





'''simple docstring'''


import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax.numpy as jnp

		from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		)






class       UpperCamelCase__  (		unittest.TestCase     ):







	def __init__(				self			:   Union[str, Any] , lowerCamelCase			:   str , lowerCamelCase			:   Optional[Any]=1_3 , lowerCamelCase			:   Optional[int]=7 , lowerCamelCase			:   List[Any]=True , lowerCamelCase			:   Any=True , lowerCamelCase			:   Any=True , lowerCamelCase			:   Optional[Any]=True , lowerCamelCase			:   Dict=9_9 , lowerCamelCase			:   Optional[Any]=3_2 , lowerCamelCase			:   int=5 , lowerCamelCase			:   Dict=4 , lowerCamelCase			:   List[Any]=3_7 , lowerCamelCase			:   List[str]="gelu" , lowerCamelCase			:   Any=0.1 , lowerCamelCase			:   Union[str, Any]=0.1 , lowerCamelCase			:   str=5_1_2 , lowerCamelCase			:   Optional[Any]=1_6 , lowerCamelCase			:   Any=2 , lowerCamelCase			:   Tuple=0.02 , lowerCamelCase			:   str=4 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_attention_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_choices







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size		)

				a__     = None
				if self.use_attention_mask:
							a__     = random_attention_mask([self.batch_size, self.seq_length]		)

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

				a__     = RobertaPreLayerNormConfig(
				    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 , is_decoder=lowerCamelCase , initializer_range=self.initializer_range , )

				return config, input_ids, token_type_ids, attention_mask







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				a__   ,				a__   ,				a__   ,				a__     = config_and_inputs
				a__     = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict







	def 						__a   (				self			:   Any		):




				'''simple docstring'''
				a__     = self.prepare_config_and_inputs()
				a__   ,				a__   ,				a__   ,				a__     = config_and_inputs

				a__     = True
				a__     = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				a__     = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2		)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)






@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class       UpperCamelCase__  (		__lowerCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Optional[Any]								=						True

	lowerCAmelCase__					:			Union[str, Any]								=						(
	    (
	        FlaxRobertaPreLayerNormModel,
	        FlaxRobertaPreLayerNormForCausalLM,
	        FlaxRobertaPreLayerNormForMaskedLM,
	        FlaxRobertaPreLayerNormForSequenceClassification,
	        FlaxRobertaPreLayerNormForTokenClassification,
	        FlaxRobertaPreLayerNormForMultipleChoice,
	        FlaxRobertaPreLayerNormForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				a__     = FlaxRobertaPreLayerNormModelTester(self		)







	@slow
	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				for model_class_name in self.all_model_classes:
							a__     = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=lowerCamelCase		)
							a__     = model(np.ones((1, 1)		)		)
							self.assertIsNotNone(lowerCamelCase		)






@require_flax
class       UpperCamelCase__  (		unittest.TestCase     ):







	@slow
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=lowerCamelCase		)

				a__     = np.array([[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]] , dtype=jnp.intaa		)
				a__     = model(lowerCamelCase		)[0]
				a__     = [1, 1_1, 5_0_2_6_5]
				self.assertEqual(list(output.shape		) , lowerCamelCase		)
				# compare the actual values for a slice.
				a__     = np.array(
				    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]] , dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase , atol=1e-4		)		)







	@slow
	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" , from_pt=lowerCamelCase		)

				a__     = np.array([[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]] , dtype=jnp.intaa		)
				a__     = model(lowerCamelCase		)[0]
				# compare the actual values for a slice.
				a__     = np.array(
				    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]] , dtype=np.floataa		)
				self.assertTrue(np.allclose(output[:, :3, :3] , lowerCamelCase , atol=1e-4		)		)










