



'''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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


lowerCAmelCase:      Union[str, Any]		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):
    lowercase__				=					["""pixel_values"""]







    def __init__(   self				:						str		,    __snake_case				:						bool = True		,    __snake_case				:						Dict[str, int] = None		,    __snake_case				:						float = None		,    __snake_case				:						PILImageResampling = PILImageResampling.BILINEAR		,    __snake_case				:						bool = True		,    __snake_case				:						Union[int, float] = 1 / 2_55		,    __snake_case				:						bool = True		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    **__snake_case				:						str		,    ):
      super().__init__(**__snake_case			)
      a		:      Tuple       =  size if size is not None else {'shortest_edge': 3_84}
      a		:      Optional[int]       =  get_size_dict(__snake_case		,    default_to_square=__snake_case			)

      a		:      List[str]       =  do_resize
      a		:      List[Any]       =  size
      # Default value set here for backwards compatibility where the value in config is None
      a		:      int       =  crop_pct if crop_pct is not None else 2_24 / 2_56
      a		:      str       =  resample
      a		:      Any       =  do_rescale
      a		:      Optional[int]       =  rescale_factor
      a		:      List[str]       =  do_normalize
      a		:      Optional[Any]       =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      a		:      Optional[int]       =  image_std if image_std is not None else IMAGENET_STANDARD_STD







    def     lowercase_ (   self				:						int		,    __snake_case				:						np.ndarray		,    __snake_case				:						Dict[str, int]		,    __snake_case				:						float		,    __snake_case				:						PILImageResampling = PILImageResampling.BICUBIC		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						List[Any]		,    ):
      a		:      List[str]       =  get_size_dict(__snake_case		,    default_to_square=__snake_case			)
      if "shortest_edge" not in size:
        raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""			)
      a		:      List[Any]       =  size['shortest_edge']

      if shortest_edge < 3_84:
        # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
        a		:      Optional[Any]       =  int(shortest_edge / crop_pct			)
        a		:      int       =  get_resize_output_image_size(__snake_case		,    size=__snake_case		,    default_to_square=__snake_case			)
        a		:      Union[str, Any]       =  resize(image=__snake_case		,    size=__snake_case		,    resample=__snake_case		,    data_format=__snake_case		,    **__snake_case			)
        # then crop to (shortest_edge, shortest_edge)
        return center_crop(image=__snake_case		,    size=(shortest_edge, shortest_edge)		,    data_format=__snake_case		,    **__snake_case			)
      else:
        # warping (no cropping) when evaluated at 384 or larger
        return resize(
            __snake_case		,    size=(shortest_edge, shortest_edge)		,    resample=__snake_case		,    data_format=__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						np.ndarray		,    __snake_case				:						Union[int, float]		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						str		,    ):
      return rescale(__snake_case		,    scale=__snake_case		,    data_format=__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						np.ndarray		,    __snake_case				:						Union[float, List[float]]		,    __snake_case				:						Union[float, List[float]]		,    __snake_case				:						Optional[Union[str, ChannelDimension]] = None		,    **__snake_case				:						Union[str, Any]		,    ):
      return normalize(__snake_case		,    mean=__snake_case		,    std=__snake_case		,    data_format=__snake_case		,    **__snake_case			)







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						ImageInput		,    __snake_case				:						bool = None		,    __snake_case				:						Dict[str, int] = None		,    __snake_case				:						float = None		,    __snake_case				:						PILImageResampling = None		,    __snake_case				:						bool = None		,    __snake_case				:						float = None		,    __snake_case				:						bool = None		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    __snake_case				:						Optional[Union[float, List[float]]] = None		,    __snake_case				:						Optional[Union[str, TensorType]] = None		,    __snake_case				:						ChannelDimension = ChannelDimension.FIRST		,    **__snake_case				:						Any		,    ):
      a		:      List[str]       =  do_resize if do_resize is not None else self.do_resize
      a		:      List[Any]       =  crop_pct if crop_pct is not None else self.crop_pct
      a		:      Tuple       =  resample if resample is not None else self.resample
      a		:      List[Any]       =  do_rescale if do_rescale is not None else self.do_rescale
      a		:      List[str]       =  rescale_factor if rescale_factor is not None else self.rescale_factor
      a		:      Optional[int]       =  do_normalize if do_normalize is not None else self.do_normalize
      a		:      int       =  image_mean if image_mean is not None else self.image_mean
      a		:      Optional[Any]       =  image_std if image_std is not None else self.image_std

      a		:      Union[str, Any]       =  size if size is not None else self.size
      a		:      List[Any]       =  get_size_dict(__snake_case		,    default_to_square=__snake_case			)

      a		:      Dict       =  make_list_of_images(__snake_case			)

      if not valid_images(__snake_case			):
        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_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
        raise ValueError('crop_pct must be specified if size < 384.'			)

