


'''simple docstring'''






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()


def       _snake_case     (  A				,					A				,					A				,					A )		->					Optional[Any]:
							# Initialise PyTorch model
							lowerCAmelCase__      =			FunnelConfig.from_json_file(lowercase_ )
							print(F"""Building PyTorch model from configuration: {config}""" )
							lowerCAmelCase__      =			FunnelBaseModel(lowercase_ ) if base_model else FunnelModel(lowercase_ )

							# Load weights from tf checkpoint
							load_tf_weights_in_funnel(lowercase_				,					lowercase_				,					lowercase_ )

							# Save pytorch-model
							print(F"""Save PyTorch model to {pytorch_dump_path}""" )
							torch.save(model.state_dict()				,					lowercase_ )


if __name__ == "__main__":
		__UpperCAmelCase					       =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		parser.add_argument(
		    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
		)
		__UpperCAmelCase					       =							parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
		)










'''simple docstring'''






from manim import *




class 	a__       (							a__						):





							'''simple docstring'''






							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							List[str]:
														lowerCAmelCase__      =			Rectangle(height=0.5      ,					width=0.5     )
														lowerCAmelCase__      =			Rectangle(height=0.46      ,					width=0.46     ).set_stroke(width=0     )

														lowerCAmelCase__      =			[mem.copy() for i in range(6     )]
														lowerCAmelCase__      =			[mem.copy() for i in range(6     )]
														lowerCAmelCase__      =			VGroup(*lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )
														lowerCAmelCase__      =			VGroup(*lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )
														lowerCAmelCase__      =			VGroup(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )
														lowerCAmelCase__      =			Text('''CPU'''      ,					font_size=24     )
														lowerCAmelCase__      =			Group(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0.5      ,					aligned_edge=lowerCamelCase_     )
														cpu.move_to([-2.5, -0.5, 0]     )
														self.add(lowerCamelCase_     )

														lowerCAmelCase__      =			[mem.copy() for i in range(1     )]
														lowerCAmelCase__      =			VGroup(*lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0     )
														lowerCAmelCase__      =			Text('''GPU'''      ,					font_size=24     )
														lowerCAmelCase__      =			Group(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0.5      ,					aligned_edge=lowerCamelCase_     )
														gpu.align_to(lowerCamelCase_      ,					lowerCamelCase_     )
														gpu.set_x(gpu.get_x() - 1     )

														self.add(lowerCamelCase_     )

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

														lowerCAmelCase__      =			Text('''Model'''      ,					font_size=24     )
														lowerCAmelCase__      =			Group(lowerCamelCase_      ,					lowerCamelCase_     ).arrange(lowerCamelCase_      ,					buff=0.5      ,					aligned_edge=lowerCamelCase_     )
														model.move_to([3, -1.0, 0]     )

														self.play(
														    Create(lowerCamelCase_      ,					run_time=1     )      ,					Create(lowerCamelCase_      ,					run_time=1     )      ,					Create(lowerCamelCase_      ,					run_time=1     )      ,					)

														lowerCAmelCase__      =			MarkupText(
														    F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""      ,					font_size=24      ,					)

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

														lowerCAmelCase__      =			MarkupText(
														    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""      ,					font_size=18      ,					)

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

														step_a.move_to([2, 2, 0]     )
														self.play(Write(lowerCamelCase_      ,					run_time=2.5     )      ,					Write(lowerCamelCase_     )      ,					Write(lowerCamelCase_     )     )

														self.add(lowerCamelCase_     )

														lowerCAmelCase__      =			[]
														lowerCAmelCase__      =			[]
														lowerCAmelCase__      =			[]
														for i, rect in enumerate(lowerCamelCase_     ):
																					lowerCAmelCase__      =			Rectangle(height=0.46      ,					width=0.46     ).set_stroke(width=0.0     ).set_fill(lowerCamelCase_      ,					opacity=0.7     )
																					cpu_target.move_to(lowerCamelCase_     )
																					cpu_target.generate_target()
																					lowerCAmelCase__      =			0.46 / 4
																					lowerCAmelCase__      =			0.46 / 3

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

																					first_animations.append(rect.animate(run_time=0.5     ).set_stroke(lowerCamelCase_     )     )
																					second_animations.append(MoveToTarget(lowerCamelCase_      ,					run_time=1.5     )     )

														self.play(*lowerCamelCase_     )
														self.play(*lowerCamelCase_     )

														self.wait()