






import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
UpperCamelCase__						:      Optional[int]    		=					"""stas/tiny-wmt19-en-ru"""







class 				lowerCamelCase_     (	lowerCAmelCase__ ,						unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			FSMTTokenizer
							SCREAMING_SNAKE_CASE_						     =			False
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								a		=			[
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								a		=			dict(zip(_lowerCamelCase							,range(len(_lowerCamelCase					)					)					)					)
								a		=			["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								a		=			["""en""", """ru"""]
								a		=			{
								    """langs""": self.langs,
								    """src_vocab_size""": 10,
								    """tgt_vocab_size""": 20,
								}

								a		=			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES['''src_vocab_file''']					)
								a		=			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES['''tgt_vocab_file''']					)
								a		=			os.path.join(self.tmpdirname							,'''tokenizer_config.json'''					)
								a		=			os.path.join(self.tmpdirname							,VOCAB_FILES_NAMES['''merges_file''']					)
								with open(self.src_vocab_file							,'''w'''					) as fp:
									fp.write(json.dumps(_lowerCamelCase					)					)
								with open(self.tgt_vocab_file							,'''w'''					) as fp:
									fp.write(json.dumps(_lowerCamelCase					)					)
								with open(self.merges_file							,'''w'''					) as fp:
									fp.write('''\n'''.join(_lowerCamelCase					)					)
								with open(_lowerCamelCase							,'''w'''					) as fp:
									fp.write(json.dumps(_lowerCamelCase					)					)
							@cached_property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''					)
							@cached_property
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								a		=			FSMTTokenizer.from_pretrained(_lowerCamelCase					)
								self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]							,['''en''', '''ru''']					)
								self.assertEqual(tokenizer.src_vocab_size							,21					)
								self.assertEqual(tokenizer.tgt_vocab_size							,21					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			FSMTTokenizer(self.langs							,self.src_vocab_file							,self.tgt_vocab_file							,self.merges_file					)

								a		=			"""lower"""
								a		=			["""low""", """er</w>"""]
								a		=			tokenizer.tokenize(_lowerCamelCase					)
								self.assertListEqual(_lowerCamelCase							,_lowerCamelCase					)

								a		=			tokens + ["""<unk>"""]
								a		=			[14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase					)							,_lowerCamelCase					)
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			self.tokenizer_ru_en

								a		=			tokenizer.encode('''sequence builders'''							,add_special_tokens=_lowerCamelCase					)
								a		=			tokenizer.encode('''multi-sequence build'''							,add_special_tokens=_lowerCamelCase					)

								a		=			tokenizer.build_inputs_with_special_tokens(_lowerCamelCase					)
								a		=			tokenizer.build_inputs_with_special_tokens(_lowerCamelCase							,_lowerCamelCase					)

								assert encoded_sentence == text + [2]
								assert encoded_pair == text + [2] + text_a + [2]
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								a		=			self.tokenizer_en_ru
								a		=			self.tokenizer_ru_en

								a		=			[
								    [
								        """Here's a little song I wrote. Don't worry, be happy.""",
								        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
								    ],
								    ["""This is it. No more. I'm done!""", [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
								]

								# if data needs to be recreated or added, run:
								# import torch
								# model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
								# for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

								for src_text, tgt_input_ids in targets:
									a		=			tokenizer_enc.encode(_lowerCamelCase							,return_tensors=_lowerCamelCase					)
									self.assertListEqual(_lowerCamelCase							,_lowerCamelCase					)

									# and decode backward, using the reversed languages model
									a		=			tokenizer_dec.decode(_lowerCamelCase							,skip_special_tokens=_lowerCamelCase					)
									self.assertEqual(_lowerCamelCase							,_lowerCamelCase					)
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								a		=			FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''							,do_lower_case=_lowerCamelCase					)
								a		=			tokenizer.tokenize('''USA is United States of America'''					)
								a		=			["""us""", """a</w>""", """is</w>""", """un""", """i""", """ted</w>""", """st""", """ates</w>""", """of</w>""", """am""", """er""", """ica</w>"""]
								self.assertListEqual(_lowerCamelCase							,_lowerCamelCase					)
							@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								pass




							@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								pass













from __future__ import annotations

import sys







class 				lowerCamelCase_     :
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			letter
								a		=			freq
								a		=			{}




							def __repr__(						self		:					List[str]					):





								'''simple docstring'''



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







class 				lowerCamelCase_     :
							def __init__(						self		:					Dict							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Letter | TreeNode							,__lowerCamelCase		:					Letter | TreeNode					):





								'''simple docstring'''



								a		=			freq
								a		=			left
								a		=			right





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	a		=			{}
	with open(snake_case_  ) 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(snake_case_,  snake_case_  ) for c, f in chars.items()),  key=lambda snake_case_  : x.freq  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Letter | TreeNode:


	"""simple docstring"""



	a		=			letters  # type: ignore
	while len(snake_case_  ) > 1:
		a		=			response.pop(0  )
		a		=			response.pop(0  )
		a		=			left.freq + right.freq
		a		=			TreeNode(snake_case_,  snake_case_,  snake_case_  )
		response.append(snake_case_  )
		response.sort(key=lambda snake_case_  : x.freq  )
	return response[0]





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	if isinstance(snake_case_,  snake_case_  ):
		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__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			parse_file(snake_case_  )
	a		=			build_tree(snake_case_  )
	a		=			{
	    k: v for letter in traverse_tree(snake_case_,  ''''''  ) for k, v in letter.bitstring.items()
	}
	print(f"""Huffman Coding  of {file_path}: """  )
	with open(snake_case_  ) 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])





