'''simple docstring'''

import unittest
from unittest.util import safe_repr

from transformers import AutoTokenizer, RwkvConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
           RwkvForCausalLM,
           RwkvModel,
       )
       from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_0
else:
       lowerCAmelCase_			:			int    	=   False





class 				lowercase    :


   def __init__( self					:						Any ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						List[Any]=14 ,		__lowerCAmelCase					:						Tuple=7 ,		__lowerCAmelCase					:						Union[str, Any]=True ,		__lowerCAmelCase					:						Optional[int]=False ,		__lowerCAmelCase					:						int=True ,		__lowerCAmelCase					:						Any=True ,		__lowerCAmelCase					:						str=True ,		__lowerCAmelCase					:						Union[str, Any]=99 ,		__lowerCAmelCase					:						Optional[Any]=32 ,		__lowerCAmelCase					:						List[str]=5 ,		__lowerCAmelCase					:						List[str]=37 ,		__lowerCAmelCase					:						Union[str, Any]="gelu" ,		__lowerCAmelCase					:						Dict=0.1 ,		__lowerCAmelCase					:						Tuple=0.1 ,		__lowerCAmelCase					:						Tuple=512 ,		__lowerCAmelCase					:						List[str]=16 ,		__lowerCAmelCase					:						Tuple=2 ,		__lowerCAmelCase					:						List[Any]=3 ,		__lowerCAmelCase					:						List[Any]=4 ,		__lowerCAmelCase					:						str=None ,		)   ->       Optional[int]:
         lowercase_     			=   parent
         lowercase_     			=   batch_size
         lowercase_     			=   seq_length
         lowercase_     			=   is_training
         lowercase_     			=   use_token_type_ids
         lowercase_     			=   use_input_mask
         lowercase_     			=   use_labels
         lowercase_     			=   use_mc_token_ids
         lowercase_     			=   vocab_size
         lowercase_     			=   hidden_size
         lowercase_     			=   num_hidden_layers
         lowercase_     			=   intermediate_size
         lowercase_     			=   hidden_act
         lowercase_     			=   hidden_dropout_prob
         lowercase_     			=   attention_probs_dropout_prob
         lowercase_     			=   max_position_embeddings
         lowercase_     			=   type_vocab_size
         lowercase_     			=   type_sequence_label_size
         lowercase_     			=   num_labels
         lowercase_     			=   num_choices
         lowercase_     			=   scope
         lowercase_     			=   vocab_size - 1
         lowercase_     			=   vocab_size - 1
         lowercase_     			=   vocab_size - 1


   def __UpperCAmelCase							( self					:						int)   ->       Tuple:
         return RwkvConfig.from_pretrained("sgugger/rwkv-4-pile-7b")


   def __UpperCAmelCase							( self					:						Dict ,		__lowerCAmelCase					:						Any=False ,		__lowerCAmelCase					:						Any=False ,		__lowerCAmelCase					:						Dict=False)   ->       Dict:
         lowercase_     			=   ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size)

         lowercase_     			=   None
         if self.use_input_mask:
               lowercase_     			=   random_attention_mask([self.batch_size, self.seq_length])

         lowercase_     			=   None
         if self.use_token_type_ids:
               lowercase_     			=   ids_tensor([self.batch_size, self.seq_length] ,		self.type_vocab_size)

         lowercase_     			=   None
         if self.use_mc_token_ids:
               lowercase_     			=   ids_tensor([self.batch_size, self.num_choices] ,		self.seq_length)

         lowercase_     			=   None
         lowercase_     			=   None
         lowercase_     			=   None
         if self.use_labels:
               lowercase_     			=   ids_tensor([self.batch_size] ,		self.type_sequence_label_size)
               lowercase_     			=   ids_tensor([self.batch_size, self.seq_length] ,		self.num_labels)
               lowercase_     			=   ids_tensor([self.batch_size] ,		self.num_choices)

         lowercase_     			=   self.get_config(
             gradient_checkpointing=_lowercase ,		scale_attn_by_inverse_layer_idx=_lowercase ,		reorder_and_upcast_attn=_lowercase ,		)

         return (
             config,
             input_ids,
             input_mask,
             None,
             token_type_ids,
             mc_token_ids,
             sequence_labels,
             token_labels,
             choice_labels,
         )


