
from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDebertaForMaskedLM,
         TFDebertaForQuestionAnswering,
         TFDebertaForSequenceClassification,
         TFDebertaForTokenClassification,
         TFDebertaModel,
     )




class    _SCREAMING_SNAKE_CASE					:
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase=13						,		lowerCamelCase=7						,		lowerCamelCase=True						,		lowerCamelCase=True						,		lowerCamelCase=True						,		lowerCamelCase=True						,		lowerCamelCase=99						,		lowerCamelCase=32						,		lowerCamelCase=2						,		lowerCamelCase=4						,		lowerCamelCase=37						,		lowerCamelCase="gelu"						,		lowerCamelCase=0.1						,		lowerCamelCase=0.1						,		lowerCamelCase=5_12						,		lowerCamelCase=16						,		lowerCamelCase=2						,		lowerCamelCase=0.0_2						,		lowerCamelCase=3						,		lowerCamelCase=4						,		lowerCamelCase=None						,		):
          snake_case__  =			parent
          snake_case__  =			13
          snake_case__  =			7
          snake_case__  =			True
          snake_case__  =			True
          snake_case__  =			True
          snake_case__  =			True
          snake_case__  =			99
          snake_case__  =			32
          snake_case__  =			2
          snake_case__  =			4
          snake_case__  =			37
          snake_case__  =			"gelu"
          snake_case__  =			0.1
          snake_case__  =			0.1
          snake_case__  =			5_12
          snake_case__  =			16
          snake_case__  =			False
          snake_case__  =			-1
          snake_case__  =			True
          snake_case__  =			2
          snake_case__  =			0.0_2
          snake_case__  =			3
          snake_case__  =			4
          snake_case__  =			None
      def A_     (     self	):
          snake_case__  =			ids_tensor([self.batch_size, self.seq_length]						,		self.vocab_size	)

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

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

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

          snake_case__  =			DebertaConfig(
              vocab_size=self.vocab_size						,		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						,		max_position_embeddings=self.max_position_embeddings						,		type_vocab_size=self.type_vocab_size						,		relative_attention=self.relative_attention						,		max_relative_positions=self.max_relative_positions						,		position_biased_input=self.position_biased_input						,		initializer_range=self.initializer_range						,		return_dict=lowerCamelCase						,		)

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			TFDebertaModel(config=lowerCamelCase	)
          snake_case__  =			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

          snake_case__  =			[input_ids, input_mask]
          snake_case__  =			model(lowerCamelCase	)

          snake_case__  =			model(lowerCamelCase	)

          self.parent.assertEqual(result.last_hidden_state.shape						,		(self.batch_size, self.seq_length, self.hidden_size)	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			TFDebertaForMaskedLM(config=lowerCamelCase	)
          snake_case__  =			{
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          snake_case__  =			model(lowerCamelCase	)
          self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.vocab_size)	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			self.num_labels
          snake_case__  =			TFDebertaForSequenceClassification(config=lowerCamelCase	)
          snake_case__  =			{
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          snake_case__  =			model(lowerCamelCase	)
          self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.num_labels)	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			self.num_labels
          snake_case__  =			TFDebertaForTokenClassification(config=lowerCamelCase	)
          snake_case__  =			{
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          snake_case__  =			model(lowerCamelCase	)
          self.parent.assertEqual(result.logits.shape						,		(self.batch_size, self.seq_length, self.num_labels)	)
      def A_     (     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):
          snake_case__  =			TFDebertaForQuestionAnswering(config=lowerCamelCase	)
          snake_case__  =			{
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          snake_case__  =			model(lowerCamelCase	)
          self.parent.assertEqual(result.start_logits.shape						,		(self.batch_size, self.seq_length)	)
          self.parent.assertEqual(result.end_logits.shape						,		(self.batch_size, self.seq_length)	)



      def A_     (     self	):
          snake_case__  =			self.prepare_config_and_inputs()
          (
              (
              snake_case__
          )  ,   (
              snake_case__
          )  ,   (
              snake_case__
          )  ,   (
              snake_case__
          )  ,   (
              snake_case__
          )  ,   (
              snake_case__
          )  ,   (
              snake_case__
          )  ,   
          )  =			config_and_inputs
          snake_case__  =			{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict





@require_tf
class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase    ,   __UpperCamelCase    ,   unittest.TestCase	):
      _A  :						Tuple      		=       (
          (
              TFDebertaModel,
              TFDebertaForMaskedLM,
              TFDebertaForQuestionAnswering,
              TFDebertaForSequenceClassification,
              TFDebertaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      _A  :						Optional[int]      		=       (
          {
              'feature-extraction': TFDebertaModel,
              'fill-mask': TFDebertaForMaskedLM,
              'question-answering': TFDebertaForQuestionAnswering,
              'text-classification': TFDebertaForSequenceClassification,
              'token-classification': TFDebertaForTokenClassification,
              'zero-shot': TFDebertaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      _A  :						str      		=       False
      _A  :						Optional[Any]      		=       False
      def A_     (     self	):
          snake_case__  =			TFDebertaModelTester(self	)
          snake_case__  =			ConfigTester(self						,		config_class=lowerCamelCase						,		hidden_size=37	)
      def A_     (     self	):
          self.config_tester.run_common_tests()
      def A_     (     self	):
          snake_case__  =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase	)
      def A_     (     self	):
          snake_case__  =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowerCamelCase	)



