



'''simple docstring'''






# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()



class 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase   ,   _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase		      =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase		      =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase		      =    frozenset({"""control_image"""}							)  # skip `image` and `mask` for now, only test for control_image
 UpperCAmelCase		      =    TEXT_TO_IMAGE_IMAGE_PARAMS







 def _snake_case					(				self				)						->  List[Any]:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					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    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Union[str, Any]     	=					ControlNetModel(
            block_out_channels=(32, 64)    ,layers_per_block=2    ,in_channels=4    ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,cross_attention_dim=32    ,conditioning_embedding_out_channels=(16, 32)    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Optional[Any]     	=					DDIMScheduler(
            beta_start=0.0_0085    ,beta_end=0.012    ,beta_schedule="""scaled_linear"""    ,clip_sample=a_    ,set_alpha_to_one=a_    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		str     	=					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    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		str     	=					CLIPTextConfig(
            bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1E-0_5    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,)
        _UpperCAmelCase    :		str     	=					CLIPTextModel(a_				)
        _UpperCAmelCase    :		Optional[Any]     	=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

        _UpperCAmelCase    :		Optional[int]     	=					{
            """unet""": unet,
            """controlnet""": controlnet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







 def _snake_case					(				self    ,a_    ,a_=0				)						->  Any:
        if str(a_				).startswith("""mps"""				):
               _UpperCAmelCase    :		int     	=					torch.manual_seed(a_				)
        else:
               _UpperCAmelCase    :		List[Any]     	=					torch.Generator(device=a_				).manual_seed(a_				)

        _UpperCAmelCase    :		int     	=					2
        _UpperCAmelCase    :		Optional[Any]     	=					randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,generator=a_    ,device=torch.device(a_				)    ,)
        _UpperCAmelCase    :		Optional[Any]     	=					floats_tensor((1, 3, 32, 32)    ,rng=random.Random(a_				)				).to(a_				)
        _UpperCAmelCase    :		Dict     	=					init_image.cpu().permute(0    ,2    ,3    ,1				)[0]

        _UpperCAmelCase    :		Any     	=					Image.fromarray(np.uinta(a_				)				).convert("""RGB"""				).resize((64, 64)				)
        _UpperCAmelCase    :		int     	=					Image.fromarray(np.uinta(init_image + 4				)				).convert("""RGB"""				).resize((64, 64)				)

        _UpperCAmelCase    :		Any     	=					{
            """prompt""": """A painting of a squirrel eating a burger""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
            """image""": image,
            """mask_image""": mask_image,
            """control_image""": control_image,
        }

        return inputs







 def _snake_case					(				self				)						->  str:
        return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)







 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()    ,reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,)
 def _snake_case					(				self				)						->  List[str]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)







 def _snake_case					(				self				)						->  str:
        self._test_inference_batch_single_identical(expected_max_diff=2E-3				)







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase		      =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase		      =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 UpperCAmelCase		      =    frozenset([]							)







 def _snake_case					(				self				)						->  List[Any]:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		str     	=					UNetaDConditionModel(
            block_out_channels=(32, 64)    ,layers_per_block=2    ,sample_size=32    ,in_channels=4    ,out_channels=4    ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,cross_attention_dim=32    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Dict     	=					ControlNetModel(
            block_out_channels=(32, 64)    ,layers_per_block=2    ,in_channels=4    ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,cross_attention_dim=32    ,conditioning_embedding_out_channels=(16, 32)    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Union[str, Any]     	=					DDIMScheduler(
            beta_start=0.0_0085    ,beta_end=0.012    ,beta_schedule="""scaled_linear"""    ,clip_sample=a_    ,set_alpha_to_one=a_    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Any     	=					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    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Tuple     	=					CLIPTextConfig(
            bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1E-0_5    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,)
        _UpperCAmelCase    :		Dict     	=					CLIPTextModel(a_				)
        _UpperCAmelCase    :		Any     	=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

        _UpperCAmelCase    :		Tuple     	=					{
            """unet""": unet,
            """controlnet""": controlnet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







class 				lowercase			(    _lowerCamelCase   ,   _lowerCamelCase   ,   unittest.TestCase							):


 """simple docstring"""