'''simple docstring'''


from manim import *






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				a__     = Rectangle(height=0.5 , width=0.5		)
				a__     = Rectangle(height=0.25 , width=0.25		)
				a__     = Rectangle(height=0.46 , width=0.46		).set_stroke(width=0		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("CPU" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				cpu.move_to([-2.5, -0.5, 0]		)
				self.add(lowerCamelCase		)

				a__     = [mem.copy() for i in range(4		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("GPU" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				gpu.move_to([-1, -1, 0]		)
				self.add(lowerCamelCase		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)

				a__     = Text("Model" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				model.move_to([3, -1.0, 0]		)
				self.add(lowerCamelCase		)

				a__     = []
				a__     = []
				a__     = []

				for i, rect in enumerate(lowerCamelCase		):
							rect.set_stroke(lowerCamelCase		)

							a__     = Rectangle(height=0.46 / 4 , width=0.46 / 3		).set_stroke(width=0.0		).set_fill(lowerCamelCase , opacity=0.7		)

							if i == 0:
										cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		) , buff=0.02 , direction=lowerCamelCase		)
										cpu_target.set_x(cpu_target.get_x() + 0.1		)
							elif i == 3:
										cpu_target.next_to(model_cpu_arr[0] , direction=lowerCamelCase , buff=0.0		)
							else:
										cpu_target.next_to(model_cpu_arr[i - 1] , direction=lowerCamelCase , buff=0.0		)
							self.add(lowerCamelCase		)
							model_cpu_arr.append(lowerCamelCase		)

				self.add(*lowerCamelCase , *lowerCamelCase , *lowerCamelCase		)

				a__     = [mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)

				a__     = Text("Loaded Checkpoint" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				checkpoint.move_to([3, 0.5, 0]		)

				self.add(lowerCamelCase		)

				a__     = []
				a__     = []

				for i, rect in enumerate(lowerCamelCase		):
							a__     = fill.copy().set_fill(lowerCamelCase , opacity=0.7		)
							target.move_to(lowerCamelCase		)
							ckpt_arr.append(lowerCamelCase		)

							a__     = target.copy()
							if i < 5:
										cpu_target.move_to(cpu_left_col_base[i + 1]		)
							else:
										cpu_target.move_to(cpu_right_col_base[i - 5]		)
							ckpt_cpu_arr.append(lowerCamelCase		)
				self.add(*lowerCamelCase , *lowerCamelCase		)

				a__     = Square(side_length=2.2		)
				key.move_to([-5, 2, 0]		)

				a__     = MarkupText(
				    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=1_8 , )

				key_text.move_to([-5, 2.4, 0]		)

				self.add(lowerCamelCase , lowerCamelCase		)

				a__     = MarkupText(
				    F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=1_8 , )

				blue_text.next_to(lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left()		)
				self.add(lowerCamelCase		)

				a__     = MarkupText(
				    F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' , font_size=2_4 , )
				step_a.move_to([2, 2, 0]		)

				a__     = [meta_mem.copy() for i in range(6		)]
				a__     = [meta_mem.copy() for i in range(6		)]
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(*lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = VGroup(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0		)
				a__     = Text("Disk" , font_size=2_4		)
				a__     = Group(lowerCamelCase , lowerCamelCase		).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase		)
				disk.move_to([-4.0, -1.25, 0]		)
				self.play(Write(lowerCamelCase , run_time=3		) , Write(lowerCamelCase , run_time=1		) , Create(lowerCamelCase , run_time=1		)		)

				a__     = []
				for i, rect in enumerate(lowerCamelCase		):
							a__     = rect.copy()
							target.generate_target()
							target.target.move_to(disk_left_col_base[i]		).scale(0.5		)
							animations.append(MoveToTarget(lowerCamelCase , run_time=1.5		)		)
				self.play(*lowerCamelCase		)

				self.play(FadeOut(lowerCamelCase		)		)

				a__     = MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' , font_size=2_4		)
				step_a.move_to([2, 2, 0]		)

				self.play(Write(lowerCamelCase , run_time=3		)		)

				self.play(
				    FadeOut(lowerCamelCase , lowerCamelCase , *lowerCamelCase , *lowerCamelCase		) , )

				self.wait()



