




from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

__A								=       "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n        >>> pipe.to(\"cuda\")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save(\"cat.png\")\n        ```\n"







def      lowerCamelCase_						(			UpperCamelCase__							: str					,	UpperCamelCase__							: str					,	UpperCamelCase__							: Optional[Any]=8 )	->			str:





				"""simple docstring"""


				__lowerCamelCase									=       h // scale_factor**2
				if h % scale_factor**2 != 0:
								new_h += 1
				__lowerCamelCase									=       w // scale_factor**2
				if w % scale_factor**2 != 0:
								new_w += 1
				return new_h * scale_factor, new_w * scale_factor







class        __lowerCAmelCase					(			__UpperCamelCase					):

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		)       -> Any:



							'''simple docstring'''
							super().__init__()

							self.register_modules(
							    text_encoder=snake_case__     ,		tokenizer=snake_case__     ,		unet=snake_case__     ,		scheduler=snake_case__     ,		movq=snake_case__     ,		)
							__lowerCamelCase									=       2 ** (len(self.movq.config.block_out_channels				) - 1)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							if latents is None:
											__lowerCamelCase									=       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}"""				)
											__lowerCamelCase									=       latents.to(snake_case__				)

							__lowerCamelCase									=       latents * scheduler.init_noise_sigma
							return latents


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__=None     ,		)       -> List[str]:



							'''simple docstring'''
							__lowerCamelCase									=       len(snake_case__				) if isinstance(snake_case__     ,		snake_case__				) else 1
							# get prompt text embeddings
							__lowerCamelCase									=       self.tokenizer(
							    snake_case__     ,		padding='max_length'     ,		truncation=snake_case__     ,		max_length=77     ,		return_attention_mask=snake_case__     ,		add_special_tokens=snake_case__     ,		return_tensors='pt'     ,		)

							__lowerCamelCase									=       text_inputs.input_ids
							__lowerCamelCase									=       self.tokenizer(snake_case__     ,		padding='longest'     ,		return_tensors='pt'				).input_ids

							if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case__     ,		snake_case__				):
											__lowerCamelCase									=       self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]				)
											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}"""				)

							__lowerCamelCase									=       text_input_ids.to(snake_case__				)
							__lowerCamelCase									=       text_inputs.attention_mask.to(snake_case__				)

							__lowerCamelCase			,		__lowerCamelCase									=       self.text_encoder(
							    input_ids=snake_case__     ,		attention_mask=snake_case__				)

							__lowerCamelCase									=       prompt_embeds.repeat_interleave(snake_case__     ,		dim=0				)
							__lowerCamelCase									=       text_encoder_hidden_states.repeat_interleave(snake_case__     ,		dim=0				)
							__lowerCamelCase									=       text_mask.repeat_interleave(snake_case__     ,		dim=0				)

							if do_classifier_free_guidance:
											__lowerCamelCase									=       42
											if negative_prompt is None:
															__lowerCamelCase									=       [''] * batch_size
											elif type(snake_case__				) is not type(snake_case__				):
															raise TypeError(
															    f"""`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__				)} !="""
															    f""" {type(snake_case__				)}."""				)
											elif isinstance(snake_case__     ,		snake_case__				):
															__lowerCamelCase									=       [negative_prompt]
											elif batch_size != len(snake_case__				):
															raise ValueError(
															    f"""`negative_prompt`: {negative_prompt} has batch size {len(snake_case__				)}, but `prompt`:"""
															    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
															    ' the batch size of `prompt`.'				)
											else:
															__lowerCamelCase									=       negative_prompt

											__lowerCamelCase									=       self.tokenizer(
											    snake_case__     ,		padding='max_length'     ,		max_length=77     ,		truncation=snake_case__     ,		return_attention_mask=snake_case__     ,		add_special_tokens=snake_case__     ,		return_tensors='pt'     ,		)
											__lowerCamelCase									=       uncond_input.input_ids.to(snake_case__				)
											__lowerCamelCase									=       uncond_input.attention_mask.to(snake_case__				)

											__lowerCamelCase			,		__lowerCamelCase									=       self.text_encoder(
											    input_ids=snake_case__     ,		attention_mask=snake_case__				)