 UpperCAmelCase		      =    StableDiffusionControlNetInpaintPipeline
 UpperCAmelCase		      =    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 UpperCAmelCase		      =    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS







 def _snake_case					(				self				)						->  str:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					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    ,)
        torch.manual_seed(0				)

        def init_weights(a_				):
               if isinstance(a_    ,torch.nn.Convad				):
                      torch.nn.init.normal(m.weight				)
                      m.bias.data.fill_(1.0				)

        _UpperCAmelCase    :		Union[str, Any]     	=					ControlNetModel(
            block_out_channels=(32, 64)    ,layers_per_block=2    ,in_channels=4    ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,cross_attention_dim=32    ,conditioning_embedding_out_channels=(16, 32)    ,)
        controlneta.controlnet_down_blocks.apply(a_				)

        torch.manual_seed(0				)
        _UpperCAmelCase    :		Any     	=					ControlNetModel(
            block_out_channels=(32, 64)    ,layers_per_block=2    ,in_channels=4    ,down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,cross_attention_dim=32    ,conditioning_embedding_out_channels=(16, 32)    ,)
        controlneta.controlnet_down_blocks.apply(a_				)

        torch.manual_seed(0				)
        _UpperCAmelCase    :		Optional[int]     	=					DDIMScheduler(
            beta_start=0.0_0085    ,beta_end=0.012    ,beta_schedule="""scaled_linear"""    ,clip_sample=a_    ,set_alpha_to_one=a_    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Optional[int]     	=					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    ,)
        torch.manual_seed(0				)
        _UpperCAmelCase    :		Tuple     	=					CLIPTextConfig(
            bos_token_id=0    ,eos_token_id=2    ,hidden_size=32    ,intermediate_size=37    ,layer_norm_eps=1E-0_5    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,)
        _UpperCAmelCase    :		Tuple     	=					CLIPTextModel(a_				)
        _UpperCAmelCase    :		Tuple     	=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

        _UpperCAmelCase    :		str     	=					MultiControlNetModel([controlneta, controlneta]				)

        _UpperCAmelCase    :		List[Any]     	=					{
            """unet""": unet,
            """controlnet""": controlnet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







 def _snake_case					(				self    ,a_    ,a_=0				)						->  Optional[Any]:
        if str(a_				).startswith("""mps"""				):
               _UpperCAmelCase    :		Optional[int]     	=					torch.manual_seed(a_				)
        else:
               _UpperCAmelCase    :		List[Any]     	=					torch.Generator(device=a_				).manual_seed(a_				)

        _UpperCAmelCase    :		List[str]     	=					2

        _UpperCAmelCase    :		List[str]     	=					[
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,generator=a_    ,device=torch.device(a_				)    ,),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)    ,generator=a_    ,device=torch.device(a_				)    ,),
        ]
        _UpperCAmelCase    :		Dict     	=					floats_tensor((1, 3, 32, 32)    ,rng=random.Random(a_				)				).to(a_				)
        _UpperCAmelCase    :		Dict     	=					init_image.cpu().permute(0    ,2    ,3    ,1				)[0]

        _UpperCAmelCase    :		Any     	=					Image.fromarray(np.uinta(a_				)				).convert("""RGB"""				).resize((64, 64)				)
        _UpperCAmelCase    :		Optional[int]     	=					Image.fromarray(np.uinta(init_image + 4				)				).convert("""RGB"""				).resize((64, 64)				)

        _UpperCAmelCase    :		List[str]     	=					{
            """prompt""": """A painting of a squirrel eating a burger""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
            """image""": image,
            """mask_image""": mask_image,
            """control_image""": control_image,
        }

        return inputs







 def _snake_case					(				self				)						->  Optional[int]:
        _UpperCAmelCase    :		Optional[Any]     	=					self.get_dummy_components()
        _UpperCAmelCase    :		Optional[Any]     	=					self.pipeline_class(**a_				)
        pipe.to(a_				)

        _UpperCAmelCase    :		Optional[int]     	=					10.0
        _UpperCAmelCase    :		Dict     	=					4

        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_inputs(a_				)
        _UpperCAmelCase    :		Optional[int]     	=					steps
        _UpperCAmelCase    :		List[str]     	=					scale
        _UpperCAmelCase    :		Dict     	=					pipe(**a_				)[0]

