






import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

A_		:		List[str]          =			{
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
A_		:		Optional[int]          =			[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Any:
  for attribute in key.split('.'  ):
    if is_finetuned:
      if attribute in ["quantizer", "project_q", "project_hid"]:
        # those layers are only relevant for pretraining and should be dropped
        return

      if attribute == "ctc_proj":
        # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
        UpperCamelCase_: Union[str, Any]   				=    """lm_head"""

    UpperCamelCase_: Union[str, Any]   				=    getattr(snake_case_     ,   snake_case_  )

  if weight_type is not None:
    UpperCamelCase_: List[str]   				=    getattr(snake_case_     ,   snake_case_  ).shape
  else:
    UpperCamelCase_: Union[str, Any]   				=    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":
    UpperCamelCase_: Dict   				=    value
  elif weight_type == "weight_g":
    UpperCamelCase_: List[Any]   				=    value
  elif weight_type == "weight_v":
    UpperCamelCase_: Tuple   				=    value
  elif weight_type == "bias":
    UpperCamelCase_: Union[str, Any]   				=    value
  else:
    UpperCamelCase_: int   				=    value

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

def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    int:
  UpperCamelCase_: Union[str, Any]   				=    []
  UpperCamelCase_: List[Any]   				=    fairseq_model.state_dict()

  UpperCamelCase_: str   				=    hf_model.unispeech.feature_extractor

  for name, value in fairseq_dict.items():
    UpperCamelCase_: Any   				=    False
    if "conv_layers" in name:
      load_conv_layer(
          snake_case_     ,   snake_case_     ,   snake_case_     ,   snake_case_     ,   hf_model.config.feat_extract_norm == 'group'     ,   )
      UpperCamelCase_: int   				=    True
    else:
      for key, mapped_key in MAPPING.items():
        UpperCamelCase_: Optional[int]   				=    """unispeech.""" + 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]:
          UpperCamelCase_: Optional[int]   				=    True
          if "*" in mapped_key:
            UpperCamelCase_: List[Any]   				=    name.split(snake_case_  )[0].split('.'  )[-2]
            UpperCamelCase_: Optional[Any]   				=    mapped_key.replace('*'     ,   snake_case_  )
          if "weight_g" in name:
            UpperCamelCase_: Optional[int]   				=    """weight_g"""
          elif "weight_v" in name:
            UpperCamelCase_: Optional[Any]   				=    """weight_v"""
          elif "bias" in name:
            UpperCamelCase_: List[Any]   				=    """bias"""
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            UpperCamelCase_: Optional[int]   				=    """weight"""
          else:
            UpperCamelCase_: Optional[int]   				=    None
          set_recursively(snake_case_     ,   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   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Tuple:
  UpperCamelCase_: Tuple   				=    full_name.split('conv_layers.'  )[-1]
  UpperCamelCase_: List[str]   				=    name.split('.'  )
  UpperCamelCase_: Dict   				=    int(items[0]  )
  UpperCamelCase_: str   				=    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.'''
      )
      UpperCamelCase_: Dict   				=    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.'''
      )
      UpperCamelCase_: Dict   				=    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."
      )
      UpperCamelCase_: Dict   				=    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.'''
      )
      UpperCamelCase_: Optional[Any]   				=    value
      logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
  else:
    unused_weights.append(snake_case_  )

@torch.no_grad()
def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=None     ,   UpperCAmelCase__=True  )	->    List[str]:
  if config_path is not None:
    UpperCamelCase_: Optional[int]   				=    UniSpeechConfig.from_pretrained(snake_case_  )
  else:
    UpperCamelCase_: Tuple   				=    UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      UpperCamelCase_: Tuple   				=    Dictionary.load_from_json(snake_case_  )

      # important change bos & pad token id since CTC symbol is <pad> and
      # not <s> as in fairseq
      UpperCamelCase_: Union[str, Any]   				=    target_dict.pad_index
      UpperCamelCase_: List[Any]   				=    target_dict.bos_index
      UpperCamelCase_: str   				=    target_dict.eos_index
      UpperCamelCase_: Tuple   				=    len(target_dict.symbols  )
      UpperCamelCase_: Union[str, Any]   				=    os.path.join(snake_case_     ,   'vocab.json'  )
      if not os.path.isdir(snake_case_  ):
        logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(snake_case_  )  )
        return
      os.makedirs(snake_case_     ,   exist_ok=snake_case_  )
      UpperCamelCase_: str   				=    target_dict.indices

      # fairseq has the <pad> and <s> switched
      UpperCamelCase_: int   				=    4_2
      UpperCamelCase_: str   				=    4_3
      with open(snake_case_     ,   'w'     ,   encoding='utf-8'  ) as vocab_handle:
        json.dump(snake_case_     ,   snake_case_  )
      UpperCamelCase_: Dict   				=    WavaVecaPhonemeCTCTokenizer(
          snake_case_     ,   unk_token=target_dict.unk_word     ,   pad_token=target_dict.pad_word     ,   bos_token=target_dict.bos_word     ,   eos_token=target_dict.eos_word     ,   word_delimiter_token='|'     ,   do_lower_case=snake_case_     ,   )
      UpperCamelCase_: List[Any]   				=    True if config.feat_extract_norm == """layer""" else False
      UpperCamelCase_: Optional[int]   				=    WavaVecaFeatureExtractor(
          feature_size=1     ,   sampling_rate=1_6_0_0_0     ,   padding_value=0     ,   do_normalize=snake_case_     ,   return_attention_mask=snake_case_     ,   )
      UpperCamelCase_: Any   				=    WavaVecaProcessor(feature_extractor=snake_case_     ,   tokenizer=snake_case_  )
      processor.save_pretrained(snake_case_  )

