


'''simple docstring'''





from manim import *






class       _UpperCamelCase    (					__snake_case				):

				"""simple docstring"""







				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				Rectangle(height=0.5				,   width=0.5   )
					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(*a__   ).arrange(a__				,   buff=0   )
					A								=				VGroup(*a__   ).arrange(a__				,   buff=0   )
					A								=				VGroup(a__				,   a__   ).arrange(a__				,   buff=0   )
					A								=				Text("""CPU"""				,   font_size=24   )
					A								=				Group(a__				,   a__   ).arrange(a__				,   buff=0.5				,   aligned_edge=a__   )
					cpu.move_to([-2.5, -0.5, 0]   )
					self.add(a__   )

					A								=				[mem.copy() for i in range(4   )]
					A								=				VGroup(*a__   ).arrange(a__				,   buff=0   )
					A								=				Text("""GPU"""				,   font_size=24   )
					A								=				Group(a__				,   a__   ).arrange(a__				,   buff=0.5				,   aligned_edge=a__   )
					gpu.move_to([-1, -1, 0]   )
					self.add(a__   )

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

					A								=				Text("""Model"""				,   font_size=24   )
					A								=				Group(a__				,   a__   ).arrange(a__				,   buff=0.5				,   aligned_edge=a__   )
					model.move_to([3, -1.0, 0]   )
					self.add(a__   )

					A								=				[]
					for i, rect in enumerate(a__   ):
						rect.set_stroke(a__   )
						# target = fill.copy().set_fill(YELLOW, opacity=0.7)
						# target.move_to(rect)
						# self.add(target)

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

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

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

					A								=				Text("""Loaded Checkpoint"""				,   font_size=24   )
					A								=				Group(a__				,   a__   ).arrange(a__				,   aligned_edge=a__				,   buff=0.4   )
					checkpoint.move_to([3, 0.5, 0]   )

					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=18				,   )

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

					self.add(a__				,   a__   )

					A								=				MarkupText(
					    f'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'				,   font_size=18				,   )

					blue_text.next_to(a__				,   DOWN * 2.4				,   aligned_edge=key_text.get_left()   )

					A								=				MarkupText(
					    f'Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.'				,   font_size=24				,   )
					step_a.move_to([2, 2, 0]   )
					self.play(Write(a__   )				,   Write(a__   )   )

					self.play(Write(a__				,   run_time=1   )				,   Create(a__				,   run_time=1   )   )

					A								=				[]
					A								=				[]
					for i, rect in enumerate(a__   ):
						A								=				fill.copy().set_fill(a__				,   opacity=0.7   )
						target.move_to(a__   )
						first_animations.append(GrowFromCenter(a__				,   run_time=1   )   )

						A								=				target.copy()
						cpu_target.generate_target()
						if i < 5:
							cpu_target.target.move_to(cpu_left_col_base[i + 1]   )
						else:
							cpu_target.target.move_to(cpu_right_col_base[i - 5]   )
						second_animations.append(MoveToTarget(a__				,   run_time=1.5   )   )

					self.play(*a__   )
					self.play(*a__   )
					self.wait()







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







	assert masked_input.count("""<mask>"""						) == 1
	A								=				torch.tensor(tokenizer.encode(UpperCamelCase__							,					add_special_tokens=UpperCamelCase__						)						).unsqueeze(0						)  # Batch size 1
	A								=				model(UpperCamelCase__						)[0]  # The last hidden-state is the first element of the output tuple
	A								=				(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
	A								=				logits[0, masked_index, :]
	A								=				logits.softmax(dim=0						)
	A					,			A								=				prob.topk(k=UpperCamelCase__							,					dim=0						)
	A								=				""" """.join(
	    [tokenizer.convert_ids_to_tokens(indices[i].item()						) for i in range(len(UpperCamelCase__						)						)]						)
	A								=				tokenizer.mask_token
	A								=				[]
	for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """						)						):
		A								=				predicted_token_bpe.replace("""\u2581"""							,					""" """						)
		if " {0}".format(UpperCamelCase__						) in masked_input:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(""" {0}""".format(UpperCamelCase__						)							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
		else:
			topk_filled_outputs.append(
			    (
			        masked_input.replace(UpperCamelCase__							,					UpperCamelCase__						),
			        values[index].item(),
			        predicted_token,
			    )						)
	return topk_filled_outputs


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



