






import unittest

import numpy as np

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 OwlViTImageProcessor







class 				lowerCamelCase_     (	unittest.TestCase     ):
							def __init__(						self		:					int							,__lowerCamelCase		:					List[Any]							,__lowerCamelCase		:					List[str]=7							,__lowerCamelCase		:					Dict=3							,__lowerCamelCase		:					Union[str, Any]=18							,__lowerCamelCase		:					str=30							,__lowerCamelCase		:					int=4_00							,__lowerCamelCase		:					List[str]=True							,__lowerCamelCase		:					Any=None							,__lowerCamelCase		:					Optional[int]=True							,__lowerCamelCase		:					Optional[Any]=None							,__lowerCamelCase		:					Dict=True							,__lowerCamelCase		:					Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073]							,__lowerCamelCase		:					Optional[int]=[0.26_862_954, 0.26_130_258, 0.27_577_711]							,__lowerCamelCase		:					Optional[Any]=True							,):





								'''simple docstring'''



								a		=			parent
								a		=			batch_size
								a		=			num_channels
								a		=			image_size
								a		=			min_resolution
								a		=			max_resolution
								a		=			do_resize
								a		=			size if size is not None else {'height': 18, 'width': 18}
								a		=			do_center_crop
								a		=			crop_size if crop_size is not None else {'height': 18, 'width': 18}
								a		=			do_normalize
								a		=			image_mean
								a		=			image_std
								a		=			do_convert_rgb




							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_convert_rgb": self.do_convert_rgb,
								}







@require_torch
@require_vision
class 				lowerCamelCase_     (	a__ ,						unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			OwlViTImageProcessor if is_vision_available() else None
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								a		=			OwlViTImageProcessingTester(self					)
							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								return self.image_processor_tester.prepare_image_processor_dict()
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,'''do_resize'''					)					)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,'''size'''					)					)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,'''do_center_crop'''					)					)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,'''center_crop'''					)					)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,'''do_normalize'''					)					)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,'''image_mean'''					)					)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,'''image_std'''					)					)
								self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_							,'''do_convert_rgb'''					)					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								a		=			self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size							,{'''height''': 18, '''width''': 18}					)
								self.assertEqual(image_processor.crop_size							,{'''height''': 18, '''width''': 18}					)

								a		=			self.image_processing_class.from_dict(self.image_processor_dict							,size=42							,crop_size=84					)
								self.assertEqual(image_processor.size							,{'''height''': 42, '''width''': 42}					)
								self.assertEqual(image_processor.crop_size							,{'''height''': 84, '''width''': 84}					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								a		=			prepare_image_inputs(self.image_processor_tester							,equal_resolution=SCREAMING_SNAKE_CASE_					)

								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_							,Image.Image					)

								# Test not batched input
								a		=			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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )							,)

								# Test batched
								a		=			image_processing(SCREAMING_SNAKE_CASE_							,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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )							,)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								a		=			self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								a		=			prepare_image_inputs(self.image_processor_tester							,equal_resolution=SCREAMING_SNAKE_CASE_							,numpify=SCREAMING_SNAKE_CASE_					)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_							,np.ndarray					)

								# Test not batched input
								a		=			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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )							,)

								# Test batched
								a		=			image_processing(SCREAMING_SNAKE_CASE_							,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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )							,)




							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								a		=			self.image_processing_class(**self.image_processor_dict					)
								# create random PyTorch tensors
								a		=			prepare_image_inputs(self.image_processor_tester							,equal_resolution=SCREAMING_SNAKE_CASE_							,torchify=SCREAMING_SNAKE_CASE_					)
								for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_							,torch.Tensor					)

								# Test not batched input
								a		=			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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )							,)

								# Test batched
								a		=			image_processing(SCREAMING_SNAKE_CASE_							,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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    )							,)













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])





