

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()
lowerCamelCase           =					logging.get_logger(__name__)

lowerCamelCase           =					{
    '''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''',
}
lowerCamelCase           =					[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def 			UpperCAmelCase__				(			_A      : Union[str, Any]	,      _A      : Optional[Any]	,      _A      : Tuple	,      _A      : Tuple	,      _A      : Optional[Any]	,      _A      : Union[str, Any]							):





  '''simple docstring'''







  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
        a__  ='''lm_head'''

    a__  =getattr(_A	,      _A							)

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

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




def 			UpperCAmelCase__				(			_A      : int	,      _A      : Tuple	,      _A      : str							):





  '''simple docstring'''







  a__  =[]
  a__  =fairseq_model.state_dict()

  a__  =hf_model.unispeech.feature_extractor

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

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




def 			UpperCAmelCase__				(			_A      : List[Any]	,      _A      : Optional[Any]	,      _A      : str	,      _A      : Dict	,      _A      : Dict							):





  '''simple docstring'''







  a__  =full_name.split('''conv_layers.'''							)[-1]
  a__  =name.split('''.'''							)
  a__  =int(items[0]							)
  a__  =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."""
      )
      a__  =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."""
      )
      a__  =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."
      )
      a__  =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."""
      )
      a__  =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 			UpperCAmelCase__				(			_A      : Optional[int]	,      _A      : str	,      _A      : List[Any]=None	,      _A      : Dict=None	,      _A      : int=True							):





  '''simple docstring'''







  if config_path is not None:
    a__  =UniSpeechConfig.from_pretrained(_A							)
  else:
    a__  =UniSpeechConfig()

  if is_finetuned:
    if dict_path:
      a__  =Dictionary.load_from_json(_A							)

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

      # fairseq has the <pad> and <s> switched
      a__  =42
      a__  =43
      with open(_A	,      '''w'''	,      encoding='''utf-8'''							) as vocab_handle:
        json.dump(_A	,      _A							)
      a__  =WavaVecaPhonemeCTCTokenizer(
          _A	,      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=_A	,      )
      a__  =True if config.feat_extract_norm == '''layer''' else False
      a__  =WavaVecaFeatureExtractor(
          feature_size=1	,      sampling_rate=1_60_00	,      padding_value=0	,      do_normalize=_A	,      return_attention_mask=_A	,      )
      a__  =WavaVecaProcessor(feature_extractor=_A	,      tokenizer=_A							)
      processor.save_pretrained(_A							)

    a__  =UniSpeechForCTC(_A							)
  else:
    a__  =UniSpeechForPreTraining(_A							)

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

  a__  =model[0].eval()

  recursively_load_weights(_A	,      _A	,      _A							)

  hf_unispeech.save_pretrained(_A							)


if __name__ == "__main__":
       lowerCamelCase           =					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           =					parser.parse_args()
       convert_unispeech_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


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

lowerCamelCase           =					torch.device('''cpu''')




def 			UpperCAmelCase__				(			):





  '''simple docstring'''







  a__  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
  a__  =Image.open(requests.get(_A	,      stream=_A							).raw							)
  return im




def 			UpperCAmelCase__				(			_A      : Optional[int]							):





  '''simple docstring'''







  if swiftformer_name == "swiftformer_xs":
    return torch.tensor([-2.17_03E00, 2.11_07E00, -2.08_11E00, 8.86_85E-01, 2.43_60E-01]							)

  elif swiftformer_name == "swiftformer_s":
    return torch.tensor([3.96_36E-01, 2.34_78E-01, -1.69_63E00, -1.73_81E00, -8.63_37E-01]							)

  elif swiftformer_name == "swiftformer_l1":
    return torch.tensor([-4.27_68E-01, -4.74_29E-01, -1.08_97E00, -1.02_48E00, 3.55_23E-02]							)

  elif swiftformer_name == "swiftformer_l3":
    return torch.tensor([-2.53_30E-01, 2.42_11E-01, -6.01_85E-01, -8.27_89E-01, -6.04_46E-02]							)




def 			UpperCAmelCase__				(			_A      : List[str]	,      _A      : Union[str, Any]	,      _A      : str							):





  '''simple docstring'''







  a__  =dct.pop(_A							)
  a__  =val




def 			UpperCAmelCase__				(			_A      : Any							):





