






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCamelCase__						:      Any								=					logging.get_logger(__name__)

UpperCamelCase__						:      int								=					{
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}







class 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			'mbart'
							SCREAMING_SNAKE_CASE_						     =			['past_key_values']
							SCREAMING_SNAKE_CASE_						     =			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
							def __init__(						self		:					Tuple							,__lowerCamelCase		:					str=5_02_65							,__lowerCamelCase		:					Optional[int]=10_24							,__lowerCamelCase		:					Tuple=12							,__lowerCamelCase		:					Tuple=40_96							,__lowerCamelCase		:					Optional[Any]=16							,__lowerCamelCase		:					Any=12							,__lowerCamelCase		:					Optional[int]=40_96							,__lowerCamelCase		:					str=16							,__lowerCamelCase		:					Tuple=0.0							,__lowerCamelCase		:					int=0.0							,__lowerCamelCase		:					Optional[Any]=True							,__lowerCamelCase		:					Optional[Any]=True							,__lowerCamelCase		:					List[str]="gelu"							,__lowerCamelCase		:					Tuple=10_24							,__lowerCamelCase		:					Union[str, Any]=0.1							,__lowerCamelCase		:					int=0.0							,__lowerCamelCase		:					int=0.0							,__lowerCamelCase		:					List[str]=0.02							,__lowerCamelCase		:					Any=0.0							,__lowerCamelCase		:					Dict=False							,__lowerCamelCase		:					Optional[int]=1							,__lowerCamelCase		:					str=0							,__lowerCamelCase		:					Union[str, Any]=2							,__lowerCamelCase		:					Optional[Any]=2							,**__lowerCamelCase		:					Optional[int]							,):





								'''simple docstring'''



								a		=			vocab_size
								a		=			max_position_embeddings
								a		=			d_model
								a		=			encoder_ffn_dim
								a		=			encoder_layers
								a		=			encoder_attention_heads
								a		=			decoder_ffn_dim
								a		=			decoder_layers
								a		=			decoder_attention_heads
								a		=			dropout
								a		=			attention_dropout
								a		=			activation_dropout
								a		=			activation_function
								a		=			init_std
								a		=			encoder_layerdrop
								a		=			decoder_layerdrop
								a		=			classifier_dropout
								a		=			use_cache
								a		=			encoder_layers
								a		=			scale_embedding  # scale factor will be sqrt(d_model) if True
								super().__init__(
								    pad_token_id=__lowerCamelCase							,bos_token_id=__lowerCamelCase							,eos_token_id=__lowerCamelCase							,is_encoder_decoder=__lowerCamelCase							,forced_eos_token_id=__lowerCamelCase							,**__lowerCamelCase							,)







class 				lowerCamelCase_     (	a_     ):
							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
									a		=			OrderedDict(
									    [
									        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
									        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
									    ]					)

									if self.use_past:
										a		=			{0: '''batch'''}
										a		=			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
									else:
										a		=			{0: '''batch''', 1: '''decoder_sequence'''}
										a		=			{0: '''batch''', 1: '''decoder_sequence'''}

									if self.use_past:
										self.fill_with_past_key_values_(__lowerCamelCase							,direction='''inputs'''					)
								elif self.task == "causal-lm":
									# TODO: figure this case out.
									a		=			OrderedDict(
									    [
									        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
									        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
									    ]					)
									if self.use_past:
										a	, a		=			self.num_layers
										for i in range(__lowerCamelCase					):
											a		=			{0: '''batch''', 2: '''past_sequence + sequence'''}
											a		=			{0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
									a		=			OrderedDict(
									    [
									        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
									        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
									        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
									        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
									    ]					)

								return common_inputs
							@property
							def  SCREAMING_SNAKE_CASE_ (						self		:					Union[str, Any]					):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
									a		=			super().outputs
								else:
									a		=			super(__lowerCamelCase							,self					).outputs
									if self.use_past:
										a	, a		=			self.num_layers
										for i in range(__lowerCamelCase					):
											a		=			{0: '''batch''', 2: '''past_sequence + sequence'''}
											a		=			{0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					Tuple							,__lowerCamelCase		:					PreTrainedTokenizer							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[TensorType] = None							,):





								'''simple docstring'''



								a		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)

