
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 	A_       :




	'''simple docstring'''


	def __init__(	self		,   snake_case		,   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=512		,   snake_case=16		,   snake_case=2		,   snake_case=0.02		,   snake_case=3		,   snake_case=4		,   snake_case=None		,   ):
		lowercase					  =   parent
		lowercase					  =   13
		lowercase					  =   7
		lowercase					  =   True
		lowercase					  =   True
		lowercase					  =   True
		lowercase					  =   True
		lowercase					  =   99
		lowercase					  =   32
		lowercase					  =   2
		lowercase					  =   4
		lowercase					  =   37
		lowercase					  =   "gelu"
		lowercase					  =   0.1
		lowercase					  =   0.1
		lowercase					  =   512
		lowercase					  =   16
		lowercase					  =   False
		lowercase					  =   -1
		lowercase					  =   True
		lowercase					  =   2
		lowercase					  =   0.02
		lowercase					  =   3
		lowercase					  =   4
		lowercase					  =   None





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   ids_tensor([self.batch_size, self.seq_length]		,   self.vocab_size	)

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

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

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

		lowercase					  =   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=SCREAMING_SNAKE_CASE__		,   )

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





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   TFDebertaModel(config=SCREAMING_SNAKE_CASE__	)
		lowercase					  =   {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

		lowercase					  =   [input_ids, input_mask]
		lowercase					  =   model(SCREAMING_SNAKE_CASE__	)

		lowercase					  =   model(SCREAMING_SNAKE_CASE__	)

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





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   TFDebertaForMaskedLM(config=SCREAMING_SNAKE_CASE__	)
		lowercase					  =   {
		    "input_ids": input_ids,
		    "attention_mask": input_mask,
		    "token_type_ids": token_type_ids,
		}
		lowercase					  =   model(SCREAMING_SNAKE_CASE__	)
		self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.seq_length, self.vocab_size)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   self.num_labels
		lowercase					  =   TFDebertaForSequenceClassification(config=SCREAMING_SNAKE_CASE__	)
		lowercase					  =   {
		    "input_ids": input_ids,
		    "attention_mask": input_mask,
		    "token_type_ids": token_type_ids,
		}

		lowercase					  =   model(SCREAMING_SNAKE_CASE__	)
		self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.num_labels)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   self.num_labels
		lowercase					  =   TFDebertaForTokenClassification(config=SCREAMING_SNAKE_CASE__	)
		lowercase					  =   {
		    "input_ids": input_ids,
		    "attention_mask": input_mask,
		    "token_type_ids": token_type_ids,
		}
		lowercase					  =   model(SCREAMING_SNAKE_CASE__	)
		self.parent.assertEqual(result.logits.shape		,   (self.batch_size, self.seq_length, self.num_labels)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case		,   snake_case	):
		lowercase					  =   TFDebertaForQuestionAnswering(config=SCREAMING_SNAKE_CASE__	)
		lowercase					  =   {
		    "input_ids": input_ids,
		    "attention_mask": input_mask,
		    "token_type_ids": token_type_ids,
		}

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







@require_tf
class 	A_       (      _a				,     _a				,     unittest.TestCase       ):




	'''simple docstring'''


	_UpperCamelCase					:					Any										=	(
	    (
	        TFDebertaModel,
	        TFDebertaForMaskedLM,
	        TFDebertaForQuestionAnswering,
	        TFDebertaForSequenceClassification,
	        TFDebertaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	_UpperCamelCase					:					List[str]										=	(
	    {
	        '''feature-extraction''': TFDebertaModel,
	        '''fill-mask''': TFDebertaForMaskedLM,
	        '''question-answering''': TFDebertaForQuestionAnswering,
	        '''text-classification''': TFDebertaForSequenceClassification,
	        '''token-classification''': TFDebertaForTokenClassification,
	        '''zero-shot''': TFDebertaForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	_UpperCamelCase					:					Any										=	False
	_UpperCamelCase					:					List[Any]										=	False





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   TFDebertaModelTester(self	)
		lowercase					  =   ConfigTester(self		,   config_class=SCREAMING_SNAKE_CASE__		,   hidden_size=37	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		self.config_tester.run_common_tests()





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE__	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE__	)





	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE__	)





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   TFDebertaModel.from_pretrained('kamalkraj/deberta-base'	)
		self.assertIsNotNone(SCREAMING_SNAKE_CASE__	)







@require_tf
class 	A_       (      unittest.TestCase       ):




	'''simple docstring'''


	@unittest.skip(reason='Model not available yet'	)
	def      SCREAMING_SNAKE_CASE__						(	self	):
		pass





	@slow
	def      SCREAMING_SNAKE_CASE__						(	self	):
		lowercase					  =   TFDebertaModel.from_pretrained('kamalkraj/deberta-base'	)
		lowercase					  =   tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]	)
		lowercase					  =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]	)
		lowercase					  =   model(SCREAMING_SNAKE_CASE__		,   attention_mask=SCREAMING_SNAKE_CASE__	)[0]

		lowercase					  =   tf.constant(
		    [
		        [
		            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
		            [1.4_484_025, -0.93_483_794, -0.80_593_085],
		            [0.3_122_741, 0.00_316_059, -1.4_131_377],
		        ]
		    ]	)
		tf.debugging.assert_near(output[:, 1:4, 1:4]		,   SCREAMING_SNAKE_CASE__		,   atol=1E-4	)



from __future__ import annotations

from queue import Queue







class 	A_       :




	'''simple docstring'''


	def __init__(	self	):
		lowercase					  =   {}





	def      SCREAMING_SNAKE_CASE__						(	self	):
		for i in self.vertices:
			print(snake_case		,   ' : '		,   ' -> '.join([str(snake_case	) for j in self.vertices[i]]	)	)





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case		,   snake_case	):
		if from_vertex in self.vertices:
			self.vertices[from_vertex].append(snake_case	)
		else:
			lowercase					  =   [to_vertex]





	def      SCREAMING_SNAKE_CASE__						(	self		,   snake_case	):
		lowercase					  =   set()

		# create a first in first out queue to store all the vertices for BFS
		lowercase					  =   Queue()

		# mark the source node as visited and enqueue it
		visited.add(snake_case	)
		queue.put(snake_case	)

		while not queue.empty():
			lowercase					  =   queue.get()

			# loop through all adjacent vertex and enqueue it if not yet visited
			for adjacent_vertex in self.vertices[vertex]:
				if adjacent_vertex not in visited:
					queue.put(snake_case	)
					visited.add(snake_case	)
		return visited


if __name__ == "__main__":
	from doctest import testmod

	testmod(verbose=True)

	UpperCAmelCase     				=			Graph()
	g.add_edge(0, 1)
	g.add_edge(0, 2)
	g.add_edge(1, 2)
	g.add_edge(2, 0)
	g.add_edge(2, 3)
	g.add_edge(3, 3)

	g.print_graph()
	# 0  :  1 -> 2
	# 1  :  2
	# 2  :  0 -> 3
	# 3  :  3

	assert sorted(g.bfs(2)) == [0, 1, 2, 3]

