"""simple docstring"""




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest

class       a_      (     __UpperCamelCase			):
					UpperCamelCase_ :	int			=    (IPNDMScheduler,)
					UpperCamelCase_ :	Optional[int]			=    (("num_inference_steps", 50),)




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Union[str, Any]  ,						**snake_case__						:     Union[str, Any]     ):
							lowerCAmelCase__        =   {"""num_train_timesteps""": 1000}
							config.update(**snake_case__     )
							return config




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     Optional[int]=0  ,						**snake_case__						:     Dict     ):
							lowerCAmelCase__        =   dict(self.forward_default_kwargs     )
							lowerCAmelCase__        =   kwargs.pop("""num_inference_steps"""  ,						snake_case__     )
							lowerCAmelCase__        =   self.dummy_sample
							lowerCAmelCase__        =   0.1 * sample
							lowerCAmelCase__        =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
									lowerCAmelCase__        =   self.get_scheduler_config(**snake_case__     )
									lowerCAmelCase__        =   scheduler_class(**snake_case__     )
									scheduler.set_timesteps(snake_case__     )
									# copy over dummy past residuals
									lowerCAmelCase__        =   dummy_past_residuals[:]

									if time_step is None:
											lowerCAmelCase__        =   scheduler.timesteps[len(scheduler.timesteps     ) // 2]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(snake_case__     )
											lowerCAmelCase__        =   scheduler_class.from_pretrained(snake_case__     )
											new_scheduler.set_timesteps(snake_case__     )
											# copy over dummy past residuals
											lowerCAmelCase__        =   dummy_past_residuals[:]

									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample
									lowerCAmelCase__        =   new_scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample

									assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample
									lowerCAmelCase__        =   new_scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample

									assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							pass




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]=0  ,						**snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   dict(self.forward_default_kwargs     )
							lowerCAmelCase__        =   kwargs.pop("""num_inference_steps"""  ,						snake_case__     )
							lowerCAmelCase__        =   self.dummy_sample
							lowerCAmelCase__        =   0.1 * sample
							lowerCAmelCase__        =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

							for scheduler_class in self.scheduler_classes:
									lowerCAmelCase__        =   self.get_scheduler_config()
									lowerCAmelCase__        =   scheduler_class(**snake_case__     )
									scheduler.set_timesteps(snake_case__     )

									# copy over dummy past residuals (must be after setting timesteps)
									lowerCAmelCase__        =   dummy_past_residuals[:]

									if time_step is None:
											lowerCAmelCase__        =   scheduler.timesteps[len(scheduler.timesteps     ) // 2]

									with tempfile.TemporaryDirectory() as tmpdirname:
											scheduler.save_config(snake_case__     )
											lowerCAmelCase__        =   scheduler_class.from_pretrained(snake_case__     )
											# copy over dummy past residuals
											new_scheduler.set_timesteps(snake_case__     )

											# copy over dummy past residual (must be after setting timesteps)
											lowerCAmelCase__        =   dummy_past_residuals[:]

									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample
									lowerCAmelCase__        =   new_scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample

									assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"

									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample
									lowerCAmelCase__        =   new_scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample

									assert torch.sum(torch.abs(output - new_output     )     ) < 1E-5, "Scheduler outputs are not identical"




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any  ,						**snake_case__						:     Optional[int]     ):
							lowerCAmelCase__        =   self.scheduler_classes[0]
							lowerCAmelCase__        =   self.get_scheduler_config(**snake_case__     )
							lowerCAmelCase__        =   scheduler_class(**snake_case__     )

							lowerCAmelCase__        =   10
							lowerCAmelCase__        =   self.dummy_model()
							lowerCAmelCase__        =   self.dummy_sample_deter
							scheduler.set_timesteps(snake_case__     )

							for i, t in enumerate(scheduler.timesteps     ):
									lowerCAmelCase__        =   model(snake_case__  ,						snake_case__     )
									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__     ).prev_sample

