

"""simple docstring"""


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

__A						   =			{
    '''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''',
}
__A						   =			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	int:
		for attribute in key.split("." ):
				__lowerCAmelCase:       Optional[int]          =			getattr(__lowerCAmelCase					, __lowerCAmelCase )

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

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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	Optional[Any]:
		__lowerCAmelCase:       int          =			[]
		__lowerCAmelCase:       List[Any]          =			fairseq_model.state_dict()

		__lowerCAmelCase:       Union[str, Any]          =			hf_model.unispeech_sat.feature_extractor

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

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






def     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE )	->	str:
		__lowerCAmelCase:       Dict          =			full_name.split("conv_layers." )[-1]
		__lowerCAmelCase:       List[str]          =			name.split("." )
		__lowerCAmelCase:       List[Any]          =			int(items[0] )
		__lowerCAmelCase:       Optional[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." )
						__lowerCAmelCase:       Any          =			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:       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:
						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:       str          =			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:       List[Any]          =			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     a__    (    __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE					, __SCREAMING_SNAKE_CASE=None					, __SCREAMING_SNAKE_CASE=None					, __SCREAMING_SNAKE_CASE=True )	->	Optional[Any]:
		if config_path is not None:
				__lowerCAmelCase:       str          =			UniSpeechSatConfig.from_pretrained(__lowerCAmelCase )
		else:
				__lowerCAmelCase:       List[str]          =			UniSpeechSatConfig()

		__lowerCAmelCase:       Optional[Any]          =			""

		if is_finetuned:
				__lowerCAmelCase:       str          =			UniSpeechSatForCTC(__lowerCAmelCase )
		else:
				__lowerCAmelCase:       Optional[Any]          =			UniSpeechSatForPreTraining(__lowerCAmelCase )

		__lowerCAmelCase:       List[Any]          =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]					, arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
		__lowerCAmelCase:       Union[str, Any]          =			model[0].eval()

		recursively_load_weights(__lowerCAmelCase					, __lowerCAmelCase )

		hf_wavavec.save_pretrained(__lowerCAmelCase )


if __name__ == "__main__":
			__A						   =			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						   =			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
			)










import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


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





@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       List[nn.Module]        = field(default_factory=A__       )
 _lowerCAmelCase       :       list        = field(default_factory=A__       )







 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   Tensor							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  len(list(m.modules()						)						) == 1 or isinstance(SCREAMING_SNAKE_CASE							,   nn.Convad						) or isinstance(SCREAMING_SNAKE_CASE							,   nn.BatchNormad						)
      if has_not_submodules:
           self.traced.append(SCREAMING_SNAKE_CASE						)







 def __call__(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      for m in self.module.modules():
           self.handles.append(m.register_forward_hook(self._forward_hook						)						)
      self.module(SCREAMING_SNAKE_CASE						)
      [x.remove() for x in self.handles]
      return self







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return list(filter(lambda SCREAMING_SNAKE_CASE						: len(list(x.state_dict().keys()						)						) > 0							,   self.traced						)						)







@dataclass
class 			__a  :
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       nn.Module
 _lowerCAmelCase       :       int        = 0
 _lowerCAmelCase       :       List        = field(default_factory=A__       )
 _lowerCAmelCase       :       List        = field(default_factory=A__       )







 def __call__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Tensor						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  Tracker(self.dest						)(SCREAMING_SNAKE_CASE						).parametrized
      UpperCamelCase__			:   Any       				=  Tracker(self.src						)(SCREAMING_SNAKE_CASE						).parametrized