        _UpperCAmelCase    :		Tuple     	=					self.get_dummy_inputs(a_				)
        _UpperCAmelCase    :		Any     	=					steps
        _UpperCAmelCase    :		str     	=					scale
        _UpperCAmelCase    :		Optional[int]     	=					pipe(**a_    ,control_guidance_start=0.1    ,control_guidance_end=0.2				)[0]

        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_inputs(a_				)
        _UpperCAmelCase    :		Dict     	=					steps
        _UpperCAmelCase    :		Dict     	=					scale
        _UpperCAmelCase    :		int     	=					pipe(**a_    ,control_guidance_start=[0.1, 0.3]    ,control_guidance_end=[0.2, 0.7]				)[0]

        _UpperCAmelCase    :		Dict     	=					self.get_dummy_inputs(a_				)
        _UpperCAmelCase    :		Optional[Any]     	=					steps
        _UpperCAmelCase    :		List[Any]     	=					scale
        _UpperCAmelCase    :		Tuple     	=					pipe(**a_    ,control_guidance_start=0.4    ,control_guidance_end=[0.5, 0.8]				)[0]

        # make sure that all outputs are different
        assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
        assert np.sum(np.abs(output_a - output_a				)				) > 1E-3
        assert np.sum(np.abs(output_a - output_a				)				) > 1E-3







 def _snake_case					(				self				)						->  Tuple:
        return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3				)







 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()    ,reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,)
 def _snake_case					(				self				)						->  List[str]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3				)







 def _snake_case					(				self				)						->  str:
        self._test_inference_batch_single_identical(expected_max_diff=2E-3				)







 def _snake_case					(				self				)						->  Any:
        _UpperCAmelCase    :		Any     	=					self.get_dummy_components()
        _UpperCAmelCase    :		List[Any]     	=					self.pipeline_class(**a_				)
        pipe.to(a_				)
        pipe.set_progress_bar_config(disable=a_				)
        with tempfile.TemporaryDirectory() as tmpdir:
               try:
                      # save_pretrained is not implemented for Multi-ControlNet
                      pipe.save_pretrained(a_				)
               except NotImplementedError:
                      pass







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


 """simple docstring"""


 def _snake_case					(				self				)						->  Union[str, Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		int     	=					ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""				)

        _UpperCAmelCase    :		str     	=					StableDiffusionControlNetInpaintPipeline.from_pretrained(
            """runwayml/stable-diffusion-inpainting"""    ,safety_checker=a_    ,controlnet=a_				)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		List[str]     	=					torch.Generator(device="""cpu"""				).manual_seed(0				)
        _UpperCAmelCase    :		Tuple     	=					load_image(
            """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""				).resize((512, 512)				)

        _UpperCAmelCase    :		Any     	=					load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"""
            """/stable_diffusion_inpaint/input_bench_mask.png"""				).resize((512, 512)				)

        _UpperCAmelCase    :		int     	=					"""pitch black hole"""

        _UpperCAmelCase    :		Union[str, Any]     	=					load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""				).resize((512, 512)				)

        _UpperCAmelCase    :		int     	=					pipe(
            a_    ,image=a_    ,mask_image=a_    ,control_image=a_    ,generator=a_    ,output_type="""np"""    ,num_inference_steps=3    ,)

        _UpperCAmelCase    :		List[Any]     	=					output.images[0]

        assert image.shape == (512, 512, 3)

        _UpperCAmelCase    :		str     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy"""				)

        assert np.abs(expected_image - image				).max() < 9E-2







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Optional[int]     	=					ControlNetModel.from_pretrained("""lllyasviel/control_v11p_sd15_inpaint"""				)

        _UpperCAmelCase    :		List[Any]     	=					StableDiffusionControlNetInpaintPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""    ,safety_checker=a_    ,controlnet=a_				)
        _UpperCAmelCase    :		str     	=					DDIMScheduler.from_config(pipe.scheduler.config				)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Dict     	=					torch.Generator(device="""cpu"""				).manual_seed(33				)

        _UpperCAmelCase    :		int     	=					load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png"""				)
        _UpperCAmelCase    :		List[Any]     	=					init_image.resize((512, 512)				)

