'''simple docstring'''




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

__UpperCAmelCase												={
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__UpperCAmelCase												=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:






    """simple docstring"""




    for attribute in key.split('''.'''    ):
        __lowerCamelCase          =  getattr(UpperCamelCase__    ,  UpperCamelCase__    )

    if weight_type is not None:
        __lowerCamelCase          =  getattr(UpperCamelCase__    ,  UpperCamelCase__    ).shape
    else:
        __lowerCamelCase          =  hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            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			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:






    """simple docstring"""




    __lowerCamelCase          =  []
    __lowerCamelCase          =  fairseq_model.state_dict()

    __lowerCamelCase          =  hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        __lowerCamelCase          =  False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  hf_model.config.feat_extract_norm == '''group'''    ,  )
            __lowerCamelCase          =  True
        else:
            for key, mapped_key in MAPPING.items():
                __lowerCamelCase          =  '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split('''w2v_model.'''    )[-1] == name.split('''.'''    )[0]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''    )[:-1]    ) != key):
                        # special case since naming is very similar
                        continue
                    __lowerCamelCase          =  True
                    if "*" in mapped_key:
                        __lowerCamelCase          =  name.split(UpperCamelCase__    )[0].split('''.'''    )[-2]
                        __lowerCamelCase          =  mapped_key.replace('''*'''    ,  UpperCamelCase__    )
                    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:
                        # TODO: don't match quantizer.weight_proj
                        __lowerCamelCase          =  '''weight'''
                    else:
                        __lowerCamelCase          =  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 __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:






    """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:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    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:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    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:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {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:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    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(UpperCamelCase__    )






@torch.no_grad()
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=None    ,  UpperCamelCase__=None    ,  UpperCamelCase__=True    )     ->   Optional[Any]:






    """simple docstring"""




    if config_path is not None:
        __lowerCamelCase          =  UniSpeechSatConfig.from_pretrained(UpperCamelCase__    )
    else:
        __lowerCamelCase          =  UniSpeechSatConfig()

    __lowerCamelCase          =  ''''''

    if is_finetuned:
        __lowerCamelCase          =  UniSpeechSatForCTC(UpperCamelCase__    )
    else:
        __lowerCamelCase          =  UniSpeechSatForPreTraining(UpperCamelCase__    )

    __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,  arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    )}    )
    __lowerCamelCase          =  model[0].eval()

    recursively_load_weights(UpperCamelCase__    ,  UpperCamelCase__    )

    hf_wavavec.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
  __UpperCAmelCase												=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_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  __UpperCAmelCase												=parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )

'''simple docstring'''




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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__UpperCAmelCase												=logging.get_logger(__name__)




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Dict							=["pixel_values"]






     def __init__(			self		:		List[str]  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		int = 0.9  ,    a		:		PILImageResampling = PILImageResampling.BICUBIC  ,    a		:		bool = True  ,    a		:		Dict[str, int] = None  ,    a		:		Union[int, float] = 1 / 2_55  ,    a		:		bool = True  ,    a		:		bool = True  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         super().__init__(**a   )
         __lowerCamelCase          =  size if size is not None else {'''shortest_edge''': 2_24}
         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         __lowerCamelCase          =  crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
         __lowerCamelCase          =  get_size_dict(a  ,    param_name='''crop_size'''   )

         __lowerCamelCase          =  do_resize
         __lowerCamelCase          =  size
         __lowerCamelCase          =  crop_pct
         __lowerCamelCase          =  resample
         __lowerCamelCase          =  do_center_crop
         __lowerCamelCase          =  crop_size
         __lowerCamelCase          =  do_rescale
         __lowerCamelCase          =  rescale_factor
         __lowerCamelCase          =  do_normalize
         __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        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		np.ndarray  ,    a		:		Dict[str, int]  ,    a		:		Optional[float] = None  ,    a		:		PILImageResampling = PILImageResampling.BICUBIC  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		List[str]  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  get_size_dict(a  ,    default_to_square=a   )
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f"""size must contain 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""   )
         if crop_pct is not None:
             if "shortest_edge" in size:
                 __lowerCamelCase          =  int(size['''shortest_edge'''] / crop_pct   )
             elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                     __lowerCamelCase          =  int(size['''height'''] / crop_pct   )
                 else:
                     __lowerCamelCase          =  (int(size['''height'''] / crop_pct   ), int(size['''width'''] / crop_pct   ))
             else:
                 raise ValueError('''Invalid size for resize: {}'''.format(a   )   )

