


import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)
def      snake_case_						( __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      List[str]						=   XCLIPTextConfig()

   # derive patch size from model name
   UpperCAmelCase_							:      Union[str, Any]						=   model_name.find('''patch'''       )
   UpperCAmelCase_							:      Optional[Any]						=   int(model_name[start_idx + len('''patch'''       ) : start_idx + len('''patch'''       ) + 2]       )
   UpperCAmelCase_							:      Optional[Any]						=   XCLIPVisionConfig(patch_size=__lowercase			,       num_frames=__lowercase       )

   if "large" in model_name:
      UpperCAmelCase_							:      Tuple						=   7_6_8
      UpperCAmelCase_							:      Union[str, Any]						=   3_0_7_2
      UpperCAmelCase_							:      Optional[int]						=   1_2

      UpperCAmelCase_							:      Dict						=   1_0_2_4
      UpperCAmelCase_							:      List[str]						=   4_0_9_6
      UpperCAmelCase_							:      int						=   1_6
      UpperCAmelCase_							:      int						=   2_4
      UpperCAmelCase_							:      Tuple						=   7_6_8
      UpperCAmelCase_							:      str						=   3_0_7_2

   if model_name == "xclip-large-patch14-16-frames":
      UpperCAmelCase_							:      Tuple						=   3_3_6

   UpperCAmelCase_							:      List[str]						=   XCLIPConfig.from_text_vision_configs(__lowercase			,       __lowercase       )

   if "large" in model_name:
      UpperCAmelCase_							:      Dict						=   7_6_8

   return config
def      snake_case_						( __lowercase       ):
   # text encoder
   if name == "token_embedding.weight":
      UpperCAmelCase_							:      List[Any]						=   name.replace('''token_embedding.weight'''			,       '''text_model.embeddings.token_embedding.weight'''       )
   if name == "positional_embedding":
      UpperCAmelCase_							:      Union[str, Any]						=   name.replace('''positional_embedding'''			,       '''text_model.embeddings.position_embedding.weight'''       )
   if "ln_1" in name:
      UpperCAmelCase_							:      Optional[int]						=   name.replace('''ln_1'''			,       '''layer_norm1'''       )
   if "ln_2" in name:
      UpperCAmelCase_							:      Union[str, Any]						=   name.replace('''ln_2'''			,       '''layer_norm2'''       )
   if "c_fc" in name:
      UpperCAmelCase_							:      Union[str, Any]						=   name.replace('''c_fc'''			,       '''fc1'''       )
   if "c_proj" in name:
      UpperCAmelCase_							:      Dict						=   name.replace('''c_proj'''			,       '''fc2'''       )
   if name.startswith('''transformer.resblocks'''       ):
      UpperCAmelCase_							:      Tuple						=   name.replace('''transformer.resblocks'''			,       '''text_model.encoder.layers'''       )
   if "attn.out_proj" in name and "message" not in name:
      UpperCAmelCase_							:      Optional[int]						=   name.replace('''attn.out_proj'''			,       '''self_attn.out_proj'''       )
   if "ln_final" in name:
      UpperCAmelCase_							:      Tuple						=   name.replace('''ln_final'''			,       '''text_model.final_layer_norm'''       )
   # visual encoder
   if name == "visual.class_embedding":
      UpperCAmelCase_							:      Optional[Any]						=   name.replace('''visual.class_embedding'''			,       '''vision_model.embeddings.class_embedding'''       )
   if name == "visual.positional_embedding":
      UpperCAmelCase_							:      Optional[Any]						=   name.replace('''visual.positional_embedding'''			,       '''vision_model.embeddings.position_embedding.weight'''       )
   if name.startswith('''visual.transformer.resblocks'''       ):
      UpperCAmelCase_							:      Union[str, Any]						=   name.replace('''visual.transformer.resblocks'''			,       '''vision_model.encoder.layers'''       )
   if "visual.conv1" in name:
      UpperCAmelCase_							:      Any						=   name.replace('''visual.conv1'''			,       '''vision_model.embeddings.patch_embedding'''       )
   if "visual.ln_pre" in name:
      UpperCAmelCase_							:      List[Any]						=   name.replace('''visual.ln_pre'''			,       '''vision_model.pre_layernorm'''       )
   if "visual.ln_post" in name:
      UpperCAmelCase_							:      str						=   name.replace('''visual.ln_post'''			,       '''vision_model.post_layernorm'''       )
   if "visual.proj" in name:
      UpperCAmelCase_							:      List[Any]						=   name.replace('''visual.proj'''			,       '''visual_projection.weight'''       )
   if "text_projection" in name:
      UpperCAmelCase_							:      List[str]						=   name.replace('''text_projection'''			,       '''text_projection.weight'''       )
   # things on top
   if "prompts_visual_proj" in name:
      UpperCAmelCase_							:      Dict						=   name.replace('''prompts_visual_proj'''			,       '''prompts_visual_projection'''       )
   if "prompts_visual_ln" in name:
      UpperCAmelCase_							:      Tuple						=   name.replace('''prompts_visual_ln'''			,       '''prompts_visual_layernorm'''       )
   # mit
   if name == "mit.positional_embedding":
      UpperCAmelCase_							:      int						=   name.replace('''positional'''			,       '''position'''       )
   if name.startswith('''mit.resblocks'''       ):
      UpperCAmelCase_							:      List[Any]						=   name.replace('''mit.resblocks'''			,       '''mit.encoder.layers'''       )
   # prompts generator
   if name.startswith('''prompts_generator.norm'''       ):
      UpperCAmelCase_							:      int						=   name.replace('''prompts_generator.norm'''			,       '''prompts_generator.layernorm'''       )