        _UpperCAmelCase    :		List[Any]     	=					load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png"""				)
        _UpperCAmelCase    :		Dict     	=					mask_image.resize((512, 512)				)

        _UpperCAmelCase    :		Tuple     	=					"""a handsome man with ray-ban sunglasses"""

        def make_inpaint_condition(a_    ,a_				):
               _UpperCAmelCase    :		Union[str, Any]     	=					np.array(image.convert("""RGB"""				)				).astype(np.floataa				) / 255.0
               _UpperCAmelCase    :		List[str]     	=					np.array(image_mask.convert("""L"""				)				).astype(np.floataa				) / 255.0

               assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
               _UpperCAmelCase    :		str     	=					-1.0  # set as masked pixel
               _UpperCAmelCase    :		str     	=					np.expand_dims(a_    ,0				).transpose(0    ,3    ,1    ,2				)
               _UpperCAmelCase    :		int     	=					torch.from_numpy(a_				)
               return image

        _UpperCAmelCase    :		Union[str, Any]     	=					make_inpaint_condition(a_    ,a_				)

        _UpperCAmelCase    :		List[Any]     	=					pipe(
            a_    ,image=a_    ,mask_image=a_    ,control_image=a_    ,guidance_scale=9.0    ,eta=1.0    ,generator=a_    ,num_inference_steps=20    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		int     	=					output.images[0]

        assert image.shape == (512, 512, 3)

        _UpperCAmelCase    :		Any     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy"""				)

        assert np.abs(expected_image - image				).max() < 9E-2





'''simple docstring'''


import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import (
	    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)

if is_tf_available() and is_tensorflow_probability_available():
	import tensorflow as tf
	import tensorflow_probability as tfp

	from ..models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)




class     __snake_case			(    _a	):




		def __call__(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:List[Any]=None			,    _SCREAMING_SNAKE_CASE					:Tuple=None			,    **_SCREAMING_SNAKE_CASE					:int)					->			str:






								'''simple docstring'''






								requires_backends(self			,    """pandas""")
								import pandas as pd

								if table is None:
														raise ValueError("""Keyword argument `table` cannot be None.""")
								elif query is None:
														if isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) and table.get("""query""") is not None and table.get("""table""") is not None:
																				UpperCamelCase_     :	Union[str, Any]					     =     [table]
														elif isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) and len(_SCREAMING_SNAKE_CASE) > 0:
																				if not all(isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) for d in table):
																										raise ValueError(
																										    F"""Keyword argument `table` should be a list of dict, but is {(type(_SCREAMING_SNAKE_CASE) for d in table)}""")

																				if table[0].get("""query""") is not None and table[0].get("""table""") is not None:
																										UpperCamelCase_     :	Any					     =     table
																				else:
																										raise ValueError(
																										    """If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"""
																										    F""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.""")
														elif Dataset is not None and isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) or isinstance(_SCREAMING_SNAKE_CASE			,    types.GeneratorType):
																				return table
														else:
																				raise ValueError(
																				    """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
																				    F"""is {type(_SCREAMING_SNAKE_CASE)})""")
								else:
														UpperCamelCase_     :	int					     =     [{"""table""": table, """query""": query}]

								for tqa_pipeline_input in tqa_pipeline_inputs:
														if not isinstance(tqa_pipeline_input["""table"""]			,    pd.DataFrame):
																				if tqa_pipeline_input["table"] is None:
																										raise ValueError("""Table cannot be None.""")

																				UpperCamelCase_     :	Any					     =     pd.DataFrame(tqa_pipeline_input["""table"""])

								return tqa_pipeline_inputs







@add_end_docstrings(_a	)
class     __snake_case			(    _a	):
		a_          =					"table,query"




		def __init__(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]=TableQuestionAnsweringArgumentHandler()			,    *_SCREAMING_SNAKE_CASE					:Any			,    **_SCREAMING_SNAKE_CASE					:Any)					->			List[Any]:






								'''simple docstring'''






								super().__init__(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Optional[Any]					     =     args_parser

								self.check_model_type(
								    dict(
								        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
								        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())
								    if self.framework == """tf"""
								    else dict(
								        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()))

