'''simple docstring'''

import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM



def  _lowerCAmelCase				(				__a      )  ->      Any:
     '''simple docstring'''







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

     return orig_key



def  _lowerCAmelCase				(				__a			,      __a      )  ->      Optional[int]:
     '''simple docstring'''







     for key in orig_state_dict.copy().keys():
          _UpperCamelCase  :List[Any]       =orig_state_dict.pop(__a      )

          if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
               continue
          else:
               _UpperCamelCase  :str       =val

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

     return orig_state_dict



def  _lowerCAmelCase				(				__a			,      __a			,      __a      )  ->      str:
     '''simple docstring'''







     _UpperCamelCase  :List[Any]       =torch.load(__a			,      map_location="""cpu"""      )["""model_state_dict"""]
     _UpperCamelCase  :Dict       =NystromformerConfig.from_json_file(__a      )
     _UpperCamelCase  :str       =NystromformerForMaskedLM(__a      )

     _UpperCamelCase  :Optional[int]       =convert_checkpoint_helper(__a			,      __a      )

     model.load_state_dict(__a      )
     model.eval()
     model.save_pretrained(__a      )

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


if __name__ == "__main__":
      _lowerCamelCase :				Dict														= 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."""
      )
      _lowerCamelCase :				int														= parser.parse_args()
      convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





'''simple docstring'''







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
   from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor


class      _a    (      __a					):
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.config_class(**self.inputs_dict							)
           self.parent.assertTrue(hasattr(lowercase  ,							'''hidden_sizes'''							)							)
           self.parent.assertTrue(hasattr(lowercase  ,							'''num_encoder_blocks'''							)							)






class      _a    :
       def __init__(      self		:      Any  ,							lowercase		:      Any  ,							lowercase		:      Optional[Any]=13  ,							lowercase		:      List[str]=64  ,							lowercase		:      Optional[Any]=3  ,							lowercase		:      List[str]=4  ,							lowercase		:      Optional[int]=[2, 2, 2, 2]  ,							lowercase		:      Tuple=[8, 4, 2, 1]  ,							lowercase		:      Optional[int]=[16, 32, 64, 128]  ,							lowercase		:      Tuple=[1, 4, 8, 16]  ,							lowercase		:      List[Any]=False  ,							lowercase		:      List[Any]=True  ,							lowercase		:      List[str]="gelu"  ,							lowercase		:      Any=0.1  ,							lowercase		:      Dict=0.02  ,							lowercase		:      str=3  ,							lowercase		:      Optional[Any]=None  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						parent
           UpperCAmelCase     			=						batch_size
           UpperCAmelCase     			=						image_size
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						num_encoder_blocks
           UpperCAmelCase     			=						sr_ratios
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						downsampling_rates
           UpperCAmelCase     			=						is_training
           UpperCAmelCase     			=						use_labels
           UpperCAmelCase     			=						hidden_act
           UpperCAmelCase     			=						hidden_dropout_prob
           UpperCAmelCase     			=						initializer_range
           UpperCAmelCase     			=						num_labels
           UpperCAmelCase     			=						scope
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

           UpperCAmelCase     			=						None
           if self.use_labels:
               UpperCAmelCase     			=						ids_tensor([self.batch_size, self.image_size, self.image_size]  ,							self.num_labels							)

           UpperCAmelCase     			=						PoolFormerConfig(
               image_size=self.image_size  ,							num_channels=self.num_channels  ,							num_encoder_blocks=self.num_encoder_blocks  ,							depths=self.depths  ,							hidden_sizes=self.hidden_sizes  ,							hidden_act=self.hidden_act  ,							hidden_dropout_prob=self.hidden_dropout_prob  ,							initializer_range=self.initializer_range  ,							)

           return config, pixel_values, labels
       def        A (      self		:      Optional[Any]  ,							lowercase		:      List[Any]  ,							lowercase		:      Dict  ,							lowercase		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerModel(config=lowercase							)
           model.to(lowercase							)
           model.eval()
           UpperCAmelCase     			=						model(lowercase							)
           UpperCAmelCase     			=						UpperCAmelCase     			=						self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape  ,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)







