



"""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_:	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_:	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_:	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_:	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_:	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_:	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(
         [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]]    ,						device=lowerCAmelCase    ,						)

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









"""simple docstring"""
from collections.abc import Callable
from math import log
from os import name, system
from random import gauss, seed
from typing import TypeVar





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   seed(1	)
   return [gauss(lowercase							,     lowercase	) for _ in range(lowercase	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return [k for k in range(lowercase	) for _ in range(instance_count[k]	)]





def        __magic_name__			(							lowercase							,     lowercase	):
   return sum(lowercase	) / instance_count





def        __magic_name__			(							lowercase							,     lowercase	):
   return instance_count / total_count





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store all squared differences
   # iterate over number of elements in items
   for i in range(len(lowercase	)	):
      # for loop iterates over number of elements in inner layer of items
      for j in range(len(items[i]	)	):
         # appending squared differences to 'squared_diff' list
         squared_diff.append((items[i][j] - means[i]) ** 2	)

    # one divided by (the number of all instances - number of classes) multiplied by
    # sum of all squared differences
   SCREAMING_SNAKE_CASE_:	Union[str, Any]            =len(lowercase	)  # Number of classes in dataset
   return 1 / (total_count - n_classes) * sum(lowercase	)





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
   # for loop iterates over number of elements in list
   for i in range(len(lowercase	)	):
      # for loop iterates over number of inner items of each element
      for j in range(len(x_items[i]	)	):
         SCREAMING_SNAKE_CASE_:	Optional[int]            =[]  # to store all discriminant values of each item as a list
         # for loop iterates over number of classes we have in our dataset
         for k in range(len(lowercase	)	):
            # appending values of discriminants for each class to 'temp' list
            temp.append(
                x_items[i][j] * (means[k] / variance)
                - (means[k] ** 2 / (2 * variance))
                + log(probabilities[k]	)	)
         # appending discriminant values of each item to 'results' list
         results.append(lowercase	)

   return [result.index(max(lowercase	)	) for result in results]





def        __magic_name__			(							lowercase							,     lowercase	):
   SCREAMING_SNAKE_CASE_:	Dict            =sum(1 for i, j in zip(lowercase							,     lowercase	) if i == j	)
   # percentage of accuracy equals to number of correct predictions divided by number
   # of all data and multiplied by 100
   return (correct / len(lowercase	)) * 100


_UpperCAmelCase       =     TypeVar("""num""")





def        __magic_name__			(							lowercase							,     lowercase							,     lowercase							,     lowercase = lambda lowercase	: True							,     lowercase = None							,     ):
   while True:
      try:
         SCREAMING_SNAKE_CASE_:	List[Any]            =input_type(input(lowercase	).strip() or default	)
         if condition(lowercase	):
            return user_input
         else:
            print(f'''{user_input}: {err_msg}'''	)
            continue
      except ValueError:
         print(
             f'''{user_input}: Incorrect input type, expected {input_type.__name__!r}'''	)





def        __magic_name__			(							):
   while True:
      print(""" Linear Discriminant Analysis """.center(50							,     """*"""	)	)
      print("""*""" * 50							,     """\n"""	)
      print("""First of all we should specify the number of classes that"""	)
      print("""we want to generate as training dataset"""	)
      # Trying to get number of classes
      SCREAMING_SNAKE_CASE_:	List[Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: x > 0							,     input_msg="""Enter the number of classes (Data Groupings): """							,     err_msg="""Number of classes should be positive!"""							,     )

      print("""-""" * 100	)

      # Trying to get the value of standard deviation
      SCREAMING_SNAKE_CASE_:	Union[str, Any]            =valid_input(
          input_type=lowercase							,     condition=lambda lowercase	: x >= 0							,     input_msg=(
              """Enter the value of standard deviation"""
              """(Default value is 1.0 for all classes): """
          )							,     err_msg="""Standard deviation should not be negative!"""							,     default="""1.0"""							,     )

      print("""-""" * 100	)

