



'''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_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						Union[str, Any]  )      ->   List[str]:
							# 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
							UpperCAmelCase :       List[str] 							=		TapasConfig.from_json_file(_lowerCAmelCase  )
							# set absolute/relative position embeddings parameter
							UpperCAmelCase :       Optional[Any] 							=		reset_position_index_per_cell

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

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

														UpperCAmelCase :       Tuple 							=		TapasForQuestionAnswering(config=_lowerCAmelCase  )
							elif task == "TABFACT":
														UpperCAmelCase :       List[str] 							=		TapasForSequenceClassification(config=_lowerCAmelCase  )
							elif task == "MLM":
														UpperCAmelCase :       List[str] 							=		TapasForMaskedLM(config=_lowerCAmelCase  )
							elif task == "INTERMEDIATE_PRETRAINING":
														UpperCAmelCase :       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}"""  )
							UpperCAmelCase :       Dict 							=		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__":
						UpperCamelCase__:   Tuple								=			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__:   Optional[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,
						)











import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Union[str, Any]    		=   StableDiffusionInpaintPipeline
      UpperCAmelCase__      :	Dict    		=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase__      :	str    		=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase__      :	int    		=   frozenset(
          [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCAmelCase__      :	Union[str, Any]    		=   frozenset([])
      def 			lowerCAmelCase__						(				self: str   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						UNetaDConditionModel(
                 block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=9    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       attention_head_dim=(2, 4)    ,       use_linear_projection=UpperCamelCase_    ,       )
             __lowerCamelCase   =						PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
             torch.manual_seed(0   )
             __lowerCamelCase   =						AutoencoderKL(
                 block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=4    ,       sample_size=1_28    ,       )
             torch.manual_seed(0   )
             __lowerCamelCase   =						CLIPTextConfig(
                 bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1E-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=10_00    ,       hidden_act="""gelu"""    ,       projection_dim=5_12    ,       )
             __lowerCamelCase   =						CLIPTextModel(UpperCamelCase_   )
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

             __lowerCamelCase   =						{
                 """unet""": unet,
                 """scheduler""": scheduler,
                 """vae""": vae,
                 """text_encoder""": text_encoder,
                 """tokenizer""": tokenizer,
                 """safety_checker""": None,
                 """feature_extractor""": None,
             }
             return components
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: List[Any]=0   ):
             # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
             __lowerCamelCase   =						floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(UpperCamelCase_   )   ).to(UpperCamelCase_   )
             __lowerCamelCase   =						image.cpu().permute(0    ,       2    ,       3    ,       1   )[0]
             __lowerCamelCase   =						Image.fromarray(np.uinta(UpperCamelCase_   )   ).convert("""RGB"""   ).resize((64, 64)   )
             __lowerCamelCase   =						Image.fromarray(np.uinta(image + 4   )   ).convert("""RGB"""   ).resize((64, 64)   )
             if str(UpperCamelCase_   ).startswith("""mps"""   ):
                    __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						{
                 """prompt""": """A painting of a squirrel eating a burger""",
                 """image""": init_image,
                 """mask_image""": mask_image,
                 """generator""": generator,
                 """num_inference_steps""": 2,
                 """guidance_scale""": 6.0,
                 """output_type""": """numpy""",
             }
             return inputs
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
             __lowerCamelCase   =						self.get_dummy_components()
             __lowerCamelCase   =						StableDiffusionInpaintPipeline(**UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						self.get_dummy_inputs(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(**UpperCamelCase_   ).images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __lowerCamelCase   =						np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]   )

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




      def 			lowerCAmelCase__						(				self: int   ):
             super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





@slow
@require_torch_gpu
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: str   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
                 """/yellow_cat_sitting_on_a_park_bench.npy"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_    ,       safety_checker=UpperCamelCase_   )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       output_type="""np"""    ,       )
             __lowerCamelCase   =						output.images[0]

             assert image.shape == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 9E-3
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
                 """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(
                 UpperCamelCase_    ,       torch_dtype=torch.floataa    ,       safety_checker=UpperCamelCase_    ,       )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       output_type="""np"""    ,       )
             __lowerCamelCase   =						output.images[0]

             assert image.shape == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 5E-1




      def 			lowerCAmelCase__						(				self: int   ):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						PNDMScheduler.from_pretrained(UpperCamelCase_    ,       subfolder="""scheduler"""   )
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(
                 UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       torch_dtype=torch.floataa    ,       )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing(1   )
             pipe.enable_sequential_cpu_offload()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=2    ,       output_type="""np"""    ,       )

             __lowerCamelCase   =						torch.cuda.max_memory_allocated()
             # make sure that less than 2.65 GB is allocated
             assert mem_bytes < 2.65 * 10**9

