






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   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   TapasConfig.from_json_file(snake_case			)
				# set absolute/relative position embeddings parameter
				__magic_name__		:Optional[int]   =   reset_position_index_per_cell

				# set remaining parameters of TapasConfig as well as the model based on the task
				if task == "SQA":
								__magic_name__		:Any   =   TapasForQuestionAnswering(config=snake_case			)
				elif task == "WTQ":
								# run_task_main.py hparams
								__magic_name__		:Any   =   4
								__magic_name__		:int   =   True
								# hparam_utils.py hparams
								__magic_name__		:int   =   0.66_4694
								__magic_name__		:str   =   0.20_7951
								__magic_name__		:List[Any]   =   0.12_1194
								__magic_name__		:List[Any]   =   True
								__magic_name__		:int   =   True
								__magic_name__		:Tuple   =   False
								__magic_name__		:Tuple   =   0.035_2513

								__magic_name__		:str   =   TapasForQuestionAnswering(config=snake_case			)
				elif task == "WIKISQL_SUPERVISED":
								# run_task_main.py hparams
								__magic_name__		:Optional[Any]   =   4
								__magic_name__		:Union[str, Any]   =   False
								# hparam_utils.py hparams
								__magic_name__		:Optional[int]   =   36.4519
								__magic_name__		:Dict   =   0.90_3421
								__magic_name__		:Any   =   222.088
								__magic_name__		:str   =   True
								__magic_name__		:int   =   True
								__magic_name__		:int   =   True
								__magic_name__		:List[str]   =   0.76_3141

								__magic_name__		:Optional[Any]   =   TapasForQuestionAnswering(config=snake_case			)
				elif task == "TABFACT":
								__magic_name__		:str   =   TapasForSequenceClassification(config=snake_case			)
				elif task == "MLM":
								__magic_name__		:Any   =   TapasForMaskedLM(config=snake_case			)
				elif task == "INTERMEDIATE_PRETRAINING":
								__magic_name__		:str   =   TapasModel(config=snake_case			)
				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(snake_case,	snake_case,	snake_case			)

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

				# Save tokenizer files
				print(f'''Save tokenizer files to {pytorch_dump_path}'''			)
				__magic_name__		:List[Any]   =   TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + '''vocab.txt''',	model_max_length=5_1_2			)
				tokenizer.save_pretrained(snake_case			)

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      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."""
					)
					SCREAMING_SNAKE_CASE__   :      int				=		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 XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 	__lowerCamelCase			(_a				,      unittest.TestCase						):
       _lowercase											=     KandinskyInpaintPipeline
       _lowercase											=     ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
       _lowercase											=     [
           """prompt""",
           """negative_prompt""",
           """image_embeds""",
           """negative_image_embeds""",
           """image""",
           """mask_image""",
       ]
       _lowercase											=     [
           """generator""",
           """height""",
           """width""",
           """latents""",
           """guidance_scale""",
           """negative_prompt""",
           """num_inference_steps""",
           """return_dict""",
           """guidance_scale""",
           """num_images_per_prompt""",
           """output_type""",
           """return_dict""",
       ]
       _lowercase											=     False







       @property
       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            return 32







       @property
       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            return self.time_input_dim







       @property
       def 		snake_case_	(			self: Union[str, Any]					):






            '''simple docstring'''




            return self.time_input_dim * 4







       @property
       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            return 100







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            __UpperCamelCase =			XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
            return tokenizer







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			MCLIPConfig(
                numDims=self.cross_attention_dim,transformerDimensions=self.text_embedder_hidden_size,hidden_size=self.text_embedder_hidden_size,intermediate_size=37,num_attention_heads=4,num_hidden_layers=5,vocab_size=1005,)

            __UpperCamelCase =			MultilingualCLIP(A_					)
            __UpperCamelCase =			text_encoder.eval()

            return text_encoder







       @property
       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            torch.manual_seed(0					)

            __UpperCamelCase =			{
                'in_channels': 9,
                # Out channels is double in channels because predicts mean and variance
                'out_channels': 8,
                'addition_embed_type': 'text_image',
                'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
                'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
                'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
                'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
                'layers_per_block': 1,
                'encoder_hid_dim': self.text_embedder_hidden_size,
                'encoder_hid_dim_type': 'text_image_proj',
                'cross_attention_dim': self.cross_attention_dim,
                'attention_head_dim': 4,
                'resnet_time_scale_shift': 'scale_shift',
                'class_embed_type': None,
            }

            __UpperCamelCase =			UNetaDConditionModel(**A_					)
            return model







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            return {
                "block_out_channels": [32, 64],
                "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
                "in_channels": 3,
                "latent_channels": 4,
                "layers_per_block": 1,
                "norm_num_groups": 8,
                "norm_type": "spatial",
                "num_vq_embeddings": 12,
                "out_channels": 3,
                "up_block_types": [
                    "AttnUpDecoderBlock2D",
                    "UpDecoderBlock2D",
                ],
                "vq_embed_dim": 4,
            }