      # Trying to get number of instances in classes and theirs means to generate
      # dataset
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]  # An empty list to store instance counts of classes in dataset
      for i in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	int            =valid_input(
             input_type=lowercase							,     condition=lambda lowercase	: x > 0							,     input_msg=(f'''Enter The number of instances for class_{i+1}: ''')							,     err_msg="""Number of instances should be positive!"""							,     )
         counts.append(lowercase	)
      print("""-""" * 100	)

      # An empty list to store values of user-entered means of classes
      SCREAMING_SNAKE_CASE_:	Optional[Any]            =[]
      for a in range(lowercase	):
         SCREAMING_SNAKE_CASE_:	Tuple            =valid_input(
             input_type=lowercase							,     input_msg=(f'''Enter the value of mean for class_{a+1}: ''')							,     err_msg="""This is an invalid value."""							,     )
         user_means.append(lowercase	)
      print("""-""" * 100	)

      print("""Standard deviation: """							,     lowercase	)
      # print out the number of instances in classes in separated line
      for i, count in enumerate(lowercase							,     1	):
         print(f'''Number of instances in class_{i} is: {count}'''	)
      print("""-""" * 100	)

      # print out mean values of classes separated line
      for i, user_mean in enumerate(lowercase							,     1	):
         print(f'''Mean of class_{i} is: {user_mean}'''	)
      print("""-""" * 100	)

      # Generating training dataset drawn from gaussian distribution
      SCREAMING_SNAKE_CASE_:	Tuple            =[
          gaussian_distribution(user_means[j]							,     lowercase							,     counts[j]	)
          for j in range(lowercase	)
      ]
      print("""Generated Normal Distribution: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Generating Ys to detecting corresponding classes
      SCREAMING_SNAKE_CASE_:	int            =y_generator(lowercase							,     lowercase	)
      print("""Generated Corresponding Ys: \n"""							,     lowercase	)
      print("""-""" * 100	)

      # Calculating the value of actual mean for each class
      SCREAMING_SNAKE_CASE_:	str            =[calculate_mean(counts[k]							,     x[k]	) for k in range(lowercase	)]
      # for loop iterates over number of elements in 'actual_means' list and print
      # out them in separated line
      for i, actual_mean in enumerate(lowercase							,     1	):
         print(f'''Actual(Real) mean of class_{i} is: {actual_mean}'''	)
      print("""-""" * 100	)

      # Calculating the value of probabilities for each class
      SCREAMING_SNAKE_CASE_:	List[Any]            =[
          calculate_probabilities(counts[i]							,     sum(lowercase	)	) for i in range(lowercase	)
      ]

      # for loop iterates over number of elements in 'probabilities' list and print
      # out them in separated line
      for i, probability in enumerate(lowercase							,     1	):
         print(f'''Probability of class_{i} is: {probability}'''	)
      print("""-""" * 100	)

      # Calculating the values of variance for each class
      SCREAMING_SNAKE_CASE_:	str            =calculate_variance(lowercase							,     lowercase							,     sum(lowercase	)	)
      print("""Variance: """							,     lowercase	)
      print("""-""" * 100	)

      # Predicting Y values
      # storing predicted Y values in 'pre_indexes' variable
      SCREAMING_SNAKE_CASE_:	Dict            =predict_y_values(lowercase							,     lowercase							,     lowercase							,     lowercase	)
      print("""-""" * 100	)

      # Calculating Accuracy of the model
      print(f'''Accuracy: {accuracy(lowercase							,     lowercase	)}'''	)
      print("""-""" * 100	)
      print(""" DONE """.center(100							,     """+"""	)	)

      if input("""Press any key to restart or 'q' for quit: """	).strip().lower() == "q":
         print("""\n""" + """GoodBye!""".center(100							,     """-"""	) + """\n"""	)
         break
      system("""cls""" if name == """nt""" else """clear"""	)  # noqa: S605


if __name__ == "__main__":
   main()