   return name
def      snake_case_						( __lowercase			,       __lowercase       ):
   for key in orig_state_dict.copy().keys():
      UpperCAmelCase_							:      Any						=   orig_state_dict.pop(__lowercase       )

      if "attn.in_proj" in key:
         UpperCAmelCase_							:      List[Any]						=   key.split('''.'''       )
         if key.startswith('''visual'''       ):
            UpperCAmelCase_							:      Any						=   key_split[3]
            UpperCAmelCase_							:      int						=   config.vision_config.hidden_size
            if "message_attn" in key:
               if "weight" in key:
                  UpperCAmelCase_							:      List[Any]						=   val[
                      :dim, :
                  ]
                  UpperCAmelCase_							:      Any						=   val[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_							:      Tuple						=   val[
                      -dim:, :
                  ]
               else:
                  UpperCAmelCase_							:      Dict						=   val[
                      :dim
                  ]
                  UpperCAmelCase_							:      Union[str, Any]						=   val[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_							:      List[Any]						=   val[
                      -dim:
                  ]
            else:
               if "weight" in key:
                  UpperCAmelCase_							:      Union[str, Any]						=   val[
                      :dim, :
                  ]
                  UpperCAmelCase_							:      Tuple						=   val[
                      dim : dim * 2, :
                  ]
                  UpperCAmelCase_							:      Tuple						=   val[
                      -dim:, :
                  ]
               else:
                  UpperCAmelCase_							:      int						=   val[:dim]
                  UpperCAmelCase_							:      int						=   val[
                      dim : dim * 2
                  ]
                  UpperCAmelCase_							:      List[str]						=   val[-dim:]
         elif key.startswith('''mit'''       ):
            UpperCAmelCase_							:      List[str]						=   key_split[2]
            UpperCAmelCase_							:      int						=   config.vision_config.mit_hidden_size
            if "weight" in key:
               UpperCAmelCase_							:      Optional[int]						=   val[:dim, :]
               UpperCAmelCase_							:      Any						=   val[dim : dim * 2, :]
               UpperCAmelCase_							:      Any						=   val[-dim:, :]
            else:
               UpperCAmelCase_							:      Any						=   val[:dim]
               UpperCAmelCase_							:      str						=   val[dim : dim * 2]
               UpperCAmelCase_							:      Any						=   val[-dim:]
         else:
            UpperCAmelCase_							:      str						=   key_split[2]
            UpperCAmelCase_							:      str						=   config.text_config.hidden_size
            if "weight" in key:
               UpperCAmelCase_							:      Dict						=   val[:dim, :]
               UpperCAmelCase_							:      Optional[Any]						=   val[
                   dim : dim * 2, :
               ]
               UpperCAmelCase_							:      List[str]						=   val[-dim:, :]
            else:
               UpperCAmelCase_							:      Any						=   val[:dim]
               UpperCAmelCase_							:      Optional[int]						=   val[
                   dim : dim * 2
               ]
               UpperCAmelCase_							:      int						=   val[-dim:]
      else:
         UpperCAmelCase_							:      Optional[Any]						=   rename_key(__lowercase       )
         if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
            UpperCAmelCase_							:      List[str]						=   val.T
         UpperCAmelCase_							:      int						=   val