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any]=False ,		__lowerCAmelCase					:						Dict=False ,		__lowerCAmelCase					:						Optional[Any]=False)   ->       Optional[int]:
         return RwkvConfig(
             vocab_size=self.vocab_size ,		hidden_size=self.hidden_size ,		num_hidden_layers=self.num_hidden_layers ,		intermediate_size=self.intermediate_size ,		activation_function=self.hidden_act ,		resid_pdrop=self.hidden_dropout_prob ,		attn_pdrop=self.attention_probs_dropout_prob ,		n_positions=self.max_position_embeddings ,		type_vocab_size=self.type_vocab_size ,		use_cache=_lowercase ,		bos_token_id=self.bos_token_id ,		eos_token_id=self.eos_token_id ,		pad_token_id=self.pad_token_id ,		gradient_checkpointing=_lowercase ,		scale_attn_by_inverse_layer_idx=_lowercase ,		reorder_and_upcast_attn=_lowercase ,		)


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       Optional[int]:
         lowercase_     			=   self.get_config()
         lowercase_     			=   300
         return config


   def __UpperCAmelCase							( self					:						int)   ->       Tuple:
         (
             lowercase_	
         )     			=   self.prepare_config_and_inputs()

         lowercase_     			=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
         lowercase_     			=   ids_tensor([self.batch_size, self.seq_length] ,		vocab_size=2)

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
             token_type_ids,
             sequence_labels,
             token_labels,
             choice_labels,
             encoder_hidden_states,
             encoder_attention_mask,
         )


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						Any ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Union[str, Any] ,		*__lowerCAmelCase					:						Dict)   ->       List[Any]:
         lowercase_     			=   True
         lowercase_     			=   RwkvModel(config=_lowercase)
         model.to(_lowercase)
         model.eval()

         lowercase_     			=   model(_lowercase)

         self.parent.assertEqual(result.last_hidden_state.shape ,		(self.batch_size, self.seq_length, self.hidden_size))
         self.parent.assertEqual(len(result.hidden_states) ,		config.num_hidden_layers + 1)


   def __UpperCAmelCase							( self					:						Optional[int] ,		__lowerCAmelCase					:						str ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						int ,		*__lowerCAmelCase					:						int)   ->       Any:
         lowercase_     			=   RwkvForCausalLM(_lowercase)
         model.to(_lowercase)
         model.eval()

         lowercase_     			=   model(_lowercase ,		labels=_lowercase)
         self.parent.assertEqual(result.loss.shape ,		())
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.vocab_size))


   def __UpperCAmelCase							( self					:						List[str] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Dict ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						int ,		__lowerCAmelCase					:						Optional[Any] ,		*__lowerCAmelCase					:						Tuple)   ->       int:
         lowercase_     			=   RwkvModel(config=_lowercase)
         model.to(_lowercase)
         model.eval()

         lowercase_     			=   model(_lowercase)
         lowercase_     			=   outputs.last_hidden_state

         lowercase_     			=   model(input_ids[:, :2])
         lowercase_     			=   outputs.last_hidden_state

         # Using the state computed on the first inputs, we will get the same output
         lowercase_     			=   model(input_ids[:, 2:] ,		state=outputs.state)
         lowercase_     			=   outputs.last_hidden_state

         self.parent.assertTrue(torch.allclose(torch.cat([output_one, output_two] ,		dim=1) ,		_lowercase ,		atol=1e-5))


   def __UpperCAmelCase							( self					:						Any ,		__lowerCAmelCase					:						Tuple ,		__lowerCAmelCase					:						List[str] ,		__lowerCAmelCase					:						List[Any] ,		__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						List[str] ,		*__lowerCAmelCase					:						Optional[int] ,		__lowerCAmelCase					:						Union[str, Any]=False)   ->       List[str]:
         lowercase_     			=   RwkvForCausalLM(_lowercase)
         model.to(_lowercase)
         if gradient_checkpointing:
               model.gradient_checkpointing_enable()

         lowercase_     			=   model(_lowercase ,		labels=_lowercase)
         self.parent.assertEqual(result.loss.shape ,		())
         self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.seq_length, self.vocab_size))
         result.loss.backward()


