





import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


UpperCAmelCase_       : List[str]							=    namedtuple("""ExampleNamedTuple""", """a b c""")





class   lowercase__     (				unittest.TestCase	):







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							List[Any]			 =		torch.randn(5     ,    2    )
       lowerCAmelCase_       :							List[Any]			 =		torch.device("""cuda"""    ) if torch.cuda.is_available() else torch.device("""cpu"""    )

       lowerCAmelCase_       :							Optional[Any]			 =		send_to_device(_lowercase     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta.cpu()     ,    _lowercase    )    )

       lowerCAmelCase_       :							Dict			 =		send_to_device((tensor, [tensor, tensor], 1)     ,    _lowercase    )
       self.assertIsInstance(_lowercase     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta[0].cpu()     ,    _lowercase    )    )
       self.assertIsInstance(resulta[1]     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta[1][0].cpu()     ,    _lowercase    )    )
       self.assertTrue(torch.equal(resulta[1][1].cpu()     ,    _lowercase    )    )
       self.assertEqual(resulta[2]     ,    1    )

       lowerCAmelCase_       :							List[Any]			 =		send_to_device({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}     ,    _lowercase    )
       self.assertIsInstance(_lowercase     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta["""a"""].cpu()     ,    _lowercase    )    )
       self.assertIsInstance(resulta["""b"""]     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta["""b"""][0].cpu()     ,    _lowercase    )    )
       self.assertTrue(torch.equal(resulta["""b"""][1].cpu()     ,    _lowercase    )    )
       self.assertEqual(resulta["""c"""]     ,    1    )

       lowerCAmelCase_       :							Optional[int]			 =		send_to_device(ExampleNamedTuple(a=_lowercase     ,    b=[tensor, tensor]     ,    c=1    )     ,    _lowercase    )
       self.assertIsInstance(_lowercase     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta.a.cpu()     ,    _lowercase    )    )
       self.assertIsInstance(resulta.b     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta.b[0].cpu()     ,    _lowercase    )    )
       self.assertTrue(torch.equal(resulta.b[1].cpu()     ,    _lowercase    )    )
       self.assertEqual(resulta.c     ,    1    )

       lowerCAmelCase_       :							str			 =		send_to_device(UserDict({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}    )     ,    _lowercase    )
       self.assertIsInstance(_lowercase     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta["""a"""].cpu()     ,    _lowercase    )    )
       self.assertIsInstance(resulta["""b"""]     ,    _lowercase    )
       self.assertTrue(torch.equal(resulta["""b"""][0].cpu()     ,    _lowercase    )    )
       self.assertTrue(torch.equal(resulta["""b"""][1].cpu()     ,    _lowercase    )    )
       self.assertEqual(resulta["""c"""]     ,    1    )







  def 						UpperCAmelCase__		(						self    ):
       with self.assertRaises(_lowercase    ) as cm:
            lowerCAmelCase_       :							List[str]			 =		recursively_apply(torch.tensor     ,    (torch.tensor(1    ), 1)     ,    error_on_other_type=_lowercase    )
       self.assertEqual(
           str(cm.exception    )     ,    """Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed."""     ,    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Dict			 =		torch.tensor([1, 2, 3, 4, 5]    )
       self.assertEqual(listify(_lowercase    )     ,    [1, 2, 3, 4, 5]    )

       lowerCAmelCase_       :							int			 =		torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]    )
       self.assertEqual(listify(_lowercase    )     ,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]    )

       lowerCAmelCase_       :							Optional[int]			 =		torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]    )
       self.assertEqual(
           listify(_lowercase    )     ,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]    )







  def 						UpperCAmelCase__		(						self    ):
       with patch_environment(aa=1     ,    BB=2    ):
            self.assertEqual(os.environ.get("""AA"""    )     ,    """1"""    )
            self.assertEqual(os.environ.get("""BB"""    )     ,    """2"""    )

       self.assertNotIn("""AA"""     ,    os.environ    )
       self.assertNotIn("""BB"""     ,    os.environ    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		RegressionModel()
       lowerCAmelCase_       :							Union[str, Any]			 =		model.forward
       lowerCAmelCase_       :							Dict			 =		convert_outputs_to_fpaa(model.forward    )
       lowerCAmelCase_       :							Optional[int]			 =		extract_model_from_parallel(_lowercase     ,    keep_fpaa_wrapper=_lowercase    )
       lowerCAmelCase_       :							List[Any]			 =		pickle.dumps(_lowercase    )







  @require_cuda
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Dict			 =		RegressionModel()
       lowerCAmelCase_       :							List[str]			 =		model.forward
       lowerCAmelCase_       :							Any			 =		torch.cuda.amp.autocast(dtype=torch.floataa    )(model.forward    )
       lowerCAmelCase_       :							str			 =		convert_outputs_to_fpaa(model.forward    )
       lowerCAmelCase_       :							Optional[int]			 =		extract_model_from_parallel(_lowercase     ,    keep_fpaa_wrapper=_lowercase    )
       lowerCAmelCase_       :							Optional[Any]			 =		pickle.dumps(_lowercase    )







