






import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       SwinvaConfig()
     __lowerCAmelCase             =       swinva_name.split("_"							)

     __lowerCAmelCase             =       name_split[1]
     if "to" in name_split[3]:
          __lowerCAmelCase             =       int(name_split[3][-3:]							)
     else:
          __lowerCAmelCase             =       int(name_split[3]							)
     if "to" in name_split[2]:
          __lowerCAmelCase             =       int(name_split[2][-2:]							)
     else:
          __lowerCAmelCase             =       int(name_split[2][6:]							)

     if model_size == "tiny":
          __lowerCAmelCase             =       96
          __lowerCAmelCase             =       (2, 2, 6, 2)
          __lowerCAmelCase             =       (3, 6, 12, 24)
     elif model_size == "small":
          __lowerCAmelCase             =       96
          __lowerCAmelCase             =       (2, 2, 18, 2)
          __lowerCAmelCase             =       (3, 6, 12, 24)
     elif model_size == "base":
          __lowerCAmelCase             =       128
          __lowerCAmelCase             =       (2, 2, 18, 2)
          __lowerCAmelCase             =       (4, 8, 16, 32)
     else:
          __lowerCAmelCase             =       192
          __lowerCAmelCase             =       (2, 2, 18, 2)
          __lowerCAmelCase             =       (6, 12, 24, 48)

     if "to" in swinva_name:
          __lowerCAmelCase             =       (12, 12, 12, 6)

     if ("22k" in swinva_name) and ("to" not in swinva_name):
          __lowerCAmelCase             =       2_1841
          __lowerCAmelCase             =       "huggingface/label-files"
          __lowerCAmelCase             =       "imagenet-22k-id2label.json"
          __lowerCAmelCase             =       json.load(open(hf_hub_download(A__       ,			A__       ,			repo_type="dataset"							)       ,			"r"							)							)
          __lowerCAmelCase             =       {int(A__							): v for k, v in idalabel.items()}
          __lowerCAmelCase             =       idalabel
          __lowerCAmelCase             =       {v: k for k, v in idalabel.items()}

     else:
          __lowerCAmelCase             =       1000
          __lowerCAmelCase             =       "huggingface/label-files"
          __lowerCAmelCase             =       "imagenet-1k-id2label.json"
          __lowerCAmelCase             =       json.load(open(hf_hub_download(A__       ,			A__       ,			repo_type="dataset"							)       ,			"r"							)							)
          __lowerCAmelCase             =       {int(A__							): v for k, v in idalabel.items()}
          __lowerCAmelCase             =       idalabel
          __lowerCAmelCase             =       {v: k for k, v in idalabel.items()}

     __lowerCAmelCase             =       img_size
     __lowerCAmelCase             =       num_classes
     __lowerCAmelCase             =       embed_dim
     __lowerCAmelCase             =       depths
     __lowerCAmelCase             =       num_heads
     __lowerCAmelCase             =       window_size

     return config





def    __lowerCAmelCase   (    __snake_case							):
     if "patch_embed.proj" in name:
          __lowerCAmelCase             =       name.replace("patch_embed.proj"       ,			"embeddings.patch_embeddings.projection"							)
     if "patch_embed.norm" in name:
          __lowerCAmelCase             =       name.replace("patch_embed.norm"       ,			"embeddings.norm"							)
     if "layers" in name:
          __lowerCAmelCase             =       "encoder." + name
     if "attn.proj" in name:
          __lowerCAmelCase             =       name.replace("attn.proj"       ,			"attention.output.dense"							)
     if "attn" in name:
          __lowerCAmelCase             =       name.replace("attn"       ,			"attention.self"							)
     if "norm1" in name:
          __lowerCAmelCase             =       name.replace("norm1"       ,			"layernorm_before"							)
     if "norm2" in name:
          __lowerCAmelCase             =       name.replace("norm2"       ,			"layernorm_after"							)
     if "mlp.fc1" in name:
          __lowerCAmelCase             =       name.replace("mlp.fc1"       ,			"intermediate.dense"							)
     if "mlp.fc2" in name:
          __lowerCAmelCase             =       name.replace("mlp.fc2"       ,			"output.dense"							)
     if "q_bias" in name:
          __lowerCAmelCase             =       name.replace("q_bias"       ,			"query.bias"							)
     if "k_bias" in name:
          __lowerCAmelCase             =       name.replace("k_bias"       ,			"key.bias"							)
     if "v_bias" in name:
          __lowerCAmelCase             =       name.replace("v_bias"       ,			"value.bias"							)
     if "cpb_mlp" in name:
          __lowerCAmelCase             =       name.replace("cpb_mlp"       ,			"continuous_position_bias_mlp"							)
     if name == "norm.weight":
          __lowerCAmelCase             =       "layernorm.weight"
     if name == "norm.bias":
          __lowerCAmelCase             =       "layernorm.bias"

