



"""simple docstring"""
import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class    a		(					unittest.TestCase    ):



  @patch("""sys.argv"""    ,						["""fakeprogrampath""", """env"""]						)
  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   List[str]:


     '''simple docstring'''




     import transformers.commands.transformers_cli

     with CaptureStd() as cs:
        transformers.commands.transformers_cli.main()
     self.assertIn("""Python version"""    ,						cs.out						)
     self.assertIn("""Platform"""    ,						cs.out						)
     self.assertIn("""Using distributed or parallel set-up in script?"""    ,						cs.out						)



  @is_pt_tf_cross_test
  @patch(
      """sys.argv"""    ,						["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]						)
  def 		lowerCamelCase__    (  self	:   Any						)       ->   Optional[int]:


     '''simple docstring'''




     import transformers.commands.transformers_cli

     shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""    ,						ignore_errors=lowerCAmelCase						)  # cleans potential past runs
     transformers.commands.transformers_cli.main()

     # The original repo has no TF weights -- if they exist, they were created by the CLI
     self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""						)						)



  @require_torch
  @patch("""sys.argv"""    ,						["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]						)
  def 		lowerCamelCase__    (  self	:   int						)       ->   str:


     '''simple docstring'''




     import transformers.commands.transformers_cli

     # # remove any previously downloaded model to start clean
     shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""    ,						ignore_errors=lowerCAmelCase						)

     # run the command
     transformers.commands.transformers_cli.main()

     # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""						)						)
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""						)						)
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""						)						)





  @require_torch
  @patch(
      """sys.argv"""    ,						[
          """fakeprogrampath""",
          """download""",
          """hf-internal-testing/test_dynamic_model_with_tokenizer""",
          """--trust-remote-code""",
          """--cache-dir""",
          """/tmp""",
      ]    ,						)
  def 		lowerCamelCase__    (  self	:   int						)       ->   Union[str, Any]:


     '''simple docstring'''




     import transformers.commands.transformers_cli

     # # remove any previously downloaded model to start clean
     shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""    ,						ignore_errors=lowerCAmelCase						)

     # run the command
     transformers.commands.transformers_cli.main()

     # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""						)						)
     self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""						)						)
     self.assertTrue(
         os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""						)						)








"""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])