								UpperCamelCase_     :	List[str]					     =     bool(getattr(self.model.config			,    """aggregation_labels"""			,    _SCREAMING_SNAKE_CASE)) and bool(
								    getattr(self.model.config			,    """num_aggregation_labels"""			,    _SCREAMING_SNAKE_CASE))
								UpperCamelCase_     :	List[Any]					     =     """tapas""" if hasattr(self.model.config			,    """aggregation_labels""") else None




		def 	_snake_case	(  self					:List[Any]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			Tuple:






								'''simple docstring'''






								return self.model(**_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[int]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			Tuple:






								'''simple docstring'''






								if self.framework == "pt":
														UpperCamelCase_     :	Optional[int]					     =     []
														UpperCamelCase_     :	Union[str, Any]					     =     []
														UpperCamelCase_     :	int					     =     None
														UpperCamelCase_     :	Dict					     =     inputs["""input_ids"""].shape[0]

														UpperCamelCase_     :	List[str]					     =     inputs["""input_ids"""].to(self.device)
														UpperCamelCase_     :	int					     =     inputs["""attention_mask"""].to(self.device)
														UpperCamelCase_     :	List[Any]					     =     inputs["""token_type_ids"""].to(self.device)
														UpperCamelCase_     :	List[str]					     =     None

														for index in range(_SCREAMING_SNAKE_CASE):
																				# If sequences have already been processed, the token type IDs will be created according to the previous
																				# answer.
																				if prev_answers is not None:
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 3]  # shape (seq_len,)
																										UpperCamelCase_     :	int					     =     np.zeros_like(prev_labels_example.cpu().numpy())  # shape (seq_len,)

																										UpperCamelCase_     :	int					     =     token_type_ids[index]  # shape (seq_len, 7)
																										for i in range(model_labels.shape[0]):
																																UpperCamelCase_     :	Optional[Any]					     =     token_type_ids_example[:, 0].tolist()[i]
																																UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																																UpperCamelCase_     :	Union[str, Any]					     =     token_type_ids_example[:, 2].tolist()[i] - 1

																																if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																						UpperCamelCase_     :	Tuple					     =     int(prev_answers[(col_id, row_id)])

																										UpperCamelCase_     :	Union[str, Any]					     =     torch.from_numpy(_SCREAMING_SNAKE_CASE).type(torch.long).to(self.device)

																				UpperCamelCase_     :	Optional[int]					     =     input_ids[index]
																				UpperCamelCase_     :	int					     =     attention_mask[index]  # shape (seq_len,)
																				UpperCamelCase_     :	Any					     =     token_type_ids[index]  # shape (seq_len, 7)
																				UpperCamelCase_     :	Union[str, Any]					     =     self.model(
																				    input_ids=input_ids_example.unsqueeze(0)			,    attention_mask=attention_mask_example.unsqueeze(0)			,    token_type_ids=token_type_ids_example.unsqueeze(0)			,    )
																				UpperCamelCase_     :	int					     =     outputs.logits

																				if self.aggregate:
																										all_aggregations.append(outputs.logits_aggregation)

																				all_logits.append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	Any					     =     torch.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	List[Any]					     =     dist_per_token.probs * attention_mask_example.type(torch.floataa).to(
																				    dist_per_token.probs.device)

																				UpperCamelCase_     :	Tuple					     =     collections.defaultdict(_SCREAMING_SNAKE_CASE)
																				for i, p in enumerate(probabilities.squeeze().tolist()):
																										UpperCamelCase_     :	str					     =     token_type_ids_example[:, 0].tolist()[i]
																										UpperCamelCase_     :	Optional[Any]					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																										UpperCamelCase_     :	Tuple					     =     token_type_ids_example[:, 2].tolist()[i] - 1
																										if col >= 0 and row >= 0 and segment_id == 1:
																																coords_to_probs[(col, row)].append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	str					     =     {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}

														UpperCamelCase_     :	str					     =     torch.cat(tuple(_SCREAMING_SNAKE_CASE)			,    0)

														return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_SCREAMING_SNAKE_CASE)			,    0))
								else:
														UpperCamelCase_     :	Any					     =     []
														UpperCamelCase_     :	Dict					     =     []
														UpperCamelCase_     :	Union[str, Any]					     =     None
														UpperCamelCase_     :	List[Any]					     =     inputs["""input_ids"""].shape[0]

