import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def      lowercase_							(     _lowerCamelCase				:			Dict):
    if "model" in orig_key:
        lowercase__					:  Tuple          =							orig_key.replace("model."						,      "")
    if "norm1" in orig_key:
        lowercase__					:  int          =							orig_key.replace("norm1"						,      "attention.output.LayerNorm")
    if "norm2" in orig_key:
        lowercase__					:  Dict          =							orig_key.replace("norm2"						,      "output.LayerNorm")
    if "norm" in orig_key:
        lowercase__					:  int          =							orig_key.replace("norm"						,      "LayerNorm")
    if "transformer" in orig_key:
        lowercase__					:  Any          =							orig_key.split(".")[0].split("_")[-1]
        lowercase__					:  Dict          =							orig_key.replace(f'''transformer_{layer_num}'''						,      f'''encoder.layer.{layer_num}''')
    if "mha.attn" in orig_key:
        lowercase__					:  Tuple          =							orig_key.replace("mha.attn"						,      "attention.self")
    if "mha" in orig_key:
        lowercase__					:  Optional[Any]          =							orig_key.replace("mha"						,      "attention")
    if "W_q" in orig_key:
        lowercase__					:  Optional[int]          =							orig_key.replace("W_q"						,      "self.query")
    if "W_k" in orig_key:
        lowercase__					:  List[str]          =							orig_key.replace("W_k"						,      "self.key")
    if "W_v" in orig_key:
        lowercase__					:  Union[str, Any]          =							orig_key.replace("W_v"						,      "self.value")
    if "ff1" in orig_key:
        lowercase__					:  Dict          =							orig_key.replace("ff1"						,      "intermediate.dense")
    if "ff2" in orig_key:
        lowercase__					:  List[Any]          =							orig_key.replace("ff2"						,      "output.dense")
    if "ff" in orig_key:
        lowercase__					:  List[Any]          =							orig_key.replace("ff"						,      "output.dense")
    if "mlm_class" in orig_key:
        lowercase__					:  int          =							orig_key.replace("mlm.mlm_class"						,      "cls.predictions.decoder")
    if "mlm" in orig_key:
        lowercase__					:  str          =							orig_key.replace("mlm"						,      "cls.predictions.transform")
    if "cls" not in orig_key:
        lowercase__					:  str          =							"nystromformer." + orig_key

    return orig_key

def      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			List[str]):
    for key in orig_state_dict.copy().keys():
        lowercase__					:  Optional[int]          =							orig_state_dict.pop(_lowerCamelCase)

        if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
            continue
        else:
            lowercase__					:  Optional[Any]          =							val

    lowercase__					:  Optional[Any]          =							orig_state_dict["cls.predictions.decoder.bias"]
    lowercase__					:  Union[str, Any]          =							(
        torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
    )

    return orig_state_dict

def      lowercase_							(     _lowerCamelCase				:			str						,      _lowerCamelCase				:			List[Any]						,      _lowerCamelCase				:			str):
    lowercase__					:  List[Any]          =							torch.load(_lowerCamelCase						,      map_location="cpu")["model_state_dict"]
    lowercase__					:  Tuple          =							NystromformerConfig.from_json_file(_lowerCamelCase)
    lowercase__					:  int          =							NystromformerForMaskedLM(_lowerCamelCase)

    lowercase__					:  int          =							convert_checkpoint_helper(_lowerCamelCase						,      _lowerCamelCase)

    model.load_state_dict(_lowerCamelCase)
    model.eval()
    model.save_pretrained(_lowerCamelCase)

    print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}''')


if __name__ == "__main__":
      UpperCamelCase    				=		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Nystromformer pytorch checkpoint.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help='''The json file for Nystromformer model config.''',
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      UpperCamelCase    				=		parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
      from PIL import Image



class     snake_case_			(						unittest.TestCase    ,__A			):






   def  __UpperCamelCase	( self							:	int    )   ->		Union[str, Any]:
       lowercase__					:  Any          =							load_tool("image-segmentation"    )
       self.tool.setup()
       lowercase__					:  Optional[int]          =							load_tool("image-segmentation"      ,		remote=lowercase_    )






   def  __UpperCamelCase	( self							:	Any    )   ->		Tuple:
       lowercase__					:  Any          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Tuple          =							self.tool(lowercase_      ,		"cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )






   def  __UpperCamelCase	( self							:	Optional[int]    )   ->		Dict:
       lowercase__					:  List[Any]          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Optional[int]          =							self.remote_tool(lowercase_      ,		"cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )






   def  __UpperCamelCase	( self							:	List[Any]    )   ->		Dict:
       lowercase__					:  Optional[Any]          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  int          =							self.tool(image=lowercase_      ,		label="cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )







   def  __UpperCamelCase	( self							:	Dict    )   ->		List[Any]:
       lowercase__					:  Tuple          =							Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"    )    ) / "000000039769.png"    )
       lowercase__					:  Union[str, Any]          =							self.remote_tool(image=lowercase_      ,		label="cat"    )
       self.assertTrue(isinstance(lowercase_      ,		Image.Image    )    )



