




import unittest

from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SqueezeBertForMaskedLM,
		    SqueezeBertForMultipleChoice,
		    SqueezeBertForQuestionAnswering,
		    SqueezeBertForSequenceClassification,
		    SqueezeBertForTokenClassification,
		    SqueezeBertModel,
		)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def __init__(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Optional[int]=13					,      __lowerCamelCase				:     List[str]=7					,      __lowerCamelCase				:     Union[str, Any]=True					,      __lowerCamelCase				:     Dict=True					,      __lowerCamelCase				:     Optional[int]=False					,      __lowerCamelCase				:     str=True					,      __lowerCamelCase				:     List[str]=99					,      __lowerCamelCase				:     List[Any]=32					,      __lowerCamelCase				:     Any=5					,      __lowerCamelCase				:     str=4					,      __lowerCamelCase				:     List[Any]=64					,      __lowerCamelCase				:     Dict="gelu"					,      __lowerCamelCase				:     int=0.1					,      __lowerCamelCase				:     List[str]=0.1					,      __lowerCamelCase				:     Union[str, Any]=5_12					,      __lowerCamelCase				:     List[Any]=16					,      __lowerCamelCase				:     str=2					,      __lowerCamelCase				:     Optional[Any]=0.02					,      __lowerCamelCase				:     Optional[int]=3					,      __lowerCamelCase				:     List[Any]=4					,      __lowerCamelCase				:     Tuple=None					,      __lowerCamelCase				:     List[Any]=2					,      __lowerCamelCase				:     List[Any]=2					,      __lowerCamelCase				:     Optional[Any]=2					,      __lowerCamelCase				:     str=2					,      __lowerCamelCase				:     Dict=4					,      __lowerCamelCase				:     Optional[int]=1					,      )					->     Any:
							a    =			parent
							a    =			batch_size
							a    =			seq_length
							a    =			is_training
							a    =			use_input_mask
							a    =			use_token_type_ids
							a    =			use_labels
							a    =			vocab_size
							a    =			hidden_size
							a    =			num_hidden_layers
							a    =			num_attention_heads
							a    =			intermediate_size
							a    =			hidden_act
							a    =			hidden_dropout_prob
							a    =			attention_probs_dropout_prob
							a    =			max_position_embeddings
							a    =			type_vocab_size
							a    =			type_sequence_label_size
							a    =			initializer_range
							a    =			num_labels
							a    =			num_choices
							a    =			scope
							a    =			q_groups
							a    =			k_groups
							a    =			v_groups
							a    =			post_attention_groups
							a    =			intermediate_groups
							a    =			output_groups





				def 							__UpperCAmelCase							(      self				:     int							)					->     Optional[int]:
							a    =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size							)

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

							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]					,      self.num_choices							)

							a    =			self.get_config()

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





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							return SqueezeBertConfig(
							    embedding_size=self.hidden_size					,      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					,      attention_probs_dropout_prob=self.hidden_dropout_prob					,      attention_dropout=self.attention_probs_dropout_prob					,      max_position_embeddings=self.max_position_embeddings					,      initializer_range=self.initializer_range					,      q_groups=self.q_groups					,      k_groups=self.k_groups					,      v_groups=self.v_groups					,      post_attention_groups=self.post_attention_groups					,      intermediate_groups=self.intermediate_groups					,      output_groups=self.output_groups					,      )





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[Any]							)					->     str:
							a    =			SqueezeBertModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      __lowerCamelCase							)
							a    =			model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     List[Any]					,      __lowerCamelCase				:     List[Any]							)					->     Dict:
							a    =			SqueezeBertForMaskedLM(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     str							)					->     Union[str, Any]:
							a    =			SqueezeBertForQuestionAnswering(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(
							    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      start_positions=__lowerCamelCase					,      end_positions=__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 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     Optional[int]					,      __lowerCamelCase				:     Any					,      __lowerCamelCase				:     str							)					->     Any:
							a    =			self.num_labels
							a    =			SqueezeBertForSequenceClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[Any]					,      __lowerCamelCase				:     Tuple							)					->     Any:
							a    =			self.num_labels
							a    =			SqueezeBertForTokenClassification(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

							a    =			model(__lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     int					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     List[str]					,      __lowerCamelCase				:     Dict					,      __lowerCamelCase				:     Tuple							)					->     Optional[int]:
							a    =			self.num_choices
							a    =			SqueezeBertForMultipleChoice(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a    =			input_ids.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
							a    =			input_mask.unsqueeze(1							).expand(-1					,      self.num_choices					,      -1							).contiguous()
							a    =			model(
							    __lowerCamelCase					,      attention_mask=__lowerCamelCase					,      labels=__lowerCamelCase					,      )
							self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_choices)							)





				def 							__UpperCAmelCase							(      self				:     Any							)					->     Optional[int]:
							a    =			self.prepare_config_and_inputs()
							((a)    ,		(a)    ,		(a)    ,		(a)    ,		(a)    ,		(a))    =			config_and_inputs
							a    =			{"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict




@require_torch
class     snake_case__							(_UpperCamelCase    ,       _UpperCamelCase    ,       unittest.TestCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					(
				    (
				        SqueezeBertModel,
				        SqueezeBertForMaskedLM,
				        SqueezeBertForMultipleChoice,
				        SqueezeBertForQuestionAnswering,
				        SqueezeBertForSequenceClassification,
				        SqueezeBertForTokenClassification,
				    )
				    if is_torch_available()
				    else None
				)
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					(
				    {
				        """feature-extraction""": SqueezeBertModel,
				        """fill-mask""": SqueezeBertForMaskedLM,
				        """question-answering""": SqueezeBertForQuestionAnswering,
				        """text-classification""": SqueezeBertForSequenceClassification,
				        """token-classification""": SqueezeBertForTokenClassification,
				        """zero-shot""": SqueezeBertForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False
				SCREAMING_SNAKE_CASE_       :							str							 =					True
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					False





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Any:
							a    =			SqueezeBertModelTester(self							)
							a    =			ConfigTester(self					,      config_class=__lowerCamelCase					,      dim=37							)





				def 							__UpperCAmelCase							(      self				:     int							)					->     int:
							self.config_tester.run_common_tests()





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[int]							)					->     Dict:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_masked_lm(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     Optional[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_question_answering(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Any:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_sequence_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     str:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_token_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[Any]:
							a    =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_squeezebert_for_multiple_choice(*__lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(      self				:     Any							)					->     Any:
							for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a    =			SqueezeBertModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




@require_sentencepiece
@require_tokenizers
@require_torch
class     snake_case__							(unittest.TestCase   ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(      self				:     str							)					->     Union[str, Any]:
							a    =			SqueezeBertForSequenceClassification.from_pretrained("squeezebert/squeezebert-mnli"							)

							a    =			torch.tensor([[1, 2_94_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 13, 15_88, 2]]							)
							a    =			model(__lowerCamelCase							)[0]
							a    =			torch.Size((1, 3)							)
							self.assertEqual(output.shape					,      __lowerCamelCase							)
							a    =			torch.tensor([[0.6_401, -0.0_349, -0.6_041]]							)
							self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1e-4							)							)












from __future__ import annotations

from scipy.special import comb  # type: ignore




class     snake_case__							:
				"""simple docstring"""



				def __init__(      self				:     Any					,      __lowerCamelCase				:     list[tuple[float, float]]							)					->     Tuple:
							a    =			list_of_points
							# Degree determines the flexibility of the curve.
							# Degree = 1 will produce a straight line.
							a    =			len(__lowerCamelCase							) - 1





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     float							)					->     list[float]:
							assert 0 <= t <= 1, "Time t must be between 0 and 1."
							a    =			[]
							for i in range(len(self.list_of_points							)							):
										# basis function for each i
										output_values.append(
										    comb(self.degree					,      __lowerCamelCase							) * ((1 - t) ** (self.degree - i)) * (t**i)							)
							# the basis must sum up to 1 for it to produce a valid Bezier curve.
							assert round(sum(__lowerCamelCase							)					,      5							) == 1
							return output_values





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     float							)					->     tuple[float, float]:
							assert 0 <= t <= 1, "Time t must be between 0 and 1."

							a    =			self.basis_function(__lowerCamelCase							)
							a    =			0.0
							a    =			0.0
							for i in range(len(self.list_of_points							)							):
										# For all points, sum up the product of i-th basis function and i-th point.
										x += basis_function[i] * self.list_of_points[i][0]
										y += basis_function[i] * self.list_of_points[i][1]
							return (x, y)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     float = 0.01							)					->     List[str]:
							from matplotlib import pyplot as plt  # type: ignore

							a    =			[]  # x coordinates of points to plot
							a    =			[]  # y coordinates of points to plot

							a    =			0.0
							while t <= 1:
										a    =			self.bezier_curve_function(__lowerCamelCase							)
										to_plot_x.append(value[0]							)
										to_plot_y.append(value[1]							)
										t += step_size

							a    =			[i[0] for i in self.list_of_points]
							a    =			[i[1] for i in self.list_of_points]

							plt.plot(
							    __lowerCamelCase					,      __lowerCamelCase					,      color="blue"					,      label="Curve of Degree " + str(self.degree							)					,      )
							plt.scatter(__lowerCamelCase					,      __lowerCamelCase					,      color="red"					,      label="Control Points"							)
							plt.legend()
							plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		BezierCurve([(1, 2), (3, 5)]).plot_curve()  # degree 1
		BezierCurve([(0, 0), (5, 5), (5, 0)]).plot_curve()  # degree 2
		BezierCurve([(0, 0), (5, 5), (5, 0), (2.5, -2.5)]).plot_curve()  # degree 3