      UpperCamelCase__			:   str       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.src_skip							,   SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   List[Any]       				=  list(filter(lambda SCREAMING_SNAKE_CASE						: type(SCREAMING_SNAKE_CASE						) not in self.dest_skip							,   SCREAMING_SNAKE_CASE						)						)

      if len(SCREAMING_SNAKE_CASE						) != len(SCREAMING_SNAKE_CASE						):
           raise Exception(
               F'Numbers of operations are different. Source module has {len(SCREAMING_SNAKE_CASE						)} operations while'
               F' destination module has {len(SCREAMING_SNAKE_CASE						)}.'						)

      for dest_m, src_m in zip(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           dest_m.load_state_dict(src_m.state_dict()						)
           if self.verbose == 1:
                print(F'Transfered from={src_m} to={dest_m}'						)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase = True			) ->	Optional[int]:
     print(f'Converting {name}...'			)
     with torch.no_grad():
          UpperCamelCase__			:   Union[str, Any]       				=  timm.create_model(__lowerCAmelCase					,    pretrained=__lowerCAmelCase			).eval()
          UpperCamelCase__			:   List[Any]       				=  ResNetForImageClassification(__lowerCAmelCase			).eval()
          UpperCamelCase__			:   Optional[Any]       				=  ModuleTransfer(src=__lowerCAmelCase					,    dest=__lowerCAmelCase			)
          UpperCamelCase__			:   int       				=  torch.randn((1, 3, 224, 224)			)
          module_transfer(__lowerCAmelCase			)

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

     UpperCamelCase__			:   List[Any]       				=  f'resnet{"-".join(name.split("resnet"			)			)}'
     print(__lowerCAmelCase			)

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name					,    commit_message="Add model"					,    use_temp_dir=__lowerCAmelCase					,    )

          # we can use the convnext one
          UpperCamelCase__			:   List[Any]       				=  AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"			)
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name					,    commit_message="Add image processor"					,    use_temp_dir=__lowerCAmelCase					,    )

          print(f'Pushed {checkpoint_name}'			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase = None					,    __lowerCAmelCase = True			) ->	Dict:
     UpperCamelCase__			:   Dict       				=  "imagenet-1k-id2label.json"
     UpperCamelCase__			:   Optional[int]       				=  1000
     UpperCamelCase__			:   Any       				=  (1, num_labels)

     UpperCamelCase__			:   Union[str, Any]       				=  "huggingface/label-files"
     UpperCamelCase__			:   Optional[int]       				=  num_labels
     UpperCamelCase__			:   str       				=  json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type="dataset"			)					,    "r"			)			)
     UpperCamelCase__			:   Optional[int]       				=  {int(__lowerCAmelCase			): v for k, v in idalabel.items()}

     UpperCamelCase__			:   Tuple       				=  idalabel
     UpperCamelCase__			:   Union[str, Any]       				=  {v: k for k, v in idalabel.items()}

     UpperCamelCase__			:   Union[str, Any]       				=  partial(__lowerCAmelCase					,    num_labels=__lowerCAmelCase					,    idalabel=__lowerCAmelCase					,    labelaid=__lowerCAmelCase			)

     UpperCamelCase__			:   Union[str, Any]       				=  {
         "resnet18": ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,    hidden_sizes=[64, 128, 256, 512]					,    layer_type="basic"			),
         "resnet26": ImageNetPreTrainedConfig(
             depths=[2, 2, 2, 2]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet34": ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,    hidden_sizes=[64, 128, 256, 512]					,    layer_type="basic"			),
         "resnet50": ImageNetPreTrainedConfig(
             depths=[3, 4, 6, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet101": ImageNetPreTrainedConfig(
             depths=[3, 4, 23, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
         "resnet152": ImageNetPreTrainedConfig(
             depths=[3, 8, 36, 3]					,    hidden_sizes=[256, 512, 1024, 2048]					,    layer_type="bottleneck"			),
     }

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


if __name__ == "__main__":
 lowerCamelCase	:							Union[str, Any]				=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 resnet* architecture,'''
         ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
     ),
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=Path,
     required=True,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument(
     '''--push_to_hub''',
     default=True,
     type=bool,
     required=False,
     help='''If True, push model and image processor to the hub.''',
 )

 lowerCamelCase	:							int				=parser.parse_args()
 lowerCamelCase	:							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)