






import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
				import torch
				from torch import nn

				from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
				from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from transformers import VivitImageProcessor







class 				lowerCamelCase_     :
							def __init__(						self		:					int							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Union[str, Any]=2							,__lowerCamelCase		:					Tuple=True							,__lowerCamelCase		:					Tuple=True							,__lowerCamelCase		:					Union[str, Any]=10							,__lowerCamelCase		:					int=10							,__lowerCamelCase		:					Dict=8							,__lowerCamelCase		:					Union[str, Any]=[2, 4, 4]							,__lowerCamelCase		:					Union[str, Any]=3							,__lowerCamelCase		:					Tuple=7_68							,__lowerCamelCase		:					Any=5							,__lowerCamelCase		:					Tuple=4							,__lowerCamelCase		:					Optional[int]=37							,__lowerCamelCase		:					List[str]="gelu_fast"							,__lowerCamelCase		:					List[str]=0.0							,__lowerCamelCase		:					List[str]=0.0							,__lowerCamelCase		:					Any=0.02							,__lowerCamelCase		:					List[str]=1e-06							,__lowerCamelCase		:					List[str]=True							,__lowerCamelCase		:					Optional[int]=None							,):





								'''simple docstring'''



								a		=			parent
								a		=			batch_size
								a		=			is_training
								a		=			use_labels
								a		=			num_labels
								a		=			image_size
								a		=			num_frames
								a		=			tubelet_size
								a		=			num_channels
								a		=			hidden_size
								a		=			num_hidden_layers
								a		=			num_attention_heads
								a		=			intermediate_size
								a		=			hidden_act
								a		=			hidden_dropout_prob
								a		=			attention_probs_dropout_prob
								a		=			initializer_range
								a		=			layer_norm_eps
								a		=			qkv_bias
								a		=			scope

								a		=			(
								    (self.image_size // self.tubelet_size[2])
								    * (self.image_size // self.tubelet_size[1])
								    * (self.num_frames // self.tubelet_size[0])
								) + 1  # CLS token
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



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

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

								a		=			self.get_config()

								return config, pixel_values, labels
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



								a		=			VivitConfig(
								    num_frames=self.num_frames							,image_size=self.image_size							,tubelet_size=self.tubelet_size							,num_channels=self.num_channels							,hidden_size=self.hidden_size							,num_hidden_layers=self.num_hidden_layers							,num_attention_heads=self.num_attention_heads							,intermediate_size=self.intermediate_size							,hidden_act=self.hidden_act							,hidden_dropout_prob=self.hidden_dropout_prob							,attention_probs_dropout_prob=self.attention_probs_dropout_prob							,initializer_range=self.initializer_range							,layer_norm_eps=self.layer_norm_eps							,qkv_bias=self.qkv_bias							,)
								a		=			self.num_labels
								return config
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict							,__lowerCamelCase		:					Tuple							,__lowerCamelCase		:					List[Any]							,__lowerCamelCase		:					List[Any]					):





								'''simple docstring'''



								a		=			VivitModel(config=__lowerCamelCase					)
								model.to(__lowerCamelCase					)
								model.eval()
								a		=			model(__lowerCamelCase					)
								self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]							,__lowerCamelCase		:					List[Any]							,__lowerCamelCase		:					Optional[int]							,__lowerCamelCase		:					str					):





								'''simple docstring'''



								a		=			VivitForVideoClassification(__lowerCamelCase					)
								model.to(__lowerCamelCase					)
								model.eval()

								a		=			model(__lowerCamelCase					)

								# verify the logits shape
								a		=			torch.Size((self.batch_size, self.num_labels)					)
								self.parent.assertEqual(result.logits.shape							,__lowerCamelCase					)




							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''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_ ,						unittest.TestCase     ):
							SCREAMING_SNAKE_CASE_						     =			(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							SCREAMING_SNAKE_CASE_						     =			False
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								a		=			VivitModelTester(self					)
								a		=			ConfigTester(self							,config_class=__lowerCamelCase							,has_text_modality=__lowerCamelCase							,hidden_size=37					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					Any							,__lowerCamelCase		:					List[str]							,__lowerCamelCase		:					str=False					):





