




"""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 	_snake_case     (							lowercase__				,       lowercase__				,       lowercase__				,       lowercase__				,       lowercase__						):
				_lowerCamelCase	:     Union[str, Any]       =       TapasConfig.from_json_file(a_						)
				# set absolute/relative position embeddings parameter
				_lowerCamelCase	:     List[Any]       =       reset_position_index_per_cell

				# set remaining parameters of TapasConfig as well as the model based on the task
				if task == "SQA":
								_lowerCamelCase	:     str       =       TapasForQuestionAnswering(config=a_						)
				elif task == "WTQ":
								# run_task_main.py hparams
								_lowerCamelCase	:     int       =       4
								_lowerCamelCase	:     Optional[Any]       =       True
								# hparam_utils.py hparams
								_lowerCamelCase	:     str       =       0.6_6_4_6_9_4
								_lowerCamelCase	:     Tuple       =       0.2_0_7_9_5_1
								_lowerCamelCase	:     Tuple       =       0.1_2_1_1_9_4
								_lowerCamelCase	:     str       =       True
								_lowerCamelCase	:     Dict       =       True
								_lowerCamelCase	:     Union[str, Any]       =       False
								_lowerCamelCase	:     List[Any]       =       0.0_3_5_2_5_1_3

								_lowerCamelCase	:     Any       =       TapasForQuestionAnswering(config=a_						)
				elif task == "WIKISQL_SUPERVISED":
								# run_task_main.py hparams
								_lowerCamelCase	:     Optional[int]       =       4
								_lowerCamelCase	:     int       =       False
								# hparam_utils.py hparams
								_lowerCamelCase	:     List[str]       =       3_6.4_5_1_9
								_lowerCamelCase	:     Any       =       0.9_0_3_4_2_1
								_lowerCamelCase	:     Dict       =       2_2_2.0_8_8
								_lowerCamelCase	:     List[Any]       =       True
								_lowerCamelCase	:     List[str]       =       True
								_lowerCamelCase	:     Union[str, Any]       =       True
								_lowerCamelCase	:     str       =       0.7_6_3_1_4_1

								_lowerCamelCase	:     int       =       TapasForQuestionAnswering(config=a_						)
				elif task == "TABFACT":
								_lowerCamelCase	:     Optional[Any]       =       TapasForSequenceClassification(config=a_						)
				elif task == "MLM":
								_lowerCamelCase	:     Any       =       TapasForMaskedLM(config=a_						)
				elif task == "INTERMEDIATE_PRETRAINING":
								_lowerCamelCase	:     Any       =       TapasModel(config=a_						)
				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(a_				,       a_				,       a_						)

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

				# Save tokenizer files
				print(f'''Save tokenizer files to {pytorch_dump_path}'''						)
				_lowerCamelCase	:     Tuple       =       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,       model_max_length=512						)
				tokenizer.save_pretrained(a_						)

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


if __name__ == "__main__":
			lowercase__			=		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."""
			)
			lowercase__			=		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 numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @property
 def 	_UpperCAmelCase							(				self			):
       torch.manual_seed(0			)
       UpperCamelCase_					: int            =				UNetaDModel(
           block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=3      ,  out_channels=3      ,  down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")      ,  up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")      ,  )
       return model





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Union[str, Any]            =				self.dummy_uncond_unet
       UpperCamelCase_					: List[str]            =				PNDMScheduler()

       UpperCamelCase_					: Dict            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Tuple            =				pndm(generator=__lowerCAmelCase      ,  num_inference_steps=20      ,  output_type="""numpy"""      ,  return_dict=__lowerCAmelCase			)[0]

       UpperCamelCase_					: Union[str, Any]            =				image[0, -3:, -3:, -1]
       UpperCamelCase_					: int            =				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Optional[int]            =				np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2






@slow
@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[Any]            =				"""google/ddpm-cifar10-32"""

       UpperCamelCase_					: List[str]            =				UNetaDModel.from_pretrained(__lowerCAmelCase			)
       UpperCamelCase_					: int            =				PNDMScheduler()

       UpperCamelCase_					: Any            =				PNDMPipeline(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)
       pndm.to(__lowerCAmelCase			)
       pndm.set_progress_bar_config(disable=__lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				torch.manual_seed(0			)
       UpperCamelCase_					: Optional[int]            =				pndm(generator=__lowerCAmelCase      ,  output_type="""numpy"""			).images

       UpperCamelCase_					: Tuple            =				image[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       UpperCamelCase_					: Union[str, Any]            =				np.array([0.15_64, 0.1_46_45, 0.14_06, 0.1_47_15, 0.1_24_25, 0.1_40_45, 0.1_31_15, 0.1_21_75, 0.1_25]			)

       assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2






