




'''simple docstring'''





import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		_SCREAMING_SNAKE_CASE							:       Union[str, Any]						=			'''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Union[str, Any]							,    snake_case							:	Optional[Any]=None					):

							'''simple docstring'''






							if rng is None:
														snake_case_      =				random.Random()

							snake_case_      =				1
							for dim in shape:
														total_dims *= dim

							snake_case_      =				[]
							for _ in range(snake_case					):
														values.append(rng.randint(0							,    vocab_size - 1					)					)

							snake_case_      =				np.array(snake_case							,    dtype=jnp.intaa					).reshape(snake_case					)

							return output


def 					UpperCamelCase_(     snake_case							:	Dict							,    snake_case							:	Optional[int]=None					):

							'''simple docstring'''






							snake_case_      =				ids_tensor(snake_case							,    vocab_size=2							,    rng=snake_case					)
							# make sure that at least one token is attended to for each batch
							snake_case_      =				1
							return attn_mask




@require_flax
class 			_snake_case		:
			lowerCAmelCase_					:				Union[str, Any]							=   None
			lowerCAmelCase_					:				str							=   ()




			def        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				self.model_tester.prepare_config_and_inputs_for_common()

										# cut to half length & take max batch_size 3
										snake_case_      =				2
										snake_case_      =				inputs["""input_ids"""].shape[-1] // 2
										snake_case_      =				inputs["""input_ids"""][:max_batch_size, :sequence_length]

										snake_case_      =				jnp.ones_like(__lowerCamelCase							)
										snake_case_      =				attention_mask[:max_batch_size, :sequence_length]

										# generate max 5 tokens
										snake_case_      =				input_ids.shape[-1] + 5
										if config.eos_token_id is not None and config.pad_token_id is None:
																	# hack to allow generate for models such as GPT2 as is done in `generate()`
																	snake_case_      =				config.eos_token_id
										return config, input_ids, attention_mask, max_length




			@is_pt_flax_cross_test
			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()
										snake_case_      =				False
										snake_case_      =				max_length
										snake_case_      =				0

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
																	snake_case_      =				getattr(__lowerCamelCase    ,							__lowerCamelCase							)
																	snake_case_      =				pt_model_class(__lowerCamelCase							).eval()
																	snake_case_      =				load_flax_weights_in_pytorch_model(__lowerCamelCase    ,							flax_model.params							)

																	snake_case_      =				flax_model.generate(__lowerCamelCase							).sequences
																	snake_case_      =				pt_model.generate(torch.tensor(__lowerCamelCase    ,							dtype=torch.long							)							)

																	if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
																								snake_case_      =				flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

																	self.assertListEqual(pt_generation_outputs.numpy().tolist()    ,							flax_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()
										snake_case_      =				False
										snake_case_      =				max_length

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()
										snake_case_      =				True
										snake_case_      =				max_length

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()
										snake_case_      =				False
										snake_case_      =				max_length
										snake_case_      =				2

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()
										snake_case_      =				False
										snake_case_      =				max_length
										snake_case_      =				2
										snake_case_      =				2

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[0]    ,							input_ids.shape[0] * config.num_return_sequences							)




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

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()
										snake_case_      =				True
										snake_case_      =				max_length
										snake_case_      =				0.8
										snake_case_      =				10
										snake_case_      =				0.3
										snake_case_      =				1
										snake_case_      =				8
										snake_case_      =				9

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()
										snake_case_      =				max_length
										snake_case_      =				1
										snake_case_      =				8
										snake_case_      =				9

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()
										snake_case_      =				max_length
										snake_case_      =				2
										snake_case_      =				1
										snake_case_      =				8
										snake_case_      =				9

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()

										# pad attention mask on the left
										snake_case_      =				attention_mask.at[(0, 0)].set(0							)

										snake_case_      =				False
										snake_case_      =				max_length

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase    ,							attention_mask=__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase    ,							attention_mask=__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()

										# pad attention mask on the left
										snake_case_      =				attention_mask.at[(0, 0)].set(0							)

										snake_case_      =				True
										snake_case_      =				max_length

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase    ,							attention_mask=__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase    ,							attention_mask=__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										snake_case_      =				self._get_input_ids_and_config()

										# pad attention mask on the left
										snake_case_      =				attention_mask.at[(0, 0)].set(0							)

										snake_case_      =				2
										snake_case_      =				max_length

										for model_class in self.all_generative_model_classes:
																	snake_case_      =				model_class(__lowerCamelCase							)

																	snake_case_      =				model.generate(__lowerCamelCase    ,							attention_mask=__lowerCamelCase							).sequences
																	self.assertEqual(generation_outputs.shape[-1]    ,							__lowerCamelCase							)

																	snake_case_      =				jit(model.generate							)
																	snake_case_      =				jit_generate(__lowerCamelCase    ,							attention_mask=__lowerCamelCase							).sequences

																	self.assertListEqual(generation_outputs.tolist()    ,							jit_generation_outputs.tolist()							)




@require_flax
class 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										snake_case_      =				AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert"							)
										snake_case_      =				FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only"							)

										snake_case_      =				"""Hello world"""
										snake_case_      =				tokenizer(__lowerCamelCase    ,							return_tensors="np"							).input_ids

										# typos are quickly detected (the correct argument is `do_sample`)
										with self.assertRaisesRegex(__lowerCamelCase    ,							"do_samples"							):
																	model.generate(__lowerCamelCase    ,							do_samples=__lowerCamelCase							)

										# arbitrary arguments that will not be used anywhere are also not accepted
										with self.assertRaisesRegex(__lowerCamelCase    ,							"foo"							):
																	snake_case_      =				{"""foo""": """bar"""}
																	model.generate(__lowerCamelCase    ,							**__lowerCamelCase							)










'''simple docstring'''





from __future__ import annotations

from scipy.special import comb  # type: ignore




class 			_snake_case		:




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

										'''simple docstring'''




										snake_case_      =				list_of_points
										# Degree determines the flexibility of the curve.
										# Degree = 1 will produce a straight line.
										snake_case_      =				len(a__							) - 1




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    list[float]:

										'''simple docstring'''




										assert 0 <= t <= 1, "Time t must be between 0 and 1."
										snake_case_      =				[]
										for i in range(len(self.list_of_points							)							):
																	# basis function for each i
																	output_values.append(
																	    comb(self.degree    ,							a__							) * ((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(a__							)    ,							5							) == 1
										return output_values




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    tuple[float, float]:

										'''simple docstring'''




										assert 0 <= t <= 1, "Time t must be between 0 and 1."

										snake_case_      =				self.basis_function(a__							)
										snake_case_      =				0.0
										snake_case_      =				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        lowerCAmelCase__    ( self    ,							a__ = 0.0_1							)    ->    Any:

										'''simple docstring'''




										from matplotlib import pyplot as plt  # type: ignore

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

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

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

										plt.plot(
										    a__    ,							a__    ,							color="blue"    ,							label="Curve of Degree " + str(self.degree							)    ,							)
										plt.scatter(a__    ,							a__    ,							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




