






"""simple docstring"""



import collections
import inspect
import unittest

from transformers import DinatConfig
from transformers.testing_utils import require_natten, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import DinatBackbone, DinatForImageClassification, DinatModel
    from transformers.models.dinat.modeling_dinat import DINAT_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor

class 			a	:







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase=1_3				,						_lowerCamelCase=6_4				,						_lowerCamelCase=4				,						_lowerCamelCase=3				,						_lowerCamelCase=1_6				,						_lowerCamelCase=[1, 2, 1]				,						_lowerCamelCase=[2, 4, 8]				,						_lowerCamelCase=3				,						_lowerCamelCase=[[3], [1, 2], [1]]				,						_lowerCamelCase=2.0				,						_lowerCamelCase=True				,						_lowerCamelCase=0.0				,						_lowerCamelCase=0.0				,						_lowerCamelCase=0.1				,						_lowerCamelCase="gelu"				,						_lowerCamelCase=True				,						_lowerCamelCase=0.0_2				,						_lowerCamelCase=1e-5				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=1_0				,						_lowerCamelCase=["stage1", "stage2"]				,						_lowerCamelCase=[1, 2]				,						):
      lowercase				       =			parent
      lowercase				       =			batch_size
      lowercase				       =			image_size
      lowercase				       =			patch_size
      lowercase				       =			num_channels
      lowercase				       =			embed_dim
      lowercase				       =			depths
      lowercase				       =			num_heads
      lowercase				       =			kernel_size
      lowercase				       =			dilations
      lowercase				       =			mlp_ratio
      lowercase				       =			qkv_bias
      lowercase				       =			hidden_dropout_prob
      lowercase				       =			attention_probs_dropout_prob
      lowercase				       =			drop_path_rate
      lowercase				       =			hidden_act
      lowercase				       =			patch_norm
      lowercase				       =			layer_norm_eps
      lowercase				       =			initializer_range
      lowercase				       =			is_training
      lowercase				       =			scope
      lowercase				       =			use_labels
      lowercase				       =			num_labels
      lowercase				       =			out_features
      lowercase				       =			out_indices







    def      UpperCamelCase_ (     self			):
      lowercase				       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

      lowercase				       =			None
      if self.use_labels:
        lowercase				       =			ids_tensor([self.batch_size]				,						self.num_labels			)

      lowercase				       =			self.get_config()

      return config, pixel_values, labels







    def      UpperCamelCase_ (     self			):
      return DinatConfig(
          num_labels=self.num_labels				,						image_size=self.image_size				,						patch_size=self.patch_size				,						num_channels=self.num_channels				,						embed_dim=self.embed_dim				,						depths=self.depths				,						num_heads=self.num_heads				,						kernel_size=self.kernel_size				,						dilations=self.dilations				,						mlp_ratio=self.mlp_ratio				,						qkv_bias=self.qkv_bias				,						hidden_dropout_prob=self.hidden_dropout_prob				,						attention_probs_dropout_prob=self.attention_probs_dropout_prob				,						drop_path_rate=self.drop_path_rate				,						hidden_act=self.hidden_act				,						patch_norm=self.patch_norm				,						layer_norm_eps=self.layer_norm_eps				,						initializer_range=self.initializer_range				,						out_features=self.out_features				,						out_indices=self.out_indices				,						)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			DinatModel(config=_SCREAMING_SNAKE_CASE			)
      model.to(_SCREAMING_SNAKE_CASE			)
      model.eval()
      lowercase				       =			model(_SCREAMING_SNAKE_CASE			)

      lowercase				       =			(config.image_size // config.patch_size) // (2 ** (len(config.depths			) - 1))
      lowercase				       =			int(config.embed_dim * 2 ** (len(config.depths			) - 1)			)

      self.parent.assertEqual(
          result.last_hidden_state.shape				,						(self.batch_size, expected_height, expected_width, expected_dim)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			DinatForImageClassification(_SCREAMING_SNAKE_CASE			)
      model.to(_SCREAMING_SNAKE_CASE			)
      model.eval()
      lowercase				       =			model(_SCREAMING_SNAKE_CASE				,						labels=_SCREAMING_SNAKE_CASE			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)			)

