




"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin




@skip_mps
class       UpperCAmelCase_    (		A_,  A_,  unittest.TestCase				):
    lowercase__		   = IFSuperResolutionPipeline
    lowercase__		   = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    lowercase__		   = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowercase__		   = PipelineTesterMixin.required_optional_params - {'''latents'''}




    def 				__magic_name__				(       self		:			List[str] ) ->				Tuple:




     '''simple docstring'''

     return self._get_superresolution_dummy_components()




    def 				__magic_name__				(       self		:			Optional[Any]      ,					snake_case_		:			List[str]      ,					snake_case_		:			Optional[int]=0 ) ->				Tuple:




     '''simple docstring'''

     if str(snake_case_ ).startswith("mps" ):
      A__			=							torch.manual_seed(snake_case_ )
     else:
      A__			=							torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )

     A__			=							floats_tensor((1, 3, 32, 32)      ,					rng=random.Random(snake_case_ ) ).to(snake_case_ )

     A__			=							{
         "prompt": "A painting of a squirrel eating a burger",
         "image": image,
         "generator": generator,
         "num_inference_steps": 2,
         "output_type": "numpy",
     }

     return inputs




    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available()      ,					reason="XFormers attention is only available with CUDA and `xformers` installed"      ,					)
    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				str:




     '''simple docstring'''

     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )




    def 				__magic_name__				(       self		:			List[str] ) ->				int:




     '''simple docstring'''

     self._test_save_load_optional_components()




    @unittest.skipIf(torch_device != "cuda"      ,					reason="float16 requires CUDA" )
    def 				__magic_name__				(       self		:			List[str] ) ->				int:




     '''simple docstring'''

     super().test_save_load_floataa(expected_max_diff=1e-1 )




    def 				__magic_name__				(       self		:			Optional[int] ) ->				Any:




     '''simple docstring'''

     self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )




    def 				__magic_name__				(       self		:			Union[str, Any] ) ->				Any:




     '''simple docstring'''

     self._test_save_load_local()




    def 				__magic_name__				(       self		:			int ) ->				Optional[int]:




     '''simple docstring'''

     self._test_inference_batch_single_identical(
         expected_max_diff=1e-2      ,					)











"""simple docstring"""



from __future__ import annotations

import sys




class       UpperCAmelCase_    :




    def __init__(       self		:			Union[str, Any]      ,					snake_case_		:			str      ,					snake_case_		:			int ) ->				int:




     '''simple docstring'''

     A__			=							letter
     A__			=							freq
     A__			=							{}




    def __repr__(       self		:			Union[str, Any] ) ->				str:




     '''simple docstring'''

     return F"""{self.letter}:{self.freq}"""






class       UpperCAmelCase_    :




    def __init__(       self		:			Dict      ,					snake_case_		:			int      ,					snake_case_		:			Letter | TreeNode      ,					snake_case_		:			Letter | TreeNode ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							freq
     A__			=							left
     A__			=							right







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						list[Letter]:
 A__			=							{}
 with open(lowercase_		) as f:
  while True:
   A__			=							f.read(1		)
   if not c:
    break
   A__			=							chars[c] + 1 if c in chars else 1
 return sorted((Letter(lowercase_     ,					lowercase_		) for c, f in chars.items())     ,					key=lambda lowercase_		: x.freq		)







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						Letter | TreeNode:
 A__			=							letters  # type: ignore
 while len(lowercase_		) > 1:
  A__			=							response.pop(0		)
  A__			=							response.pop(0		)
  A__			=							left.freq + right.freq
  A__			=							TreeNode(lowercase_     ,					lowercase_     ,					lowercase_		)
  response.append(lowercase_		)
  response.sort(key=lambda lowercase_		: x.freq		)
 return response[0]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						list[Letter]:
 if isinstance(lowercase_     ,					lowercase_		):
  A__			=							bitstring
  return [root]
 A__			=							root  # type: ignore
 A__			=							[]
 letters += traverse_tree(treenode.left     ,					bitstring + "0"		)
 letters += traverse_tree(treenode.right     ,					bitstring + "1"		)
 return letters







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						None:
 A__			=							parse_file(lowercase_		)
 A__			=							build_tree(lowercase_		)
 A__			=							{
     k: v for letter in traverse_tree(lowercase_     ,					""		) for k, v in letter.bitstring.items()
 }
 print(f"""Huffman Coding  of {file_path}: """		)
 with open(lowercase_		) as f:
  while True:
   A__			=							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])





