


"""simple docstring"""






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,
)


_lowerCamelCase  =	namedtuple('ExampleNamedTuple', 'a b c')


class       lowerCamelCase_		(      unittest.TestCase ):





				"""simple docstring"""







				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					torch.randn(5							,		2  )
								SCREAMING_SNAKE_CASE__  				=					torch.device("cuda"  ) if torch.cuda.is_available() else torch.device("cpu"  )

								SCREAMING_SNAKE_CASE__  				=					send_to_device(_lowercase							,		_lowercase  )
								self.assertTrue(torch.equal(resulta.cpu()							,		_lowercase  )  )

								SCREAMING_SNAKE_CASE__  				=					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  )

								SCREAMING_SNAKE_CASE__  				=					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  )

								SCREAMING_SNAKE_CASE__  				=					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  )

								SCREAMING_SNAKE_CASE__  				=					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 	lowerCAmelCase__		(    self  ):
								with self.assertRaises(_lowercase  ) as cm:
												SCREAMING_SNAKE_CASE__  				=					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 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					torch.tensor([1, 2, 3, 4, 5]  )
								self.assertEqual(listify(_lowercase  )							,		[1, 2, 3, 4, 5]  )

								SCREAMING_SNAKE_CASE__  				=					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]]  )

								SCREAMING_SNAKE_CASE__  				=					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 	lowerCAmelCase__		(    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 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					RegressionModel()
								SCREAMING_SNAKE_CASE__  				=					model.forward
								SCREAMING_SNAKE_CASE__  				=					convert_outputs_to_fpaa(model.forward  )
								SCREAMING_SNAKE_CASE__  				=					extract_model_from_parallel(_lowercase							,		keep_fpaa_wrapper=_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					pickle.dumps(_lowercase  )



				@require_cuda
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					RegressionModel()
								SCREAMING_SNAKE_CASE__  				=					model.forward
								SCREAMING_SNAKE_CASE__  				=					torch.cuda.amp.autocast(dtype=torch.floataa  )(model.forward  )
								SCREAMING_SNAKE_CASE__  				=					convert_outputs_to_fpaa(model.forward  )
								SCREAMING_SNAKE_CASE__  				=					extract_model_from_parallel(_lowercase							,		keep_fpaa_wrapper=_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					pickle.dumps(_lowercase  )



				@require_cuda
				@require_torch_min_version(version="2.0"  )
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					RegressionModel()
								SCREAMING_SNAKE_CASE__  				=					model.forward
								SCREAMING_SNAKE_CASE__  				=					torch.cuda.amp.autocast(dtype=torch.floataa  )(model.forward  )
								SCREAMING_SNAKE_CASE__  				=					convert_outputs_to_fpaa(model.forward  )
								SCREAMING_SNAKE_CASE__  				=					torch.compile(model.forward							,		backend="inductor"  )
								SCREAMING_SNAKE_CASE__  				=					torch.randn(4							,		10  ).cuda()
								SCREAMING_SNAKE_CASE__  				=					model(_lowercase  )



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					RegressionModel()
								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								SCREAMING_SNAKE_CASE__  				=					torch.nn.parallel.DataParallel(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					extract_model_from_parallel(_lowercase  )

								self.assertEqual(_lowercase							,		_lowercase  )



				@require_torch_min_version(version="2.0"  )
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					RegressionModel()
								SCREAMING_SNAKE_CASE__  				=					torch.compile(_lowercase  )

								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								SCREAMING_SNAKE_CASE__  				=					torch.nn.parallel.DataParallel(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					torch.compile(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					extract_model_from_parallel(_lowercase  )

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






				def 	lowerCAmelCase__		(    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])