   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         lowercase_     			=   self.prepare_config_and_inputs()

         (
             lowercase_	
         )     			=   config_and_inputs

         lowercase_     			=   {"""input_ids""": input_ids}

         return config, inputs_dict





@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0			,					reason='See https://github.com/huggingface/transformers/pull/24204'			)
@require_torch
class 				lowercase    (    __A			,					__A			,					__A			,					unittest.TestCase			):
   lowerCamelCase_            =(RwkvModel, RwkvForCausalLM) if is_torch_available() else ()
   lowerCamelCase_            =(
       {'feature-extraction': RwkvModel, 'text-generation': RwkvForCausalLM} if is_torch_available() else {}
   )
   # all_generative_model_classes = (RwkvForCausalLM,) if is_torch_available() else ()
   lowerCamelCase_            =False
   lowerCamelCase_            =False
   lowerCamelCase_            =False
   lowerCamelCase_            =False
   lowerCamelCase_            =False  # Rwkv does not support head masking


   def __UpperCAmelCase							( self					:						Union[str, Any])   ->       Union[str, Any]:
         lowercase_     			=   RwkvModelTester(self)
         lowercase_     			=   ConfigTester(
             self ,		config_class=_lowercase ,		n_embd=37 ,		common_properties=["hidden_size", "num_hidden_layers"])


   def __UpperCAmelCase							( self					:						List[Any] ,		__lowerCAmelCase					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						Tuple=None)   ->       Union[str, Any]:
         if isinstance(_lowercase ,		torch.Tensor):
               lowercase_     			=   member.max().item(), member.min().item()
         elif isinstance(_lowercase ,		_lowercase) or isinstance(_lowercase ,		_lowercase):
               lowercase_     			=   max(_lowercase), min(_lowercase)

         if not isinstance(_lowercase ,		_lowercase):
               raise TypeError("container should be a list or tuple")
         elif len(_lowercase) != 2:
               raise ValueError("container should have 2 elements")

         lowercase_     			=   container

         lowercase_     			=   (min_value >= expected_min) and (max_value <= expected_max)

         if not is_inside_interval:
               lowercase_     			=   """%s not found in %s""" % (safe_repr(_lowercase), safe_repr(_lowercase))
               self.fail(self._formatMessage(_lowercase ,		_lowercase))


   def __UpperCAmelCase							( self					:						str)   ->       int:
         self.config_tester.run_common_tests()


   def __UpperCAmelCase							( self					:						Tuple)   ->       Optional[int]:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_rwkv_model(*_lowercase)


   def __UpperCAmelCase							( self					:						Optional[int])   ->       int:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_causl_lm(*_lowercase)


   def __UpperCAmelCase							( self					:						Optional[Any])   ->       Optional[Any]:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_state_equivalency(*_lowercase)


   def __UpperCAmelCase							( self					:						List[Any])   ->       Dict:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               lowercase_     			=   model_class(config=_lowercase)
               for name, param in model.named_parameters():
                     if "time_decay" in name:
                           if param.requires_grad:
                                 self.assertTrue(param.data.max().item() == 3.0)
                                 self.assertTrue(param.data.min().item() == -5.0)
                     elif "time_first" in name:
                           if param.requires_grad:
                                 # check if it's a ones like
                                 self.assertTrue(torch.allclose(param.data ,		torch.ones_like(param.data) ,		atol=1e-5 ,		rtol=1e-5))
                     elif any(x in name for x in ["time_mix_key", "time_mix_receptance"]):
                           if param.requires_grad:
                                 self.assertInterval(
                                     param.data ,		[0.0, 1.0] ,		msg=F'Parameter {name} of model {model_class} seems not properly initialized' ,		)
                     elif "time_mix_value" in name:
                           if param.requires_grad:
                                 self.assertInterval(
                                     param.data ,		[0.0, 1.3] ,		msg=F'Parameter {name} of model {model_class} seems not properly initialized' ,		)


   def __UpperCAmelCase							( self					:						str)   ->       Optional[int]:
         lowercase_     			=   self.model_tester.prepare_config_and_inputs_for_common()
         lowercase_     			=   True

         lowercase_     			=   getattr(self.model_tester ,		"seq_length" ,		_lowercase)

         for model_class in self.all_model_classes:
               lowercase_     			=   True
               lowercase_     			=   False
               lowercase_     			=   True
               lowercase_     			=   model_class(_lowercase)
               model.to(_lowercase)
               model.eval()

               lowercase_     			=   self._prepare_for_class(_lowercase ,		_lowercase)
               lowercase_     			=   inputs["""input_ids"""].shape[0]
               with torch.no_grad():
                     lowercase_     			=   model(**_lowercase)
               lowercase_     			=   outputs.attentions
               self.assertEqual(len(_lowercase) ,		self.model_tester.num_hidden_layers)

