
"""simple docstring"""







import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin







@skip_mps
class 							__magic_name__     (		lowercase__      ,		lowercase__      ,		unittest.TestCase   ):
     _SCREAMING_SNAKE_CASE       :					List[str]     							=   IFPipeline
     _SCREAMING_SNAKE_CASE       :					Dict     							=   TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
     _SCREAMING_SNAKE_CASE       :					List[Any]     							=   TEXT_TO_IMAGE_BATCH_PARAMS
     _SCREAMING_SNAKE_CASE       :					int     							=   PipelineTesterMixin.required_optional_params - {'latents'}





     def 							lowerCAmelCase      (				self   :							Dict    ):
            return self._get_dummy_components()





     def 							lowerCAmelCase      (				self   :							Optional[Any]							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]=0    ):
            if str(snake_case_    ).startswith("mps"    ):
                   __snake_case     			=       torch.manual_seed(snake_case_    )
            else:
                   __snake_case     			=       torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )

            __snake_case     			=       {
                "prompt": "A painting of a squirrel eating a burger",
                "generator": generator,
                "num_inference_steps": 2,
                "output_type": "numpy",
            }

            return inputs





     def 							lowerCAmelCase      (				self   :							Tuple    ):
            self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != "cuda"							,						reason="float16 requires CUDA"    )
     def 							lowerCAmelCase      (				self   :							str    ):
            # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
            super().test_save_load_floataa(expected_max_diff=1e-1    )





     def 							lowerCAmelCase      (				self   :							str    ):
            self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )





     def 							lowerCAmelCase      (				self   :							str    ):
            self._test_save_load_local()





     def 							lowerCAmelCase      (				self   :							str    ):
            self._test_inference_batch_single_identical(
                expected_max_diff=1e-2							,						)





     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()							,						reason="XFormers attention is only available with CUDA and `xformers` installed"							,						)
     def 							lowerCAmelCase      (				self   :							List[Any]    ):
            self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )







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





     def 							lowerCAmelCase      (				self   :							List[Any]    ):
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





     def 							lowerCAmelCase      (				self   :							str    ):
            # if

            __snake_case     			=       IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"							,						variant="fp16"							,						torch_dtype=torch.floataa    )

            __snake_case     			=       IFSuperResolutionPipeline.from_pretrained(
                "DeepFloyd/IF-II-L-v1.0"							,						variant="fp16"							,						torch_dtype=torch.floataa							,						text_encoder=snake_case_							,						tokenizer=snake_case_    )

            # pre compute text embeddings and remove T5 to save memory

            pipe_a.text_encoder.to("cuda"    )

            __snake_case			,   __snake_case     			=       pipe_a.encode_prompt("anime turtle"							,						device="cuda"    )

            del pipe_a.tokenizer
            del pipe_a.text_encoder
            gc.collect()

            __snake_case     			=       None
            __snake_case     			=       None

            pipe_a.enable_model_cpu_offload()
            pipe_a.enable_model_cpu_offload()

            pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
            pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

            self._test_if(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

            pipe_a.remove_all_hooks()
            pipe_a.remove_all_hooks()

            # img2img

            __snake_case     			=       IFImgaImgPipeline(**pipe_a.components    )
            __snake_case     			=       IFImgaImgSuperResolutionPipeline(**pipe_a.components    )

            pipe_a.enable_model_cpu_offload()
            pipe_a.enable_model_cpu_offload()

            pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
            pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

            self._test_if_imgaimg(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )

            pipe_a.remove_all_hooks()
            pipe_a.remove_all_hooks()

            # inpainting

            __snake_case     			=       IFInpaintingPipeline(**pipe_a.components    )
            __snake_case     			=       IFInpaintingSuperResolutionPipeline(**pipe_a.components    )

            pipe_a.enable_model_cpu_offload()
            pipe_a.enable_model_cpu_offload()

            pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )
            pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()    )

            self._test_if_inpainting(snake_case_							,						snake_case_							,						snake_case_							,						snake_case_    )





     def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							str							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[Any]							,						snake_case_   :							List[str]    ):
            # pipeline 1

            _start_torch_memory_measurement()

            __snake_case     			=       torch.Generator(device="cpu"    ).manual_seed(0    )
            __snake_case     			=       pipe_a(
                prompt_embeds=snake_case_							,						negative_prompt_embeds=snake_case_							,						num_inference_steps=2							,						generator=snake_case_							,						output_type="np"							,						)

            __snake_case     			=       output.images[0]

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

            __snake_case     			=       torch.cuda.max_memory_allocated()
            assert mem_bytes < 13 * 10**9

            __snake_case     			=       load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"    )
            assert_mean_pixel_difference(snake_case_							,						snake_case_    )

            # pipeline 2

            _start_torch_memory_measurement()

            __snake_case     			=       torch.Generator(device="cpu"    ).manual_seed(0    )

            __snake_case     			=       floats_tensor((1, 3, 64, 64)							,						rng=random.Random(0    )    ).to(snake_case_    )

            __snake_case     			=       pipe_a(
                prompt_embeds=snake_case_							,						negative_prompt_embeds=snake_case_							,						image=snake_case_							,						generator=snake_case_							,						num_inference_steps=2							,						output_type="np"							,						)

            __snake_case     			=       output.images[0]

            assert image.shape == (256, 256, 3)