														UpperCamelCase_     :	List[str]					     =     inputs["""input_ids"""]
														UpperCamelCase_     :	int					     =     inputs["""attention_mask"""]
														UpperCamelCase_     :	Optional[Any]					     =     inputs["""token_type_ids"""].numpy()
														UpperCamelCase_     :	Dict					     =     None

														for index in range(_SCREAMING_SNAKE_CASE):
																				# If sequences have already been processed, the token type IDs will be created according to the previous
																				# answer.
																				if prev_answers is not None:
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 3]  # shape (seq_len,)
																										UpperCamelCase_     :	Optional[Any]					     =     np.zeros_like(_SCREAMING_SNAKE_CASE			,    dtype=np.intaa)  # shape (seq_len,)

																										UpperCamelCase_     :	Union[str, Any]					     =     token_type_ids[index]  # shape (seq_len, 7)
																										for i in range(model_labels.shape[0]):
																																UpperCamelCase_     :	Any					     =     token_type_ids_example[:, 0].tolist()[i]
																																UpperCamelCase_     :	str					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																																UpperCamelCase_     :	Union[str, Any]					     =     token_type_ids_example[:, 2].tolist()[i] - 1

																																if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																						UpperCamelCase_     :	int					     =     int(prev_answers[(col_id, row_id)])

																										UpperCamelCase_     :	Optional[int]					     =     model_labels

																				UpperCamelCase_     :	Any					     =     input_ids[index]
																				UpperCamelCase_     :	Optional[int]					     =     attention_mask[index]  # shape (seq_len,)
																				UpperCamelCase_     :	List[Any]					     =     token_type_ids[index]  # shape (seq_len, 7)
																				UpperCamelCase_     :	Union[str, Any]					     =     self.model(
																				    input_ids=np.expand_dims(_SCREAMING_SNAKE_CASE			,    axis=0)			,    attention_mask=np.expand_dims(_SCREAMING_SNAKE_CASE			,    axis=0)			,    token_type_ids=np.expand_dims(_SCREAMING_SNAKE_CASE			,    axis=0)			,    )
																				UpperCamelCase_     :	List[Any]					     =     outputs.logits

																				if self.aggregate:
																										all_aggregations.append(outputs.logits_aggregation)

																				all_logits.append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	List[Any]					     =     tfp.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	str					     =     dist_per_token.probs_parameter() * tf.cast(_SCREAMING_SNAKE_CASE			,    tf.floataa)

																				UpperCamelCase_     :	str					     =     collections.defaultdict(_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	Any					     =     token_type_ids_example
																				for i, p in enumerate(tf.squeeze(_SCREAMING_SNAKE_CASE).numpy().tolist()):
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 0].tolist()[i]
																										UpperCamelCase_     :	List[str]					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 2].tolist()[i] - 1
																										if col >= 0 and row >= 0 and segment_id == 1:
																																coords_to_probs[(col, row)].append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	str					     =     {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}

														UpperCamelCase_     :	Optional[Any]					     =     tf.concat(tuple(_SCREAMING_SNAKE_CASE)			,    0)

														return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(_SCREAMING_SNAKE_CASE)			,    0))




		def __call__(  self					:Union[str, Any]			,    *_SCREAMING_SNAKE_CASE					:str			,    **_SCREAMING_SNAKE_CASE					:Any)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self._args_parser(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	List[str]					     =     super().__call__(_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								if len(_SCREAMING_SNAKE_CASE) == 1:
														return results[0]
								return results




		def 	_snake_case	(  self					:Any			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=None			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=None			,    **_SCREAMING_SNAKE_CASE					:List[str])					->			Tuple:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     {}
								if padding is not None:
														UpperCamelCase_     :	Tuple					     =     padding
								if truncation is not None:
														UpperCamelCase_     :	Tuple					     =     truncation