							for i, t in enumerate(scheduler.timesteps     ):
									lowerCAmelCase__        =   model(snake_case__  ,						snake_case__     )
									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__     ).prev_sample

							return sample




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict     ):
							lowerCAmelCase__        =   dict(self.forward_default_kwargs     )

							lowerCAmelCase__        =   kwargs.pop("""num_inference_steps"""  ,						snake_case__     )

							for scheduler_class in self.scheduler_classes:
									lowerCAmelCase__        =   self.get_scheduler_config()
									lowerCAmelCase__        =   scheduler_class(**snake_case__     )

									lowerCAmelCase__        =   self.dummy_sample
									lowerCAmelCase__        =   0.1 * sample

									if num_inference_steps is not None and hasattr(snake_case__  ,						"""set_timesteps"""     ):
											scheduler.set_timesteps(snake_case__     )
									elif num_inference_steps is not None and not hasattr(snake_case__  ,						"""set_timesteps"""     ):
											lowerCAmelCase__        =   num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									lowerCAmelCase__        =   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
									lowerCAmelCase__        =   dummy_past_residuals[:]

									lowerCAmelCase__        =   scheduler.timesteps[5]
									lowerCAmelCase__        =   scheduler.timesteps[6]

									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample
									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample

									self.assertEqual(output_a.shape  ,						sample.shape     )
									self.assertEqual(output_a.shape  ,						output_a.shape     )

									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample
									lowerCAmelCase__        =   scheduler.step(snake_case__  ,						snake_case__  ,						snake_case__  ,						**snake_case__     ).prev_sample

									self.assertEqual(output_a.shape  ,						sample.shape     )
									self.assertEqual(output_a.shape  ,						output_a.shape     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[Any]     ):
							for timesteps in [100, 1000]:
									self.check_over_configs(num_train_timesteps=snake_case__  ,						time_step=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Any     ):
							for t, num_inference_steps in zip([1, 5, 10]  ,						[10, 50, 100]     ):
									self.check_over_forward(num_inference_steps=snake_case__  ,						time_step=snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]     ):
							lowerCAmelCase__        =   self.full_loop()
							lowerCAmelCase__        =   torch.mean(torch.abs(snake_case__     )     )

							assert abs(result_mean.item() - 2540529     ) < 10





"""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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   TapasConfig.from_json_file(lowerCamelCase__     )
		# set absolute/relative position embeddings parameter
		lowerCAmelCase__        =   reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WTQ":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   True
				# hparam_utils.py hparams
				lowerCAmelCase__        =   0.66_46_94
				lowerCAmelCase__        =   0.20_79_51
				lowerCAmelCase__        =   0.12_11_94
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   False
				lowerCAmelCase__        =   0.0_35_25_13

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowerCAmelCase__        =   4
				lowerCAmelCase__        =   False
				# hparam_utils.py hparams
				lowerCAmelCase__        =   36.45_19
				lowerCAmelCase__        =   0.90_34_21
				lowerCAmelCase__        =   2_22.0_88
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   True
				lowerCAmelCase__        =   0.76_31_41

				lowerCAmelCase__        =   TapasForQuestionAnswering(config=lowerCamelCase__     )
		elif task == "TABFACT":
				lowerCAmelCase__        =   TapasForSequenceClassification(config=lowerCamelCase__     )
		elif task == "MLM":
				lowerCAmelCase__        =   TapasForMaskedLM(config=lowerCamelCase__     )
		elif task == "INTERMEDIATE_PRETRAINING":
				lowerCAmelCase__        =   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}"""     )
		lowerCAmelCase__        =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""			,  model_max_length=512     )
		tokenizer.save_pretrained(lowerCamelCase__     )

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


if __name__ == "__main__":
			__lowerCAmelCase    :							List[str]													=						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."
			)
			__lowerCAmelCase    :							Union[str, Any]													=						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,
			)




