

'''simple docstring'''

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 (
						    MODEL_MAPPING,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						)
						from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor







class lowerCamelCase   :

				def __init__(       self,						lowercase_,						lowercase_=100,						lowercase_=13,						lowercase_=30,						lowercase_=2,						lowercase_=3,						lowercase_=True,						lowercase_=True,						lowercase_=32,						lowercase_=4,						lowercase_=4,						lowercase_=37,						lowercase_="gelu",						lowercase_=0.1,						lowercase_=0.1,						lowercase_=10,						lowercase_=0.02,						lowercase_=3,						lowercase_=None,						lowercase_=[0, 1, 2, 3],						)    ->							Dict:
					snake_case							=					parent
					snake_case							=					100
					snake_case							=					batch_size
					snake_case							=					image_size
					snake_case							=					patch_size
					snake_case							=					num_channels
					snake_case							=					is_training
					snake_case							=					use_labels
					snake_case							=					hidden_size
					snake_case							=					num_hidden_layers
					snake_case							=					num_attention_heads
					snake_case							=					intermediate_size
					snake_case							=					hidden_act
					snake_case							=					hidden_dropout_prob
					snake_case							=					attention_probs_dropout_prob
					snake_case							=					type_sequence_label_size
					snake_case							=					initializer_range
					snake_case							=					scope
					snake_case							=					out_indices
					snake_case							=					num_labels

					# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
					snake_case							=					(image_size // patch_size) ** 2
					snake_case							=					num_patches + 1

				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					snake_case							=					None
					snake_case							=					None
					if self.use_labels:
						snake_case							=					ids_tensor([self.batch_size],						self.type_sequence_label_size )
						snake_case							=					ids_tensor([self.batch_size, self.image_size, self.image_size],						self.num_labels )

					snake_case							=					self.get_config()

					return config, pixel_values, labels, pixel_labels

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					return DataaVecVisionConfig(
					    vocab_size=self.vocab_size,						image_size=self.image_size,						patch_size=self.patch_size,						num_channels=self.num_channels,						hidden_size=self.hidden_size,						num_hidden_layers=self.num_hidden_layers,						num_attention_heads=self.num_attention_heads,						intermediate_size=self.intermediate_size,						hidden_act=self.hidden_act,						hidden_dropout_prob=self.hidden_dropout_prob,						attention_probs_dropout_prob=self.attention_probs_dropout_prob,						is_decoder=lowercase_,						initializer_range=self.initializer_range,						out_indices=self.out_indices,						)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Tuple:
					snake_case							=					DataaVecVisionModel(config=lowercase_ )
					model.to(lowercase_ )
					model.eval()
					snake_case							=					model(lowercase_ )
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
					snake_case							=					(self.image_size // self.patch_size) ** 2
					self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, num_patches + 1, self.hidden_size) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							Any:
					snake_case							=					self.type_sequence_label_size
					snake_case							=					DataaVecVisionForImageClassification(lowercase_ )
					model.to(lowercase_ )
					model.eval()
					snake_case							=					model(lowercase_,						labels=lowercase_ )
					self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.type_sequence_label_size) )

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )    ->							str:
					snake_case							=					self.num_labels
					snake_case							=					DataaVecVisionForSemanticSegmentation(lowercase_ )
					model.to(lowercase_ )
					model.eval()
					snake_case							=					model(lowercase_ )
					self.parent.assertEqual(
					    result.logits.shape,						(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
					snake_case							=					model(lowercase_,						labels=lowercase_ )
					self.parent.assertEqual(
					    result.logits.shape,						(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )






				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case							=					self.prepare_config_and_inputs()
					snake_case			,			snake_case			,			snake_case			,			snake_case							=					config_and_inputs
					snake_case							=					{'pixel_values': pixel_values}
					return config, inputs_dict







@require_torch
class lowerCamelCase   (    __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = (
				    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				snake_case_							     = (
				    {
				        '''feature-extraction''': DataaVecVisionModel,
				        '''image-classification''': DataaVecVisionForImageClassification,
				        '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				snake_case_							     = False
				snake_case_							     = False
				snake_case_							     = False

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					DataaVecVisionModelTester(self )
					snake_case							=					ConfigTester(
					    self,						config_class=lowercase_,						has_text_modality=lowercase_,						hidden_size=37 )

				def 					_lowerCamelCase    (       self )    ->							Any:
					self.config_tester.run_common_tests()

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					# Data2VecVision does not use inputs_embeds
					pass

				@require_torch_multi_gpu
				@unittest.skip(
				    reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					pass