   return orig_state_dict
def      snake_case_						( __lowercase       ):
   if num_frames == 8:
      UpperCAmelCase_							:      Optional[Any]						=   '''eating_spaghetti_8_frames.npy'''
   elif num_frames == 1_6:
      UpperCAmelCase_							:      int						=   '''eating_spaghetti.npy'''
   elif num_frames == 3_2:
      UpperCAmelCase_							:      Union[str, Any]						=   '''eating_spaghetti_32_frames.npy'''
   UpperCAmelCase_							:      str						=   hf_hub_download(
       repo_id='''hf-internal-testing/spaghetti-video'''			,       filename=__lowercase			,       repo_type='''dataset'''			,       )
   UpperCAmelCase_							:      Optional[Any]						=   np.load(__lowercase       )
   return list(__lowercase       )
def      snake_case_						( __lowercase			,       __lowercase=None			,       __lowercase=False       ):
   UpperCAmelCase_							:      Tuple						=   {
       # fully supervised kinetics-400 checkpoints
       '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
       '''xclip-base-patch32-16-frames''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
       ),
       '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
       '''xclip-base-patch16-16-frames''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
       ),
       '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
       '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
       # fully supervised kinetics-600 checkpoints
       '''xclip-base-patch16-kinetics-600''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
       ),
       '''xclip-base-patch16-kinetics-600-16-frames''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
       ),
       '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
       # few shot
       '''xclip-base-patch16-hmdb-2-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
       ),
       '''xclip-base-patch16-hmdb-4-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
       ),
       '''xclip-base-patch16-hmdb-8-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
       ),
       '''xclip-base-patch16-hmdb-16-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
       ),
       '''xclip-base-patch16-ucf-2-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
       ),
       '''xclip-base-patch16-ucf-4-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
       ),
       '''xclip-base-patch16-ucf-8-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
       ),
       '''xclip-base-patch16-ucf-16-shot''': (
           '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
       ),
       # zero shot
       '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
   }

   UpperCAmelCase_							:      Dict						=   model_to_url[model_name]
   UpperCAmelCase_							:      Union[str, Any]						=   8
   if "16-frames" in model_name:
      UpperCAmelCase_							:      str						=   1_6
   elif "shot" in model_name:
      UpperCAmelCase_							:      Tuple						=   3_2

   UpperCAmelCase_							:      List[str]						=   get_xclip_config(__lowercase			,       __lowercase       )
   UpperCAmelCase_							:      Any						=   XCLIPModel(__lowercase       )
   model.eval()

   if "drive" in checkpoint_url:
      UpperCAmelCase_							:      Dict						=   '''pytorch_model.bin'''
      gdown.cached_download(__lowercase			,       __lowercase			,       quiet=__lowercase       )
      UpperCAmelCase_							:      int						=   torch.load(__lowercase			,       map_location='''cpu'''       )['''model''']
   else:
      UpperCAmelCase_							:      Tuple						=   torch.hub.load_state_dict_from_url(__lowercase       )['''model''']

   UpperCAmelCase_							:      List[Any]						=   convert_state_dict(__lowercase			,       __lowercase       )