      # test greyscale images
      lowercase				       =			1
      lowercase				       =			DinatForImageClassification(_SCREAMING_SNAKE_CASE			)
      model.to(_SCREAMING_SNAKE_CASE			)
      model.eval()

      lowercase				       =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
      lowercase				       =			model(_SCREAMING_SNAKE_CASE			)
      self.parent.assertEqual(result.logits.shape				,						(self.batch_size, self.num_labels)			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			DinatBackbone(config=_SCREAMING_SNAKE_CASE			)
      model.to(_SCREAMING_SNAKE_CASE			)
      model.eval()
      lowercase				       =			model(_SCREAMING_SNAKE_CASE			)

      # verify hidden states
      self.parent.assertEqual(len(result.feature_maps			)				,						len(config.out_features			)			)
      self.parent.assertListEqual(list(result.feature_maps[0].shape			)				,						[self.batch_size, model.channels[0], 1_6, 1_6]			)

      # verify channels
      self.parent.assertEqual(len(model.channels			)				,						len(config.out_features			)			)

      # verify backbone works with out_features=None
      lowercase				       =			None
      lowercase				       =			DinatBackbone(config=_SCREAMING_SNAKE_CASE			)
      model.to(_SCREAMING_SNAKE_CASE			)
      model.eval()
      lowercase				       =			model(_SCREAMING_SNAKE_CASE			)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps			)				,						1			)
      self.parent.assertListEqual(list(result.feature_maps[0].shape			)				,						[self.batch_size, model.channels[-1], 4, 4]			)

      # verify channels
      self.parent.assertEqual(len(model.channels			)				,						1			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.prepare_config_and_inputs()
      lowercase				       =			config_and_inputs
      lowercase				       =			{"""pixel_values""": pixel_values}
      return config, inputs_dict


@require_natten
@require_torch
class 			a	(  _a,							_a,							unittest.TestCase      ):
    UpperCAmelCase_					:	List[Any]      	=(
        (
            DinatModel,
            DinatForImageClassification,
            DinatBackbone,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase_					:	str      	=(
        {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
        if is_torch_available()
        else {}
    )
    UpperCAmelCase_					:	str      	=False

    UpperCAmelCase_					:	Any      	=False
    UpperCAmelCase_					:	int      	=False
    UpperCAmelCase_					:	Any      	=False
    UpperCAmelCase_					:	str      	=False







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DinatModelTester(self			)
      lowercase				       =			ConfigTester(self				,						config_class=_SCREAMING_SNAKE_CASE				,						embed_dim=3_7			)







    def      UpperCamelCase_ (     self			):
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()







    def      UpperCamelCase_ (     self			):
      return







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE			)







    @unittest.skip(reason='Dinat does not use inputs_embeds'			)
    def      UpperCamelCase_ (     self			):
      pass







    @unittest.skip(reason='Dinat does not use feedforward chunking'			)
    def      UpperCamelCase_ (     self			):
      pass







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			model_class(_SCREAMING_SNAKE_CASE			)
        self.assertIsInstance(model.get_input_embeddings()				,						(nn.Module)			)
        lowercase				       =			model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE				,						nn.Linear			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase				       =			model_class(_SCREAMING_SNAKE_CASE			)
        lowercase				       =			inspect.signature(model.forward			)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase				       =			[*signature.parameters.keys()]

        lowercase				       =			["""pixel_values"""]
        self.assertListEqual(arg_names[:1]				,						_SCREAMING_SNAKE_CASE			)







    def      UpperCamelCase_ (     self			):
      self.skipTest('Dinat\'s attention operation is handled entirely by NATTEN.'			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase			):
      lowercase				       =			model_class(_SCREAMING_SNAKE_CASE			)
      model.to(_SCREAMING_SNAKE_CASE			)
      model.eval()

      with torch.no_grad():
        lowercase				       =			model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)			)

      lowercase				       =			outputs.hidden_states

      lowercase				       =			getattr(
          self.model_tester				,						'expected_num_hidden_layers'				,						len(self.model_tester.depths			) + 1			)
      self.assertEqual(len(_SCREAMING_SNAKE_CASE			)				,						_SCREAMING_SNAKE_CASE			)