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

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

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

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

						snake_case							=					['pixel_values']
						self.assertListEqual(arg_names[:1],						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_segmentation(*lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					if not self.model_tester.is_training:
						return

					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()
					snake_case							=					True

					for model_class in self.all_model_classes:
						if model_class in [*get_values(lowercase_ )]:
							continue

						snake_case							=					model_class(lowercase_ )
						model.to(lowercase_ )
						model.train()
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					model(**lowercase_ ).loss
						loss.backward()

				def 					_lowerCamelCase    (       self )    ->							List[Any]:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()
					if not self.model_tester.is_training:
						return

					snake_case							=					False
					snake_case							=					True

					for model_class in self.all_model_classes:
						if model_class in [*get_values(lowercase_ )] or not model_class.supports_gradient_checkpointing:
							continue
						# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
						# this can then be incorporated into _prepare_for_class in test_modeling_common.py
						elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
							snake_case			,			snake_case			,			snake_case			,			snake_case							=					inputs_dict['pixel_values'].shape
							snake_case							=					torch.zeros(
							    [self.model_tester.batch_size, height, width],						device=lowercase_ ).long()
						snake_case							=					model_class(lowercase_ )
						model.gradient_checkpointing_enable()
						model.to(lowercase_ )
						model.train()
						snake_case							=					self._prepare_for_class(lowercase_,						lowercase_,						return_labels=lowercase_ )
						snake_case							=					model(**lowercase_ ).loss
						loss.backward()

				def 					_lowerCamelCase    (       self )    ->							int:
					snake_case			,			snake_case							=					self.model_tester.prepare_config_and_inputs_for_common()

					snake_case							=					_config_zero_init(lowercase_ )
					for model_class in self.all_model_classes:
						snake_case							=					model_class(config=lowercase_ )
						for name, param in model.named_parameters():
							# we skip lambda parameters as these require special initial values
							# determined by config.layer_scale_init_value
							if "lambda" in name:
								continue
							if 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''',						)

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_,						lowercase_,						lowercase_=2E-4,						lowercase_="outputs",						lowercase_=None )    ->							Tuple:
					# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
					super().check_pt_tf_outputs(lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_,						lowercase_ )

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowercase_ )






				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						snake_case							=					DataaVecVisionModel.from_pretrained(lowercase_ )
						self.assertIsNotNone(lowercase_ )

def      __magic_name__							(			)      ->		Tuple:
	snake_case							=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image







@require_torch
@require_vision
class lowerCamelCase   (    unittest.TestCase				):

				@cached_property
				def 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					return (
					    BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k' ) if is_vision_available() else None
					)






				@slow
				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k' ).to(
					    lowercase_ )

					snake_case							=					self.default_image_processor
					snake_case							=					prepare_img()
					snake_case							=					image_processor(images=lowercase_,						return_tensors='pt' ).to(lowercase_ )

					# forward pass
					with torch.no_grad():
						snake_case							=					model(**lowercase_ )
					snake_case							=					outputs.logits

					# verify the logits
					snake_case							=					torch.Size((1, 1000) )
					self.assertEqual(logits.shape,						lowercase_ )

					snake_case							=					torch.tensor([0.3_277, -0.1_395, 0.0_911] ).to(lowercase_ )

					self.assertTrue(torch.allclose(logits[0, :3],						lowercase_,						atol=1E-4 ) )

					snake_case							=					[model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
					self.assertEqual(logits[0].topk(2 ).indices.cpu().tolist(),						lowercase_ )












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  _a  (					lowercase__				):






							"""simple docstring"""

							def __call__(   self					:  List[Any]				,						a					:  str=None				,						a					:  Dict=None				,						**a					:  Tuple     )						->Dict:
													# Returns tqa_pipeline_inputs of shape:
													# [
													#   {"table": pd.DataFrame, "query": List[str]},
													#   ...,
													#   {"table": pd.DataFrame, "query" : List[str]}
													# ]
													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(a				,						a     ) and table.get("query"     ) is not None and table.get("table"     ) is not None:
																									SCREAMING_SNAKE_CASE__  :					int =       [table]
																			elif isinstance(a				,						a     ) and len(a     ) > 0:
																									if not all(isinstance(a				,						a     ) for d in table     ):
																															raise ValueError(
																															    f"""Keyword argument `table` should be a list of dict, but is {(type(a     ) for d in table)}"""     )

																									if table[0].get("query"     ) is not None and table[0].get("table"     ) is not None:
																															SCREAMING_SNAKE_CASE__  :					List[str] =       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(a				,						a     ) or isinstance(a				,						types.GeneratorType     ):
																									return table
																			else:
																									raise ValueError(
																									    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
																									    f"""is {type(a     )})"""     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[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."     )