       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.prepare_config_and_inputs()
           UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase     			=						config_and_inputs
           UpperCAmelCase     			=						{'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_torch
class      _a    (      __a				,				__a				,				unittest.TestCase					):
       __a							:	Optional[int]  							=   (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
       __a							:	Dict  							=   (
           {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       __a							:	str  							=   False
       __a							:	Optional[int]  							=   False
       __a							:	Tuple  							=   False
       __a							:	Union[str, Any]  							=   False
       __a							:	Dict  							=   False
       def        A (      self		:      Any							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerModelTester(self							)
           UpperCAmelCase     			=						PoolFormerConfigTester(self  ,							config_class=lowercase							)
       def        A (      self		:      Any							):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def        A (      self		:      Optional[int]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase							)
       @unittest.skip('''PoolFormer does not use inputs_embeds'''							)
       def        A (      self		:      Dict							):



           '''simple docstring'''

           pass
       @unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''							)
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           pass
       def        A (      self		:      Dict							):



           '''simple docstring'''

           def check_hidden_states_output(lowercase		:      int  ,							lowercase		:      Any  ,							lowercase		:      Union[str, Any]							):
               UpperCAmelCase     			=						model_class(lowercase							)
               model.to(lowercase							)
               model.eval()

               with torch.no_grad():
                   UpperCAmelCase     			=						model(**self._prepare_for_class(lowercase  ,							lowercase							)							)

               UpperCAmelCase     			=						outputs.hidden_states

               UpperCAmelCase     			=						self.model_tester.num_encoder_blocks
               self.assertEqual(len(lowercase							)  ,							lowercase							)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]							)  ,							[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]  ,							)

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     			=						True
               check_hidden_states_output(lowercase  ,							lowercase  ,							lowercase							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase     			=						True

               check_hidden_states_output(lowercase  ,							lowercase  ,							lowercase							)
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           if not self.model_tester.is_training:
               return

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase     			=						True

           for model_class in self.all_model_classes:
               if model_class in get_values(lowercase							):
                   continue
               UpperCAmelCase     			=						model_class(lowercase							)
               model.to(lowercase							)
               model.train()
               UpperCAmelCase     			=						self._prepare_for_class(lowercase  ,							lowercase  ,							return_labels=lowercase							)
               UpperCAmelCase     			=						model(**lowercase							).loss
               loss.backward()
       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     			=						model_class(lowercase							)
               UpperCAmelCase     			=						inspect.signature(model.forward							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               UpperCAmelCase     			=						[*signature.parameters.keys()]

               UpperCAmelCase     			=						['''pixel_values''']
               self.assertListEqual(arg_names[:1]  ,							lowercase							)







       @slow
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     			=						PoolFormerModel.from_pretrained(lowercase							)
               self.assertIsNotNone(lowercase							)






def 		snake_case_				():
    UpperCAmelCase     			=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image







@require_torch
class      _a    (      unittest.TestCase					):
       @slow
       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerImageProcessor()
           UpperCAmelCase     			=						PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''							).to(lowercase							)

           UpperCAmelCase     			=						image_processor(images=prepare_img()  ,							return_tensors='''pt'''							).to(lowercase							)

           # forward pass
           with torch.no_grad():
               UpperCAmelCase     			=						model(**lowercase							)

           # verify the logits
           UpperCAmelCase     			=						torch.Size((1, 1_000)							)
           self.assertEqual(outputs.logits.shape  ,							lowercase							)

           UpperCAmelCase     			=						torch.tensor([-0.6113, 0.1685, -0.0492]							).to(lowercase							)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,							lowercase  ,							atol=1E-4							)							)




