



'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModelWithProjection

from diffusers import (
    AutoencoderKL,
    DPMSolverMultistepScheduler,
    PNDMScheduler,
    StableDiffusionImageVariationPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 							lowerCAmelCase_		(	a_  ,      a_  ,      a_  ,      unittest.TestCase						):
    __UpperCAmelCase							=	StableDiffusionImageVariationPipeline
    __UpperCAmelCase							=	IMAGE_VARIATION_PARAMS
    __UpperCAmelCase							=	IMAGE_VARIATION_BATCH_PARAMS
    __UpperCAmelCase							=	frozenset([]						)
    # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    __UpperCAmelCase							=	frozenset([]						)





    def 					__snake_case       (    self    :    List[str]     ):



       '''simple docstring'''







       torch.manual_seed(0     )
       snake_case   :							Tuple						  =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,    )
       snake_case   :							Any						  =PNDMScheduler(skip_prk_steps=_snake_case     )
       torch.manual_seed(0     )
       snake_case   :							List[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     )
       snake_case   :							str						  =CLIPVisionConfig(
           hidden_size=32,    projection_dim=32,    intermediate_size=37,    layer_norm_eps=1E-05,    num_attention_heads=4,    num_hidden_layers=5,    image_size=32,    patch_size=4,    )
       snake_case   :							int						  =CLIPVisionModelWithProjection(_snake_case     )
       snake_case   :							Tuple						  =CLIPImageProcessor(crop_size=32,    size=32     )

       snake_case   :							List[str]						  ={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''image_encoder''': image_encoder,
           '''feature_extractor''': feature_extractor,
           '''safety_checker''': None,
       }
       return components





    def 					__snake_case       (    self    :    str,    _snake_case    :    Union[str, Any],    _snake_case    :    Optional[int]=0     ):



       '''simple docstring'''







       snake_case   :							Dict						  =floats_tensor((1, 3, 32, 32),    rng=random.Random(_snake_case     )     )
       snake_case   :							Union[str, Any]						  =image.cpu().permute(0,    2,    3,    1     )[0]
       snake_case   :							Union[str, Any]						  =Image.fromarray(np.uinta(_snake_case     )     ).convert('''RGB'''     ).resize((32, 32)     )
       if str(_snake_case     ).startswith('''mps'''     ):
          snake_case   :							int						  =torch.manual_seed(_snake_case     )
       else:
          snake_case   :							List[Any]						  =torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )
       snake_case   :							int						  ={
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs





    def 					__snake_case       (    self    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case   :							List[Any]						  =self.get_dummy_components()
       snake_case   :							Any						  =StableDiffusionImageVariationPipeline(**_snake_case     )
       snake_case   :							str						  =sd_pipe.to(_snake_case     )
       sd_pipe.set_progress_bar_config(disable=_snake_case     )

       snake_case   :							Optional[Any]						  =self.get_dummy_inputs(_snake_case     )
       snake_case   :							List[str]						  =sd_pipe(**_snake_case     ).images
       snake_case   :							int						  =image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       snake_case   :							Tuple						  =np.array([0.5239, 0.5723, 0.4796, 0.5049, 0.5550, 0.4685, 0.5329, 0.4891, 0.4921]     )

       assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3





    def 					__snake_case       (    self    :    Optional[Any]     ):



       '''simple docstring'''







       snake_case   :							Union[str, Any]						  ='''cpu'''  # ensure determinism for the device-dependent torch.Generator
       snake_case   :							Tuple						  =self.get_dummy_components()
       snake_case   :							List[Any]						  =StableDiffusionImageVariationPipeline(**_snake_case     )
       snake_case   :							Dict						  =sd_pipe.to(_snake_case     )
       sd_pipe.set_progress_bar_config(disable=_snake_case     )

       snake_case   :							Optional[Any]						  =self.get_dummy_inputs(_snake_case     )
       snake_case   :							List[Any]						  =2 * [inputs['''image''']]
       snake_case   :							List[Any]						  =sd_pipe(**_snake_case     )

       snake_case   :							Union[str, Any]						  =output.images

       snake_case   :							Dict						  =image[-1, -3:, -3:, -1]

       assert image.shape == (2, 64, 64, 3)
       snake_case   :							Optional[int]						  =np.array([0.6892, 0.5637, 0.5836, 0.5771, 0.6254, 0.6409, 0.5580, 0.5569, 0.5289]     )

       assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3






    def 					__snake_case       (    self    :    int     ):



       '''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    :    Optional[int]     ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    Optional[Any],    _snake_case    :    Tuple,    _snake_case    :    Union[str, Any]="cpu",    _snake_case    :    List[str]=torch.floataa,    _snake_case    :    List[str]=0     ):