																									SCREAMING_SNAKE_CASE__  :					Dict =       pd.DataFrame(tqa_pipeline_input["table"]     )

													return tqa_pipeline_inputs







@add_end_docstrings(lowercase__				)
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    "table,query"

							def __init__(   self					:  List[str]				,						a					:  Optional[int]=TableQuestionAnsweringArgumentHandler()				,						*a					:  List[str]				,						**a					:  Dict     )						->Any:
													super().__init__(*a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       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()     )     )

													SCREAMING_SNAKE_CASE__  :					str =       bool(getattr(self.model.config				,						"aggregation_labels"				,						a     )     ) and bool(
													    getattr(self.model.config				,						"num_aggregation_labels"				,						a     )     )
													SCREAMING_SNAKE_CASE__  :					int =       "tapas" if hasattr(self.model.config				,						"aggregation_labels"     ) else None

							def   A_	(   self					:  List[str]				,						**a					:  Any     )						->Optional[Any]:
													return self.model(**a     )

							def   A_	(   self					:  Any				,						**a					:  Any     )						->Dict:
													if self.framework == "pt":
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       []
																			SCREAMING_SNAKE_CASE__  :					int =       []
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"].shape[0]

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

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

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

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

																															SCREAMING_SNAKE_CASE__  :					Dict =       torch.from_numpy(a     ).type(torch.long     ).to(self.device     )

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

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

																									all_logits.append(a     )

																									SCREAMING_SNAKE_CASE__  :					Any =       torch.distributions.Bernoulli(logits=a     )
																									SCREAMING_SNAKE_CASE__  :					Tuple =       dist_per_token.probs * attention_mask_example.type(torch.floataa     ).to(
																									    dist_per_token.probs.device     )

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

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

																			SCREAMING_SNAKE_CASE__  :					List[str] =       torch.cat(tuple(a     )				,						0     )

																			return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(a     )				,						0     ))
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       []
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"].shape[0]

																			SCREAMING_SNAKE_CASE__  :					Tuple =       inputs["input_ids"]
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["attention_mask"]
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       inputs["token_type_ids"].numpy()
																			SCREAMING_SNAKE_CASE__  :					Tuple =       None

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

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

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

																															SCREAMING_SNAKE_CASE__  :					Any =       model_labels

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

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

																									all_logits.append(a     )

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

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

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

																			SCREAMING_SNAKE_CASE__  :					List[Any] =       tf.concat(tuple(a     )				,						0     )

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

							def __call__(   self					:  Optional[Any]				,						*a					:  Any				,						**a					:  Optional[int]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					Tuple =       self._args_parser(*a				,						**a     )

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

							def   A_	(   self					:  Any				,						a					:  Dict=None				,						a					:  Dict=None				,						a					:  Union[str, Any]=None				,						**a					:  int     )						->Any:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       {}
													if padding is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padding
													if truncation is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       truncation

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

							def   A_	(   self					:  Any				,						a					:  Any				,						a					:  Union[str, Any]=None				,						a					:  Union[str, Any]=True				,						a					:  Dict=None     )						->int:
													if truncation is None:
																			if self.type == "tapas":
																									SCREAMING_SNAKE_CASE__  :					Any =       "drop_rows_to_fit"
																			else:
																									SCREAMING_SNAKE_CASE__  :					List[str] =       "do_not_truncate"

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[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"     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       self.tokenizer(a				,						a				,						return_tensors=self.framework				,						truncation=a				,						padding=a     )
													SCREAMING_SNAKE_CASE__  :					str =       table
													return inputs

							def   A_	(   self					:  Any				,						a					:  Tuple				,						a					:  Union[str, Any]=False     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model_inputs.pop("table"     )

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






							def   A_	(   self					:  str				,						a					:  Any     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Dict =       model_outputs["model_inputs"]
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model_outputs["table"]
													SCREAMING_SNAKE_CASE__  :					Dict =       model_outputs["outputs"]
													if self.type == "tapas":
																			if self.aggregate:
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       outputs[:2]
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.tokenizer.convert_logits_to_predictions(a				,						a				,						a     )
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[Any] =       predictions
																									SCREAMING_SNAKE_CASE__  :					Tuple =       {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(a     )}

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

																									answers.append(a     )
																			if len(a     ) == 0:
																									raise PipelineException("Empty answer"     )
													else:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [{"answer": answer} for answer in self.tokenizer.batch_decode(a				,						skip_special_tokens=a     )]

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