     if "head" in name:
          __lowerCAmelCase             =       name.replace("head"       ,			"classifier"							)
     else:
          __lowerCAmelCase             =       "swinv2." + name

     return name





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     for key in orig_state_dict.copy().keys():
          __lowerCAmelCase             =       orig_state_dict.pop(A__							)

          if "mask" in key:
               continue
          elif "qkv" in key:
               __lowerCAmelCase             =       key.split("."							)
               __lowerCAmelCase             =       int(key_split[1]							)
               __lowerCAmelCase             =       int(key_split[3]							)
               __lowerCAmelCase             =       model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    __lowerCAmelCase             =       val[:dim, :]
                    __lowerCAmelCase             =       val[dim : dim * 2, :]
                    __lowerCAmelCase             =       val[-dim:, :]
               else:
                    __lowerCAmelCase             =       val[:dim]
                    __lowerCAmelCase             =       val[
                        dim : dim * 2
                    ]
                    __lowerCAmelCase             =       val[-dim:]
          else:
               __lowerCAmelCase             =       val

     return orig_state_dict





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       timm.create_model(A__       ,			pretrained=A__							)
     timm_model.eval()

     __lowerCAmelCase             =       get_swinva_config(A__							)
     __lowerCAmelCase             =       SwinvaForImageClassification(A__							)
     model.eval()

     __lowerCAmelCase             =       convert_state_dict(timm_model.state_dict()       ,			A__							)
     model.load_state_dict(A__							)

     __lowerCAmelCase             =       "http://images.cocodataset.org/val2017/000000039769.jpg"

     __lowerCAmelCase             =       AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_"       ,			"-"							)							)							)
     __lowerCAmelCase             =       Image.open(requests.get(A__       ,			stream=A__							).raw							)
     __lowerCAmelCase             =       image_processor(images=A__       ,			return_tensors="pt"							)

     __lowerCAmelCase             =       timm_model(inputs["pixel_values"]							)
     __lowerCAmelCase             =       model(**A__							).logits

     assert torch.allclose(A__       ,			A__       ,			atol=1E-3							)

     print(F"""Saving model {swinva_name} to {pytorch_dump_folder_path}"""							)
     model.save_pretrained(A__							)

     print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
     image_processor.save_pretrained(A__							)

     model.push_to_hub(
         repo_path_or_name=Path(A__       ,			A__							)       ,			organization="nandwalritik"       ,			commit_message="Add model"       ,			)


if __name__ == "__main__":
 lowerCamelCase      :       Tuple    =						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--swinv2_name''',
     default='''swinv2_tiny_patch4_window8_256''',
     type=str,
     help='''Name of the Swinv2 timm model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 lowerCamelCase      :       List[Any]    =						parser.parse_args()
 convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)











import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin



@require_jieba
class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Optional[int]												=     CpmAntTokenizer
   A     :	Optional[int]												=     False


   def       UpperCAmelCase		(    self	:      List[str])						->					str:




         """simple docstring"""

         super().setUp()

         lowercase__  =	[
             '<d>',
             '</d>',
             '<s>',
             '</s>',
             '</_>',
             '<unk>',
             '<pad>',
             '</n>',
             '我',
             '是',
             'C',
             'P',
             'M',
             'A',
             'n',
             't',
         ]
         lowercase__  =	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]))



   @tooslow
   def       UpperCAmelCase		(    self	:      str)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b')
         lowercase__  =	'今天天气真好！'
         lowercase__  =	['今天', '天气', '真', '好', '！']
         lowercase__  =	tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	'今天天气真好！'
         lowercase__  =	[tokenizer.bos_token] + tokens

         lowercase__  =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					lowerCAmelCase)

         lowercase__  =	tokenizer.decode(lowerCAmelCase)
         self.assertEqual(lowerCAmelCase      ,					lowerCAmelCase)
