



"""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)





"""simple docstring"""




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
 from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments






@require_torch
class       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Optional[Any]		):
           for model_result in results.values():
                for batch_size, sequence_length in zip(model_result["bs"]	,      model_result["ss"]		):
                     _UpperCAmelCase     :      List[str]														=       model_result["result"][batch_size][sequence_length]
                     self.assertIsNotNone(A		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Any														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      int														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[str]														=       "sgugger/tiny-distilbert-classification"
           _UpperCAmelCase     :      Optional[int]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      only_pretrain_model=A	,      )
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Any														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      torchscript=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      @unittest.skipIf(torch_device == "cpu"	,      "Cant do half precision"		)
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Optional[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      fpaa=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[str]														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      int														=       AutoConfig.from_pretrained(A		)
           # set architectures equal to `None`
           _UpperCAmelCase     :      Optional[Any]														=       None
           _UpperCAmelCase     :      Optional[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Optional[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Dict														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Union[str, Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      Tuple														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      Union[str, Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      @unittest.skipIf(torch_device == "cpu"	,      "Can't do half precision"		)
      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[int]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      str														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      fpaa=A	,      multi_process=A	,      )
           _UpperCAmelCase     :      Tuple														=       PyTorchBenchmark(A		)
           _UpperCAmelCase     :      List[Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Union[str, Any]														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      Dict														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      List[str]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      List[str]														=       "sshleifer/tinier_bart"
           _UpperCAmelCase     :      Any														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      Optional[int]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      int														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Union[str, Any]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_inference_result		)
           self.check_results_dict_not_empty(results.memory_inference_result		)



      def 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Tuple														=       "sshleifer/tiny-gpt2"
           _UpperCAmelCase     :      Tuple														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      int														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      Optional[int]														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Optional[int]														=       "sshleifer/tinier_bart"
           _UpperCAmelCase     :      int														=       AutoConfig.from_pretrained(A		)
           _UpperCAmelCase     :      List[Any]														=       PyTorchBenchmarkArguments(
               models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      multi_process=A	,      )
           _UpperCAmelCase     :      str														=       PyTorchBenchmark(A	,      configs=[config]		)
           _UpperCAmelCase     :      int														=       benchmark.run()
           self.check_results_dict_not_empty(results.time_train_result		)
           self.check_results_dict_not_empty(results.memory_train_result		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      int														=       "sshleifer/tiny-gpt2"
           with tempfile.TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase     :      Union[str, Any]														=       PyTorchBenchmarkArguments(
                    models=[MODEL_ID]	,      training=A	,      inference=A	,      save_to_csv=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      inference_time_csv_file=os.path.join(A	,      "inf_time.csv"		)	,      train_memory_csv_file=os.path.join(A	,      "train_mem.csv"		)	,      inference_memory_csv_file=os.path.join(A	,      "inf_mem.csv"		)	,      train_time_csv_file=os.path.join(A	,      "train_time.csv"		)	,      env_info_csv_file=os.path.join(A	,      "env.csv"		)	,      multi_process=A	,      )
                _UpperCAmelCase     :      int														=       PyTorchBenchmark(A		)
                benchmark.run()
                self.assertTrue(Path(os.path.join(A	,      "inf_time.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "train_time.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "inf_mem.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "train_mem.csv"		)		).exists()		)
                self.assertTrue(Path(os.path.join(A	,      "env.csv"		)		).exists()		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[Any]														=       "sshleifer/tiny-gpt2"

           def _check_summary_is_not_empty(A	:      int		):
                self.assertTrue(hasattr(A	,      "sequential"		)		)
                self.assertTrue(hasattr(A	,      "cumulative"		)		)
                self.assertTrue(hasattr(A	,      "current"		)		)
                self.assertTrue(hasattr(A	,      "total"		)		)

           with tempfile.TemporaryDirectory() as tmp_dir:
                _UpperCAmelCase     :      int														=       PyTorchBenchmarkArguments(
                    models=[MODEL_ID]	,      training=A	,      inference=A	,      sequence_lengths=[8]	,      batch_sizes=[1]	,      log_filename=os.path.join(A	,      "log.txt"		)	,      log_print=A	,      trace_memory_line_by_line=A	,      multi_process=A	,      )
                _UpperCAmelCase     :      int														=       PyTorchBenchmark(A		)
                _UpperCAmelCase     :      Any														=       benchmark.run()
                _check_summary_is_not_empty(result.inference_summary		)
                _check_summary_is_not_empty(result.train_summary		)
                self.assertTrue(Path(os.path.join(A	,      "log.txt"		)		).exists()		)