      @slow
      def A_     (     self	):
          snake_case__  =			TFDebertaModel.from_pretrained("kamalkraj/deberta-base"	)
          self.assertIsNotNone(lowerCamelCase	)





@require_tf
class    _SCREAMING_SNAKE_CASE					( unittest.TestCase	):
      @unittest.skip(reason="Model not available yet"	)
      def A_     (     self	):
          pass



      @slow
      def A_     (     self	):
          snake_case__  =			TFDebertaModel.from_pretrained("kamalkraj/deberta-base"	)
          snake_case__  =			tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]	)
          snake_case__  =			tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
          snake_case__  =			model(lowerCamelCase						,		attention_mask=lowerCamelCase	)[0]

          snake_case__  =			tf.constant(
              [
                  [
                      [-0.5_9_8_5_5_8_9_6, -0.8_0_5_5_2_5_6_6, -0.8_4_6_2_1_3_5],
                      [1.4_4_8_4_0_2_5, -0.9_3_4_8_3_7_9_4, -0.8_0_5_9_3_0_8_5],
                      [0.3_1_2_2_7_4_1, 0.0_0_3_1_6_0_5_9, -1.4_1_3_1_3_7_7],
                  ]
              ]	)
          tf.debugging.assert_near(output[:, 1:4, 1:4]						,		lowerCamelCase						,		atol=1e-4	)





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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    seed(1							)
    return [gauss(__lowerCAmelCase      ,						__lowerCAmelCase							) for _ in range(__lowerCAmelCase							)]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return [k for k in range(__lowerCAmelCase							) for _ in range(instance_count[k]							)]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return sum(__lowerCAmelCase							) / instance_count





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    return instance_count / total_count





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			[]  # An empty list to store all squared differences
    # iterate over number of elements in items
    for i in range(len(__lowerCAmelCase							)							):
        # 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
    snake_case__  =			len(__lowerCAmelCase							)  # Number of classes in dataset
    return 1 / (total_count - n_classes) * sum(__lowerCAmelCase							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			[]
    # for loop iterates over number of elements in list
    for i in range(len(__lowerCAmelCase							)							):
        # for loop iterates over number of inner items of each element
        for j in range(len(x_items[i]							)							):
            snake_case__  =			[]  # 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(__lowerCAmelCase							)							):
                # 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(__lowerCAmelCase							)

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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			sum(1 for i, j in zip(__lowerCAmelCase      ,						__lowerCAmelCase							) 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(__lowerCAmelCase							)) * 100


__magic_name__           =					TypeVar('''num''')





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase = lambda __lowerCAmelCase							: True      ,						__lowerCAmelCase = None      ,						):
    while True:
        try:
            snake_case__  =			input_type(input(__lowerCAmelCase							).strip() or default							)
            if condition(__lowerCAmelCase							):
                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 		SCREAMING_SNAKE_CASE__      ( ):
    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
        snake_case__  =			valid_input(
            input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: 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
        snake_case__  =			valid_input(
            input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: 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
        snake_case__  =			[]  # An empty list to store instance counts of classes in dataset
        for i in range(__lowerCAmelCase							):
            snake_case__  =			valid_input(
                input_type=__lowerCAmelCase      ,						condition=lambda __lowerCAmelCase							: x > 0      ,						input_msg=(F"""Enter The number of instances for class_{i+1}: """)      ,						err_msg="Number of instances should be positive!"      ,						)
            counts.append(__lowerCAmelCase							)
        print("-" * 100							)

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

        print("Standard deviation: "      ,						__lowerCAmelCase							)
        # print out the number of instances in classes in separated line
        for i, count in enumerate(__lowerCAmelCase      ,						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(__lowerCAmelCase      ,						1							):
            print(F"""Mean of class_{i} is: {user_mean}"""							)
        print("-" * 100							)

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

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

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

        # Calculating the value of probabilities for each class
        snake_case__  =			[
            calculate_probabilities(counts[i]      ,						sum(__lowerCAmelCase							)							) for i in range(__lowerCAmelCase							)
        ]

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

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

        # Predicting Y values
        # storing predicted Y values in 'pre_indexes' variable
        snake_case__  =			predict_y_values(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							)
        print("-" * 100							)

        # Calculating Accuracy of the model
        print(F"""Accuracy: {accuracy(__lowerCAmelCase      ,						__lowerCAmelCase							)}"""							)
        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()



