




'''simple docstring'''


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_      :  int				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  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",
    "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_      :  str				     =  [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					List[str]				):




     """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_     :							Union[str, Any]     						=  """lm_head"""

          a_     :							Any     						=  getattr(lowerCAmelCase__ ,    lowerCAmelCase__				)

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

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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     a_     :							str     						=  []
     a_     :							str     						=  fairseq_model.state_dict()

     a_     :							Optional[Any]     						=  hf_model.unispeech.feature_extractor

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

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



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Tuple				):




     """simple docstring"""


     a_     :							List[str]     						=  full_name.split("""conv_layers."""				)[-1]
     a_     :							Union[str, Any]     						=  name.split("""."""				)
     a_     :							Union[str, Any]     						=  int(items[0]				)
     a_     :							Optional[int]     						=  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_     :							str     						=  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_     :							Any     						=  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_     :							List[str]     						=  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_     :							Dict     						=  value
               logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."				)
     else:
          unused_weights.append(lowerCAmelCase__				)



@torch.no_grad()
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					int ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					Optional[Any]=None ,    UpperCamelCase__    :					Optional[int]=None ,    UpperCamelCase__    :					Tuple=True				):




     """simple docstring"""


     if config_path is not None:
          a_     :							List[Any]     						=  UniSpeechConfig.from_pretrained(lowerCAmelCase__				)
     else:
          a_     :							Dict     						=  UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               a_     :							List[Any]     						=  Dictionary.load_from_json(lowerCAmelCase__				)

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

               # fairseq has the <pad> and <s> switched
               a_     :							Dict     						=  42
               a_     :							int     						=  43
               with open(lowerCAmelCase__ ,    """w""" ,    encoding="""utf-8"""				) as vocab_handle:
                    json.dump(lowerCAmelCase__ ,    lowerCAmelCase__				)
               a_     :							Any     						=  WavaVecaPhonemeCTCTokenizer(
                   lowerCAmelCase__ ,    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=lowerCAmelCase__ ,    )
               a_     :							Optional[int]     						=  True if config.feat_extract_norm == """layer""" else False
               a_     :							Tuple     						=  WavaVecaFeatureExtractor(
                   feature_size=1 ,    sampling_rate=1_6000 ,    padding_value=0 ,    do_normalize=lowerCAmelCase__ ,    return_attention_mask=lowerCAmelCase__ ,    )
               a_     :							Tuple     						=  WavaVecaProcessor(feature_extractor=lowerCAmelCase__ ,    tokenizer=lowerCAmelCase__				)
               processor.save_pretrained(lowerCAmelCase__				)

          a_     :							Any     						=  UniSpeechForCTC(lowerCAmelCase__				)
     else:
          a_     :							str     						=  UniSpeechForPreTraining(lowerCAmelCase__				)

     if is_finetuned:
          a_						,     a_						,     a_     :							str     						=  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_     :							Optional[Any]     						=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

     a_     :							List[str]     						=  model[0].eval()

     recursively_load_weights(lowerCAmelCase__ ,    lowerCAmelCase__ ,    lowerCAmelCase__				)

     hf_unispeech.save_pretrained(lowerCAmelCase__				)


if __name__ == "__main__":
 lowerCAmelCase_      :  int				     =  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_      :  List[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
 )



'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:				Dict     	=			logging.get_logger()




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					str		,lowerCAmelCase__:					LevitConfig		,lowerCAmelCase__:					Path		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							print(F"""Converting {name}..."""   )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128s'		,pretrained=lowerCAmelCase__   )
																					else:
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_192'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_256'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_384'		,pretrained=lowerCAmelCase__   )

														from_model.eval()
														SCREAMING_SNAKE_CASE_				=				LevitForImageClassificationWithTeacher(lowerCAmelCase__   ).eval()
														SCREAMING_SNAKE_CASE_				=				OrderedDict()

														SCREAMING_SNAKE_CASE_				=				from_model.state_dict()
														SCREAMING_SNAKE_CASE_				=				list(from_model.state_dict().keys()   )
														SCREAMING_SNAKE_CASE_				=				list(our_model.state_dict().keys()   )
														print(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
														for i in range(len(lowerCAmelCase__   )   ):
																					SCREAMING_SNAKE_CASE_				=				weights[og_keys[i]]
														our_model.load_state_dict(lowerCAmelCase__   )

														SCREAMING_SNAKE_CASE_				=				torch.randn((2, 3, 224, 224)   )
														SCREAMING_SNAKE_CASE_				=				from_model(lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				our_model(lowerCAmelCase__   ).logits

							assert torch.allclose(lowerCAmelCase__		,lowerCAmelCase__   ), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE_				=				name
							print(lowerCAmelCase__   )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name   )
														SCREAMING_SNAKE_CASE_				=				LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name   )

														print(F"""Pushed {checkpoint_name}"""   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					Path		,lowerCAmelCase__:					str = None		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				(1, num_labels)

							SCREAMING_SNAKE_CASE_				=				'huggingface/label-files'
							SCREAMING_SNAKE_CASE_				=				num_labels
							SCREAMING_SNAKE_CASE_				=				json.load(open(hf_hub_download(lowerCAmelCase__		,lowerCAmelCase__		,repo_type='dataset'   )		,'r'   )   )
							SCREAMING_SNAKE_CASE_				=				{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				idalabel
							SCREAMING_SNAKE_CASE_				=				{v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				partial(lowerCAmelCase__		,num_labels=lowerCAmelCase__		,idalabel=lowerCAmelCase__		,labelaid=lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 6, 8]		,depths=[2, 3, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 8, 12]		,depths=[4, 4, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]		,num_attention_heads=[3, 5, 6]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]		,num_attention_heads=[4, 6, 8]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]		,num_attention_heads=[6, 9, 12]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0.1		,),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,names_to_config[model_name]		,lowerCAmelCase__		,lowerCAmelCase__   )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
							return config, expected_shape


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				str     	=			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default=None,
							    type=str,
							    help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default="levit-dump-folder/",
							    type=Path,
							    required=False,
							    help="Path to the output PyTorch model directory.",
							)
							parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
							parser.add_argument(
							    "--no-push_to_hub",
							    dest="push_to_hub",
							    action="store_false",
							    help="Do not push model and image processor to the hub",
							)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							SCREAMING_SNAKE_CASE							:				Path     	=			args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