             __lowerCamelCase          =  get_resize_output_image_size(a  ,    size=a  ,    default_to_square=a   )
         else:
             if "shortest_edge" in size:
                 __lowerCamelCase          =  get_resize_output_image_size(a  ,    size=size['''shortest_edge''']  ,    default_to_square=a   )
             elif "height" in size and "width" in size:
                 __lowerCamelCase          =  (size['''height'''], size['''width'''])
             else:
                 raise ValueError('''Invalid size for resize: {}'''.format(a   )   )

         return resize(a  ,    size=a  ,    resample=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		np.ndarray  ,    a		:		Dict[str, int]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  get_size_dict(a   )
         if "height" not in size or "width" not in size:
             raise ValueError(f"""size must contain 'height' and 'width' as keys. Got {size.keys()}"""   )
         return center_crop(a  ,    size=(size['''height'''], size['''width'''])  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		np.ndarray  ,    a		:		Union[int, float]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Union[str, Any]  ,    ):





         """simple docstring"""



         return rescale(a  ,    scale=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]  ,    a		:		np.ndarray  ,    a		:		Union[float, List[float]]  ,    a		:		Union[float, List[float]]  ,    a		:		Optional[Union[str, ChannelDimension]] = None  ,    **a		:		Any  ,    ):





         """simple docstring"""



         return normalize(a  ,    mean=a  ,    std=a  ,    data_format=a  ,    **a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]  ,    a		:		ImageInput  ,    a		:		bool = None  ,    a		:		Dict[str, int] = None  ,    a		:		int = None  ,    a		:		PILImageResampling = None  ,    a		:		bool = None  ,    a		:		Dict[str, int] = None  ,    a		:		bool = None  ,    a		:		float = None  ,    a		:		bool = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[float, List[float]]] = None  ,    a		:		Optional[Union[str, TensorType]] = None  ,    a		:		ChannelDimension = ChannelDimension.FIRST  ,    **a		:		Tuple  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  do_resize if do_resize is not None else self.do_resize
         __lowerCamelCase          =  crop_pct if crop_pct is not None else self.crop_pct
         __lowerCamelCase          =  resample if resample is not None else self.resample
         __lowerCamelCase          =  do_center_crop if do_center_crop is not None else self.do_center_crop
         __lowerCamelCase          =  do_rescale if do_rescale is not None else self.do_rescale
         __lowerCamelCase          =  rescale_factor if rescale_factor is not None else self.rescale_factor
         __lowerCamelCase          =  do_normalize if do_normalize is not None else self.do_normalize
         __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(a  ,    default_to_square=a   )
         __lowerCamelCase          =  crop_size if crop_size is not None else self.crop_size
         __lowerCamelCase          =  get_size_dict(a  ,    param_name='''crop_size'''   )

         __lowerCamelCase          =  make_list_of_images(a   )

         if not valid_images(a   ):
             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 or resample is None:
             raise ValueError('''Size and resample must be specified if do_resize is True.'''   )

         if do_center_crop and crop_pct is None:
             raise ValueError('''Crop_pct 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.'''   )

         if do_normalize and (image_mean is None or image_std is None):
             raise ValueError('''Image mean and std must be specified if do_normalize is True.'''   )

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

         if do_resize:
             __lowerCamelCase          =  [self.resize(image=a  ,    size=a  ,    crop_pct=a  ,    resample=a   ) for image in images]

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

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

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

         __lowerCamelCase          =  [to_channel_dimension_format(a  ,    a   ) for image in images]

         __lowerCamelCase          =  {'''pixel_values''': images}
         return BatchFeature(data=a  ,    tensor_type=a   )