								'''simple docstring'''



								a		=			copy.deepcopy(__lowerCamelCase					)

								if return_labels:
									if model_class in get_values(__lowerCamelCase					):
										a		=			torch.zeros(
										    self.model_tester.batch_size							,dtype=torch.long							,device=__lowerCamelCase					)

								return inputs_dict
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''simple docstring'''



								self.config_tester.run_common_tests()
							@unittest.skip(reason='''Vivit does not use inputs_embeds'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict					):





								'''simple docstring'''



								pass
							def  SCREAMING_SNAKE_CASE_ (						self		:					Optional[int]					):





								'''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					)
									self.assertIsInstance(model.get_input_embeddings()							,(nn.Module)					)
									a		=			model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(__lowerCamelCase							,nn.Linear					)					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''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''', '''head_mask''']
									self.assertListEqual(arg_names[:2]							,__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								a		=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]					):





								'''simple docstring'''



								a		=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_video_classification(*__lowerCamelCase					)
							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a		=			VivitModel.from_pretrained(__lowerCamelCase					)
									self.assertIsNotNone(__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					str					):





								'''simple docstring'''



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

								for model_class in self.all_model_classes:
									a		=			self.model_tester.seq_length

									a		=			True
									a		=			False
									a		=			True
									a		=			model_class(__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.eval()
									with torch.no_grad():
										a		=			model(**self._prepare_for_class(__lowerCamelCase							,__lowerCamelCase					)					)
									a		=			outputs.attentions
									self.assertEqual(len(__lowerCamelCase					)							,self.model_tester.num_hidden_layers					)

									# check that output_attentions also work using config
									del inputs_dict["output_attentions"]
									a		=			True
									a		=			model_class(__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.eval()
									with torch.no_grad():
										a		=			model(**self._prepare_for_class(__lowerCamelCase							,__lowerCamelCase					)					)
									a		=			outputs.attentions
									self.assertEqual(len(__lowerCamelCase					)							,self.model_tester.num_hidden_layers					)

									self.assertListEqual(
									    list(attentions[0].shape[-3:]					)							,[self.model_tester.num_attention_heads, seq_len, seq_len]							,)
									a		=			len(__lowerCamelCase					)

									# Check attention is always last and order is fine
									a		=			True
									a		=			True
									a		=			model_class(__lowerCamelCase					)
									model.to(__lowerCamelCase					)
									model.eval()
									with torch.no_grad():
										a		=			model(**self._prepare_for_class(__lowerCamelCase							,__lowerCamelCase					)					)

									self.assertEqual(out_len + 1							,len(__lowerCamelCase					)					)

									a		=			outputs.attentions

									self.assertEqual(len(__lowerCamelCase					)							,self.model_tester.num_hidden_layers					)

									self.assertListEqual(
									    list(self_attentions[0].shape[-3:]					)							,[self.model_tester.num_attention_heads, seq_len, seq_len]							,)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								def check_hidden_states_output(__lowerCamelCase		:					Union[str, Any]							,__lowerCamelCase		:					Any							,__lowerCamelCase		:					Any					):
									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_hidden_layers + 1
									self.assertEqual(len(__lowerCamelCase					)							,__lowerCamelCase					)

									a		=			self.model_tester.seq_length

									self.assertListEqual(
									    list(hidden_states[0].shape[-2:]					)							,[seq_length, self.model_tester.hidden_size]							,)

								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					)




							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any					):





								'''simple docstring'''



								pass





def   SCREAMING_SNAKE_CASE__						(   )   ->  Tuple:


	"""simple docstring"""



	a		=			hf_hub_download(
	    repo_id='''hf-internal-testing/spaghetti-video''',  filename='''eating_spaghetti_32_frames.npy''',  repo_type='''dataset'''  )
	a		=			np.load(snake_case_  )
	return list(snake_case_  )







@require_torch
@require_vision
class 				lowerCamelCase_     (	unittest.TestCase     ):
							@cached_property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								return VivitImageProcessor() if is_vision_available() else None




							@slow
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple					):





								'''simple docstring'''



								a		=			VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''					).to(__lowerCamelCase					)

								a		=			self.default_image_processor
								a		=			prepare_video()
								a		=			image_processor(__lowerCamelCase							,return_tensors='''pt'''					).to(__lowerCamelCase					)

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

								# verify the logits
								a		=			torch.Size((1, 4_00)					)
								self.assertEqual(outputs.logits.shape							,__lowerCamelCase					)

								# taken from original model
								a		=			torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]					).to(__lowerCamelCase					)

								self.assertTrue(torch.allclose(outputs.logits[0, :5]							,__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])





