





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import SegformerImageProcessor





class   lowercase__     (				unittest.TestCase	):







  def __init__(						self     ,    _lowercase     ,    _lowercase=7     ,    _lowercase=3     ,    _lowercase=30     ,    _lowercase=400     ,    _lowercase=True     ,    _lowercase=None     ,    _lowercase=True     ,    _lowercase=[0.5, 0.5, 0.5]     ,    _lowercase=[0.5, 0.5, 0.5]     ,    _lowercase=False     ,    ):
       lowerCAmelCase_       :							str			 =		size if size is not None else {"""height""": 30, """width""": 30}
       lowerCAmelCase_       :							Any			 =		parent
       lowerCAmelCase_       :							List[Any]			 =		batch_size
       lowerCAmelCase_       :							List[Any]			 =		num_channels
       lowerCAmelCase_       :							int			 =		min_resolution
       lowerCAmelCase_       :							Any			 =		max_resolution
       lowerCAmelCase_       :							Any			 =		do_resize
       lowerCAmelCase_       :							Union[str, Any]			 =		size
       lowerCAmelCase_       :							Any			 =		do_normalize
       lowerCAmelCase_       :							int			 =		image_mean
       lowerCAmelCase_       :							List[Any]			 =		image_std
       lowerCAmelCase_       :							str			 =		do_reduce_labels







  def 						UpperCAmelCase__		(						self    ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_reduce_labels": self.do_reduce_labels,
       }






def 				_lowerCAmelCase			(	)						->      Dict:
     lowerCAmelCase_       :							int			 =		load_dataset("""hf-internal-testing/fixtures_ade20k"""       , split="""test"""      )

     lowerCAmelCase_       :							Union[str, Any]			 =		Image.open(dataset[0]["""file"""]      )
     lowerCAmelCase_       :							Dict			 =		Image.open(dataset[1]["""file"""]      )

     return image, map






def 				_lowerCAmelCase			(	)						->      Union[str, Any]:
     lowerCAmelCase_       :							Optional[Any]			 =		load_dataset("""hf-internal-testing/fixtures_ade20k"""       , split="""test"""      )

     lowerCAmelCase_       :							Union[str, Any]			 =		Image.open(dataset[0]["""file"""]      )
     lowerCAmelCase_       :							int			 =		Image.open(dataset[1]["""file"""]      )
     lowerCAmelCase_       :							int			 =		Image.open(dataset[2]["""file"""]      )
     lowerCAmelCase_       :							List[Any]			 =		Image.open(dataset[3]["""file"""]      )

     return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class   lowercase__     (				__A  ,       unittest.TestCase	):
  __UpperCamelCase						  =	SegformerImageProcessor if is_vision_available() else None







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		SegformerImageProcessingTester(self    )







  @property
  def 						UpperCAmelCase__		(						self    ):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Optional[int]			 =		self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(_lowercase     ,    """do_resize"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """size"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """do_normalize"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """image_mean"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """image_std"""    )    )
       self.assertTrue(hasattr(_lowercase     ,    """do_reduce_labels"""    )    )







  def 						UpperCAmelCase__		(						self    ):
       lowerCAmelCase_       :							Any			 =		self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size     ,    {"""height""": 30, """width""": 30}    )
       self.assertEqual(image_processor.do_reduce_labels     ,    _lowercase    )

       lowerCAmelCase_       :							Optional[int]			 =		self.image_processing_class.from_dict(self.image_processor_dict     ,    size=42     ,    reduce_labels=_lowercase    )
       self.assertEqual(image_processor.size     ,    {"""height""": 42, """width""": 42}    )
       self.assertEqual(image_processor.do_reduce_labels     ,    _lowercase    )







  def 						UpperCAmelCase__		(						self    ):
       pass







  def 						UpperCAmelCase__		(						self    ):
       # Initialize image_processing
       lowerCAmelCase_       :							Optional[int]			 =		self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       lowerCAmelCase_       :							int			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowercase    )
       for image in image_inputs:
            self.assertIsInstance(_lowercase     ,    Image.Image    )

       # Test not batched input
       lowerCAmelCase_       :							Tuple			 =		image_processing(image_inputs[0]     ,    return_tensors="""pt"""    ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )

       # Test batched
       lowerCAmelCase_       :							int			 =		image_processing(_lowercase     ,    return_tensors="""pt"""    ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )







  def 						UpperCAmelCase__		(						self    ):
       # Initialize image_processing
       lowerCAmelCase_       :							Optional[Any]			 =		self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       lowerCAmelCase_       :							Any			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowercase     ,    numpify=_lowercase    )
       for image in image_inputs:
            self.assertIsInstance(_lowercase     ,    np.ndarray    )

       # Test not batched input
       lowerCAmelCase_       :							Union[str, Any]			 =		image_processing(image_inputs[0]     ,    return_tensors="""pt"""    ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )

       # Test batched
       lowerCAmelCase_       :							Any			 =		image_processing(_lowercase     ,    return_tensors="""pt"""    ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )







  def 						UpperCAmelCase__		(						self    ):
       # Initialize image_processing
       lowerCAmelCase_       :							Optional[int]			 =		self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       lowerCAmelCase_       :							List[Any]			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowercase     ,    torchify=_lowercase    )
       for image in image_inputs:
            self.assertIsInstance(_lowercase     ,    torch.Tensor    )

       # Test not batched input
       lowerCAmelCase_       :							Tuple			 =		image_processing(image_inputs[0]     ,    return_tensors="""pt"""    ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )

       # Test batched
       lowerCAmelCase_       :							Optional[Any]			 =		image_processing(_lowercase     ,    return_tensors="""pt"""    ).pixel_values
       self.assertEqual(
           encoded_images.shape     ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )







  def 						UpperCAmelCase__		(						self    ):
       # Initialize image_processing
       lowerCAmelCase_       :							Optional[Any]			 =		self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       lowerCAmelCase_       :							Optional[int]			 =		prepare_image_inputs(self.image_processor_tester     ,    equal_resolution=_lowercase     ,    torchify=_lowercase    )
       lowerCAmelCase_       :							Optional[Any]			 =		[]
       for image in image_inputs:
            self.assertIsInstance(_lowercase     ,    torch.Tensor    )
            maps.append(torch.zeros(image.shape[-2:]    ).long()    )

       # Test not batched input
       lowerCAmelCase_       :							int			 =		image_processing(image_inputs[0]     ,    maps[0]     ,    return_tensors="""pt"""    )
       self.assertEqual(
           encoding["""pixel_values"""].shape     ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )
       self.assertEqual(
           encoding["""labels"""].shape     ,    (
               1,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )
       self.assertEqual(encoding["""labels"""].dtype     ,    torch.long    )
       self.assertTrue(encoding["""labels"""].min().item() >= 0    )
       self.assertTrue(encoding["""labels"""].max().item() <= 255    )

       # Test batched
       lowerCAmelCase_       :							Tuple			 =		image_processing(_lowercase     ,    _lowercase     ,    return_tensors="""pt"""    )
       self.assertEqual(
           encoding["""pixel_values"""].shape     ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )
       self.assertEqual(
           encoding["""labels"""].shape     ,    (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )
       self.assertEqual(encoding["""labels"""].dtype     ,    torch.long    )
       self.assertTrue(encoding["""labels"""].min().item() >= 0    )
       self.assertTrue(encoding["""labels"""].max().item() <= 255    )

       # Test not batched input (PIL images)
       lowerCAmelCase_ ,	lowerCAmelCase_       :							List[str]			 =		prepare_semantic_single_inputs()

       lowerCAmelCase_       :							List[Any]			 =		image_processing(_lowercase     ,    _lowercase     ,    return_tensors="""pt"""    )
       self.assertEqual(
           encoding["""pixel_values"""].shape     ,    (
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )
       self.assertEqual(
           encoding["""labels"""].shape     ,    (
               1,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )
       self.assertEqual(encoding["""labels"""].dtype     ,    torch.long    )
       self.assertTrue(encoding["""labels"""].min().item() >= 0    )
       self.assertTrue(encoding["""labels"""].max().item() <= 255    )

       # Test batched input (PIL images)
       lowerCAmelCase_ ,	lowerCAmelCase_       :							List[Any]			 =		prepare_semantic_batch_inputs()

       lowerCAmelCase_       :							Tuple			 =		image_processing(_lowercase     ,    _lowercase     ,    return_tensors="""pt"""    )
       self.assertEqual(
           encoding["""pixel_values"""].shape     ,    (
               2,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )
       self.assertEqual(
           encoding["""labels"""].shape     ,    (
               2,
               self.image_processor_tester.size["""height"""],
               self.image_processor_tester.size["""width"""],
           )     ,    )
       self.assertEqual(encoding["""labels"""].dtype     ,    torch.long    )
       self.assertTrue(encoding["""labels"""].min().item() >= 0    )
       self.assertTrue(encoding["""labels"""].max().item() <= 255    )







  def 						UpperCAmelCase__		(						self    ):
       # Initialize image_processing
       lowerCAmelCase_       :							int			 =		self.image_processing_class(**self.image_processor_dict    )

       # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
       lowerCAmelCase_ ,	lowerCAmelCase_       :							int			 =		prepare_semantic_single_inputs()
       lowerCAmelCase_       :							Any			 =		image_processing(_lowercase     ,    _lowercase     ,    return_tensors="""pt"""    )
       self.assertTrue(encoding["""labels"""].min().item() >= 0    )
       self.assertTrue(encoding["""labels"""].max().item() <= 150    )

       lowerCAmelCase_       :							str			 =		True
       lowerCAmelCase_       :							Tuple			 =		image_processing(_lowercase     ,    _lowercase     ,    return_tensors="""pt"""    )
       self.assertTrue(encoding["""labels"""].min().item() >= 0    )
       self.assertTrue(encoding["""labels"""].max().item() <= 255    )







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])
