



"""simple docstring"""
import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
   from PIL import Image

   from transformers import MgpstrProcessor
class    a		:



  def __init__(  self	:   List[str]    ,						lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Optional[int]=False    ,						lowerCAmelCase	:   Union[str, Any]=13    ,						lowerCAmelCase	:   Dict=(32, 128)    ,						lowerCAmelCase	:   int=4    ,						lowerCAmelCase	:   str=3    ,						lowerCAmelCase	:   Tuple=27    ,						lowerCAmelCase	:   Dict=38    ,						lowerCAmelCase	:   Any=99    ,						lowerCAmelCase	:   str=99    ,						lowerCAmelCase	:   List[str]=32    ,						lowerCAmelCase	:   Union[str, Any]=5    ,						lowerCAmelCase	:   Union[str, Any]=4    ,						lowerCAmelCase	:   List[Any]=4.0    ,						lowerCAmelCase	:   List[Any]=257    ,						lowerCAmelCase	:   List[Any]=None    ,						)       ->   Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            =parent
     SCREAMING_SNAKE_CASE_:	Optional[int]            =is_training
     SCREAMING_SNAKE_CASE_:	Tuple            =batch_size
     SCREAMING_SNAKE_CASE_:	Optional[int]            =image_size
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =patch_size
     SCREAMING_SNAKE_CASE_:	Optional[int]            =num_channels
     SCREAMING_SNAKE_CASE_:	Optional[int]            =max_token_length
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =num_character_labels
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =num_bpe_labels
     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =num_wordpiece_labels
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =hidden_size
     SCREAMING_SNAKE_CASE_:	Optional[int]            =num_hidden_layers
     SCREAMING_SNAKE_CASE_:	str            =num_attention_heads
     SCREAMING_SNAKE_CASE_:	Optional[Any]            =mlp_ratio
     SCREAMING_SNAKE_CASE_:	Dict            =patch_embeds_hidden_size
     SCREAMING_SNAKE_CASE_:	str            =output_hidden_states



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   Union[str, Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Union[str, Any]            =floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]						)
     SCREAMING_SNAKE_CASE_:	int            =self.get_config()
     return config, pixel_values



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Union[str, Any]:


     '''simple docstring'''




     return MgpstrConfig(
         image_size=self.image_size    ,						patch_size=self.patch_size    ,						num_channels=self.num_channels    ,						max_token_length=self.max_token_length    ,						num_character_labels=self.num_character_labels    ,						num_bpe_labels=self.num_bpe_labels    ,						num_wordpiece_labels=self.num_wordpiece_labels    ,						hidden_size=self.hidden_size    ,						num_hidden_layers=self.num_hidden_layers    ,						num_attention_heads=self.num_attention_heads    ,						mlp_ratio=self.mlp_ratio    ,						output_hidden_states=self.output_hidden_states    ,						)



  def 		lowerCamelCase__    (  self	:   Any    ,						lowerCAmelCase	:   Tuple    ,						lowerCAmelCase	:   Union[str, Any]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =MgpstrForSceneTextRecognition(lowerCAmelCase						)
     model.to(lowerCAmelCase						)
     model.eval()
     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:	Dict            =model(lowerCAmelCase						)
     self.parent.assertEqual(
         generated_ids[0][0].shape    ,						(self.batch_size, self.max_token_length, self.num_character_labels)						)





  def 		lowerCamelCase__    (  self	:   Union[str, Any]						)       ->   Optional[int]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Any            =self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Tuple            =config_and_inputs
     SCREAMING_SNAKE_CASE_:	Any            ={"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class    a		(					UpperCAmelCase__		, UpperCAmelCase__		, unittest.TestCase    ):
  UpperCamelCase		:			Dict         =   (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
  UpperCamelCase		:			Optional[int]         =   {'feature-extraction': MgpstrForSceneTextRecognition} if is_torch_available() else {}
  UpperCamelCase		:			Union[str, Any]         =   False