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

											__lowerCamelCase									=       negative_prompt_embeds.shape[1]
											__lowerCamelCase									=       negative_prompt_embeds.repeat(1     ,		snake_case__				)
											__lowerCamelCase									=       negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,		snake_case__				)

											__lowerCamelCase									=       uncond_text_encoder_hidden_states.shape[1]
											__lowerCamelCase									=       uncond_text_encoder_hidden_states.repeat(1     ,		snake_case__     ,		1				)
											__lowerCamelCase									=       uncond_text_encoder_hidden_states.view(
											    batch_size * num_images_per_prompt     ,		snake_case__     ,		-1				)
											__lowerCamelCase									=       uncond_text_mask.repeat_interleave(snake_case__     ,		dim=0				)

											# done duplicates

											# 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
											__lowerCamelCase									=       torch.cat([negative_prompt_embeds, prompt_embeds]				)
											__lowerCamelCase									=       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

											__lowerCamelCase									=       torch.cat([uncond_text_mask, text_mask]				)

							return prompt_embeds, text_encoder_hidden_states, text_mask


			def  lowercase_       ( self     ,		lowerCamelCase__=0				)       -> List[str]:



							'''simple docstring'''
							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.text_encoder,
							    self.movq,
							]
							for cpu_offloaded_model in models:
											if cpu_offloaded_model is not None:
															cpu_offload(snake_case__     ,		snake_case__				)


			def  lowercase_       ( self     ,		lowerCamelCase__=0				)       -> int:



							'''simple docstring'''
							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=snake_case__				)
											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

							__lowerCamelCase									=       None
							for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
											__lowerCamelCase			,		__lowerCamelCase									=       cpu_offload_with_hook(snake_case__     ,		snake_case__     ,		prev_module_hook=snake_case__				)

							if self.safety_checker is not None:
											__lowerCamelCase			,		__lowerCamelCase									=       cpu_offload_with_hook(self.safety_checker     ,		snake_case__     ,		prev_module_hook=snake_case__				)

							# 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				)       -> List[str]:



							'''simple docstring'''
							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     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = 512     ,		lowerCamelCase__ = 512     ,		lowerCamelCase__ = 100     ,		lowerCamelCase__ = 4.0     ,		lowerCamelCase__ = 1     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = None     ,		lowerCamelCase__ = "pil"     ,		lowerCamelCase__ = True     ,		)       -> Optional[int]:



							'''simple docstring'''

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

							__lowerCamelCase									=       self._execution_device

							__lowerCamelCase									=       batch_size * num_images_per_prompt
							__lowerCamelCase									=       guidance_scale > 1.0

							__lowerCamelCase			,		__lowerCamelCase			,		__lowerCamelCase									=       self._encode_prompt(
							    snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__     ,		snake_case__				)

							if isinstance(snake_case__     ,		snake_case__				):
											__lowerCamelCase									=       torch.cat(snake_case__     ,		dim=0				)
							if isinstance(snake_case__     ,		snake_case__				):
											__lowerCamelCase									=       torch.cat(snake_case__     ,		dim=0				)

							if do_classifier_free_guidance:
											__lowerCamelCase									=       image_embeds.repeat_interleave(snake_case__     ,		dim=0				)
											__lowerCamelCase									=       negative_image_embeds.repeat_interleave(snake_case__     ,		dim=0				)

							__lowerCamelCase									=       torch.cat([negative_image_embeds, image_embeds]     ,		dim=0				).to(
							    dtype=prompt_embeds.dtype     ,		device=snake_case__				)

							self.scheduler.set_timesteps(snake_case__     ,		device=snake_case__				)
							__lowerCamelCase									=       self.scheduler.timesteps

							__lowerCamelCase									=       self.unet.config.in_channels

							__lowerCamelCase			,		__lowerCamelCase									=       get_new_h_w(snake_case__     ,		snake_case__     ,		self.movq_scale_factor				)

							# create initial latent
							__lowerCamelCase									=       self.prepare_latents(
							    (batch_size, num_channels_latents, height, width)     ,		text_encoder_hidden_states.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
											__lowerCamelCase									=       torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