   UpperCAmelCase_							:      Union[str, Any]						=   XCLIPModel(__lowercase       )
   UpperCAmelCase_							:      Tuple						=   model.load_state_dict(__lowercase			,       strict=__lowercase       )
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   UpperCAmelCase_							:      Tuple						=   3_3_6 if model_name == '''xclip-large-patch14-16-frames''' else 2_2_4
   UpperCAmelCase_							:      Tuple						=   VideoMAEImageProcessor(size=__lowercase       )
   UpperCAmelCase_							:      Optional[Any]						=   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''       )
   UpperCAmelCase_							:      int						=   CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''       )
   UpperCAmelCase_							:      Union[str, Any]						=   XCLIPProcessor(image_processor=__lowercase			,       tokenizer=__lowercase       )

   UpperCAmelCase_							:      List[Any]						=   prepare_video(__lowercase       )
   UpperCAmelCase_							:      str						=   processor(
       text=['''playing sports''', '''eating spaghetti''', '''go shopping''']			,       videos=__lowercase			,       return_tensors='''pt'''			,       padding=__lowercase       )

   print('''Shape of pixel values:'''			,       inputs.pixel_values.shape       )

   with torch.no_grad():
      UpperCAmelCase_							:      Optional[int]						=   model(**__lowercase       )

   # Verify outputs
   UpperCAmelCase_							:      Dict						=   outputs.logits_per_video
   UpperCAmelCase_							:      Any						=   logits_per_video.softmax(dim=1       )
   print('''Probs:'''			,       __lowercase       )
   # kinetics-400
   if model_name == "xclip-base-patch32":
      UpperCAmelCase_							:      Optional[int]						=   torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]       )
   elif model_name == "xclip-base-patch32-16-frames":
      UpperCAmelCase_							:      int						=   torch.tensor([[7.09_99e-04, 9.98_83e-01, 4.55_80e-04]]       )
   elif model_name == "xclip-base-patch16":
      UpperCAmelCase_							:      List[Any]						=   torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]       )
   elif model_name == "xclip-base-patch16-16-frames":
      UpperCAmelCase_							:      str						=   torch.tensor([[7.69_37e-04, 9.97_28e-01, 1.94_73e-03]]       )
   elif model_name == "xclip-large-patch14":
      UpperCAmelCase_							:      str						=   torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]       )
   elif model_name == "xclip-large-patch14-16-frames":
      UpperCAmelCase_							:      List[str]						=   torch.tensor([[3.38_77e-04, 9.99_37e-01, 2.88_88e-04]]       )
   # kinetics-600
   elif model_name == "xclip-base-patch16-kinetics-600":
      UpperCAmelCase_							:      Tuple						=   torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]       )
   elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
      UpperCAmelCase_							:      List[str]						=   torch.tensor([[3.85_54e-04, 9.99_29e-01, 3.27_54e-04]]       )
   elif model_name == "xclip-large-patch14-kinetics-600":
      UpperCAmelCase_							:      Any						=   torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]       )
   # few shot
   elif model_name == "xclip-base-patch16-hmdb-2-shot":
      UpperCAmelCase_							:      Any						=   torch.tensor([[7.18_90e-06, 9.99_94e-01, 5.65_59e-05]]       )
   elif model_name == "xclip-base-patch16-hmdb-4-shot":
      UpperCAmelCase_							:      Optional[Any]						=   torch.tensor([[1.03_20e-05, 9.99_93e-01, 6.24_35e-05]]       )
   elif model_name == "xclip-base-patch16-hmdb-8-shot":
      UpperCAmelCase_							:      Union[str, Any]						=   torch.tensor([[4.13_77e-06, 9.99_90e-01, 9.83_86e-05]]       )
   elif model_name == "xclip-base-patch16-hmdb-16-shot":
      UpperCAmelCase_							:      int						=   torch.tensor([[4.13_47e-05, 9.99_62e-01, 3.34_11e-04]]       )
   elif model_name == "xclip-base-patch16-ucf-2-shot":
      UpperCAmelCase_							:      List[str]						=   torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]       )
   elif model_name == "xclip-base-patch16-ucf-4-shot":
      UpperCAmelCase_							:      List[Any]						=   torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]       )
   elif model_name == "xclip-base-patch16-ucf-8-shot":
      UpperCAmelCase_							:      int						=   torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]       )
   elif model_name == "xclip-base-patch16-ucf-16-shot":
      UpperCAmelCase_							:      List[str]						=   torch.tensor([[9.82_19e-04, 9.95_93e-01, 3.08_63e-03]]       )
   # zero shot
   elif model_name == "xclip-base-patch16-zero-shot":
      UpperCAmelCase_							:      int						=   torch.tensor([[3.50_82e-04, 9.97_85e-01, 1.79_66e-03]]       )
   else:
      raise ValueError(F'''Model name {model_name} not supported'''       )
   assert torch.allclose(__lowercase			,       __lowercase			,       atol=1e-3       )
   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(__lowercase       )