  UpperCamelCase		:			Dict         =   False
  UpperCamelCase		:			int         =   False
  UpperCamelCase		:			Tuple         =   False
  UpperCamelCase		:			Optional[Any]         =   False



  def 		lowerCamelCase__    (  self	:   Tuple						)       ->   str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	List[str]            =MgpstrModelTester(self						)
     SCREAMING_SNAKE_CASE_:	int            =ConfigTester(self    ,						config_class=lowerCAmelCase    ,						has_text_modality=lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   str						)       ->   Dict:


     '''simple docstring'''




     self.config_tester.run_common_tests()



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Optional[int]            =self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase						)



  @unittest.skip(reason="""MgpstrModel does not use inputs_embeds"""						)
  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   str:


     '''simple docstring'''




     pass



  def 		lowerCamelCase__    (  self	:   List[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Any            =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	int            =model_class(lowerCAmelCase						)
        self.assertIsInstance(model.get_input_embeddings()    ,						(nn.Module)						)
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCAmelCase    ,						nn.Linear						)						)



  def 		lowerCamelCase__    (  self	:   List[str]						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	List[str]            =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =model_class(lowerCAmelCase						)
        SCREAMING_SNAKE_CASE_:	Optional[Any]            =inspect.signature(model.forward						)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        SCREAMING_SNAKE_CASE_:	List[str]            =[*signature.parameters.keys()]

        SCREAMING_SNAKE_CASE_:	Dict            =["""pixel_values"""]
        self.assertListEqual(arg_names[:1]    ,						lowerCAmelCase						)



  @unittest.skip(reason="""MgpstrModel does not support feedforward chunking"""						)
  def 		lowerCamelCase__    (  self	:   Any						)       ->   Union[str, Any]:


     '''simple docstring'''




     pass



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Dict            =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        if not model_class.supports_gradient_checkpointing:
           continue

        SCREAMING_SNAKE_CASE_:	str            =True
        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =model_class(lowerCAmelCase						)
        self.assertTrue(model.is_gradient_checkpointing						)



  def 		lowerCamelCase__    (  self	:   Any						)       ->   Any:


     '''simple docstring'''




     def check_hidden_states_output(lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   Optional[int]    ,						lowerCAmelCase	:   List[Any]						):
        SCREAMING_SNAKE_CASE_:	Any            =model_class(lowerCAmelCase						)
        model.to(lowerCAmelCase						)
        model.eval()

        with torch.no_grad():
           SCREAMING_SNAKE_CASE_:	Optional[int]            =model(**self._prepare_for_class(lowerCAmelCase    ,						lowerCAmelCase						)						)

        SCREAMING_SNAKE_CASE_:	int            =outputs.hidden_states

        SCREAMING_SNAKE_CASE_:	Union[str, Any]            =getattr(
            self.model_tester    ,						"""expected_num_hidden_layers"""    ,						self.model_tester.num_hidden_layers + 1						)
        self.assertEqual(len(lowerCAmelCase						)    ,						lowerCAmelCase						)

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

     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	int            =self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Tuple            =True
        check_hidden_states_output(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        SCREAMING_SNAKE_CASE_:	List[Any]            =True

        check_hidden_states_output(lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase						)



  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_     ,						SCREAMING_SNAKE_CASE_:	Dict            =self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE_:	str            =_config_zero_init(lowerCAmelCase						)
     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_:	Tuple            =model_class(config=lowerCAmelCase						)
        for name, param in model.named_parameters():
           if isinstance(lowerCAmelCase    ,						(nn.Linear, nn.Convad, nn.LayerNorm)						):
              if param.requires_grad:
                 self.assertIn(
                     ((param.data.mean() * 1E9).round() / 1E9).item()    ,						[0.0, 1.0]    ,						msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,						)





  @unittest.skip(reason="""Retain_grad is tested in individual model tests"""						)
  def 		lowerCamelCase__    (  self	:   int						)       ->   Optional[Any]:


     '''simple docstring'''




     pass







def        __magic_name__			(							):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            ="""https://i.postimg.cc/ZKwLg2Gw/367-14.png"""
   SCREAMING_SNAKE_CASE_:	List[Any]            =Image.open(requests.get(lowercase							,     stream=lowercase	).raw	).convert("""RGB"""	)
   return im
@require_vision
@require_torch
class    a		(					unittest.TestCase    ):