            __snake_case     			=       torch.cuda.max_memory_allocated()
            assert mem_bytes < 4 * 10**9

            __snake_case     			=       load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"    )
            assert_mean_pixel_difference(snake_case_							,						snake_case_    )





     def 							lowerCAmelCase      (				self   :							Tuple							,						snake_case_   :							Optional[int]							,						snake_case_   :							Any							,						snake_case_   :							Any							,						snake_case_   :							int    ):
            # pipeline 1

            _start_torch_memory_measurement()

            __snake_case     			=       floats_tensor((1, 3, 64, 64)							,						rng=random.Random(0    )    ).to(snake_case_    )

            __snake_case     			=       torch.Generator(device="cpu"    ).manual_seed(0    )

            __snake_case     			=       pipe_a(
                prompt_embeds=snake_case_							,						negative_prompt_embeds=snake_case_							,						image=snake_case_							,						num_inference_steps=2							,						generator=snake_case_							,						output_type="np"							,						)

            __snake_case     			=       output.images[0]

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

            __snake_case     			=       torch.cuda.max_memory_allocated()
            assert mem_bytes < 10 * 10**9

            __snake_case     			=       load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"    )
            assert_mean_pixel_difference(snake_case_							,						snake_case_    )

            # pipeline 2

            _start_torch_memory_measurement()

            __snake_case     			=       torch.Generator(device="cpu"    ).manual_seed(0    )

            __snake_case     			=       floats_tensor((1, 3, 256, 256)							,						rng=random.Random(0    )    ).to(snake_case_    )
            __snake_case     			=       floats_tensor((1, 3, 64, 64)							,						rng=random.Random(0    )    ).to(snake_case_    )

            __snake_case     			=       pipe_a(
                prompt_embeds=snake_case_							,						negative_prompt_embeds=snake_case_							,						image=snake_case_							,						original_image=snake_case_							,						generator=snake_case_							,						num_inference_steps=2							,						output_type="np"							,						)

            __snake_case     			=       output.images[0]

            assert image.shape == (256, 256, 3)

            __snake_case     			=       torch.cuda.max_memory_allocated()
            assert mem_bytes < 4 * 10**9

            __snake_case     			=       load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"    )
            assert_mean_pixel_difference(snake_case_							,						snake_case_    )





     def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Tuple							,						snake_case_   :							str							,						snake_case_   :							Optional[Any]							,						snake_case_   :							Optional[int]    ):
            # pipeline 1

            _start_torch_memory_measurement()

            __snake_case     			=       floats_tensor((1, 3, 64, 64)							,						rng=random.Random(0    )    ).to(snake_case_    )
            __snake_case     			=       floats_tensor((1, 3, 64, 64)							,						rng=random.Random(1    )    ).to(snake_case_    )

            __snake_case     			=       torch.Generator(device="cpu"    ).manual_seed(0    )
            __snake_case     			=       pipe_a(
                prompt_embeds=snake_case_							,						negative_prompt_embeds=snake_case_							,						image=snake_case_							,						mask_image=snake_case_							,						num_inference_steps=2							,						generator=snake_case_							,						output_type="np"							,						)

            __snake_case     			=       output.images[0]

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

            __snake_case     			=       torch.cuda.max_memory_allocated()
            assert mem_bytes < 10 * 10**9

            __snake_case     			=       load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"    )
            assert_mean_pixel_difference(snake_case_							,						snake_case_    )

            # pipeline 2

            _start_torch_memory_measurement()

            __snake_case     			=       torch.Generator(device="cpu"    ).manual_seed(0    )

            __snake_case     			=       floats_tensor((1, 3, 64, 64)							,						rng=random.Random(0    )    ).to(snake_case_    )
            __snake_case     			=       floats_tensor((1, 3, 256, 256)							,						rng=random.Random(0    )    ).to(snake_case_    )
            __snake_case     			=       floats_tensor((1, 3, 256, 256)							,						rng=random.Random(1    )    ).to(snake_case_    )

            __snake_case     			=       pipe_a(
                prompt_embeds=snake_case_							,						negative_prompt_embeds=snake_case_							,						image=snake_case_							,						mask_image=snake_case_							,						original_image=snake_case_							,						generator=snake_case_							,						num_inference_steps=2							,						output_type="np"							,						)

            __snake_case     			=       output.images[0]

            assert image.shape == (256, 256, 3)

            __snake_case     			=       torch.cuda.max_memory_allocated()
            assert mem_bytes < 4 * 10**9

            __snake_case     			=       load_numpy(
                "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"    )
            assert_mean_pixel_difference(snake_case_							,						snake_case_    )




def   __UpperCamelCase   (					)  ->     List[str]:







       """simple docstring"""


       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()


"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE )
							# 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=SCREAMING_SNAKE_CASE )
							elif task == "WTQ":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       True
														# hparam_utils.py hparams
														__snake_case     			=       0.664_694
														__snake_case     			=       0.207_951
														__snake_case     			=       0.121_194
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       False
														__snake_case     			=       0.0_352_513

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       False
														# hparam_utils.py hparams
														__snake_case     			=       36.4_519
														__snake_case     			=       0.903_421
														__snake_case     			=       222.088
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       0.763_141

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "TABFACT":
														__snake_case     			=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE )
							elif task == "MLM":
														__snake_case     			=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE )
							elif task == "INTERMEDIATE_PRETRAINING":
														__snake_case     			=       TapasModel(config=SCREAMING_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(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

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

							# 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(SCREAMING_SNAKE_CASE )

							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,
		)