  @require_cuda
  @require_torch_min_version(version="""2.0"""    )
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							str			 =		RegressionModel()
       lowerCAmelCase_       :							Union[str, Any]			 =		model.forward
       lowerCAmelCase_       :							List[str]			 =		torch.cuda.amp.autocast(dtype=torch.floataa    )(model.forward    )
       lowerCAmelCase_       :							Optional[int]			 =		convert_outputs_to_fpaa(model.forward    )
       lowerCAmelCase_       :							List[Any]			 =		torch.compile(model.forward     ,    backend="""inductor"""    )
       lowerCAmelCase_       :							Union[str, Any]			 =		torch.randn(4     ,    10    ).cuda()
       lowerCAmelCase_       :							Optional[Any]			 =		model(_lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							List[str]			 =		RegressionModel()
       # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
       lowerCAmelCase_       :							List[str]			 =		torch.nn.parallel.DataParallel(_lowercase    )
       lowerCAmelCase_       :							Optional[int]			 =		extract_model_from_parallel(_lowercase    )

       self.assertEqual(_lowercase     ,    _lowercase    )







  @require_torch_min_version(version="""2.0"""    )
  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Tuple			 =		RegressionModel()
       lowerCAmelCase_       :							Optional[int]			 =		torch.compile(_lowercase    )

       # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
       lowerCAmelCase_       :							Union[str, Any]			 =		torch.nn.parallel.DataParallel(_lowercase    )
       lowerCAmelCase_       :							Tuple			 =		torch.compile(_lowercase    )
       lowerCAmelCase_       :							Tuple			 =		extract_model_from_parallel(_lowercase    )

       self.assertEqual(compiled_model._orig_mod     ,    compiled_model_unwrapped._orig_mod    )







  def 						UpperCAmelCase__		(						self    ):
       self.assertEqual(find_device([1, """a""", torch.tensor([1, 2, 3]    )]    )     ,    torch.device("""cpu"""    )    )
       self.assertEqual(find_device({"""a""": 1, """b""": torch.tensor([1, 2, 3]    )}    )     ,    torch.device("""cpu"""    )    )
       self.assertIsNone(find_device([1, """a"""]    )    )







from __future__ import annotations

import sys





class   lowercase__     :







  def __init__(						self     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							str			 =		letter
       lowerCAmelCase_       :							int			 =		freq
       lowerCAmelCase_       :							dict[str, str]			 =		{}







  def __repr__(						self    ):
       return F'{self.letter}:{self.freq}'





class   lowercase__     :







  def __init__(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase    ):
       lowerCAmelCase_       :							int			 =		freq
       lowerCAmelCase_       :							Letter | TreeNode			 =		left
       lowerCAmelCase_       :							Letter | TreeNode			 =		right






def 				_lowerCAmelCase			(	_a :       str      )						->      list[Letter]:
     lowerCAmelCase_       :							dict[str, int]			 =		{}
     with open(_a      ) as f:
          while True:
               lowerCAmelCase_       :							Any			 =		f.read(1      )
               if not c:
                    break
               lowerCAmelCase_       :							List[str]			 =		chars[c] + 1 if c in chars else 1
     return sorted((Letter(_a       , _a      ) for c, f in chars.items())       , key=lambda _a      : x.freq      )






def 				_lowerCAmelCase			(	_a :       list[Letter]      )						->      Letter | TreeNode:
     lowerCAmelCase_       :							list[Letter | TreeNode]			 =		letters  # type: ignore
     while len(_a      ) > 1:
          lowerCAmelCase_       :							Any			 =		response.pop(0      )
          lowerCAmelCase_       :							int			 =		response.pop(0      )
          lowerCAmelCase_       :							List[Any]			 =		left.freq + right.freq
          lowerCAmelCase_       :							Union[str, Any]			 =		TreeNode(_a       , _a       , _a      )
          response.append(_a      )
          response.sort(key=lambda _a      : x.freq      )
     return response[0]






def 				_lowerCAmelCase			(	_a :       Letter | TreeNode       , _a :       str      )						->      list[Letter]:
     if isinstance(_a       , _a      ):
          lowerCAmelCase_       :							Union[str, Any]			 =		bitstring
          return [root]
     lowerCAmelCase_       :							TreeNode			 =		root  # type: ignore
     lowerCAmelCase_       :							Tuple			 =		[]
     letters += traverse_tree(treenode.left       , bitstring + """0"""      )
     letters += traverse_tree(treenode.right       , bitstring + """1"""      )
     return letters






def 				_lowerCAmelCase			(	_a :       str      )						->      None:
     lowerCAmelCase_       :							Optional[Any]			 =		parse_file(_a      )
     lowerCAmelCase_       :							Optional[int]			 =		build_tree(_a      )
     lowerCAmelCase_       :							str			 =		{
         k: v for letter in traverse_tree(_a       , """"""      ) for k, v in letter.bitstring.items()
     }
     print(F'Huffman Coding  of {file_path}: '      )
     with open(_a      ) as f:
          while True:
               lowerCAmelCase_       :							Optional[Any]			 =		f.read(1      )
               if not c:
                    break
               print(letters[c]       , end=""" """      )
     print()


if __name__ == "__main__":
      # pass the file path to the huffman function
      huffman(sys.argv[1])
