



"""simple docstring"""
import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor
class    a		(					unittest.TestCase    ):



  def 		lowerCamelCase__    (  self	:   List[Any]    ,						lowerCAmelCase	:   bool = False						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[Any]            =10

     if only_cross_attention:
        SCREAMING_SNAKE_CASE_:	Tuple            =12
     else:
        # when only cross attention is not set, the cross attention dim must be the same as the query dim
        SCREAMING_SNAKE_CASE_:	int            =query_dim

     return {
         "query_dim": query_dim,
         "cross_attention_dim": cross_attention_dim,
         "heads": 2,
         "dim_head": 4,
         "added_kv_proj_dim": 6,
         "norm_num_groups": 1,
         "only_cross_attention": only_cross_attention,
         "processor": AttnAddedKVProcessor(),
     }



  def 		lowerCamelCase__    (  self	:   Dict    ,						lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Dict						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =2

     SCREAMING_SNAKE_CASE_:	Tuple            =torch.rand(lowerCAmelCase    ,						lowerCAmelCase    ,						3    ,						2						)
     SCREAMING_SNAKE_CASE_:	Dict            =torch.rand(lowerCAmelCase    ,						4    ,						lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =None

     return {
         "hidden_states": hidden_states,
         "encoder_hidden_states": encoder_hidden_states,
         "attention_mask": attention_mask,
     }





  def 		lowerCamelCase__    (  self	:   str						)       ->   Any:


     '''simple docstring'''




     torch.manual_seed(0						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =self.get_constructor_arguments(only_cross_attention=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Optional[int]            =Attention(**lowerCAmelCase						)

     self.assertTrue(attn.to_k is not None						)
     self.assertTrue(attn.to_v is not None						)

     SCREAMING_SNAKE_CASE_:	Tuple            =self.get_forward_arguments(
         query_dim=constructor_args["""query_dim"""]    ,						added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =attn(**lowerCAmelCase						)

     # only self attention

     torch.manual_seed(0						)

     SCREAMING_SNAKE_CASE_:	Tuple            =self.get_constructor_arguments(only_cross_attention=lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	str            =Attention(**lowerCAmelCase						)

     self.assertTrue(attn.to_k is None						)
     self.assertTrue(attn.to_v is None						)

     SCREAMING_SNAKE_CASE_:	int            =self.get_forward_arguments(
         query_dim=constructor_args["""query_dim"""]    ,						added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]						)

     SCREAMING_SNAKE_CASE_:	Tuple            =attn(**lowerCAmelCase						)

     self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()						)







class    a		(					unittest.TestCase    ):



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Tuple            =DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""    ,						safety_checker=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =pipe(
         """foo"""    ,						num_inference_steps=2    ,						generator=torch.Generator("""cpu"""						).manual_seed(0						)    ,						output_type="""np"""    ,						).images

     # the initial conversion succeeds
     SCREAMING_SNAKE_CASE_:	str            =DiffusionPipeline.from_pretrained(
         """hf-internal-testing/tiny-stable-diffusion-pipe"""    ,						device_map="""sequential"""    ,						safety_checker=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	Optional[Any]            =pipe(
         """foo"""    ,						num_inference_steps=2    ,						generator=torch.Generator("""cpu"""						).manual_seed(0						)    ,						output_type="""np"""    ,						).images

     with tempfile.TemporaryDirectory() as tmpdir:
        # save the converted model
        pipe.save_pretrained(lowerCAmelCase						)

        # can also load the converted weights
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =DiffusionPipeline.from_pretrained(lowerCAmelCase    ,						device_map="""sequential"""    ,						safety_checker=lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =pipe(
         """foo"""    ,						num_inference_steps=2    ,						generator=torch.Generator("""cpu"""						).manual_seed(0						)    ,						output_type="""np"""    ,						).images

     self.assertTrue(np.allclose(lowerCAmelCase    ,						lowerCAmelCase    ,						atol=1E-5						)						)
     self.assertTrue(np.allclose(lowerCAmelCase    ,						lowerCAmelCase    ,						atol=1E-5						)						)








"""simple docstring"""
from __future__ import annotations

import sys
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   int						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =letter
     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	dict[str, str]            ={}





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


     '''simple docstring'''




     return f'''{self.letter}:{self.freq}'''







class    a		:



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


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =left
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =right







def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	dict[str, int]            ={}
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	List[Any]            =f.read(1	)
         if not c:
            break
         SCREAMING_SNAKE_CASE_:	Any            =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        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	list[Letter | TreeNode]            =letters  # type: ignore
   while len(lowercase	) > 1:
      SCREAMING_SNAKE_CASE_:	Any            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =left.freq + right.freq
      SCREAMING_SNAKE_CASE_:	Any            =TreeNode(lowercase							,     lowercase							,     lowercase	)
      response.append(lowercase	)
      response.sort(key=lambda lowercase	: x.freq	)
   return response[0]





def        __magic_name__			(							lowercase							,     lowercase	):
   if isinstance(lowercase							,     lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =bitstring
      return [root]
   SCREAMING_SNAKE_CASE_:	TreeNode            =root  # type: ignore
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
   letters += traverse_tree(treenode.left							,     bitstring + """0"""	)
   letters += traverse_tree(treenode.right							,     bitstring + """1"""	)
   return letters





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =parse_file(lowercase	)
   SCREAMING_SNAKE_CASE_:	str            =build_tree(lowercase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            ={
       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:
         SCREAMING_SNAKE_CASE_:	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])