      # Dinat has a different seq_length
      lowercase				       =			(
          config.patch_size
          if isinstance(config.patch_size				,						collections.abc.Iterable			)
          else (config.patch_size, config.patch_size)
      )

      lowercase				       =			image_size[0] // patch_size[0]
      lowercase				       =			image_size[1] // patch_size[1]

      self.assertListEqual(
          list(hidden_states[0].shape[-3:]			)				,						[height, width, self.model_tester.embed_dim]				,						)

      if model_class.__name__ != "DinatBackbone":
        lowercase				       =			outputs.reshaped_hidden_states
        self.assertEqual(len(_SCREAMING_SNAKE_CASE			)				,						_SCREAMING_SNAKE_CASE			)

        lowercase				       =			reshaped_hidden_states[0].shape
        lowercase				       =			(
            reshaped_hidden_states[0].view(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			).permute(0				,						2				,						3				,						1			)
        )
        self.assertListEqual(
            list(reshaped_hidden_states.shape[-3:]			)				,						[height, width, self.model_tester.embed_dim]				,						)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      lowercase				       =			(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size				,						collections.abc.Iterable			)
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
        lowercase				       =			True
        self.check_hidden_states_output(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase				       =			True

        self.check_hidden_states_output(_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE				,						_SCREAMING_SNAKE_CASE			)







    @slow
    def      UpperCamelCase_ (     self			):






      for model_name in DINAT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase				       =			DinatModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
        self.assertIsNotNone(_SCREAMING_SNAKE_CASE			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.model_tester.prepare_config_and_inputs_for_common()

      lowercase				       =			_config_zero_init(_SCREAMING_SNAKE_CASE			)
      for model_class in self.all_model_classes:
        lowercase				       =			model_class(config=_SCREAMING_SNAKE_CASE			)
        for name, param in model.named_parameters():
          if "embeddings" not in name and param.requires_grad:
            self.assertIn(
                ((param.data.mean() * 1e9).round() / 1e9).item()				,						[0.0, 1.0]				,						msg=F'Parameter {name} of model {model_class} seems not properly initialized'				,						)


@require_natten
@require_vision
@require_torch
class 			a	(  unittest.TestCase      ):







    @cached_property
    def      UpperCamelCase_ (     self			):
      return AutoImageProcessor.from_pretrained('shi-labs/dinat-mini-in1k-224'			) if is_vision_available() else None







    @slow
    def      UpperCamelCase_ (     self			):
      lowercase				       =			DinatForImageClassification.from_pretrained('shi-labs/dinat-mini-in1k-224'			).to(_SCREAMING_SNAKE_CASE			)
      lowercase				       =			self.default_image_processor

      lowercase				       =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
      lowercase				       =			image_processor(images=_SCREAMING_SNAKE_CASE				,						return_tensors='pt'			).to(_SCREAMING_SNAKE_CASE			)

      # forward pass
      with torch.no_grad():
        lowercase				       =			model(**_SCREAMING_SNAKE_CASE			)

      # verify the logits
      lowercase				       =			torch.Size((1, 1_0_0_0)			)
      self.assertEqual(outputs.logits.shape				,						_SCREAMING_SNAKE_CASE			)
      lowercase				       =			torch.tensor([-0.1_5_4_5, -0.7_6_6_7, 0.4_6_4_2]			).to(_SCREAMING_SNAKE_CASE			)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]				,						_SCREAMING_SNAKE_CASE				,						atol=1e-4			)			)


@require_torch
@require_natten
class 			a	(  unittest.TestCase,							_a      ):
    UpperCAmelCase_					:	List[str]      	=(DinatBackbone,) if is_torch_available() else ()
    UpperCAmelCase_					:	List[str]      	=DinatConfig







    def      UpperCamelCase_ (     self			):
      lowercase				       =			DinatModelTester(self			)




'''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]