   if push_to_hub:
      print('''Pushing model, processor and slow tokenizer files to the hub...'''       )
      model.push_to_hub(__lowercase			,       organization='''nielsr'''       )
      processor.push_to_hub(__lowercase			,       organization='''nielsr'''       )
      slow_tokenizer.push_to_hub(__lowercase			,       organization='''nielsr'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  Any      					=		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='xclip-base-patch32',
           type=str,
           help='Name of the model.',
       )
       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.'
       )

       __UpperCamelCase				:  Tuple      					=		parser.parse_args()
       convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)



# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase			,       __lowercase       ):
   UpperCAmelCase_							:      int						=   multiprocessing.Manager()
   UpperCAmelCase_							:      Union[str, Any]						=   manager.list()

   UpperCAmelCase_							:      int						=   multiprocessing.Process(target=__lowercase			,       args=(check_program, result, timeout)       )
   p.start()
   p.join(timeout=timeout + 1       )
   if p.is_alive():
      p.kill()

   if not result:
      result.append('''timed out'''       )

   return {
       "task_id": task_id,
       "passed": result[0] == "passed",
       "result": result[0],
       "completion_id": completion_id,
   }
def      snake_case_						( __lowercase			,       __lowercase			,       __lowercase       ):
   with create_tempdir():
      # These system calls are needed when cleaning up tempdir.
      import os
      import shutil

      UpperCAmelCase_							:      str						=   shutil.rmtree
      UpperCAmelCase_							:      Tuple						=   os.rmdir
      UpperCAmelCase_							:      Dict						=   os.chdir

      # Disable functionalities that can make destructive changes to the test.
      reliability_guard()

      # Run program.
      try:
         UpperCAmelCase_							:      Optional[int]						=   {}
         with swallow_io():
            with time_limit(__lowercase       ):
               exec(__lowercase			,       __lowercase       )
         result.append('''passed'''       )
      except TimeoutException:
         result.append('''timed out'''       )
      except BaseException as e:
         result.append(F'''failed: {e}'''       )

      # Needed for cleaning up.
      UpperCAmelCase_							:      Optional[int]						=   rmtree
      UpperCAmelCase_							:      Optional[Any]						=   rmdir
      UpperCAmelCase_							:      Optional[Any]						=   chdir
@contextlib.contextmanager
def      snake_case_						( __lowercase       ):
   def signal_handler(__lowercase			,       __lowercase       ):
      raise TimeoutException('''Timed out!'''       )

   signal.setitimer(signal.ITIMER_REAL			,       __lowercase       )
   signal.signal(signal.SIGALRM			,       __lowercase       )
   try:
      yield
   finally:
      signal.setitimer(signal.ITIMER_REAL			,       0       )
@contextlib.contextmanager
def      snake_case_						( ):
   UpperCAmelCase_							:      Optional[Any]						=   WriteOnlyStringIO()
   with contextlib.redirect_stdout(__lowercase       ):
      with contextlib.redirect_stderr(__lowercase       ):
         with redirect_stdin(__lowercase       ):
            yield
@contextlib.contextmanager
def      snake_case_						( ):
   with tempfile.TemporaryDirectory() as dirname:
      with chdir(__lowercase       ):
         yield dirname





class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    pass





class  lowerCAmelCase__(    io.StringIO						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   Dict       ,   *__snake_case				:   List[Any]       ,   **__snake_case				:   int	):