											__lowerCamelCase									=       {'text_embeds': prompt_embeds, 'image_embeds': image_embeds}
											__lowerCamelCase									=       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:
															__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(
											    snake_case__     ,		snake_case__     ,		snake_case__     ,		generator=snake_case__     ,		).prev_sample
							# post-processing
							__lowerCamelCase									=       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"]:
											__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(snake_case__				)

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=snake_case__				)



"""simple docstring"""




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()
__lowerCAmelCase     :				int    				=    logging.get_logger(__name__)

__lowerCAmelCase     :				Dict    				=    {
    "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",
}
__lowerCAmelCase     :				Optional[Any]    				=    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		for attribute in key.split("""."""  ):
				lowerCAmelCase__   = getattr(lowerCamelCase__      ,	lowerCamelCase__  )

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

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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = []
		lowerCAmelCase__   = fairseq_model.state_dict()

		lowerCAmelCase__   = hf_model.feature_extractor
		lowerCAmelCase__   = hf_model.adapter

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





		"""simple docstring"""




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







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = full_name.split("""adaptor."""  )[-1]
		lowerCAmelCase__   = name.split("""."""  )

		if items[1].isdigit():
				lowerCAmelCase__   = int(items[1]  )
		else:
				lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = 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."""
								lowerCAmelCase__   = value
								logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""  )
		elif isinstance(lowerCamelCase__      ,	lowerCamelCase__  ):
				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."""
						lowerCAmelCase__   = 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."""
						lowerCAmelCase__   = value
						logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""  )
		else:
				unused_weights.append(lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__ ,       lowerCAmelCase__   = emb.weight.shape
		lowerCAmelCase__   = nn.Linear(lowerCamelCase__      ,	lowerCamelCase__      ,	bias=lowerCamelCase__  )
		lowerCAmelCase__   = emb.weight.data
		return lin_layer







@torch.no_grad()
def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	):





		"""simple docstring"""




		lowerCAmelCase__   = WavaVecaConfig.from_pretrained(
		    lowerCamelCase__      ,	add_adapter=lowerCamelCase__      ,	adapter_stride=lowerCamelCase__      ,	adapter_kernel_size=lowerCamelCase__      ,	use_auth_token=lowerCamelCase__      ,	output_hidden_size=lowerCamelCase__      ,	)
		lowerCAmelCase__   = MBartConfig.from_pretrained(lowerCamelCase__  )

		# load model
		lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = 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,
		    }      ,	)
		lowerCAmelCase__   = model[0].eval()

		# load feature extractor
		lowerCAmelCase__   = WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase__      ,	use_auth_token=lowerCamelCase__  )

		# set weights for wav2vec2 encoder
		lowerCAmelCase__   = WavaVecaModel(lowerCamelCase__  )

		recursively_load_weights_wavaveca(model.encoder      ,	lowerCamelCase__  )

		# load decoder weights
		lowerCAmelCase__   = MBartForCausalLM(lowerCamelCase__  )
		lowerCAmelCase__ ,       lowerCAmelCase__   = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()      ,	strict=lowerCamelCase__  )
		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}"""  )

		lowerCAmelCase__   = SpeechEncoderDecoderModel(encoder=lowerCamelCase__      ,	decoder=lowerCamelCase__  )
		lowerCAmelCase__   = False

		lowerCAmelCase__   = MBartaaTokenizer(lowerCamelCase__  )
		tokenizer.save_pretrained(lowerCamelCase__  )

		lowerCAmelCase__   = hf_wavavec.config.to_dict()
		lowerCAmelCase__   = tokenizer.pad_token_id
		lowerCAmelCase__   = tokenizer.bos_token_id
		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = """mbart50"""
		lowerCAmelCase__   = """wav2vec2"""

		lowerCAmelCase__   = tokenizer.eos_token_id
		lowerCAmelCase__   = 25_0004
		lowerCAmelCase__   = tokenizer.eos_token_id

		lowerCAmelCase__   = SpeechEncoderDecoderConfig.from_dict(lowerCamelCase__  )

		hf_wavavec.save_pretrained(lowerCamelCase__  )
		feature_extractor.save_pretrained(lowerCamelCase__  )


if __name__ == "__main__":
			__lowerCAmelCase     :				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("--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")

			__lowerCAmelCase     :				Union[str, Any]    				=    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,
			)