      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.
      a		:      Dict       =  [to_numpy_array(__snake_case			) for image in images]

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

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

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

      a		:      List[str]       =  [to_channel_dimension_format(__snake_case		,    __snake_case			) for image in images]

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




'''simple docstring'''






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowerCAmelCase:      Optional[int]		 =		logging.get_logger(__name__)

lowerCAmelCase:      Union[str, Any]		 =		{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_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',
}
lowerCAmelCase:      int		 =		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  for attribute in key.split('.'       ):
    a		:      Tuple       =  getattr(_A				,    _A       )

  if weight_type is not None:
    a		:      Union[str, Any]       =  getattr(_A				,    _A       ).shape
  else:
    a		:      Optional[Any]       =  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":
    a		:      str       =  value
  elif weight_type == "weight_g":
    a		:      Optional[int]       =  value
  elif weight_type == "weight_v":
    a		:      List[Any]       =  value
  elif weight_type == "bias":
    a		:      Any       =  value
  else:
    a		:      Optional[Any]       =  value

  logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""       )
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      Optional[int]       =  []
  a		:      Tuple       =  fairseq_model.state_dict()

  a		:      List[Any]       =  hf_model.unispeech_sat.feature_extractor

  for name, value in fairseq_dict.items():
    a		:      Optional[Any]       =  False
    if "conv_layers" in name:
      load_conv_layer(
          _A				,    _A				,    _A				,    _A				,    hf_model.config.feat_extract_norm == 'group'				,    )
      a		:      int       =  True
    else:
      for key, mapped_key in MAPPING.items():
        a		:      Any       =  '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
          a		:      str       =  True
          if "*" in mapped_key:
            a		:      Union[str, Any]       =  name.split(_A       )[0].split('.'       )[-2]
            a		:      List[str]       =  mapped_key.replace('*'				,    _A       )
          if "weight_g" in name:
            a		:      Optional[int]       =  'weight_g'
          elif "weight_v" in name:
            a		:      Any       =  'weight_v'
          elif "bias" in name:
            a		:      str       =  'bias'
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            a		:      List[Any]       =  'weight'
          else:
            a		:      Any       =  None
          set_recursively(_A				,    _A				,    _A				,    _A				,    _A       )
        continue
    if not is_used:
      unused_weights.append(_A       )

  logger.warning(f"""Unused weights: {unused_weights}"""       )
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A       ):
  a		:      List[str]       =  full_name.split('conv_layers.'       )[-1]
  a		:      Any       =  name.split('.'       )
  a		:      Optional[Any]       =  int(items[0]       )
  a		:      Union[str, Any]       =  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."""       )
      a		:      Optional[int]       =  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."""       )
      a		:      Optional[int]       =  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."""       )
      a		:      Union[str, Any]       =  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."""       )
      a		:      Union[str, Any]       =  value
      logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
  else:
    unused_weights.append(_A       )
@torch.no_grad()
def    lowerCamelCase__ (		_A				,    _A				,    _A=None				,    _A=None				,    _A=True       ):
  if config_path is not None:
    a		:      str       =  UniSpeechSatConfig.from_pretrained(_A       )
  else:
    a		:      Dict       =  UniSpeechSatConfig()

  a		:      Any       =  ''

  if is_finetuned:
    a		:      Tuple       =  UniSpeechSatForCTC(_A       )
  else:
    a		:      List[str]       =  UniSpeechSatForPreTraining(_A       )

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

  recursively_load_weights(_A				,    _A       )

  hf_wavavec.save_pretrained(_A       )


if __name__ == "__main__":
     lowerCAmelCase:      Tuple		 =		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'
     )
     lowerCAmelCase:      Optional[int]		 =		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
     )