       '''simple docstring'''
       raise OSError





    def 					_lowerCamelCase (	self				:   Dict       ,   *__snake_case				:   int       ,   **__snake_case				:   Any	):





       '''simple docstring'''
       raise OSError





    def 					_lowerCamelCase (	self				:   int       ,   *__snake_case				:   List[str]       ,   **__snake_case				:   Optional[Any]	):





       '''simple docstring'''
       raise OSError





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   *__snake_case				:   Optional[Any]       ,   **__snake_case				:   List[Any]	):





       '''simple docstring'''
       return False





class  lowerCAmelCase__(    contextlib._RedirectStream						):  # type: ignore






    '''simple docstring'''





    A_  :     Optional[Any]			= 'stdin'





@contextlib.contextmanager
def      snake_case_						( __lowercase       ):
   if root == ".":
      yield
      return
   UpperCAmelCase_							:      Tuple						=   os.getcwd()
   os.chdir(__lowercase       )
   try:
      yield
   except BaseException as exc:
      raise exc
   finally:
      os.chdir(__lowercase       )


def      snake_case_						( __lowercase=None       ):

   if maximum_memory_bytes is not None:
      import resource

      resource.setrlimit(resource.RLIMIT_AS			,       (maximum_memory_bytes, maximum_memory_bytes)       )
      resource.setrlimit(resource.RLIMIT_DATA			,       (maximum_memory_bytes, maximum_memory_bytes)       )
      if not platform.uname().system == "Darwin":
         resource.setrlimit(resource.RLIMIT_STACK			,       (maximum_memory_bytes, maximum_memory_bytes)       )

   faulthandler.disable()

   import builtins

   UpperCAmelCase_							:      Any						=   None
   UpperCAmelCase_							:      Any						=   None

   import os

   UpperCAmelCase_							:      Union[str, Any]						=   '''1'''

   UpperCAmelCase_							:      List[str]						=   None
   UpperCAmelCase_							:      Dict						=   None
   UpperCAmelCase_							:      Any						=   None
   UpperCAmelCase_							:      Optional[int]						=   None
   UpperCAmelCase_							:      List[str]						=   None
   UpperCAmelCase_							:      List[Any]						=   None
   UpperCAmelCase_							:      Union[str, Any]						=   None
   UpperCAmelCase_							:      str						=   None
   UpperCAmelCase_							:      Dict						=   None
   UpperCAmelCase_							:      int						=   None
   UpperCAmelCase_							:      Dict						=   None
   UpperCAmelCase_							:      List[Any]						=   None
   UpperCAmelCase_							:      List[Any]						=   None
   UpperCAmelCase_							:      Optional[Any]						=   None
   UpperCAmelCase_							:      Optional[Any]						=   None
   UpperCAmelCase_							:      Dict						=   None
   UpperCAmelCase_							:      Optional[Any]						=   None
   UpperCAmelCase_							:      Optional[Any]						=   None
   UpperCAmelCase_							:      Optional[Any]						=   None
   UpperCAmelCase_							:      List[Any]						=   None
   UpperCAmelCase_							:      int						=   None
   UpperCAmelCase_							:      Union[str, Any]						=   None
   UpperCAmelCase_							:      List[Any]						=   None
   UpperCAmelCase_							:      Dict						=   None
   UpperCAmelCase_							:      Union[str, Any]						=   None
   UpperCAmelCase_							:      Dict						=   None
   UpperCAmelCase_							:      Any						=   None

   import shutil

   UpperCAmelCase_							:      Union[str, Any]						=   None
   UpperCAmelCase_							:      Dict						=   None
   UpperCAmelCase_							:      Tuple						=   None

   import subprocess

   UpperCAmelCase_							:      Dict						=   None  # type: ignore

   UpperCAmelCase_							:      Union[str, Any]						=   None

   import sys

   UpperCAmelCase_							:      Optional[Any]						=   None
   UpperCAmelCase_							:      List[Any]						=   None
   UpperCAmelCase_							:      List[Any]						=   None
   UpperCAmelCase_							:      List[str]						=   None
   UpperCAmelCase_							:      Dict						=   None