






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			:					Dict         =	logging.get_logger(__name__)

lowerCamelCase			:					int         =	{
    '''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			:					List[str]         =	[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     for attribute in key.split("."       ):
          __lowerCAmelCase								=					getattr(__snake_case					, __snake_case       )

     if weight_type is not None:
          __lowerCAmelCase								=					getattr(__snake_case					, __snake_case       ).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       __lowerCAmelCase      (						__snake_case					, __snake_case       ):
     __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(
                   __snake_case					, __snake_case					, __snake_case					, __snake_case					, 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(__snake_case					, __snake_case					, __snake_case					, __snake_case       )
               __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(__snake_case       )[0].split("."       )[-2]
                              __lowerCAmelCase								=					mapped_key.replace("*"					, __snake_case       )
                         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(__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       )
                    continue
          if not is_used:
               unused_weights.append(__snake_case       )

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





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     __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(__snake_case       )





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case       ):
     __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(__snake_case					, __snake_case       ):
          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(__snake_case       )





def       __lowerCAmelCase      (						__snake_case       ):
     __lowerCAmelCase			, __lowerCAmelCase								=					emb.weight.shape
     __lowerCAmelCase								=					nn.Linear(__snake_case					, __snake_case					, bias=__snake_case       )
     __lowerCAmelCase								=					emb.weight.data
     return lin_layer





@torch.no_grad()
def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case					, ):
     __lowerCAmelCase								=					WavaVecaConfig.from_pretrained(
         __snake_case					, add_adapter=__snake_case					, adapter_stride=__snake_case					, adapter_kernel_size=__snake_case					, use_auth_token=__snake_case					, output_hidden_size=__snake_case					, )
     __lowerCAmelCase								=					MBartConfig.from_pretrained(__snake_case       )

     # 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(__snake_case					, use_auth_token=__snake_case       )

     # set weights for wav2vec2 encoder
     __lowerCAmelCase								=					WavaVecaModel(__snake_case       )

     recursively_load_weights_wavaveca(model.encoder					, __snake_case       )

     # load decoder weights
     __lowerCAmelCase								=					MBartForCausalLM(__snake_case       )
     __lowerCAmelCase			, __lowerCAmelCase								=					hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					, strict=__snake_case       )
     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=__snake_case					, decoder=__snake_case       )
     __lowerCAmelCase								=					False

     __lowerCAmelCase								=					MBartaaTokenizer(__snake_case       )
     tokenizer.save_pretrained(__snake_case       )

     __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(__snake_case       )

     hf_wavavec.save_pretrained(__snake_case       )
     feature_extractor.save_pretrained(__snake_case       )


if __name__ == "__main__":
 lowerCamelCase			:					Dict         =	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=1024, type=int, help='''encoder output dim''')
 parser.add_argument('''--start_token_id''', default=250004, type=int, help='''`decoder_start_token_id` of model config''')

 lowerCamelCase			:					List[str]         =	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,
 )














from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


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



class 							_UpperCamelCase			(a_			):
      snake_case_  =							["""pixel_values"""]


      def __init__(	self					,						__UpperCamelCase = True					,						__UpperCamelCase = None					,						__UpperCamelCase = PILImageResampling.BICUBIC					,						__UpperCamelCase = True					,						__UpperCamelCase = True					,						__UpperCamelCase = 1 / 2_5_5					,						__UpperCamelCase = None					,						__UpperCamelCase = True					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	None:
           super().__init__(**__UpperCamelCase	)
           __lowerCAmelCase								=					size if size is not None else {"height": 2_2_4, "width": 2_2_4}
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase	)
           __lowerCAmelCase								=					crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase					,						default_to_square=__UpperCamelCase					,						param_name="crop_size"	)

           __lowerCAmelCase								=					do_resize
           __lowerCAmelCase								=					do_rescale
           __lowerCAmelCase								=					do_normalize
           __lowerCAmelCase								=					do_center_crop
           __lowerCAmelCase								=					crop_size
           __lowerCAmelCase								=					size
           __lowerCAmelCase								=					resample
           __lowerCAmelCase								=					rescale_factor
           __lowerCAmelCase								=					image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           __lowerCAmelCase								=					image_std if image_std is not None else IMAGENET_DEFAULT_STD


