
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()
lowercase  : List[str]						   =							logging.get_logger()

@dataclass
class __snake_case		:
    _a		:						nn.Module
    _a		:						List[nn.Module]=    field(default_factory=lowerCAmelCase					)
    _a		:						list=    field(default_factory=lowerCAmelCase					)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case				):



           '''simple docstring'''







           lowercase							:					Any	     =				len(list(m.modules()				)				) == 1 or isinstance(snake_case		,nn.Convad				) or isinstance(snake_case		,nn.BatchNormad				)
           if has_not_submodules:
                  self.traced.append(snake_case				)
    def __call__(  self		,snake_case				):



           '''simple docstring'''







           for m in self.module.modules():
                  self.handles.append(m.register_forward_hook(self._forward_hook				)				)
           self.module(snake_case				)
           [x.remove() for x in self.handles]
           return self
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







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






@dataclass
class __snake_case		:
    _a		:						nn.Module
    _a		:						nn.Module
    _a		:						int=    0
    _a		:						List=    field(default_factory=lowerCAmelCase					)
    _a		:						List=    field(default_factory=lowerCAmelCase					)
    def __call__(  self		,snake_case				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				Tracker(self.dest				)(snake_case				).parametrized
           lowercase							:					int	     =				Tracker(self.src				)(snake_case				).parametrized

           lowercase							:					Union[str, Any]	     =				list(filter(lambda snake_case				: type(snake_case				) not in self.src_skip		,snake_case				)				)
           lowercase							:					Tuple	     =				list(filter(lambda snake_case				: type(snake_case				) not in self.dest_skip		,snake_case				)				)

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

           for dest_m, src_m in zip(snake_case		,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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = True    )			->   str:
       print(f"Converting {name}..."    )
       with torch.no_grad():
              lowercase							:					List[str]	     =				timm.create_model(SCREAMING_SNAKE_CASE__ ,     pretrained=SCREAMING_SNAKE_CASE__    ).eval()
              lowercase							:					Union[str, Any]	     =				ResNetForImageClassification(SCREAMING_SNAKE_CASE__    ).eval()
              lowercase							:					List[Any]	     =				ModuleTransfer(src=SCREAMING_SNAKE_CASE__ ,     dest=SCREAMING_SNAKE_CASE__    )
              lowercase							:					List[Any]	     =				torch.randn((1, 3, 224, 224)    )
              module_transfer(SCREAMING_SNAKE_CASE__    )

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

       lowercase							:					Dict	     =				f"resnet{'-'.join(name.split('resnet'    )    )}"
       print(SCREAMING_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=SCREAMING_SNAKE_CASE__ ,     )

              # we can use the convnext one
              lowercase							:					Optional[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=SCREAMING_SNAKE_CASE__ ,     )

              print(f"Pushed {checkpoint_name}"    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ = None ,     SCREAMING_SNAKE_CASE__ = True    )			->   Any:
       lowercase							:					Optional[int]	     =				"""imagenet-1k-id2label.json"""
       lowercase							:					Any	     =				1_000
       lowercase							:					Optional[int]	     =				(1, num_labels)

       lowercase							:					Tuple	     =				"""huggingface/label-files"""
       lowercase							:					int	     =				num_labels
       lowercase							:					List[str]	     =				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     repo_type="""dataset"""    ) ,     """r"""    )    )
       lowercase							:					str	     =				{int(SCREAMING_SNAKE_CASE__    ): v for k, v in idalabel.items()}

       lowercase							:					Any	     =				idalabel
       lowercase							:					List[str]	     =				{v: k for k, v in idalabel.items()}

       lowercase							:					str	     =				partial(SCREAMING_SNAKE_CASE__ ,     num_labels=SCREAMING_SNAKE_CASE__ ,     idalabel=SCREAMING_SNAKE_CASE__ ,     labelaid=SCREAMING_SNAKE_CASE__    )

       lowercase							:					List[str]	     =				{
           """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, 1_024, 2_048] ,     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, 1_024, 2_048] ,     layer_type="""bottleneck"""    ),
           """resnet101""": ImageNetPreTrainedConfig(
               depths=[3, 4, 23, 3] ,     hidden_sizes=[256, 512, 1_024, 2_048] ,     layer_type="""bottleneck"""    ),
           """resnet152""": ImageNetPreTrainedConfig(
               depths=[3, 8, 36, 3] ,     hidden_sizes=[256, 512, 1_024, 2_048] ,     layer_type="""bottleneck"""    ),
       }

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


if __name__ == "__main__":
      lowercase  : 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.""",
      )

      lowercase  : Optional[int]						   =							parser.parse_args()
      lowercase  : 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 json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor

@require_vision
class __snake_case		(				unittest.TestCase					):
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				tempfile.mkdtemp()

           lowercase							:					List[str]	     =				[
               """[UNK]""",
               """[CLS]""",
               """[SEP]""",
               """[PAD]""",
               """[MASK]""",
               """的""",
               """价""",
               """格""",
               """是""",
               """15""",
               """便""",
               """alex""",
               """##andra""",
               """，""",
               """。""",
               """-""",
               """t""",
               """shirt""",
           ]
           lowercase							:					Dict	     =				os.path.join(self.tmpdirname		,VOCAB_FILES_NAMES["""vocab_file"""]				)
           with open(self.vocab_file		,"""w"""		,encoding="""utf-8"""				) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

           lowercase							:					Any	     =				{
               """do_resize""": True,
               """size""": {"""height""": 224, """width""": 224},
               """do_center_crop""": True,
               """crop_size""": {"""height""": 18, """width""": 18},
               """do_normalize""": True,
               """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
               """do_convert_rgb""": True,
           }
           lowercase							:					List[str]	     =				os.path.join(self.tmpdirname		,snake_case				)
           with open(self.image_processor_file		,"""w"""		,encoding="""utf-8"""				) as fp:
                  json.dump(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizer.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return BertTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           shutil.rmtree(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Optional[int]	     =				[np.random.randint(255		,size=(3, 30, 400)		,dtype=np.uinta				)]

           lowercase							:					str	     =				[Image.fromarray(np.moveaxis(snake_case		,0		,-1				)				) for x in image_inputs]

           return image_inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[str]	     =				self.get_tokenizer()
           lowercase							:					Dict	     =				self.get_rust_tokenizer()
           lowercase							:					Union[str, Any]	     =				self.get_image_processor()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_slow.save_pretrained(self.tmpdirname				)
           lowercase							:					Any	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,use_fast=snake_case				)

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)
           processor_fast.save_pretrained(self.tmpdirname				)
           lowercase							:					Tuple	     =				ChineseCLIPProcessor.from_pretrained(self.tmpdirname				)

           self.assertEqual(processor_slow.tokenizer.get_vocab()		,tokenizer_slow.get_vocab()				)
           self.assertEqual(processor_fast.tokenizer.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertEqual(tokenizer_slow.get_vocab()		,tokenizer_fast.get_vocab()				)
           self.assertIsInstance(processor_slow.tokenizer		,snake_case				)
           self.assertIsInstance(processor_fast.tokenizer		,snake_case				)

           self.assertEqual(processor_slow.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertEqual(processor_fast.image_processor.to_json_string()		,image_processor.to_json_string()				)
           self.assertIsInstance(processor_slow.image_processor		,snake_case				)
           self.assertIsInstance(processor_fast.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=self.get_tokenizer()		,image_processor=self.get_image_processor()				)
           processor.save_pretrained(self.tmpdirname				)

           lowercase							:					int	     =				self.get_tokenizer(cls_token="""(CLS)"""		,sep_token="""(SEP)"""				)
           lowercase							:					Dict	     =				self.get_image_processor(do_normalize=snake_case				)

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor.from_pretrained(
               self.tmpdirname		,cls_token="""(CLS)"""		,sep_token="""(SEP)"""		,do_normalize=snake_case				)

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

           self.assertEqual(processor.image_processor.to_json_string()		,image_processor_add_kwargs.to_json_string()				)
           self.assertIsInstance(processor.image_processor		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					List[str]	     =				self.get_tokenizer()

           lowercase							:					Optional[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Optional[int]	     =				self.prepare_image_inputs()

           lowercase							:					Tuple	     =				image_processor(snake_case		,return_tensors="""np"""				)
           lowercase							:					Optional[int]	     =				processor(images=snake_case		,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 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					Union[str, Any]	     =				self.get_tokenizer()

           lowercase							:					Any	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					Any	     =				"""Alexandra，T-shirt的价格是15便士。"""

           lowercase							:					int	     =				processor(text=snake_case				)

           lowercase							:					Dict	     =				tokenizer(snake_case				)

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



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				self.get_image_processor()
           lowercase							:					Optional[Any]	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					List[str]	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Any	     =				self.prepare_image_inputs()

           lowercase							:					Optional[Any]	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""]				)

           # test if it raises when no input is passed
           with pytest.raises(snake_case				):
                  processor()
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					str	     =				self.get_image_processor()
           lowercase							:					str	     =				self.get_tokenizer()

           lowercase							:					Dict	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           lowercase							:					Any	     =				processor.batch_decode(snake_case				)
           lowercase							:					str	     =				tokenizer.batch_decode(snake_case				)

           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Dict	     =				self.get_image_processor()
           lowercase							:					Dict	     =				self.get_tokenizer()

           lowercase							:					List[Any]	     =				ChineseCLIPProcessor(tokenizer=snake_case		,image_processor=snake_case				)

           lowercase							:					str	     =				"""Alexandra，T-shirt的价格是15便士。"""
           lowercase							:					Optional[Any]	     =				self.prepare_image_inputs()

           lowercase							:					Dict	     =				processor(text=snake_case		,images=snake_case				)

           self.assertListEqual(list(inputs.keys()				)		,processor.model_input_names				)
