






import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
				from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import PoolFormerImageProcessor







class 				lowerCamelCase_     (	a_     ):
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								a		=			self.config_class(**self.inputs_dict					)
								self.parent.assertTrue(hasattr(__lowerCamelCase							,'''hidden_sizes'''					)					)
								self.parent.assertTrue(hasattr(__lowerCamelCase							,'''num_encoder_blocks'''					)					)







class 				lowerCamelCase_     :
							def __init__(						self		:					int							,__lowerCamelCase		:					Optional[Any]							,__lowerCamelCase		:					List[Any]=13							,__lowerCamelCase		:					List[str]=64							,__lowerCamelCase		:					str=3							,__lowerCamelCase		:					Tuple=4							,__lowerCamelCase		:					int=[2, 2, 2, 2]							,__lowerCamelCase		:					Dict=[8, 4, 2, 1]							,__lowerCamelCase		:					str=[16, 32, 64, 1_28]							,__lowerCamelCase		:					Optional[Any]=[1, 4, 8, 16]							,__lowerCamelCase		:					Any=False							,__lowerCamelCase		:					List[Any]=True							,__lowerCamelCase		:					Any="gelu"							,__lowerCamelCase		:					Union[str, Any]=0.1							,__lowerCamelCase		:					Dict=0.02							,__lowerCamelCase		:					Dict=3							,__lowerCamelCase		:					Dict=None							,):





								'''simple docstring'''



								a		=			parent
								a		=			batch_size
								a		=			image_size
								a		=			num_channels
								a		=			num_encoder_blocks
								a		=			sr_ratios
								a		=			depths
								a		=			hidden_sizes
								a		=			downsampling_rates
								a		=			is_training
								a		=			use_labels
								a		=			hidden_act
								a		=			hidden_dropout_prob
								a		=			initializer_range
								a		=			num_labels
								a		=			scope
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]					):





								'''simple docstring'''



								a		=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								a		=			None
								if self.use_labels:
									a		=			ids_tensor([self.batch_size, self.image_size, self.image_size]							,self.num_labels					)

								a		=			PoolFormerConfig(
								    image_size=self.image_size							,num_channels=self.num_channels							,num_encoder_blocks=self.num_encoder_blocks							,depths=self.depths							,hidden_sizes=self.hidden_sizes							,hidden_act=self.hidden_act							,hidden_dropout_prob=self.hidden_dropout_prob							,initializer_range=self.initializer_range							,)

								return config, pixel_values, labels
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					Union[str, Any]							,__lowerCamelCase		:					Dict							,__lowerCamelCase		:					List[str]					):





								'''simple docstring'''



								a		=			PoolFormerModel(config=__lowerCamelCase					)
								model.to(__lowerCamelCase					)
								model.eval()
								a		=			model(__lowerCamelCase					)
								a		=			a		=			self.image_size // 32.0
								self.parent.assertEqual(
								    result.last_hidden_state.shape							,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			self.prepare_config_and_inputs()
								a	, a	, a		=			config_and_inputs
								a		=			{'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class 				lowerCamelCase_     (	a_ ,						a_ ,						unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE_						     =			(
							    {'feature-extraction': PoolFormerModel, 'image-classification': PoolFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			PoolFormerModelTester(self					)
								a		=			PoolFormerConfigTester(self							,config_class=__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								self.config_tester.run_common_tests()
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCamelCase					)
							@unittest.skip('''PoolFormer does not use inputs_embeds'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								pass
							@unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								pass
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								def check_hidden_states_output(__lowerCamelCase		:					Optional[int]							,__lowerCamelCase		:					Union[str, Any]							,__lowerCamelCase		:					int					):
									a		=			model_class(__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.eval()

									with torch.no_grad():
										a		=			model(**self._prepare_for_class(__lowerCamelCase							,__lowerCamelCase					)					)

									a		=			outputs.hidden_states

									a		=			self.model_tester.num_encoder_blocks
									self.assertEqual(len(__lowerCamelCase					)							,__lowerCamelCase					)

									# verify the first hidden states (first block)
									self.assertListEqual(
									    list(hidden_states[0].shape[-3:]					)							,[
									        self.model_tester.hidden_sizes[0],
									        self.model_tester.image_size // 4,
									        self.model_tester.image_size // 4,
									    ]							,)

								a	, a		=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a		=			True
									check_hidden_states_output(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									a		=			True

									check_hidden_states_output(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[Any]					):





								'''simple docstring'''



								if not self.model_tester.is_training:
									return

								a	, a		=			self.model_tester.prepare_config_and_inputs_for_common()
								a		=			True

								for model_class in self.all_model_classes:
									if model_class in get_values(__lowerCamelCase					):
										continue
									a		=			model_class(__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.train()
									a		=			self._prepare_for_class(__lowerCamelCase							,__lowerCamelCase							,return_labels=__lowerCamelCase					)
									a		=			model(**__lowerCamelCase					).loss
									loss.backward()
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a	, a		=			self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									a		=			model_class(__lowerCamelCase					)
									a		=			inspect.signature(model.forward					)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									a		=			[*signature.parameters.keys()]

									a		=			['''pixel_values''']
									self.assertListEqual(arg_names[:1]							,__lowerCamelCase					)




							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					int					):





								'''simple docstring'''



								for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a		=			PoolFormerModel.from_pretrained(__lowerCamelCase					)
									self.assertIsNotNone(__lowerCamelCase					)





def   SCREAMING_SNAKE_CASE__						(   )   ->  int:


	"""simple docstring"""



	a		=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
	return image







@require_torch
class 				lowerCamelCase_     (	unittest.TestCase     ):
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			PoolFormerImageProcessor()
								a		=			PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''					).to(__lowerCamelCase					)

								a		=			image_processor(images=prepare_img()							,return_tensors='''pt'''					).to(__lowerCamelCase					)

								# forward pass
								with torch.no_grad():
									a		=			model(**__lowerCamelCase					)

								# verify the logits
								a		=			torch.Size((1, 10_00)					)
								self.assertEqual(outputs.logits.shape							,__lowerCamelCase					)

								a		=			torch.tensor([-0.6_113, 0.1_685, -0.0_492]					).to(__lowerCamelCase					)
								self.assertTrue(torch.allclose(outputs.logits[0, :3]							,__lowerCamelCase							,atol=1e-4					)					)













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])





