoder	,	UpperCamelCase_		)

					# load decoder weights
					SCREAMING_SNAKE_CASE__													= MBartForCausalLM(UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__						, SCREAMING_SNAKE_CASE__													= hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()	,	strict=UpperCamelCase_		)
					logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'		)
					logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'		)

					SCREAMING_SNAKE_CASE__													= SpeechEncoderDecoderModel(encoder=UpperCamelCase_	,	decoder=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= False

					SCREAMING_SNAKE_CASE__													= MBartaaTokenizer(UpperCamelCase_		)
					tokenizer.save_pretrained(UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= hf_wavavec.config.to_dict()
					SCREAMING_SNAKE_CASE__													= tokenizer.pad_token_id
					SCREAMING_SNAKE_CASE__													= tokenizer.bos_token_id
					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id
					SCREAMING_SNAKE_CASE__													= 'mbart50'
					SCREAMING_SNAKE_CASE__													= 'wav2vec2'

					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id
					SCREAMING_SNAKE_CASE__													= 250004
					SCREAMING_SNAKE_CASE__													= tokenizer.eos_token_id

					SCREAMING_SNAKE_CASE__													= SpeechEncoderDecoderConfig.from_dict(UpperCamelCase_		)

					hf_wavavec.save_pretrained(UpperCamelCase_		)
					feature_extractor.save_pretrained(UpperCamelCase_		)


if __name__ == "__main__":
				__snake_case								=  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("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
				parser.add_argument("""--config_yaml_path""", default=None, type=str, help="""Path to yaml file of fine-tuned model""")
				parser.add_argument(
				    """--encoder_config_path""",
				    default="""facebook/wav2vec2-xls-r-1b""",
				    type=str,
				    help="""Path to hf encoder wav2vec2 checkpoint config""",
				)
				parser.add_argument(
				    """--decoder_config_path""",
				    default="""facebook/mbart-large-50-one-to-many-mmt""",
				    type=str,
				    help="""Path to hf decoder checkpoint config""",
				)
				parser.add_argument("""--add_adapter""", default=True, type=bool, help="""whethere to add model adapter layers""")
				parser.add_argument("""--adapter_stride""", default=2, type=int, help="""stride of adapter layers""")
				parser.add_argument("""--adapter_kernel_size""", default=3, type=int, help="""kernel size of adapter layers""")
				parser.add_argument("""--encoder_output_dim""", default=10_24, type=int, help="""encoder output dim""")
				parser.add_argument("""--start_token_id""", default=25_00_04, type=int, help="""`decoder_start_token_id` of model config""")

				__snake_case								=  parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    args.config_yaml_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    add_adapter=args.add_adapter,
				    adapter_kernel_size=args.adapter_kernel_size,
				    adapter_stride=args.adapter_stride,
				    decoder_start_token_id=args.start_token_id,
				    encoder_output_dim=args.encoder_output_dim,
				)





