


"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import 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 (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMModel,
		    TFXLMWithLMHeadModel,
		    XLMConfig,
		)
class snake_case					:







		def __init__(    self					:				Tuple			,      a__					:				Any			,      )				->  Tuple:





						'''simple docstring'''







						_A         =	parent
						_A         =	13
						_A         =	7
						_A         =	True
						_A         =	True
						_A         =	True
						_A         =	True
						_A         =	True
						_A         =	False
						_A         =	False
						_A         =	False
						_A         =	2
						_A         =	99
						_A         =	0
						_A         =	32
						_A         =	2
						_A         =	4
						_A         =	0.1
						_A         =	0.1
						_A         =	5_12
						_A         =	16
						_A         =	2
						_A         =	0.0_2
						_A         =	3
						_A         =	4
						_A         =	"last"
						_A         =	True
						_A         =	None
						_A         =	0







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





						'''simple docstring'''







						_A         =	ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size						)
						_A         =	random_attention_mask([self.batch_size, self.seq_length]			,      dtype=tf.floataa						)

						_A         =	None
						if self.use_input_lengths:
										_A         =	(
										    ids_tensor([self.batch_size]			,      vocab_size=2						) + self.seq_length - 2
										)  # small variation of seq_length

						_A         =	None
						if self.use_token_type_ids:
										_A         =	ids_tensor([self.batch_size, self.seq_length]			,      self.n_langs						)

						_A         =	None
						_A         =	None
						_A         =	None
						if self.use_labels:
										_A         =	ids_tensor([self.batch_size]			,      self.type_sequence_label_size						)
										_A         =	ids_tensor([self.batch_size, self.seq_length]			,      self.num_labels						)
										_A         =	ids_tensor([self.batch_size]			,      2			,      dtype=tf.floataa						)
										_A         =	ids_tensor([self.batch_size]			,      self.num_choices						)

						_A         =	XLMConfig(
						    vocab_size=self.vocab_size			,      n_special=self.n_special			,      emb_dim=self.hidden_size			,      n_layers=self.num_hidden_layers			,      n_heads=self.num_attention_heads			,      dropout=self.hidden_dropout_prob			,      attention_dropout=self.attention_probs_dropout_prob			,      gelu_activation=self.gelu_activation			,      sinusoidal_embeddings=self.sinusoidal_embeddings			,      asm=self.asm			,      causal=self.causal			,      n_langs=self.n_langs			,      max_position_embeddings=self.max_position_embeddings			,      initializer_range=self.initializer_range			,      summary_type=self.summary_type			,      use_proj=self.use_proj			,      bos_token_id=self.bos_token_id			,      )

						return (
						    config,
						    input_ids,
						    token_type_ids,
						    input_lengths,
						    sequence_labels,
						    token_labels,
						    is_impossible_labels,
						    choice_labels,
						    input_mask,
						)







		def        a_       (    self					:				Optional[int]			,      a__					:				Dict			,      a__					:				Optional[int]			,      a__					:				int			,      a__					:				Any			,      a__					:				Dict			,      a__					:				Optional[int]			,      a__					:				Optional[Any]			,      a__					:				Optional[int]			,      a__					:				Dict			,      )				->  Any:





						'''simple docstring'''







						_A         =	TFXLMModel(config=a__						)
						_A         =	{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
						_A         =	model(a__						)

						_A         =	[input_ids, input_mask]
						_A         =	model(a__						)
						self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size)						)







		def        a_       (    self					:				Union[str, Any]			,      a__					:				int			,      a__					:				List[Any]			,      a__					:				Any			,      a__					:				Optional[int]			,      a__					:				int			,      a__					:				Dict			,      a__					:				Dict			,      a__					:				Optional[Any]			,      a__					:				Optional[Any]			,      )				->  Any:





						'''simple docstring'''







						_A         =	TFXLMWithLMHeadModel(a__						)

						_A         =	{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
						_A         =	model(a__						)

						_A         =	outputs

						self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size)						)







		def        a_       (    self					:				List[Any]			,      a__					:				Any			,      a__					:				Any			,      a__					:				List[Any]			,      a__					:				Dict			,      a__					:				Union[str, Any]			,      a__					:				Tuple			,      a__					:				int			,      a__					:				Dict			,      a__					:				Tuple			,      )				->  str:





						'''simple docstring'''







						_A         =	TFXLMForQuestionAnsweringSimple(a__						)

						_A         =	{"input_ids": input_ids, "lengths": input_lengths}

						_A         =	model(a__						)

						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					:				List[str]			,      a__					:				Any			,      a__					:				str			,      a__					:				Dict			,      a__					:				Tuple			,      a__					:				List[str]			,      a__					:				Tuple			,      a__					:				Dict			,      a__					:				Tuple			,      a__					:				int			,      )				->  Dict:





						'''simple docstring'''







						_A         =	TFXLMForSequenceClassification(a__						)

						_A         =	{"input_ids": input_ids, "lengths": input_lengths}

						_A         =	model(a__						)

						self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.type_sequence_label_size)						)







		def        a_       (    self					:				Tuple			,      a__					:				Union[str, Any]			,      a__					:				Dict			,      a__					:				Any			,      a__					:				Optional[int]			,      a__					:				Optional[int]			,      a__					:				Optional[Any]			,      a__					:				Any			,      a__					:				List[Any]			,      a__					:				Any			,      )				->  Tuple:





						'''simple docstring'''







						_A         =	self.num_labels
						_A         =	TFXLMForTokenClassification(config=a__						)
						_A         =	{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
						_A         =	model(a__						)
						self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.num_labels)						)







		def        a_       (    self					:				str			,      a__					:				Union[str, Any]			,      a__					:				int			,      a__					:				Optional[int]			,      a__					:				Optional[int]			,      a__					:				str			,      a__					:				List[str]			,      a__					:				Dict			,      a__					:				Union[str, Any]			,      a__					:				Tuple			,      )				->  List[Any]:





						'''simple docstring'''







						_A         =	self.num_choices
						_A         =	TFXLMForMultipleChoice(config=a__						)
						_A         =	tf.tile(tf.expand_dims(a__			,      1						)			,      (1, self.num_choices, 1)						)
						_A         =	tf.tile(tf.expand_dims(a__			,      1						)			,      (1, self.num_choices, 1)						)
						_A         =	tf.tile(tf.expand_dims(a__			,      1						)			,      (1, self.num_choices, 1)						)
						_A         =	{
						    "input_ids": multiple_choice_inputs_ids,
						    "attention_mask": multiple_choice_input_mask,
						    "token_type_ids": multiple_choice_token_type_ids,
						}
						_A         =	model(a__						)
						self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_choices)						)







		def        a_       (    self					:				Optional[Any]						)				->  Dict:





						'''simple docstring'''







						_A         =	self.prepare_config_and_inputs()
						(
						    (
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						(
						    _A
						)     ,						
						)         =	config_and_inputs
						_A         =	{
						    "input_ids": input_ids,
						    "token_type_ids": token_type_ids,
						    "langs": token_type_ids,
						    "lengths": input_lengths,
						}
						return config, inputs_dict





@require_tf
class snake_case					(     _UpperCamelCase   , _UpperCamelCase   , unittest.TestCase):
		__UpperCamelCase							=	(
		    (
		        TFXLMModel,
		        TFXLMWithLMHeadModel,
		        TFXLMForSequenceClassification,
		        TFXLMForQuestionAnsweringSimple,
		        TFXLMForTokenClassification,
		        TFXLMForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		__UpperCamelCase							=	(
		    (TFXLMWithLMHeadModel,) if is_tf_available() else ()
		)  # TODO (PVP): Check other models whether language generation is also applicable
		__UpperCamelCase							=	(
		    {
		        'feature-extraction': TFXLMModel,
		        'fill-mask': TFXLMWithLMHeadModel,
		        'question-answering': TFXLMForQuestionAnsweringSimple,
		        'text-classification': TFXLMForSequenceClassification,
		        'text-generation': TFXLMWithLMHeadModel,
		        'token-classification': TFXLMForTokenClassification,
		        'zero-shot': TFXLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		__UpperCamelCase							=	False
		__UpperCamelCase							=	False







		def        a_       (    self					:				int			,      a__					:				int			,      a__					:				str			,      a__					:				Any			,      a__					:				int			,      a__					:				List[str]						)				->  List[str]:





						'''simple docstring'''







						if (
						    pipeline_test_casse_name == "QAPipelineTests"
						    and tokenizer_name is not None
						    and not tokenizer_name.endswith("Fast"						)
						):
										# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
										# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
										# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
										return True

						return False







		def        a_       (    self					:				Optional[int]						)				->  Tuple:





						'''simple docstring'''







						_A         =	TFXLMModelTester(self						)
						_A         =	ConfigTester(self			,      config_class=a__			,      emb_dim=37						)







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





						'''simple docstring'''







						self.config_tester.run_common_tests()







		def        a_       (    self					:				List[Any]						)				->  int:





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_model(*a__						)







		def        a_       (    self					:				List[Any]						)				->  Dict:





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_lm_head(*a__						)







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





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_qa(*a__						)







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





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_sequence_classif(*a__						)







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





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_token_classification(*a__						)







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





						'''simple docstring'''







						_A         =	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_xlm_for_multiple_choice(*a__						)







		@slow
		def        a_       (    self					:				Optional[Any]						)				->  Union[str, Any]:





						'''simple docstring'''







						for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_A         =	TFXLMModel.from_pretrained(a__						)
										self.assertIsNotNone(a__						)





@require_tf
class snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				List[Any]						)				->  str:





						'''simple docstring'''







						_A         =	TFXLMWithLMHeadModel.from_pretrained("xlm-mlm-en-2048"						)
						_A         =	tf.convert_to_tensor([[14, 4_47]]			,      dtype=tf.intaa						)  # the president
						_A         =	[
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						    14,
						    4_47,
						]  # the president the president the president the president the president the president the president the president the president the president
						# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
						_A         =	model.generate(a__			,      do_sample=a__						)
						self.assertListEqual(output_ids[0].numpy().tolist()			,      a__						)





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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					seed(1       )
					return [gauss(UpperCAmelCase,	UpperCAmelCase       ) for _ in range(UpperCAmelCase       )]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					return [k for k in range(UpperCAmelCase       ) for _ in range(instance_count[k]       )]







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return sum(UpperCAmelCase       ) / instance_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					return instance_count / total_count







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Any		   =							[]  # An empty list to store all squared differences
					# iterate over number of elements in items
					for i in range(len(UpperCAmelCase       )       ):
										# 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
					__magic_name__					:							Optional[int]		   =							len(UpperCAmelCase       )  # Number of classes in dataset
					return 1 / (total_count - n_classes) * sum(UpperCAmelCase       )







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )  ->list:






					"""simple docstring"""






					__magic_name__					:							int		   =							[]
					# for loop iterates over number of elements in list
					for i in range(len(UpperCAmelCase       )       ):
										# for loop iterates over number of inner items of each element
										for j in range(len(x_items[i]       )       ):
															__magic_name__					:							Dict		   =							[]  # 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(UpperCAmelCase       )       ):
																				# 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(UpperCAmelCase       )

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







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase       )  ->float:






					"""simple docstring"""






					__magic_name__					:							Optional[Any]		   =							sum(1 for i, j in zip(UpperCAmelCase,	UpperCAmelCase       ) 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(UpperCAmelCase       )) * 100


lowercase_		=							TypeVar('''num''')







def  lowerCAmelCase  (	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase = lambda UpperCAmelCase       : True,	UpperCAmelCase = None,	)  ->num:






					"""simple docstring"""






					while True:
										try:
															__magic_name__					:							List[Any]		   =							input_type(input(UpperCAmelCase       ).strip() or default       )
															if condition(UpperCAmelCase       ):
																				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  lowerCAmelCase  (	)  ->str:






					"""simple docstring"""






					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
										__magic_name__					:							List[Any]		   =							valid_input(
										    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : 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
										__magic_name__					:							Any		   =							valid_input(
										    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : 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
										__magic_name__					:							Optional[Any]		   =							[]  # An empty list to store instance counts of classes in dataset
										for i in range(UpperCAmelCase       ):
															__magic_name__					:							Dict		   =							valid_input(
															    input_type=UpperCAmelCase,	condition=lambda UpperCAmelCase       : x > 0,	input_msg=(F'''Enter The number of instances for class_{i+1}: '''),	err_msg='''Number of instances should be positive!''',	)
															counts.append(UpperCAmelCase       )
										print('''-''' * 100       )

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

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

										# Generating training dataset drawn from gaussian distribution
										__magic_name__					:							Optional[int]		   =							[
										    gaussian_distribution(user_means[j],	UpperCAmelCase,	counts[j]       )
										    for j in range(UpperCAmelCase       )
										]
										print('''Generated Normal Distribution: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Generating Ys to detecting corresponding classes
										__magic_name__					:							List[str]		   =							y_generator(UpperCAmelCase,	UpperCAmelCase       )
										print('''Generated Corresponding Ys: \n''',	UpperCAmelCase       )
										print('''-''' * 100       )

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

										# Calculating the value of probabilities for each class
										__magic_name__					:							Union[str, Any]		   =							[
										    calculate_probabilities(counts[i],	sum(UpperCAmelCase       )       ) for i in range(UpperCAmelCase       )
										]

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

										# Calculating the values of variance for each class
										__magic_name__					:							List[Any]		   =							calculate_variance(UpperCAmelCase,	UpperCAmelCase,	sum(UpperCAmelCase       )       )
										print('''Variance: ''',	UpperCAmelCase       )
										print('''-''' * 100       )

										# Predicting Y values
										# storing predicted Y values in 'pre_indexes' variable
										__magic_name__					:							List[Any]		   =							predict_y_values(UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase,	UpperCAmelCase       )
										print('''-''' * 100       )

										# Calculating Accuracy of the model
										print(F'''Accuracy: {accuracy(UpperCAmelCase,	UpperCAmelCase       )}'''       )
										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()