               # check that output_attentions also work using config
               del inputs_dict["output_attentions"]
               lowercase_     			=   True
               lowercase_     			=   model_class(_lowercase)
               model.to(_lowercase)
               model.eval()

               lowercase_     			=   self._prepare_for_class(_lowercase ,		_lowercase)
               lowercase_     			=   inputs["""input_ids"""].shape[0]
               with torch.no_grad():
                     lowercase_     			=   model(**_lowercase)
               lowercase_     			=   outputs.attentions
               self.assertEqual(len(_lowercase) ,		self.model_tester.num_hidden_layers)

               self.assertListEqual(
                   list(attentions[0].shape[-3:]) ,		[batch_size, seq_len, config.hidden_size] ,		)
               lowercase_     			=   len(_lowercase)

               # Check attention is always last and order is fine
               lowercase_     			=   True
               lowercase_     			=   True
               lowercase_     			=   model_class(_lowercase)
               model.to(_lowercase)
               model.eval()

               lowercase_     			=   self._prepare_for_class(_lowercase ,		_lowercase)
               lowercase_     			=   inputs["""input_ids"""].shape[0]
               with torch.no_grad():
                     lowercase_     			=   model(**_lowercase)

               lowercase_     			=   1
               self.assertEqual(out_len + added_hidden_states ,		len(_lowercase))

               lowercase_     			=   outputs.attentions

               self.assertEqual(len(_lowercase) ,		self.model_tester.num_hidden_layers)
               self.assertListEqual(
                   list(self_attentions[0].shape[-3:]) ,		[batch_size, seq_len, config.hidden_size] ,		)


   @slow
   def __UpperCAmelCase							( self					:						Dict)   ->       Dict:
         for model_name in RWKV_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase_     			=   RwkvModel.from_pretrained(_lowercase)
               self.assertIsNotNone(_lowercase)





@unittest.skipIf(
    not is_torch_greater_or_equal_than_2_0			,					reason='See https://github.com/huggingface/transformers/pull/24204'			)
@slow
class 				lowercase    (    unittest.TestCase			):


   def __UpperCAmelCase							( self					:						str)   ->       Optional[Any]:
         lowercase_     			=   """RWKV/rwkv-4-169m-pile"""
         lowercase_     			=   AutoTokenizer.from_pretrained(self.model_id)


   def __UpperCAmelCase							( self					:						Any)   ->       Any:
         lowercase_     			=   """Hello my name is Jasmine and I am a newbie to the"""
         lowercase_     			=   RwkvForCausalLM.from_pretrained(self.model_id).to(_lowercase)

         lowercase_     			=   self.tokenizer("Hello my name is" ,		return_tensors="pt").input_ids.to(_lowercase)
         lowercase_     			=   model.generate(_lowercase ,		max_new_tokens=10)
         lowercase_     			=   self.tokenizer.decode(output[0].tolist())

         self.assertEqual(_lowercase ,		_lowercase)


   def __UpperCAmelCase							( self					:						List[str])   ->       Optional[Any]:
         lowercase_     			=   """Hello my name is Jasmine and I am a newbie to the"""

         lowercase_     			=   self.tokenizer("Hello my name is" ,		return_tensors="pt").input_ids.to(_lowercase)
         lowercase_     			=   RwkvForCausalLM.from_pretrained(self.model_id ,		torch_dtype=torch.bfloataa).to(_lowercase)

         lowercase_     			=   model.generate(_lowercase ,		max_new_tokens=10)
         lowercase_     			=   self.tokenizer.decode(output[0].tolist())

         self.assertEqual(_lowercase ,		_lowercase)













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])