								# Generate decoder inputs
								a		=			seq_length if not self.use_past else 1
								a		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
								a		=			{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								a		=			dict(**__lowerCamelCase							,**__lowerCamelCase					)

								if self.use_past:
									if not is_torch_available():
										raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
									else:
										import torch
									a	, a		=			common_inputs['''input_ids'''].shape
									a		=			common_inputs['''decoder_input_ids'''].shape[1]
									a	, a		=			self.num_attention_heads
									a		=			(
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									a		=			decoder_seq_length + 3
									a		=			(
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									a		=			torch.cat(
									    [common_inputs['''decoder_attention_mask'''], torch.ones(__lowerCamelCase							,__lowerCamelCase					)]							,dim=1					)

									a		=			[]
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									a	, a		=			self.num_layers
									a		=			min(__lowerCamelCase							,__lowerCamelCase					)
									a		=			max(__lowerCamelCase							,__lowerCamelCase					) - min_num_layers
									a		=			'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

									for _ in range(__lowerCamelCase					):
										common_inputs["past_key_values"].append(
										    (
										        torch.zeros(__lowerCamelCase					),
										        torch.zeros(__lowerCamelCase					),
										        torch.zeros(__lowerCamelCase					),
										        torch.zeros(__lowerCamelCase					),
										    )					)
									# TODO: test this.
									a		=			encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
									for _ in range(__lowerCamelCase							,__lowerCamelCase					):
										common_inputs["past_key_values"].append((torch.zeros(__lowerCamelCase					), torch.zeros(__lowerCamelCase					))					)
								return common_inputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[Any]							,__lowerCamelCase		:					PreTrainedTokenizer							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[TensorType] = None							,):





								'''simple docstring'''



								a		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)

								if self.use_past:
									if not is_torch_available():
										raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
									else:
										import torch
									a	, a		=			common_inputs['''input_ids'''].shape
									# Not using the same length for past_key_values
									a		=			seqlen + 2
									a	, a		=			self.num_layers
									a	, a		=			self.num_attention_heads
									a		=			(
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

									a		=			common_inputs['''attention_mask'''].dtype
									a		=			torch.cat(
									    [common_inputs['''attention_mask'''], torch.ones(__lowerCamelCase							,__lowerCamelCase							,dtype=__lowerCamelCase					)]							,dim=1					)
									a		=			[
									    (torch.zeros(__lowerCamelCase					), torch.zeros(__lowerCamelCase					)) for _ in range(__lowerCamelCase					)
									]
								return common_inputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					PreTrainedTokenizer							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[TensorType] = None							,):





								'''simple docstring'''



								a		=			compute_effective_axis_dimension(
								    __lowerCamelCase							,fixed_dimension=OnnxConfig.default_fixed_batch							,num_token_to_add=0					)

								# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
								a		=			tokenizer.num_special_tokens_to_add(__lowerCamelCase					)
								a		=			compute_effective_axis_dimension(
								    __lowerCamelCase							,fixed_dimension=OnnxConfig.default_fixed_sequence							,num_token_to_add=__lowerCamelCase					)

								# Generate dummy inputs according to compute batch and sequence
								a		=			[''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
								a		=			dict(tokenizer(__lowerCamelCase							,return_tensors=__lowerCamelCase					)					)
								return common_inputs
							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					PreTrainedTokenizer							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					int = -1							,__lowerCamelCase		:					bool = False							,__lowerCamelCase		:					Optional[TensorType] = None							,):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
									a		=			self._generate_dummy_inputs_for_default_and_seqaseq_lm(
									    __lowerCamelCase							,batch_size=__lowerCamelCase							,seq_length=__lowerCamelCase							,is_pair=__lowerCamelCase							,framework=__lowerCamelCase					)

								elif self.task == "causal-lm":
									a		=			self._generate_dummy_inputs_for_causal_lm(
									    __lowerCamelCase							,batch_size=__lowerCamelCase							,seq_length=__lowerCamelCase							,is_pair=__lowerCamelCase							,framework=__lowerCamelCase					)
								else:
									a		=			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __lowerCamelCase							,batch_size=__lowerCamelCase							,seq_length=__lowerCamelCase							,is_pair=__lowerCamelCase							,framework=__lowerCamelCase					)

								return common_inputs




							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					List[Any]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					str							,__lowerCamelCase		:					Optional[int]					):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
									a		=			super()._flatten_past_key_values_(__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)
								else:
									a		=			super(__lowerCamelCase							,self					)._flatten_past_key_values_(
									    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase					)













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])