       '''simple docstring'''







       snake_case   :							int						  =torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )
       snake_case   :							Optional[int]						  =load_image(
           '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
           '''/stable_diffusion_imgvar/input_image_vermeer.png'''     )
       snake_case   :							Optional[Any]						  =np.random.RandomState(_snake_case     ).standard_normal((1, 4, 64, 64)     )
       snake_case   :							Dict						  =torch.from_numpy(_snake_case     ).to(device=_snake_case,    dtype=_snake_case     )
       snake_case   :							Any						  ={
           '''image''': init_image,
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs





    def 					__snake_case       (    self    :    Optional[int]     ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  =StableDiffusionImageVariationPipeline.from_pretrained(
           '''lambdalabs/sd-image-variations-diffusers''',    safety_checker=_snake_case     )
       snake_case   :							Optional[int]						  =sd_pipe.to(_snake_case     )
       sd_pipe.set_progress_bar_config(disable=_snake_case     )

       snake_case   :							int						  =self.get_inputs(_snake_case     )
       snake_case   :							Optional[int]						  =sd_pipe(**_snake_case     ).images
       snake_case   :							Union[str, Any]						  =image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       snake_case   :							List[Any]						  =np.array([0.8_4491, 0.9_0789, 0.7_5708, 0.7_8734, 0.8_3485, 0.7_0099, 0.6_6938, 0.6_8727, 0.6_1379]     )
       assert np.abs(image_slice - expected_slice     ).max() < 6E-3





    def 					__snake_case       (    self    :    Union[str, Any]     ):



       '''simple docstring'''







       snake_case   :							List[Any]						  =0

       def callback_fn(_snake_case    :    int,    _snake_case    :    int,    _snake_case    :    torch.FloatTensor     ) -> None:
          snake_case   :							str						  =True
          nonlocal number_of_steps
          number_of_steps += 1
          if step == 1:
             snake_case   :							int						  =latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             snake_case   :							str						  =latents[0, -3:, -3:, -1]
             snake_case   :							Tuple						  =np.array(
                 [-0.1621, 0.2837, -0.7979, -0.1221, -1.3057, 0.7681, -2.1191, 0.0464, 1.6309]     )

             assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2
          elif step == 2:
             snake_case   :							List[str]						  =latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             snake_case   :							str						  =latents[0, -3:, -3:, -1]
             snake_case   :							int						  =np.array([0.6299, 1.7500, 1.1992, -2.1582, -1.8994, 0.7334, -0.7090, 1.0137, 1.5273]     )

             assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2

       snake_case   :							List[Any]						  =False

       snake_case   :							List[str]						  =StableDiffusionImageVariationPipeline.from_pretrained(
           '''fusing/sd-image-variations-diffusers''',    safety_checker=_snake_case,    torch_dtype=torch.floataa,    )
       pipe.to(_snake_case     )
       pipe.set_progress_bar_config(disable=_snake_case     )
       pipe.enable_attention_slicing()

       snake_case   :							int						  =self.get_inputs(_snake_case,    dtype=torch.floataa     )
       pipe(**_snake_case,    callback=_snake_case,    callback_steps=1     )
       assert callback_fn.has_been_called
       assert number_of_steps == inputs["num_inference_steps"]






    def 					__snake_case       (    self    :    Any     ):



       '''simple docstring'''







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

       snake_case   :							Optional[Any]						  ='''fusing/sd-image-variations-diffusers'''
       snake_case   :							int						  =StableDiffusionImageVariationPipeline.from_pretrained(
           _snake_case,    safety_checker=_snake_case,    torch_dtype=torch.floataa     )
       snake_case   :							List[Any]						  =pipe.to(_snake_case     )
       pipe.set_progress_bar_config(disable=_snake_case     )
       pipe.enable_attention_slicing(1     )
       pipe.enable_sequential_cpu_offload()

       snake_case   :							str						  =self.get_inputs(_snake_case,    dtype=torch.floataa     )
       snake_case   :							int						  =pipe(**_snake_case     )

       snake_case   :							List[Any]						  =torch.cuda.max_memory_allocated()
       # make sure that less than 2.6 GB is allocated
       assert mem_bytes < 2.6 * 10**9





@nightly
@require_torch_gpu
class 							lowerCAmelCase_		(	unittest.TestCase						):





    def 					__snake_case       (    self    :    Dict     ):



       '''simple docstring'''







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





    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    Any,    _snake_case    :    Optional[int]="cpu",    _snake_case    :    int=torch.floataa,    _snake_case    :    List[Any]=0     ):



