





'''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 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Any:
	# 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
	snake_case__	       =   TapasConfig.from_json_file(UpperCAmelCase_							)
	# set absolute/relative position embeddings parameter
	snake_case__	       =   reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case__	       =   TapasForQuestionAnswering(config=UpperCAmelCase_							)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case__	       =   4
		snake_case__	       =   True
		# hparam_utils.py hparams
		snake_case__	       =   0.66_4694
		snake_case__	       =   0.20_7951
		snake_case__	       =   0.12_1194
		snake_case__	       =   True
		snake_case__	       =   True
		snake_case__	       =   False
		snake_case__	       =   0.035_2513

		snake_case__	       =   TapasForQuestionAnswering(config=UpperCAmelCase_							)
	elif task == "WIKISQL_SUPERVISED":
		# run_task_main.py hparams
		snake_case__	       =   4
		snake_case__	       =   False
		# hparam_utils.py hparams
		snake_case__	       =   36.4519
		snake_case__	       =   0.90_3421
		snake_case__	       =   222.088
		snake_case__	       =   True
		snake_case__	       =   True
		snake_case__	       =   True
		snake_case__	       =   0.76_3141

		snake_case__	       =   TapasForQuestionAnswering(config=UpperCAmelCase_							)
	elif task == "TABFACT":
		snake_case__	       =   TapasForSequenceClassification(config=UpperCAmelCase_							)
	elif task == "MLM":
		snake_case__	       =   TapasForMaskedLM(config=UpperCAmelCase_							)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case__	       =   TapasModel(config=UpperCAmelCase_							)
	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(UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)

	# Save pytorch-model (weights and configuration)
	print(f'''Save PyTorch model to {pytorch_dump_path}'''							)
	model.save_pretrained(UpperCAmelCase_							)

	# Save tokenizer files
	print(f'''Save tokenizer files to {pytorch_dump_path}'''							)
	snake_case__	       =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'					,				model_max_length=5_12							)
	tokenizer.save_pretrained(UpperCAmelCase_							)

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


if __name__ == "__main__":
			a__					:			List[Any]          =     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.'''
			)
			a__					:			Dict          =     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'''




from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 				__snake_case     (					__magic_name__							):





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		int:
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		snake_case__	       =   DDIMScheduler.from_config(scheduler.config		)

		self.register_modules(unet=UpperCamelCase_      ,       scheduler=UpperCamelCase_		)





	@torch.no_grad()
	def __call__(		self      ,       UpperCamelCase_ = 1      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = 0.0      ,       UpperCamelCase_ = 50      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pil"      ,       UpperCamelCase_ = True      ,       )      ->		Union[ImagePipelineOutput, Tuple]:

		# Sample gaussian noise to begin loop
		if isinstance(self.unet.config.sample_size      ,       UpperCamelCase_		):
			snake_case__	       =   (
			    batch_size,
			    self.unet.config.in_channels,
			    self.unet.config.sample_size,
			    self.unet.config.sample_size,
			)
		else:
			snake_case__	       =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		) and len(UpperCamelCase_		) != batch_size:
			raise ValueError(
			    F'''You have passed a list of generators of length {len(UpperCamelCase_		)}, but requested an effective batch'''
			    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

		snake_case__	       =   randn_tensor(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       device=self.device      ,       dtype=self.unet.dtype		)

		# set step values
		self.scheduler.set_timesteps(UpperCamelCase_		)

		for t in self.progress_bar(self.scheduler.timesteps		):
			# 1. predict noise model_output
			snake_case__	       =   self.unet(UpperCamelCase_      ,       UpperCamelCase_		).sample

			# 2. predict previous mean of image x_t-1 and add variance depending on eta
			# eta corresponds to η in paper and should be between [0, 1]
			# do x_t -> x_t-1
			snake_case__	       =   self.scheduler.step(
			    UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       eta=UpperCamelCase_      ,       use_clipped_model_output=UpperCamelCase_      ,       generator=UpperCamelCase_		).prev_sample

		snake_case__	       =   (image / 2 + 0.5).clamp(0      ,       1		)
		snake_case__	       =   image.cpu().permute(0      ,       2      ,       3      ,       1		).numpy()
		if output_type == "pil":
			snake_case__	       =   self.numpy_to_pil(UpperCamelCase_		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=UpperCamelCase_		)