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = PILImageResampling.BILINEAR					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	np.ndarray:
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase	)

           if "shortest_edge" in size:
                __lowerCAmelCase								=					get_resize_output_image_size(__UpperCamelCase					,						size=size["shortest_edge"]					,						default_to_square=__UpperCamelCase	)
                # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
           elif "height" in size and "width" in size:
                __lowerCAmelCase								=					(size["height"], size["width"])
           else:
                raise ValueError(F"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}"""	)
           return resize(__UpperCamelCase					,						size=__UpperCamelCase					,						resample=__UpperCamelCase					,						data_format=__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	np.ndarray:
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase	)
           if "height" not in size or "width" not in size:
                raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""	)
           return center_crop(__UpperCamelCase					,						size=(size["height"], size["width"])					,						data_format=__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = None					,						**__UpperCamelCase	)->	np.ndarray:
           return rescale(__UpperCamelCase					,						scale=__UpperCamelCase					,						data_format=__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase = None					,						**__UpperCamelCase					,						)->	np.ndarray:
           return normalize(__UpperCamelCase					,						mean=__UpperCamelCase					,						std=__UpperCamelCase					,						data_format=__UpperCamelCase					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self					,						__UpperCamelCase					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = None					,						__UpperCamelCase = ChannelDimension.FIRST					,						**__UpperCamelCase					,						)->	BatchFeature:
           __lowerCAmelCase								=					do_resize if do_resize is not None else self.do_resize
           __lowerCAmelCase								=					do_rescale if do_rescale is not None else self.do_rescale
           __lowerCAmelCase								=					do_normalize if do_normalize is not None else self.do_normalize
           __lowerCAmelCase								=					do_center_crop if do_center_crop is not None else self.do_center_crop
           __lowerCAmelCase								=					crop_size if crop_size is not None else self.crop_size
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase					,						param_name="crop_size"					,						default_to_square=__UpperCamelCase	)
           __lowerCAmelCase								=					resample if resample is not None else self.resample
           __lowerCAmelCase								=					rescale_factor if rescale_factor is not None else self.rescale_factor
           __lowerCAmelCase								=					image_mean if image_mean is not None else self.image_mean
           __lowerCAmelCase								=					image_std if image_std is not None else self.image_std

           __lowerCAmelCase								=					size if size is not None else self.size
           __lowerCAmelCase								=					get_size_dict(__UpperCamelCase	)

           if not is_batched(__UpperCamelCase	):
                __lowerCAmelCase								=					[images]

           if not valid_images(__UpperCamelCase	):
                raise ValueError(
                    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                    "torch.Tensor, tf.Tensor or jax.ndarray."	)

           if do_resize and size is None:
                raise ValueError("Size must be specified if do_resize is True."	)

           if do_center_crop and crop_size is None:
                raise ValueError("Crop size must be specified if do_center_crop is True."	)

           if do_rescale and rescale_factor is None:
                raise ValueError("Rescale factor must be specified if do_rescale is True."	)

           # All transformations expect numpy arrays.
           __lowerCAmelCase								=					[to_numpy_array(__UpperCamelCase	) for image in images]

           if do_resize:
                __lowerCAmelCase								=					[self.resize(image=__UpperCamelCase					,						size=__UpperCamelCase					,						resample=__UpperCamelCase	) for image in images]

           if do_center_crop:
                __lowerCAmelCase								=					[self.center_crop(image=__UpperCamelCase					,						size=__UpperCamelCase	) for image in images]

           if do_rescale:
                __lowerCAmelCase								=					[self.rescale(image=__UpperCamelCase					,						scale=__UpperCamelCase	) for image in images]

           if do_normalize:
                __lowerCAmelCase								=					[self.normalize(image=__UpperCamelCase					,						mean=__UpperCamelCase					,						std=__UpperCamelCase	) for image in images]

           __lowerCAmelCase								=					[to_channel_dimension_format(__UpperCamelCase					,						__UpperCamelCase	) for image in images]

           __lowerCAmelCase								=					{"pixel_values": images}
           return BatchFeature(data=__UpperCamelCase					,						tensor_type=__UpperCamelCase	)