  '''simple docstring'''







  a__  =[]
  for k in state_dict.keys():
    a__  =k
    if ".pwconv" in k:
      a__  =k_new.replace('''.pwconv'''	,      '''.point_wise_conv'''							)
    if ".dwconv" in k:
      a__  =k_new.replace('''.dwconv'''	,      '''.depth_wise_conv'''							)
    if ".Proj." in k:
      a__  =k_new.replace('''.Proj.'''	,      '''.proj.'''							)
    if "patch_embed" in k_new:
      a__  =k_new.replace('''patch_embed'''	,      '''swiftformer.patch_embed.patch_embedding'''							)
    if "network" in k_new:
      a__  =k_new.split('''.'''							)
      if ls[2].isdigit():
        a__  ='''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]							)
      else:
        a__  =k_new.replace('''network'''	,      '''swiftformer.encoder.network'''							)
    rename_keys.append((k, k_new)							)
  return rename_keys




@torch.no_grad()
def 			UpperCAmelCase__				(			_A      : int	,      _A      : Union[str, Any]	,      _A      : int							):





  '''simple docstring'''







  a__  =SwiftFormerConfig()

  # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
  a__  =10_00
  a__  ='''huggingface/label-files'''
  a__  ='''imagenet-1k-id2label.json'''
  a__  =json.load(open(hf_hub_download(_A	,      _A	,      repo_type='''dataset'''							)	,      '''r'''							)							)
  a__  ={int(_A							): v for k, v in idalabel.items()}
  a__  =idalabel
  a__  ={v: k for k, v in idalabel.items()}

  # size of the architecture
  if swiftformer_name == "swiftformer_xs":
    a__  =[3, 3, 6, 4]
    a__  =[48, 56, 1_12, 2_20]

  elif swiftformer_name == "swiftformer_s":
    a__  =[3, 3, 9, 6]
    a__  =[48, 64, 1_68, 2_24]

  elif swiftformer_name == "swiftformer_l1":
    a__  =[4, 3, 10, 5]
    a__  =[48, 96, 1_92, 3_84]

  elif swiftformer_name == "swiftformer_l3":
    a__  =[4, 4, 12, 6]
    a__  =[64, 1_28, 3_20, 5_12]

  # load state_dict of original model, remove and rename some keys
  if original_ckpt:
    if original_ckpt.startswith('''https'''							):
      a__  =torch.hub.load_state_dict_from_url(_A	,      map_location='''cpu'''	,      check_hash=_A							)
    else:
      a__  =torch.load(_A	,      map_location='''cpu'''							)
  a__  =checkpoint

  a__  =create_rename_keys(_A							)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(_A	,      _A	,      _A							)

  # load HuggingFace model
  a__  =SwiftFormerForImageClassification(_A							).eval()
  hf_model.load_state_dict(_A							)

  # prepare test inputs
  a__  =prepare_img()
  a__  =ViTImageProcessor.from_pretrained('''preprocessor_config'''							)
  a__  =processor(images=_A	,      return_tensors='''pt'''							)

  # compare outputs from both models
  a__  =get_expected_output(_A							)
  a__  =hf_model(inputs['''pixel_values''']							).logits

  assert hf_logits.shape == torch.Size([1, 10_00]							)
  assert torch.allclose(hf_logits[0, 0:5]	,      _A	,      atol=1E-3							)

  Path(_A							).mkdir(exist_ok=_A							)
  print(F"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""							)
  hf_model.save_pretrained(_A							)


if __name__ == "__main__":
       lowerCamelCase           =					argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--swiftformer_name''',
           default='''swiftformer_xs''',
           choices=['''swiftformer_xs''', '''swiftformer_s''', '''swiftformer_l1''', '''swiftformer_l3'''],
           type=str,
           help='''Name of the SwiftFormer model you\'d like to convert.''',
       )
       parser.add_argument(
           '''--pytorch_dump_folder_path''',
           default='''./converted_outputs/''',
           type=str,
           help='''Path to the output PyTorch model directory.''',
       )
       parser.add_argument('''--original_ckpt''', default=None, type=str, help='''Path to the original model checkpoint.''')

       lowerCamelCase           =					parser.parse_args()
       convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