								UpperCamelCase_     :	Optional[int]					     =     {}
								if sequential is not None:
														UpperCamelCase_     :	Tuple					     =     sequential
								return preprocess_params, forward_params, {}




		def 	_snake_case	(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:str=None			,    _SCREAMING_SNAKE_CASE					:Tuple=True			,    _SCREAMING_SNAKE_CASE					:str=None)					->			int:






								'''simple docstring'''






								if truncation is None:
														if self.type == "tapas":
																				UpperCamelCase_     :	List[Any]					     =     """drop_rows_to_fit"""
														else:
																				UpperCamelCase_     :	Union[str, Any]					     =     """do_not_truncate"""

								UpperCamelCase_     ,UpperCamelCase_     :	Any					     =     pipeline_input["""table"""], pipeline_input["""query"""]
								if table.empty:
														raise ValueError("""table is empty""")
								if query is None or query == "":
														raise ValueError("""query is empty""")
								UpperCamelCase_     :	Optional[Any]					     =     self.tokenizer(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    return_tensors=self.framework			,    truncation=_SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	List[Any]					     =     table
								return inputs




		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]=False)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     model_inputs.pop("""table""")

								if self.type == "tapas":
														if sequential:
																				UpperCamelCase_     :	List[Any]					     =     self.sequential_inference(**_SCREAMING_SNAKE_CASE)
														else:
																				UpperCamelCase_     :	str					     =     self.batch_inference(**_SCREAMING_SNAKE_CASE)
								else:
														UpperCamelCase_     :	List[Any]					     =     self.model.generate(**_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Any					     =     {"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
								return model_outputs





		def 	_snake_case	(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:Optional[int])					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	str					     =     model_outputs["""model_inputs"""]
								UpperCamelCase_     :	int					     =     model_outputs["""table"""]
								UpperCamelCase_     :	Tuple					     =     model_outputs["""outputs"""]
								if self.type == "tapas":
														if self.aggregate:
																				UpperCamelCase_     ,UpperCamelCase_     :	int					     =     outputs[:2]
																				UpperCamelCase_     :	Any					     =     self.tokenizer.convert_logits_to_predictions(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     ,UpperCamelCase_     :	Dict					     =     predictions
																				UpperCamelCase_     :	str					     =     {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_SCREAMING_SNAKE_CASE)}

																				UpperCamelCase_     :	Tuple					     =     self.model.config.no_aggregation_label_index
																				UpperCamelCase_     :	List[Any]					     =     {
																				    i: aggregators[i] + """ > """ for i, pred in enumerate(_SCREAMING_SNAKE_CASE) if pred != no_agg_label_index
																				}
														else:
																				UpperCamelCase_     :	Dict					     =     outputs[0]
																				UpperCamelCase_     :	List[Any]					     =     self.tokenizer.convert_logits_to_predictions(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	Optional[int]					     =     predictions[0]
																				UpperCamelCase_     :	Tuple					     =     {}
																				UpperCamelCase_     :	str					     =     {}
														UpperCamelCase_     :	int					     =     []
														for index, coordinates in enumerate(_SCREAMING_SNAKE_CASE):
																				UpperCamelCase_     :	int					     =     [table.iat[coordinate] for coordinate in coordinates]
																				UpperCamelCase_     :	Optional[int]					     =     aggregators.get(_SCREAMING_SNAKE_CASE			,    """""")
																				UpperCamelCase_     :	int					     =     aggregators_prefix.get(_SCREAMING_SNAKE_CASE			,    """""")
																				UpperCamelCase_     :	Optional[Any]					     =     {
																				    """answer""": aggregator_prefix + """, """.join(_SCREAMING_SNAKE_CASE),
																				    """coordinates""": coordinates,
																				    """cells""": [table.iat[coordinate] for coordinate in coordinates],
																				}
																				if aggregator:
																										UpperCamelCase_     :	Union[str, Any]					     =     aggregator

																				answers.append(_SCREAMING_SNAKE_CASE)
														if len(_SCREAMING_SNAKE_CASE) == 0:
																				raise PipelineException("""Empty answer""")
								else:
														UpperCamelCase_     :	Optional[int]					     =     [{"""answer""": answer} for answer in self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,    skip_special_tokens=_SCREAMING_SNAKE_CASE)]

								return answers if len(_SCREAMING_SNAKE_CASE) > 1 else answers[0]




