





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 SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						@property
						def UpperCAmelCase_			( self : int		)							->      str:







							"""simple docstring"""


							torch.manual_seed(0		)
							snake_case_						:     Optional[Any]     =		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 : List[str]		)							->      List[Any]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		self.dummy_uncond_unet
							snake_case_						:     int     =		PNDMScheduler()

							snake_case_						:     Optional[Any]     =		PNDMPipeline(unet=_A						,				scheduler=_A		)
							pndm.to(_A		)
							pndm.set_progress_bar_config(disable=_A		)

							snake_case_						:     str     =		torch.manual_seed(0		)
							snake_case_						:     int     =		pndm(generator=_A						,				num_inference_steps=20						,				output_type='numpy'		).images

							snake_case_						:     Union[str, Any]     =		torch.manual_seed(0		)
							snake_case_						:     List[str]     =		pndm(generator=_A						,				num_inference_steps=20						,				output_type='numpy'						,				return_dict=_A		)[0]

							snake_case_						:     Any     =		image[0, -3:, -3:, -1]
							snake_case_						:     List[Any]     =		image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:     Optional[Any]     =		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 SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def UpperCAmelCase_			( self : Dict		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Union[str, Any]     =		'google/ddpm-cifar10-32'

							snake_case_						:     int     =		UNetaDModel.from_pretrained(_A		)
							snake_case_						:     Union[str, Any]     =		PNDMScheduler()

							snake_case_						:     Any     =		PNDMPipeline(unet=_A						,				scheduler=_A		)
							pndm.to(_A		)
							pndm.set_progress_bar_config(disable=_A		)
							snake_case_						:     Tuple     =		torch.manual_seed(0		)
							snake_case_						:     Union[str, Any]     =		pndm(generator=_A						,				output_type='numpy'		).images

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

							assert image.shape == (1, 32, 32, 3)
							snake_case_						:     Tuple     =		np.array([0.1_5_6_4, 0.1_4_6_4_5, 0.1_4_0_6, 0.1_4_7_1_5, 0.1_2_4_2_5, 0.1_4_0_4_5, 0.1_3_1_1_5, 0.1_2_1_7_5, 0.1_2_5]		)

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








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 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				,	__a				,	__a				):
	# 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_						:     Optional[int]     =		TapasConfig.from_json_file(__a				)
	# set absolute/relative position embeddings parameter
	snake_case_						:     List[Any]     =		reset_position_index_per_cell

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

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

		snake_case_						:     str     =		TapasForQuestionAnswering(config=__a				)
	elif task == "TABFACT":
		snake_case_						:     List[Any]     =		TapasForSequenceClassification(config=__a				)
	elif task == "MLM":
		snake_case_						:     Optional[int]     =		TapasForMaskedLM(config=__a				)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_						:     Tuple     =		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}"""				)
	snake_case_						:     str     =		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,	model_max_length=5_12				)
	tokenizer.save_pretrained(__a				)

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


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE 		=						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."""
						)
						_SCREAMING_SNAKE_CASE 		=						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,
						)