    UpperCamelCase_: Optional[int]   				=    UniSpeechForCTC(snake_case_  )
  else:
    UpperCamelCase_: Any   				=    UniSpeechForPreTraining(snake_case_  )

  if is_finetuned:
    UpperCamelCase_: str   				=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]     ,   arg_overrides={'data': '/'.join(dict_path.split('/'  )[:-1]  ), 'w2v_path': checkpoint_path}  )
  else:
    UpperCamelCase_: Dict   				=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  )

  UpperCamelCase_: Optional[Any]   				=    model[0].eval()

  recursively_load_weights(snake_case_     ,   snake_case_     ,   snake_case_  )

  hf_unispeech.save_pretrained(snake_case_  )


if __name__ == "__main__":
   A_		:		Optional[Any]          =			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'
   )
   A_		:		Any          =			parser.parse_args()
   convert_unispeech_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
   )







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


A_		:		int		     =			logging.get_logger(__name__)




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		str									=['''pixel_values''']





       def __init__( self  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = PILImageResampling.BICUBIC  ,      _lowerCamelCase = True  ,      _lowerCamelCase = True  ,      _lowerCamelCase = 1 / 2_5_5  ,      _lowerCamelCase = None  ,      _lowerCamelCase = True  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         super().__init__(**_lowerCamelCase							)
         UpperCamelCase_: Any   				=    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: Any   				=    get_size_dict(_lowerCamelCase							)
         UpperCamelCase_: Any   				=    crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
         UpperCamelCase_: List[Any]   				=    get_size_dict(_lowerCamelCase  ,      default_to_square=_lowerCamelCase  ,      param_name='crop_size'							)

         UpperCamelCase_: Optional[int]   				=    do_resize
         UpperCamelCase_: Tuple   				=    do_rescale
         UpperCamelCase_: Dict   				=    do_normalize
         UpperCamelCase_: Optional[int]   				=    do_center_crop
         UpperCamelCase_: Tuple   				=    crop_size
         UpperCamelCase_: Optional[int]   				=    size
         UpperCamelCase_: Dict   				=    resample
         UpperCamelCase_: Tuple   				=    rescale_factor
         UpperCamelCase_: Tuple   				=    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else IMAGENET_DEFAULT_STD





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = PILImageResampling.BILINEAR  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

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





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase							)
         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(_lowerCamelCase  ,      size=(size['height'], size['width'])  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase							):
         return rescale(_lowerCamelCase  ,      scale=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      **_lowerCamelCase  ,      ):
         return normalize(_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase  ,      data_format=_lowerCamelCase  ,      **_lowerCamelCase							)





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = None  ,      _lowerCamelCase = ChannelDimension.FIRST  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: List[str]   				=    do_resize if do_resize is not None else self.do_resize
         UpperCamelCase_: str   				=    do_rescale if do_rescale is not None else self.do_rescale
         UpperCamelCase_: Dict   				=    do_normalize if do_normalize is not None else self.do_normalize
         UpperCamelCase_: List[str]   				=    do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCamelCase_: Optional[Any]   				=    crop_size if crop_size is not None else self.crop_size
         UpperCamelCase_: List[str]   				=    get_size_dict(_lowerCamelCase  ,      param_name='crop_size'  ,      default_to_square=_lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    resample if resample is not None else self.resample
         UpperCamelCase_: str   				=    rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCamelCase_: Dict   				=    image_mean if image_mean is not None else self.image_mean
         UpperCamelCase_: Dict   				=    image_std if image_std is not None else self.image_std

         UpperCamelCase_: Any   				=    size if size is not None else self.size
         UpperCamelCase_: Optional[Any]   				=    get_size_dict(_lowerCamelCase							)

         if not is_batched(_lowerCamelCase							):
           UpperCamelCase_: Dict   				=    [images]

         if not valid_images(_lowerCamelCase							):
           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.
         UpperCamelCase_: List[str]   				=    [to_numpy_array(_lowerCamelCase							) for image in images]

         if do_resize:
           UpperCamelCase_: List[str]   				=    [self.resize(image=_lowerCamelCase  ,      size=_lowerCamelCase  ,      resample=_lowerCamelCase							) for image in images]

         if do_center_crop:
           UpperCamelCase_: List[str]   				=    [self.center_crop(image=_lowerCamelCase  ,      size=_lowerCamelCase							) for image in images]

         if do_rescale:
           UpperCamelCase_: int   				=    [self.rescale(image=_lowerCamelCase  ,      scale=_lowerCamelCase							) for image in images]

         if do_normalize:
           UpperCamelCase_: Optional[Any]   				=    [self.normalize(image=_lowerCamelCase  ,      mean=_lowerCamelCase  ,      std=_lowerCamelCase							) for image in images]

         UpperCamelCase_: List[str]   				=    [to_channel_dimension_format(_lowerCamelCase  ,      _lowerCamelCase							) for image in images]

         UpperCamelCase_: Optional[int]   				=    {'pixel_values': images}
         return BatchFeature(data=_lowerCamelCase  ,      tensor_type=_lowerCamelCase							)