from __future__ import annotations

import unittest

from transformers import RoFormerConfig, 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 (
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerModel,
				)
				from transformers.models.roformer.modeling_tf_roformer import (
				    TFRoFormerSelfAttention,
				    TFRoFormerSinusoidalPositionalEmbedding,
				)

class 						__UpperCamelCase    :




	def __init__(		self	:						List[Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Any=13		,	lowerCAmelCase	:						Optional[int]=7		,	lowerCAmelCase	:						List[str]=True		,	lowerCAmelCase	:						Union[str, Any]=True		,	lowerCAmelCase	:						str=True		,	lowerCAmelCase	:						int=True		,	lowerCAmelCase	:						str=99		,	lowerCAmelCase	:						Tuple=32		,	lowerCAmelCase	:						Optional[int]=2		,	lowerCAmelCase	:						Optional[int]=4		,	lowerCAmelCase	:						List[str]=37		,	lowerCAmelCase	:						Dict="gelu"		,	lowerCAmelCase	:						Optional[int]=0.1		,	lowerCAmelCase	:						str=0.1		,	lowerCAmelCase	:						Optional[int]=512		,	lowerCAmelCase	:						Optional[Any]=16		,	lowerCAmelCase	:						Dict=2		,	lowerCAmelCase	:						List[Any]=0.02		,	lowerCAmelCase	:						List[str]=3		,	lowerCAmelCase	:						Tuple=4		,	lowerCAmelCase	:						int=None		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			parent
		UpperCAmelCase_       				=			13
		UpperCAmelCase_       				=			7
		UpperCAmelCase_       				=			True
		UpperCAmelCase_       				=			True
		UpperCAmelCase_       				=			True
		UpperCAmelCase_       				=			True
		UpperCAmelCase_       				=			99
		UpperCAmelCase_       				=			32
		UpperCAmelCase_       				=			2
		UpperCAmelCase_       				=			4
		UpperCAmelCase_       				=			37
		UpperCAmelCase_       				=			"gelu"
		UpperCAmelCase_       				=			0.1
		UpperCAmelCase_       				=			0.1
		UpperCAmelCase_       				=			512
		UpperCAmelCase_       				=			16
		UpperCAmelCase_       				=			2
		UpperCAmelCase_       				=			0.02
		UpperCAmelCase_       				=			3
		UpperCAmelCase_       				=			4
		UpperCAmelCase_       				=			None




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







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

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

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

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

		UpperCAmelCase_       				=			RoFormerConfig(
		    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		,	initializer_range=self.initializer_range		,	return_dict=A__		,	)

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




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFRoFormerModel(config=A__							)
		UpperCAmelCase_       				=			{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

		UpperCAmelCase_       				=			[input_ids, input_mask]
		UpperCAmelCase_       				=			model(A__							)

		UpperCAmelCase_       				=			model(A__							)

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




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			True
		UpperCAmelCase_       				=			TFRoFormerForCausalLM(config=A__							)
		UpperCAmelCase_       				=			{
		    "input_ids": input_ids,
		    "attention_mask": input_mask,
		    "token_type_ids": token_type_ids,
		}
		UpperCAmelCase_       				=			model(A__							)["logits"]
		self.parent.assertListEqual(
		    list(prediction_scores.numpy().shape							)		,	[self.batch_size, self.seq_length, self.vocab_size]							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.num_labels
		UpperCAmelCase_       				=			TFRoFormerForSequenceClassification(config=A__							)
		UpperCAmelCase_       				=			{
		    "input_ids": input_ids,
		    "attention_mask": input_mask,
		    "token_type_ids": token_type_ids,
		}

		UpperCAmelCase_       				=			model(A__							)
		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







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




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Optional[int]							):


		'''simple docstring'''







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




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFRoFormerForQuestionAnswering(config=A__							)
		UpperCAmelCase_       				=			{
		    "input_ids": input_ids,
		    "attention_mask": input_mask,
		    "token_type_ids": token_type_ids,
		}

		UpperCAmelCase_       				=			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	:						Tuple							):


		'''simple docstring'''







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


@require_tf
class 						__UpperCamelCase    (					__a     ,     __a     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     (
	    (
	        TFRoFormerModel,
	        TFRoFormerForCausalLM,
	        TFRoFormerForMaskedLM,
	        TFRoFormerForQuestionAnswering,
	        TFRoFormerForSequenceClassification,
	        TFRoFormerForTokenClassification,
	        TFRoFormerForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE__    				=     (
	    {
	        'feature-extraction': TFRoFormerModel,
	        'fill-mask': TFRoFormerForMaskedLM,
	        'question-answering': TFRoFormerForQuestionAnswering,
	        'text-classification': TFRoFormerForSequenceClassification,
	        'text-generation': TFRoFormerForCausalLM,
	        'token-classification': TFRoFormerForTokenClassification,
	        'zero-shot': TFRoFormerForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     False




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		if pipeline_test_casse_name == "TextGenerationPipelineTests":
			return True

		return False




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFRoFormerModelTester(self							)
		UpperCAmelCase_       				=			ConfigTester(self		,	config_class=A__		,	hidden_size=37							)




	def       __A					(		self	:						Union[str, Any]							):


		'''simple docstring'''







		self.config_tester.run_common_tests()




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*A__							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_lm(*A__							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_lm_head(*A__							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_multiple_choice(*A__							)




