






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      :       Tuple    =						logging.get_logger()



@dataclass
class 				_UpperCamelCase      :
      snake_case_	=				42
      snake_case_	=				field(default_factory=a_      )
      snake_case_	=				field(default_factory=a_      )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						int:
           __lowerCAmelCase             =       len(list(m.modules()						)						) == 1 or isinstance(__UpperCamelCase       ,    nn.Convad						) or isinstance(__UpperCamelCase       ,    nn.BatchNormad						)
           if has_not_submodules:
                self.traced.append(__UpperCamelCase						)


      def __call__(						self       ,    __UpperCamelCase						)->						Dict:
           for m in self.module.modules():
                self.handles.append(m.register_forward_hook(self._forward_hook						)						)
           self.module(__UpperCamelCase						)
           [x.remove() for x in self.handles]
           return self


      @property
      def 			__UpperCAmelCase		(						self						)->						Any:
           # check the len of the state_dict keys to see if we have learnable params
           return list(filter(lambda __UpperCamelCase						: len(list(x.state_dict().keys()						)						) > 0       ,    self.traced						)						)



@dataclass
class 				_UpperCamelCase      :
      snake_case_	=				42
      snake_case_	=				42
      snake_case_	=				0
      snake_case_	=				field(default_factory=a_      )
      snake_case_	=				field(default_factory=a_      )


      def __call__(						self       ,    __UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       Tracker(self.dest						)(__UpperCamelCase						).parametrized
           __lowerCAmelCase             =       Tracker(self.src						)(__UpperCamelCase						).parametrized

           __lowerCAmelCase             =       list(filter(lambda __UpperCamelCase						: type(__UpperCamelCase						) not in self.src_skip       ,    __UpperCamelCase						)						)
           __lowerCAmelCase             =       list(filter(lambda __UpperCamelCase						: type(__UpperCamelCase						) not in self.dest_skip       ,    __UpperCamelCase						)						)

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

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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case = True							):
     print(F"""Converting {name}..."""							)
     with torch.no_grad():
          __lowerCAmelCase             =       timm.create_model(__snake_case       ,			pretrained=__snake_case							).eval()
          __lowerCAmelCase             =       ResNetForImageClassification(__snake_case							).eval()
          __lowerCAmelCase             =       ModuleTransfer(src=__snake_case       ,			dest=__snake_case							)
          __lowerCAmelCase             =       torch.randn((1, 3, 224, 224)							)
          module_transfer(__snake_case							)

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

     __lowerCAmelCase             =       F"""resnet{"-".join(name.split("resnet"							)							)}"""
     print(__snake_case							)

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

          # we can use the convnext one
          __lowerCAmelCase             =       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=__snake_case       ,			)

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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case = None       ,			__snake_case = True							):
     __lowerCAmelCase             =       "imagenet-1k-id2label.json"
     __lowerCAmelCase             =       1000
     __lowerCAmelCase             =       (1, num_labels)

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

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

     __lowerCAmelCase             =       partial(__snake_case       ,			num_labels=__snake_case       ,			idalabel=__snake_case       ,			labelaid=__snake_case							)

     __lowerCAmelCase             =       {
         "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(__snake_case       ,			names_to_config[model_name]       ,			__snake_case       ,			__snake_case							)
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(__snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							)
     return config, expected_shape


if __name__ == "__main__":
 lowerCamelCase      :       Dict    =						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      :       List[Any]    =						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)














import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       "laion/clap-htsat-unfused"
           __lowerCAmelCase             =       tempfile.mkdtemp()


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						Optional[int]:
           return RobertaTokenizer.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self       ,    **__UpperCamelCase						)->						List[Any]:
           return ClapFeatureExtractor.from_pretrained(self.checkpoint       ,    **__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           shutil.rmtree(self.tmpdirname						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_tokenizer()
           __lowerCAmelCase             =       self.get_feature_extractor()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           processor.save_pretrained(self.tmpdirname						)
           __lowerCAmelCase             =       ClapProcessor.from_pretrained(self.tmpdirname						)

           self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer.get_vocab()						)
           self.assertIsInstance(processor.tokenizer       ,    __UpperCamelCase						)

           self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor.to_json_string()						)
           self.assertIsInstance(processor.feature_extractor       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       ClapProcessor(tokenizer=self.get_tokenizer()       ,    feature_extractor=self.get_feature_extractor()						)
           processor.save_pretrained(self.tmpdirname						)

           __lowerCAmelCase             =       self.get_tokenizer(bos_token="(BOS)"       ,    eos_token="(EOS)"						)
           __lowerCAmelCase             =       self.get_feature_extractor(do_normalize=__UpperCamelCase       ,    padding_value=1.0						)

           __lowerCAmelCase             =       ClapProcessor.from_pretrained(
               self.tmpdirname       ,    bos_token="(BOS)"       ,    eos_token="(EOS)"       ,    do_normalize=__UpperCamelCase       ,    padding_value=1.0						)

           self.assertEqual(processor.tokenizer.get_vocab()       ,    tokenizer_add_kwargs.get_vocab()						)
           self.assertIsInstance(processor.tokenizer       ,    __UpperCamelCase						)

           self.assertEqual(processor.feature_extractor.to_json_string()       ,    feature_extractor_add_kwargs.to_json_string()						)
           self.assertIsInstance(processor.feature_extractor       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[str]:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       floats_list((3, 1_0_0_0)						)

           __lowerCAmelCase             =       feature_extractor(__UpperCamelCase       ,    return_tensors="np"						)
           __lowerCAmelCase             =       processor(audios=__UpperCamelCase       ,    return_tensors="np"						)

           for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()       ,    input_processor[key].sum()       ,    delta=1e-2						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       "This is a test string"

           __lowerCAmelCase             =       processor(text=__UpperCamelCase						)

           __lowerCAmelCase             =       tokenizer(__UpperCamelCase						)

           for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]       ,    encoded_processor[key]						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           __lowerCAmelCase             =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           __lowerCAmelCase             =       processor.batch_decode(__UpperCamelCase						)
           __lowerCAmelCase             =       tokenizer.batch_decode(__UpperCamelCase						)

           self.assertListEqual(__UpperCamelCase       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.get_feature_extractor()
           __lowerCAmelCase             =       self.get_tokenizer()

           __lowerCAmelCase             =       ClapProcessor(tokenizer=__UpperCamelCase       ,    feature_extractor=__UpperCamelCase						)

           self.assertListEqual(
               processor.model_input_names[2:]       ,    feature_extractor.model_input_names       ,    msg="`processor` and `feature_extractor` model input names do not match"       ,    )