       '''simple docstring'''







       snake_case   :							Any						  =torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )
       snake_case   :							Optional[int]						  =load_image(
           '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
           '''/stable_diffusion_imgvar/input_image_vermeer.png'''     )
       snake_case   :							List[Any]						  =np.random.RandomState(_snake_case     ).standard_normal((1, 4, 64, 64)     )
       snake_case   :							Optional[int]						  =torch.from_numpy(_snake_case     ).to(device=_snake_case,    dtype=_snake_case     )
       snake_case   :							Tuple						  ={
           '''image''': init_image,
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 50,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs





    def 					__snake_case       (    self    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							int						  =StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers'''     )
       sd_pipe.to(_snake_case     )
       sd_pipe.set_progress_bar_config(disable=_snake_case     )

       snake_case   :							Tuple						  =self.get_inputs(_snake_case     )
       snake_case   :							Dict						  =sd_pipe(**_snake_case     ).images[0]

       snake_case   :							int						  =load_numpy(
           '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
           '''/stable_diffusion_imgvar/lambdalabs_variations_pndm.npy'''     )
       snake_case   :							Any						  =np.abs(expected_image - image     ).max()
       assert max_diff < 1E-3






    def 					__snake_case       (    self    :    int     ):



       '''simple docstring'''







       snake_case   :							Optional[int]						  =StableDiffusionImageVariationPipeline.from_pretrained('''fusing/sd-image-variations-diffusers'''     )
       snake_case   :							List[str]						  =DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config     )
       sd_pipe.to(_snake_case     )
       sd_pipe.set_progress_bar_config(disable=_snake_case     )

       snake_case   :							int						  =self.get_inputs(_snake_case     )
       snake_case   :							List[str]						  =25
       snake_case   :							List[Any]						  =sd_pipe(**_snake_case     ).images[0]

       snake_case   :							Tuple						  =load_numpy(
           '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
           '''/stable_diffusion_imgvar/lambdalabs_variations_dpm_multi.npy'''     )
       snake_case   :							str						  =np.abs(expected_image - image     ).max()
       assert max_diff < 1E-3










'''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 							lowerCAmelCase_		(	a_						):





    def __call__(    self    :    str,    _snake_case    :    Dict=None,    _snake_case    :    str=None,    **_snake_case    :    Any     ):



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

             if table[0].get('''query'''     ) is not None and table[0].get('''table'''     ) is not None:
                snake_case   :							Dict						  =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(_snake_case,    _snake_case     ) or isinstance(_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(_snake_case     )})'''     )
       else:
          snake_case   :							Union[str, Any]						  =[{'''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.'''     )

             snake_case   :							Any						  =pd.DataFrame(tqa_pipeline_input['''table''']     )

       return tqa_pipeline_inputs





@add_end_docstrings(a_						)
class 							lowerCAmelCase_		(	a_						):
    __UpperCAmelCase							=	'table,query'





    def __init__(    self    :    Any,    _snake_case    :    Any=TableQuestionAnsweringArgumentHandler(),    *_snake_case    :    List[Any],    **_snake_case    :    Dict     ):



       '''simple docstring'''







       super().__init__(*_snake_case,    **_snake_case     )
       snake_case   :							str						  =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()     )     )

       snake_case   :							Dict						  =bool(getattr(self.model.config,    '''aggregation_labels''',    _snake_case     )     ) and bool(
           getattr(self.model.config,    '''num_aggregation_labels''',    _snake_case     )     )
       snake_case   :							List[str]						  ='''tapas''' if hasattr(self.model.config,    '''aggregation_labels'''     ) else None





    def 					__snake_case       (    self    :    Dict,    **_snake_case    :    Tuple     ):



       '''simple docstring'''







       return self.model(**_snake_case     )





    def 					__snake_case       (    self    :    List[Any],    **_snake_case    :    List[str]     ):



       '''simple docstring'''







       if self.framework == "pt":
          snake_case   :							str						  =[]
          snake_case   :							str						  =[]
          snake_case   :							Optional[int]						  =None
          snake_case   :							List[str]						  =inputs['''input_ids'''].shape[0]

          snake_case   :							Union[str, Any]						  =inputs['''input_ids'''].to(self.device     )
          snake_case   :							List[str]						  =inputs['''attention_mask'''].to(self.device     )
          snake_case   :							Optional[int]						  =inputs['''token_type_ids'''].to(self.device     )
          snake_case   :							Any						  =None

          for index in range(_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:
                snake_case   :							Union[str, Any]						  =token_type_ids_example[:, 3]  # shape (seq_len,)
                snake_case   :							Any						  =np.zeros_like(prev_labels_example.cpu().numpy()     )  # shape (seq_len,)