       @property
       def 		snake_case_	(			self: str					):






            '''simple docstring'''




            torch.manual_seed(0					)
            __UpperCamelCase =			VQModel(**self.dummy_movq_kwargs					)
            return model







       def 		snake_case_	(			self: Dict					):






            '''simple docstring'''




            __UpperCamelCase =			self.dummy_text_encoder
            __UpperCamelCase =			self.dummy_tokenizer
            __UpperCamelCase =			self.dummy_unet
            __UpperCamelCase =			self.dummy_movq

            __UpperCamelCase =			DDIMScheduler(
                num_train_timesteps=1000,beta_schedule='linear',beta_start=0.0_0_0_8_5,beta_end=0.0_1_2,clip_sample=A_,set_alpha_to_one=A_,steps_offset=1,prediction_type='epsilon',thresholding=A_,)

            __UpperCamelCase =			{
                'text_encoder': text_encoder,
                'tokenizer': tokenizer,
                'unet': unet,
                'scheduler': scheduler,
                'movq': movq,
            }

            return components







       def 		snake_case_	(			self: Tuple,A_: Optional[int],A_: Dict=0					):






            '''simple docstring'''




            __UpperCamelCase =			floats_tensor((1, self.cross_attention_dim),rng=random.Random(A_					)					).to(A_					)
            __UpperCamelCase =			floats_tensor((1, self.cross_attention_dim),rng=random.Random(seed + 1					)					).to(A_					)
            # create init_image
            __UpperCamelCase =			floats_tensor((1, 3, 64, 64),rng=random.Random(A_					)					).to(A_					)
            __UpperCamelCase =			image.cpu().permute(0,2,3,1					)[0]
            __UpperCamelCase =			Image.fromarray(np.uinta(A_					)					).convert('RGB'					).resize((256, 256)					)
            # create mask
            __UpperCamelCase =			np.ones((64, 64),dtype=np.floataa					)
            __UpperCamelCase =			0

            if str(A_					).startswith('mps'					):
                 __UpperCamelCase =			torch.manual_seed(A_					)
            else:
                 __UpperCamelCase =			torch.Generator(device=A_					).manual_seed(A_					)
            __UpperCamelCase =			{
                'prompt': 'horse',
                'image': init_image,
                'mask_image': mask,
                'image_embeds': image_embeds,
                'negative_image_embeds': negative_image_embeds,
                'generator': generator,
                'height': 64,
                'width': 64,
                'num_inference_steps': 2,
                'guidance_scale': 4.0,
                'output_type': 'np',
            }
            return inputs







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			'cpu'

            __UpperCamelCase =			self.get_dummy_components()

            __UpperCamelCase =			self.pipeline_class(**A_					)
            __UpperCamelCase =			pipe.to(A_					)

            pipe.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			pipe(**self.get_dummy_inputs(A_					)					)
            __UpperCamelCase =			output.images

            __UpperCamelCase =			pipe(
                **self.get_dummy_inputs(A_					),return_dict=A_,)[0]

            __UpperCamelCase =			image[0, -3:, -3:, -1]
            __UpperCamelCase =			image_from_tuple[0, -3:, -3:, -1]

            print(F'''image.shape {image.shape}'''					)

            assert image.shape == (1, 64, 64, 3)

            __UpperCamelCase =			np.array(
                [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]					)

            assert (
                np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
            ), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
            assert (
                np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2
            ), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







       def 		snake_case_	(			self: Optional[Any]					):






            '''simple docstring'''




            super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



@slow
@require_torch_gpu
class 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: Tuple					):






            '''simple docstring'''




            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()







       def 		snake_case_	(			self: Any					):






            '''simple docstring'''




            __UpperCamelCase =			load_numpy(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'					)

            __UpperCamelCase =			load_image(
                'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
            __UpperCamelCase =			np.ones((768, 768),dtype=np.floataa					)
            __UpperCamelCase =			0

            __UpperCamelCase =			'a hat'

            __UpperCamelCase =			KandinskyPriorPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-prior',torch_dtype=torch.floataa					)
            pipe_prior.to(A_					)

            __UpperCamelCase =			KandinskyInpaintPipeline.from_pretrained(
                'kandinsky-community/kandinsky-2-1-inpaint',torch_dtype=torch.floataa					)
            __UpperCamelCase =			pipeline.to(A_					)
            pipeline.set_progress_bar_config(disable=A_					)

            __UpperCamelCase =			torch.Generator(device='cpu'					).manual_seed(0					)
            __UpperCamelCase,    __UpperCamelCase =			pipe_prior(
                A_,generator=A_,num_inference_steps=5,negative_prompt='',).to_tuple()

            __UpperCamelCase =			pipeline(
                A_,image=A_,mask_image=A_,image_embeds=A_,negative_image_embeds=A_,generator=A_,num_inference_steps=100,height=768,width=768,output_type='np',)

            __UpperCamelCase =			output.images[0]

            assert image.shape == (768, 768, 3)

            assert_mean_pixel_difference(A_,A_					)