	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*A__							)




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*A__							)




	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*A__							)




	@slow
	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFRoFormerModel.from_pretrained("junnyu/roformer_chinese_base"							)
		self.assertIsNotNone(A__							)


@require_tf
class 						__UpperCamelCase    (					unittest.TestCase						):




	@slow
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"							)
		UpperCAmelCase_       				=			tf.constant([[0, 1, 2, 3, 4, 5]]							)
		UpperCAmelCase_       				=			model(A__							)[0]

		# TODO Replace vocab size
		UpperCAmelCase_       				=			50_000

		UpperCAmelCase_       				=			[1, 6, vocab_size]
		self.assertEqual(output.shape		,	A__							)

		print(output[:, :3, :3]							)

		# TODO Replace values below with what was printed above.
		UpperCAmelCase_       				=			tf.constant(
		    [
		        [
		            [-0.12_053_341, -1.0_264_901, 0.29_221_946],
		            [-1.5_133_783, 0.197_433, 0.15_190_607],
		            [-5.0_135_403, -3.900_256, -0.84_038_764],
		        ]
		    ]							)
		tf.debugging.assert_near(output[:, :3, :3]		,	A__		,	atol=1e-4							)


@require_tf
class 						__UpperCamelCase    (					unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     1E-4




	def       __A					(		self	:						Any							):


		'''simple docstring'''







		UpperCAmelCase_       				=			tf.constant([[4, 10]]							)
		UpperCAmelCase_       				=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=6		,	embedding_dim=6							)

		UpperCAmelCase_       				=			emba(input_ids.shape							)
		UpperCAmelCase_       				=			tf.constant(
		    [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]							)

		tf.debugging.assert_near(A__		,	A__		,	atol=self.tolerance							)




	def       __A					(		self	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			tf.constant(
		    [
		        [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
		        [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
		        [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
		    ]							)
		UpperCAmelCase_       				=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=512		,	embedding_dim=512							)
		emba([2, 16, 512]							)
		UpperCAmelCase_       				=			emba.weight[:3, :5]

		tf.debugging.assert_near(A__		,	A__		,	atol=self.tolerance							)


@require_tf
class 						__UpperCamelCase    (					unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     1E-4




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			tf.reshape(tf.range(2 * 12 * 16 * 64		,	dtype=tf.floataa							)		,	shape=(2, 12, 16, 64)							) / 100

		UpperCAmelCase_       				=			-tf.reshape(tf.range(2 * 12 * 16 * 64		,	dtype=tf.floataa							)		,	shape=(2, 12, 16, 64)							) / 100

		UpperCAmelCase_       				=			TFRoFormerSinusoidalPositionalEmbedding(num_positions=32		,	embedding_dim=64							)
		UpperCAmelCase_       				=			embed_positions([2, 16, 768]							)[None, None, :, :]

		UpperCAmelCase_				,   UpperCAmelCase_       				=			TFRoFormerSelfAttention.apply_rotary_position_embeddings(
		    A__		,	A__		,	A__							)

		UpperCAmelCase_       				=			tf.constant(
		    [
		        [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
		        [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
		        [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
		        [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
		        [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
		        [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
		    ]							)
		UpperCAmelCase_       				=			tf.constant(
		    [
		        [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
		        [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
		        [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
		        [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
		        [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
		        [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
		    ]							)

		tf.debugging.assert_near(query_layer[0, 0, :6, :8]		,	A__		,	atol=self.tolerance							)
		tf.debugging.assert_near(key_layer[0, 0, :6, :8]		,	A__		,	atol=self.tolerance							)


from __future__ import annotations



def       __lowerCAmelCase      (						A       ,     A    ):
	print(F"Vertex\tShortest Distance from vertex {src}"    )
	for i, d in enumerate(A    ):
		print(F"{i}\t\t{d}"    )



def       __lowerCAmelCase      (						A       ,     A       ,     A    ):
	for j in range(A    ):
		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])
		if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
			return True
	return False



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			[float("inf"    )] * vertex_count
	UpperCAmelCase_       				=			0.0

	for _ in range(vertex_count - 1    ):
		for j in range(A    ):
			UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			(graph[j][k] for k in ["src", "dst", "weight"])

			if distance[u] != float("inf"    ) and distance[u] + w < distance[v]:
				UpperCAmelCase_       				=			distance[u] + w

	UpperCAmelCase_       				=			check_negative_cycle(A       ,     A       ,     A    )
	if negative_cycle_exists:
		raise Exception("Negative cycle found"    )

	return distance


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				_a:    List[str] 							=			int(input("""Enter number of vertices: """).strip())
				_a:    str 							=			int(input("""Enter number of edges: """).strip())

				_a:    list[dict[str, int]] 							=			[{} for _ in range(E)]

				for i in range(E):
								print("""Edge """, i + 1)
								_a							,			_a							,			_a:    Any 							=			(
								    int(x)
								    for x in input("""Enter source, destination, weight: """).strip().split(""" """)
								)
								_a:    str 							=			{"""src""": src, """dst""": dest, """weight""": weight}

				_a:    List[Any] 							=			int(input("""\nEnter shortest path source:""").strip())
				_a:    Optional[int] 							=			bellman_ford(graph, V, E, source)
				print_distance(shortest_distance, 0)