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

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

                snake_case   :							List[Any]						  =torch.from_numpy(_snake_case     ).type(torch.long     ).to(self.device     )

             snake_case   :							Tuple						  =input_ids[index]
             snake_case   :							Optional[int]						  =attention_mask[index]  # shape (seq_len,)
             snake_case   :							List[Any]						  =token_type_ids[index]  # shape (seq_len, 7)
             snake_case   :							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     ),    )
             snake_case   :							int						  =outputs.logits

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

             all_logits.append(_snake_case     )

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

             snake_case   :							Dict						  =collections.defaultdict(_snake_case     )
             for i, p in enumerate(probabilities.squeeze().tolist()     ):
                snake_case   :							int						  =token_type_ids_example[:, 0].tolist()[i]
                snake_case   :							Optional[int]						  =token_type_ids_example[:, 1].tolist()[i] - 1
                snake_case   :							Dict						  =token_type_ids_example[:, 2].tolist()[i] - 1
                if col >= 0 and row >= 0 and segment_id == 1:
                   coords_to_probs[(col, row)].append(_snake_case     )

             snake_case   :							Union[str, Any]						  ={key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

          snake_case   :							Tuple						  =torch.cat(tuple(_snake_case     ),    0     )

          return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_snake_case     ),    0     ))
       else:
          snake_case   :							Tuple						  =[]
          snake_case   :							Optional[int]						  =[]
          snake_case   :							str						  =None
          snake_case   :							Dict						  =inputs['''input_ids'''].shape[0]

          snake_case   :							Optional[Any]						  =inputs['''input_ids''']
          snake_case   :							Optional[int]						  =inputs['''attention_mask''']
          snake_case   :							Union[str, Any]						  =inputs['''token_type_ids'''].numpy()
          snake_case   :							Tuple						  =None

          for index in range(_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:
                snake_case   :							Any						  =token_type_ids_example[:, 3]  # shape (seq_len,)
                snake_case   :							str						  =np.zeros_like(_snake_case,    dtype=np.intaa     )  # shape (seq_len,)

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

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

                snake_case   :							Union[str, Any]						  =model_labels

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

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

             all_logits.append(_snake_case     )

             snake_case   :							int						  =tfp.distributions.Bernoulli(logits=_snake_case     )
             snake_case   :							Union[str, Any]						  =dist_per_token.probs_parameter() * tf.cast(_snake_case,    tf.floataa     )

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

             snake_case   :							Optional[int]						  ={key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

          snake_case   :							Dict						  =tf.concat(tuple(_snake_case     ),    0     )

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





    def __call__(    self    :    List[str],    *_snake_case    :    Any,    **_snake_case    :    List[Any]     ):



       '''simple docstring'''







       snake_case   :							Tuple						  =self._args_parser(*_snake_case,    **_snake_case     )

       snake_case   :							Dict						  =super().__call__(_snake_case,    **_snake_case     )
       if len(_snake_case     ) == 1:
          return results[0]
       return results





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    Union[str, Any]=None,    _snake_case    :    Union[str, Any]=None,    _snake_case    :    List[Any]=None,    **_snake_case    :    List[str]     ):



       '''simple docstring'''







       snake_case   :							Optional[Any]						  ={}
       if padding is not None:
          snake_case   :							List[Any]						  =padding
       if truncation is not None:
          snake_case   :							int						  =truncation

       snake_case   :							Any						  ={}
       if sequential is not None:
          snake_case   :							int						  =sequential
       return preprocess_params, forward_params, {}





    def 					__snake_case       (    self    :    Dict,    _snake_case    :    Dict,    _snake_case    :    List[Any]=None,    _snake_case    :    Optional[int]=True,    _snake_case    :    Dict=None     ):



       '''simple docstring'''







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

       snake_case  ,    snake_case   :							Optional[int]						  =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'''     )
       snake_case   :							Dict						  =self.tokenizer(_snake_case,    _snake_case,    return_tensors=self.framework,    truncation=_snake_case,    padding=_snake_case     )
       snake_case   :							List[Any]						  =table
       return inputs





    def 					__snake_case       (    self    :    List[str],    _snake_case    :    List[Any],    _snake_case    :    Any=False     ):



       '''simple docstring'''







       snake_case   :							Union[str, Any]						  =model_inputs.pop('''table'''     )

       if self.type == "tapas":
          if sequential:
             snake_case   :							Dict						  =self.sequential_inference(**_snake_case     )
          else:
             snake_case   :							Optional[int]						  =self.batch_inference(**_snake_case     )
       else:
          snake_case   :							str						  =self.model.generate(**_snake_case     )
       snake_case   :							int						  ={'''model_inputs''': model_inputs, '''table''': table, '''outputs''': outputs}
       return model_outputs






    def 					__snake_case       (    self    :    Optional[int],    _snake_case    :    Optional[Any]     ):



       '''simple docstring'''







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

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

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

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





