



import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class 			__lowerCAmelCase  (						_UpperCamelCase	,						unittest.TestCase ):
 lowerCAmelCase__							       =	XLMTokenizer
 lowerCAmelCase__							       =	False




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    __lowerCamelCase    				=     [
        "l",
        "o",
        "w",
        "e",
        "r",
        "s",
        "t",
        "i",
        "d",
        "n",
        "w</w>",
        "r</w>",
        "t</w>",
        "lo",
        "low",
        "er</w>",
        "low</w>",
        "lowest</w>",
        "newer</w>",
        "wider</w>",
        "<unk>",
    ]
    __lowerCamelCase    				=     dict(zip(__UpperCAmelCase						,					range(len(__UpperCAmelCase					)					)					)					)
    __lowerCamelCase    				=     ["l o 123", "lo w 1456", "e r</w> 1789", ""]

    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''vocab_file''']					)
    __lowerCamelCase    				=     os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES['''merges_file''']					)
    with open(self.vocab_file						,					'''w'''					) as fp:
       fp.write(json.dumps(__UpperCAmelCase					)					)
    with open(self.merges_file						,					'''w'''					) as fp:
       fp.write('''\n'''.join(__UpperCAmelCase					)					)




 def 							lowerCamelCase   (    self						,					__UpperCAmelCase					):



    '''simple docstring'''
    __lowerCamelCase    				=     "lower newer"
    __lowerCamelCase    				=     "lower newer"
    return input_text, output_text




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer(self.vocab_file						,					self.merges_file					)

    __lowerCamelCase    				=     "lower"
    __lowerCamelCase    				=     ["low", "er</w>"]
    __lowerCamelCase    				=     tokenizer.tokenize(__UpperCAmelCase					)
    self.assertListEqual(__UpperCAmelCase						,					__UpperCAmelCase					)

    __lowerCamelCase    				=     tokens + ["<unk>"]
    __lowerCamelCase    				=     [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase					)						,					__UpperCAmelCase					)




 @slow
 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    __lowerCamelCase    				=     XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''					)

    __lowerCamelCase    				=     tokenizer.encode('''sequence builders'''						,					add_special_tokens=__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.encode('''multi-sequence build'''						,					add_special_tokens=__UpperCAmelCase					)

    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase					)
    __lowerCamelCase    				=     tokenizer.build_inputs_with_special_tokens(__UpperCAmelCase						,					__UpperCAmelCase					)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]











"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  List[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[Any]														=       SwinConfig(image_size=192							)

     if "base" in model_name:
          _UpperCAmelCase     :      Tuple														=       6
          _UpperCAmelCase     :      Optional[Any]														=       128
          _UpperCAmelCase     :      Dict														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      List[Any]														=       (4, 8, 16, 32)
     elif "large" in model_name:
          _UpperCAmelCase     :      int														=       12
          _UpperCAmelCase     :      Optional[int]														=       192
          _UpperCAmelCase     :      Optional[Any]														=       (2, 2, 18, 2)
          _UpperCAmelCase     :      str														=       (6, 12, 24, 48)
     else:
          raise ValueError("Model not supported, only supports base and large variants"							)

     _UpperCAmelCase     :      Optional[int]														=       window_size
     _UpperCAmelCase     :      Optional[int]														=       embed_dim
     _UpperCAmelCase     :      List[Any]														=       depths
     _UpperCAmelCase     :      Any														=       num_heads

     return config

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Dict							)    ->  str:





     '''simple docstring'''

     if "encoder.mask_token" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("encoder.mask_token"		,    "embeddings.mask_token"							)
     if "encoder.patch_embed.proj" in name:
          _UpperCAmelCase     :      Optional[int]														=       name.replace("encoder.patch_embed.proj"		,    "embeddings.patch_embeddings.projection"							)
     if "encoder.patch_embed.norm" in name:
          _UpperCAmelCase     :      Any														=       name.replace("encoder.patch_embed.norm"		,    "embeddings.norm"							)
     if "attn.proj" in name:
          _UpperCAmelCase     :      int														=       name.replace("attn.proj"		,    "attention.output.dense"							)
     if "attn" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("attn"		,    "attention.self"							)
     if "norm1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("norm1"		,    "layernorm_before"							)
     if "norm2" in name:
          _UpperCAmelCase     :      int														=       name.replace("norm2"		,    "layernorm_after"							)
     if "mlp.fc1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("mlp.fc1"		,    "intermediate.dense"							)
     if "mlp.fc2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("mlp.fc2"		,    "output.dense"							)

     if name == "encoder.norm.weight":
          _UpperCAmelCase     :      Any														=       "layernorm.weight"
     if name == "encoder.norm.bias":
          _UpperCAmelCase     :      List[Any]														=       "layernorm.bias"

     if "decoder" in name:
          pass
     else:
          _UpperCAmelCase     :      Dict														=       "swin." + name

     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Optional[Any]:





     '''simple docstring'''

     for key in orig_state_dict.copy().keys():
          _UpperCAmelCase     :      Dict														=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__							)

          if "attn_mask" in key:
               pass
          elif "qkv" in key:
               _UpperCAmelCase     :      int														=       key.split("."							)
               _UpperCAmelCase     :      List[str]														=       int(key_split[2]							)
               _UpperCAmelCase     :      Dict														=       int(key_split[4]							)
               _UpperCAmelCase     :      str														=       model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    _UpperCAmelCase     :      Optional[Any]														=       val[:dim, :]
                    _UpperCAmelCase     :      List[Any]														=       val[
                        dim : dim * 2, :
                    ]
                    _UpperCAmelCase     :      Optional[Any]														=       val[-dim:, :]
               else:
                    _UpperCAmelCase     :      Optional[int]														=       val[
                        :dim
                    ]
                    _UpperCAmelCase     :      Dict														=       val[
                        dim : dim * 2
                    ]
                    _UpperCAmelCase     :      Union[str, Any]														=       val[
                        -dim:
                    ]
          else:
               _UpperCAmelCase     :      List[Any]														=       val

     return orig_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Tuple		,    SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Any		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  List[str]:





     '''simple docstring'''

     _UpperCAmelCase     :      Any														=       torch.load(SCREAMING_SNAKE_CASE__		,    map_location="cpu"							)["model"]

     _UpperCAmelCase     :      Union[str, Any]														=       get_swin_config(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       SwinForMaskedImageModeling(SCREAMING_SNAKE_CASE__							)
     model.eval()

     _UpperCAmelCase     :      Optional[Any]														=       convert_state_dict(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     model.load_state_dict(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       "http://images.cocodataset.org/val2017/000000039769.jpg"

     _UpperCAmelCase     :      List[Any]														=       ViTImageProcessor(size={"height": 192, "width": 192}							)
     _UpperCAmelCase     :      int														=       Image.open(requests.get(SCREAMING_SNAKE_CASE__		,    stream=SCREAMING_SNAKE_CASE__							).raw							)
     _UpperCAmelCase     :      str														=       image_processor(images=SCREAMING_SNAKE_CASE__		,    return_tensors="pt"							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       model(**SCREAMING_SNAKE_CASE__							).logits

     print(outputs.keys()							)
     print("Looks ok!"							)

     if pytorch_dump_folder_path is not None:
          print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
          model.save_pretrained(SCREAMING_SNAKE_CASE__							)

          print(f'Saving image processor to {pytorch_dump_folder_path}'							)
          image_processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

     if push_to_hub:
          print(f'Pushing model and image processor for {model_name} to hub'							)
          model.push_to_hub(f'microsoft/{model_name}'							)
          image_processor.push_to_hub(f'microsoft/{model_name}'							)


if __name__ == "__main__":
 _lowerCAmelCase							:   str				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="swin-base-simmim-window6-192",
     type=str,
     choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
     help="Name of the Swin SimMIM model you'd like to convert.",
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
     type=str,
     help="Path to the original PyTorch checkpoint (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 _lowerCAmelCase							:   str				=				parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
