




def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):
					__lowerCAmelCase            = generate_pascal_triangle(SCREAMING_SNAKE_CASE_  )
					for row_idx in range(SCREAMING_SNAKE_CASE_  ):
										# Print left spaces
										for _ in range(num_rows - row_idx - 1  ):
															print(end=" "  )
										# Print row values
										for col_idx in range(row_idx + 1  ):
															if col_idx != row_idx:
																				print(triangle[row_idx][col_idx]   ,   end=" "  )
															else:
																				print(triangle[row_idx][col_idx]   ,   end=""  )
										print()







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):

					if not isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  ):
										raise TypeError("The input value of 'num_rows' should be 'int'"  )

					if num_rows == 0:
										return []
					elif num_rows < 0:
										raise ValueError(
										    "The input value of 'num_rows' should be greater than or equal to 0"  )

					__lowerCAmelCase            = []
					for current_row_idx in range(SCREAMING_SNAKE_CASE_  ):
										__lowerCAmelCase            = populate_current_row(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										triangle.append(SCREAMING_SNAKE_CASE_  )
					return triangle







def 			_a   (		SCREAMING_SNAKE_CASE_							:       list[list[int]]   ,   SCREAMING_SNAKE_CASE_							:       int  ):
					__lowerCAmelCase            = [-1] * (current_row_idx + 1)
					# first and last elements of current row are equal to 1
					__lowerCAmelCase     ,							__lowerCAmelCase            = 1, 1
					for current_col_idx in range(1   ,   SCREAMING_SNAKE_CASE_  ):
										calculate_current_element(
										    SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
					return current_row







def 			_a   (		SCREAMING_SNAKE_CASE_							:       list[list[int]]   ,   SCREAMING_SNAKE_CASE_							:       list[int]   ,   SCREAMING_SNAKE_CASE_							:       int   ,   SCREAMING_SNAKE_CASE_							:       int   ,   ):
					__lowerCAmelCase            = triangle[current_row_idx - 1][current_col_idx - 1]
					__lowerCAmelCase            = triangle[current_row_idx - 1][current_col_idx]
					__lowerCAmelCase            = above_to_left_elt + above_to_right_elt







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):

					if not isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  ):
										raise TypeError("The input value of 'num_rows' should be 'int'"  )

					if num_rows == 0:
										return []
					elif num_rows < 0:
										raise ValueError(
										    "The input value of 'num_rows' should be greater than or equal to 0"  )

					__lowerCAmelCase            = [[1]]

					for row_index in range(1   ,   SCREAMING_SNAKE_CASE_  ):
										__lowerCAmelCase            = [0] + result[-1] + [0]
										__lowerCAmelCase            = row_index + 1
										# Calculate the number of distinct elements in a row
										__lowerCAmelCase            = sum(divmod(SCREAMING_SNAKE_CASE_   ,   2  )  )
										__lowerCAmelCase            = [
										    temp_row[i - 1] + temp_row[i] for i in range(1   ,   distinct_elements + 1  )
										]
										__lowerCAmelCase            = row_first_half[: (row_index + 1) // 2]
										row_second_half.reverse()
										__lowerCAmelCase            = row_first_half + row_second_half
										result.append(SCREAMING_SNAKE_CASE_  )

					return result







def 			_a   (		):
					from collections.abc import Callable
					from timeit import timeit

					def benchmark_a_function(SCREAMING_SNAKE_CASE_							:       Callable   ,   SCREAMING_SNAKE_CASE_							:       int  ) -> None:
										__lowerCAmelCase            = F"""{func.__name__}({value})"""
										__lowerCAmelCase            = timeit(F"""__main__.{call}"""   ,   setup="import __main__"  )
										# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
										print(F"""{call:38} -- {timing:.4f} seconds"""  )

					for value in range(15  ):  # (1, 7, 14):
										for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
															benchmark_a_function(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										print()