  @slow
  def 		lowerCamelCase__    (  self	:   Optional[Any]						)       ->   Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	Dict            ="""alibaba-damo/mgp-str-base"""
     SCREAMING_SNAKE_CASE_:	List[Any]            =MgpstrForSceneTextRecognition.from_pretrained(lowerCAmelCase						).to(lowerCAmelCase						)
     SCREAMING_SNAKE_CASE_:	Any            =MgpstrProcessor.from_pretrained(lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =prepare_img()
     SCREAMING_SNAKE_CASE_:	int            =processor(images=lowerCAmelCase    ,						return_tensors="""pt"""						).pixel_values.to(lowerCAmelCase						)

     # forward pass
     with torch.no_grad():
        SCREAMING_SNAKE_CASE_:	List[str]            =model(lowerCAmelCase						)

     # verify the logits
     self.assertEqual(outputs.logits[0].shape    ,						torch.Size((1, 27, 38)						)						)

     SCREAMING_SNAKE_CASE_:	int            =processor.batch_decode(outputs.logits						)
     SCREAMING_SNAKE_CASE_:	Tuple            ="""ticket"""

     self.assertEqual(out_strs["""generated_text"""][0]    ,						lowerCAmelCase						)

     SCREAMING_SNAKE_CASE_:	List[Any]            =torch.tensor(
         [[[-3_9.5_3_9_7, -4_4.4_0_2_4, -3_6.1_8_4_4], [-6_1.4_7_0_9, -6_3.8_6_3_9, -5_8.3_4_5_4], [-7_4.0_2_2_5, -6_8.5_4_9_4, -7_1.2_1_6_4]]]    ,						device=lowerCAmelCase    ,						)

     self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]    ,						lowerCAmelCase    ,						atol=1E-4						)						)








"""simple docstring"""
from __future__ import annotations

import sys
class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   str    ,						lowerCAmelCase	:   int						)       ->   List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	str            =letter
     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	dict[str, str]            ={}





  def __repr__(  self	:   Optional[Any]						)       ->   str:


     '''simple docstring'''




     return f'''{self.letter}:{self.freq}'''







class    a		:



  def __init__(  self	:   Optional[Any]    ,						lowerCAmelCase	:   int    ,						lowerCAmelCase	:   Letter | TreeNode    ,						lowerCAmelCase	:   Letter | TreeNode						)       ->   List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:	int            =freq
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =left
     SCREAMING_SNAKE_CASE_:	Letter | TreeNode            =right







def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	dict[str, int]            ={}
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	List[Any]            =f.read(1	)
         if not c:
            break
         SCREAMING_SNAKE_CASE_:	Any            =chars[c] + 1 if c in chars else 1
   return sorted((Letter(lowercase							,     lowercase	) for c, f in chars.items())							,     key=lambda lowercase	: x.freq	)





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	list[Letter | TreeNode]            =letters  # type: ignore
   while len(lowercase	) > 1:
      SCREAMING_SNAKE_CASE_:	Any            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =response.pop(0	)
      SCREAMING_SNAKE_CASE_:	List[Any]            =left.freq + right.freq
      SCREAMING_SNAKE_CASE_:	Any            =TreeNode(lowercase							,     lowercase							,     lowercase	)
      response.append(lowercase	)
      response.sort(key=lambda lowercase	: x.freq	)
   return response[0]





def        __magic_name__			(							lowercase							,     lowercase	):
   if isinstance(lowercase							,     lowercase	):
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =bitstring
      return [root]
   SCREAMING_SNAKE_CASE_:	TreeNode            =root  # type: ignore
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =[]
   letters += traverse_tree(treenode.left							,     bitstring + """0"""	)
   letters += traverse_tree(treenode.right							,     bitstring + """1"""	)
   return letters





def        __magic_name__			(							lowercase	):
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =parse_file(lowercase	)
   SCREAMING_SNAKE_CASE_:	str            =build_tree(lowercase	)
   SCREAMING_SNAKE_CASE_:	List[Any]            ={
       k: v for letter in traverse_tree(lowercase							,     """"""	) for k, v in letter.bitstring.items()
   }
   print(f'''Huffman Coding  of {file_path}: '''	)
   with open(lowercase	) as f:
      while True:
         SCREAMING_SNAKE_CASE_:	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])



