





"""simple docstring"""






import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			Any			)    ->			str:



			'''simple docstring'''
			# Initialise PyTorch model.
			# If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
			# TapasConfig to False.

			# initialize configuration from json file
			a__  :	List[str]		 =			TapasConfig.from_json_file(lowerCAmelCase__			)
			# set absolute/relative position embeddings parameter
			a__  :	Optional[int]		 =			reset_position_index_per_cell

			# set remaining parameters of TapasConfig as well as the model based on the task
			if task == "SQA":
						a__  :	str		 =			TapasForQuestionAnswering(config=lowerCAmelCase__			)
			elif task == "WTQ":
						# run_task_main.py hparams
						a__  :	Dict		 =			4
						a__  :	Dict		 =			True
						# hparam_utils.py hparams
						a__  :	List[str]		 =			0.664694
						a__  :	Union[str, Any]		 =			0.207951
						a__  :	Any		 =			0.121194
						a__  :	List[Any]		 =			True
						a__  :	List[str]		 =			True
						a__  :	Dict		 =			False
						a__  :	Optional[int]		 =			0.0352513

						a__  :	str		 =			TapasForQuestionAnswering(config=lowerCAmelCase__			)
			elif task == "WIKISQL_SUPERVISED":
						# run_task_main.py hparams
						a__  :	Tuple		 =			4
						a__  :	Optional[int]		 =			False
						# hparam_utils.py hparams
						a__  :	Optional[Any]		 =			36.4519
						a__  :	int		 =			0.903421
						a__  :	Any		 =			222.088
						a__  :	Optional[Any]		 =			True
						a__  :	Optional[Any]		 =			True
						a__  :	Tuple		 =			True
						a__  :	Tuple		 =			0.763141

						a__  :	Optional[int]		 =			TapasForQuestionAnswering(config=lowerCAmelCase__			)
			elif task == "TABFACT":
						a__  :	Optional[int]		 =			TapasForSequenceClassification(config=lowerCAmelCase__			)
			elif task == "MLM":
						a__  :	Dict		 =			TapasForMaskedLM(config=lowerCAmelCase__			)
			elif task == "INTERMEDIATE_PRETRAINING":
						a__  :	List[Any]		 =			TapasModel(config=lowerCAmelCase__			)
			else:
						raise ValueError(F"Task {task} not supported."			)

			print(F"Building PyTorch model from configuration: {config}"			)
			# Load weights from tf checkpoint
			load_tf_weights_in_tapas(lowerCAmelCase__							,			lowerCAmelCase__							,			lowerCAmelCase__			)

			# Save pytorch-model (weights and configuration)
			print(F"Save PyTorch model to {pytorch_dump_path}"			)
			model.save_pretrained(lowerCAmelCase__			)

			# Save tokenizer files
			print(F"Save tokenizer files to {pytorch_dump_path}"			)
			a__  :	Dict		 =			TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + "vocab.txt"							,			model_max_length=5_1_2			)
			tokenizer.save_pretrained(lowerCAmelCase__			)

			print("Used relative position embeddings:"							,			model.config.reset_position_index_per_cell			)


if __name__ == "__main__":
		__UpperCAmelCase						=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--task''', default='''SQA''', type=str, help='''Model task for which to convert a checkpoint. Defaults to SQA.'''
		)
		parser.add_argument(
		    '''--reset_position_index_per_cell''',
		    default=False,
		    action='''store_true''',
		    help='''Whether to use relative position embeddings or not. Defaults to True.''',
		)
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--tapas_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained TAPAS model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__UpperCAmelCase						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)






"""simple docstring"""






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


__UpperCAmelCase						=			False



class 					__UpperCAmelCase							(						unittest.TestCase  ):
				def       UpperCAmelCase      (		self    :			Tuple	, a_    :			Optional[Any]=32    )	->	Union[str, Any]:



							'''simple docstring'''



							set_seed(0    )
							a__  :	Any		 =			UNetaDModel(sample_size=a_	, in_channels=3	, out_channels=3    )
							a__  :	Union[str, Any]		 =			torch.optim.SGD(model.parameters()	, lr=0.0001    )
							return model, optimizer






				@slow
				def       UpperCAmelCase      (		self    :			Union[str, Any]    )	->	Any:



							'''simple docstring'''



							a__  :	Optional[Any]		 =			"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
							a__  :	Dict		 =			DDPMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )
							a__  :	Optional[Any]		 =			DDIMScheduler(
							    num_train_timesteps=10_00	, beta_start=0.0001	, beta_end=0.02	, beta_schedule="linear"	, clip_sample=a_	, )

							assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

							# shared batches for DDPM and DDIM
							set_seed(0    )
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).clip(-1	, 1    ).to(a_    ) for _ in range(4    )]
							a__  :	List[str]		 =			[torch.randn((4, 3, 32, 32)    ).to(a_    ) for _ in range(4    )]
							a__  :	Optional[Any]		 =			[torch.randint(0	, 10_00	, (4,)    ).long().to(a_    ) for _ in range(4    )]

							# train with a DDPM scheduler
							a__ ,	a__  :	Union[str, Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	Optional[int]		 =			ddpm_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	List[str]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Optional[int]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							# recreate the model and optimizer, and retry with DDIM
							a__ ,	a__  :	Optional[Any]		 =			self.get_model_optimizer(resolution=32    )
							model.train().to(a_    )
							for i in range(4    ):
										optimizer.zero_grad()
										a__  :	List[str]		 =			ddim_scheduler.add_noise(clean_images[i]	, noise[i]	, timesteps[i]    )
										a__  :	Optional[int]		 =			model(a_	, timesteps[i]    ).sample
										a__  :	Union[str, Any]		 =			torch.nn.functional.mse_loss(a_	, noise[i]    )
										loss.backward()
										optimizer.step()
							del model, optimizer

							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )
							self.assertTrue(torch.allclose(a_	, a_	, atol=1E-5    )    )