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					benchmark()










from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
					    TFDebertaVaForMaskedLM,
					    TFDebertaVaForQuestionAnswering,
					    TFDebertaVaForSequenceClassification,
					    TFDebertaVaForTokenClassification,
					    TFDebertaVaModel,
					)
class     a__					:






						def __init__(     self	,    _A	,    _A=1_3	,    _A=7	,    _A=True	,    _A=True	,    _A=True	,    _A=True	,    _A=9_9	,    _A=3_2	,    _A=2	,    _A=4	,    _A=3_7	,    _A="gelu"	,    _A=0.1	,    _A=0.1	,    _A=5_1_2	,    _A=1_6	,    _A=2	,    _A=0.02	,    _A=False	,    _A=True	,    _A="None"	,    _A=3	,    _A=4	,    _A=None	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = parent
											__lowerCAmelCase            = batch_size
											__lowerCAmelCase            = seq_length
											__lowerCAmelCase            = is_training
											__lowerCAmelCase            = use_input_mask
											__lowerCAmelCase            = use_token_type_ids
											__lowerCAmelCase            = use_labels
											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = hidden_size
											__lowerCAmelCase            = num_hidden_layers
											__lowerCAmelCase            = num_attention_heads
											__lowerCAmelCase            = intermediate_size
											__lowerCAmelCase            = hidden_act
											__lowerCAmelCase            = hidden_dropout_prob
											__lowerCAmelCase            = attention_probs_dropout_prob
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = type_vocab_size
											__lowerCAmelCase            = type_sequence_label_size
											__lowerCAmelCase            = initializer_range
											__lowerCAmelCase            = num_labels
											__lowerCAmelCase            = num_choices
											__lowerCAmelCase            = relative_attention
											__lowerCAmelCase            = position_biased_input
											__lowerCAmelCase            = pos_att_type
											__lowerCAmelCase            = scope






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size    )

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

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

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

											__lowerCAmelCase            = DebertaVaConfig(
											    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	,    position_biased_input=self.position_biased_input	,    initializer_range=self.initializer_range	,    return_dict=_A	,    )

											return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaModel(config=_A    )
											__lowerCAmelCase            = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

											__lowerCAmelCase            = [input_ids, input_mask]
											__lowerCAmelCase            = model(_A    )

											__lowerCAmelCase            = model(_A    )

											self.parent.assertEqual(result.last_hidden_state.shape	,    (self.batch_size, self.seq_length, self.hidden_size)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.num_labels
											__lowerCAmelCase            = TFDebertaVaForSequenceClassification(config=_A    )
											__lowerCAmelCase            = {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}

											__lowerCAmelCase            = model(_A    )
											self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaForQuestionAnswering(config=_A    )
											__lowerCAmelCase            = {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}

											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



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






@require_tf
class     a__					(      snake_case__				,       snake_case__				,       unittest.TestCase   ):
						_a							:					Optional[Any]      =					(
						    (
						        TFDebertaVaModel,
						        TFDebertaVaForMaskedLM,
						        TFDebertaVaForQuestionAnswering,
						        TFDebertaVaForSequenceClassification,
						        TFDebertaVaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:					Union[str, Any]      =					(
						    {
						        """feature-extraction""": TFDebertaVaModel,
						        """fill-mask""": TFDebertaVaForMaskedLM,
						        """question-answering""": TFDebertaVaForQuestionAnswering,
						        """text-classification""": TFDebertaVaForSequenceClassification,
						        """token-classification""": TFDebertaVaForTokenClassification,
						        """zero-shot""": TFDebertaVaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						_a							:					str      =					False
						_a							:					List[str]      =					False






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaModelTester(self    )
											__lowerCAmelCase            = ConfigTester(self	,    config_class=_A	,    hidden_size=3_7    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A    )






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"    )
											self.assertIsNotNone(_A    )






@require_tf
class     a__					(      unittest.TestCase   ):






						@unittest.skip(reason="Model not available yet"    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"    )
											__lowerCAmelCase            = tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    )
											__lowerCAmelCase            = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
											__lowerCAmelCase            = model(_A	,    attention_mask=_A    )[0]

											__lowerCAmelCase            = tf.constant(
											    [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]    )
											tf.debugging.assert_near(output[:, 1:4, 1:4]	,    _A	,    atol=1E-4    )




