"""simple docstring"""







import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         LayoutLMForMaskedLM,
         LayoutLMForQuestionAnswering,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMModel,
     )






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=7 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=99 ,				_SCREAMING_SNAKE_CASE=32 ,				_SCREAMING_SNAKE_CASE=5 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=37 ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=512 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=1000 ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					seq_length
      __lowerCamelCase    		=					is_training
      __lowerCamelCase    		=					use_input_mask
      __lowerCamelCase    		=					use_token_type_ids
      __lowerCamelCase    		=					use_labels
      __lowerCamelCase    		=					vocab_size
      __lowerCamelCase    		=					hidden_size
      __lowerCamelCase    		=					num_hidden_layers
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					intermediate_size
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					max_position_embeddings
      __lowerCamelCase    		=					type_vocab_size
      __lowerCamelCase    		=					type_sequence_label_size
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					num_labels
      __lowerCamelCase    		=					num_choices
      __lowerCamelCase    		=					scope
      __lowerCamelCase    		=					range_bbox







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.vocab_size	)

      __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length, 4] ,				self.range_bbox	)
      # Ensure that bbox is legal
      for i in range(bbox.shape[0]	):
         for j in range(bbox.shape[1]	):
            if bbox[i, j, 3] < bbox[i, j, 1]:
               __lowerCamelCase    		=					bbox[i, j, 3]
               __lowerCamelCase    		=					bbox[i, j, 1]
               __lowerCamelCase    		=					t
            if bbox[i, j, 2] < bbox[i, j, 0]:
               __lowerCamelCase    		=					bbox[i, j, 2]
               __lowerCamelCase    		=					bbox[i, j, 0]
               __lowerCamelCase    		=					t

      __lowerCamelCase    		=					None
      if self.use_input_mask:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				vocab_size=2	)

      __lowerCamelCase    		=					None
      if self.use_token_type_ids:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.type_vocab_size	)

      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.type_sequence_label_size	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.seq_length] ,				self.num_labels	)
         __lowerCamelCase    		=					ids_tensor([self.batch_size] ,				self.num_choices	)

      __lowerCamelCase    		=					self.get_config()

      return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







   def    _A				(	self	):


      '''simple docstring'''

      return LayoutLMConfig(
          vocab_size=self.vocab_size ,				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 ,				max_position_embeddings=self.max_position_embeddings ,				type_vocab_size=self.type_vocab_size ,				initializer_range=self.initializer_range ,				)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)	)
      self.parent.assertEqual(result.pooler_output.shape ,				(self.batch_size, self.hidden_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.vocab_size)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					LayoutLMForSequenceClassification(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					LayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.seq_length, self.num_labels)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(
          _SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				start_positions=_SCREAMING_SNAKE_CASE ,				end_positions=_SCREAMING_SNAKE_CASE ,				)
      self.parent.assertEqual(result.start_logits.shape ,				(self.batch_size, self.seq_length)	)
      self.parent.assertEqual(result.end_logits.shape ,				(self.batch_size, self.seq_length)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()
      (
          (
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,(
          __lowerCamelCase
      )		,
      )    		=					config_and_inputs
      __lowerCamelCase    		=					{
          'input_ids': input_ids,
          'bbox': bbox,
          'token_type_ids': token_type_ids,
          'attention_mask': input_mask,
      }
      return config, inputs_dict






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							List[str]    =    (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   __UpperCAmelCase	:							List[Any]    =    (
       {
           "document-question-answering": LayoutLMForQuestionAnswering,
           "feature-extraction": LayoutLMModel,
           "fill-mask": LayoutLMForMaskedLM,
           "text-classification": LayoutLMForSequenceClassification,
           "token-classification": LayoutLMForTokenClassification,
           "zero-shot": LayoutLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase	:							int    =    True







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModelTester(self	)
      __lowerCamelCase    		=					ConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE ,				hidden_size=37	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __lowerCamelCase    		=					type
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE	)







   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass



def       A__    (	)  ->							Optional[int]:
   # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
   # fmt: off
   __lowerCamelCase    		=					torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]      , device=lowercase_						)  # noqa: E231
   __lowerCamelCase    		=					torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]      , device=lowercase_						)  # noqa: E231
   # these are sequence labels (i.e. at the token level)
   __lowerCamelCase    		=					torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]      , device=lowercase_						)  # noqa: E231
   # fmt: on

   return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the sequence output on [0, :3, :3]
      __lowerCamelCase    		=					torch.tensor(
          [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] ,				device=_SCREAMING_SNAKE_CASE ,				)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-3	)	)

      # test the pooled output on [1, :3]
      __lowerCamelCase    		=					torch.tensor([-0.6580, -0.0214, 0.8552] ,				device=_SCREAMING_SNAKE_CASE	)

      self.assertTrue(torch.allclose(outputs.pooler_output[1, :3] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-3	)	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased' ,				num_labels=2	).to(
          _SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(
          input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=torch.tensor([1, 1] ,				device=_SCREAMING_SNAKE_CASE	) ,				)

      # test whether we get a loss as a scalar
      __lowerCamelCase    		=					outputs.loss
      __lowerCamelCase    		=					torch.Size([]	)
      self.assertEqual(loss.shape ,				_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      __lowerCamelCase    		=					outputs.logits
      __lowerCamelCase    		=					torch.Size((2, 2)	)
      self.assertEqual(logits.shape ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased' ,				num_labels=13	).to(
          _SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(
          input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)

      # test the loss calculation to be around 2.65
      # expected_loss = torch.tensor(2.65, device=torch_device)

      # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
      # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

      # test the shape of the logits
      __lowerCamelCase    		=					outputs.logits
      __lowerCamelCase    		=					torch.Size((2, 25, 13)	)
      self.assertEqual(logits.shape ,				_SCREAMING_SNAKE_CASE	)







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					prepare_layoutlm_batch_inputs()

      # forward pass
      __lowerCamelCase    		=					model(input_ids=_SCREAMING_SNAKE_CASE ,				bbox=_SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				token_type_ids=_SCREAMING_SNAKE_CASE	)

      # test the shape of the logits
      __lowerCamelCase    		=					torch.Size((2, 25)	)
      self.assertEqual(outputs.start_logits.shape ,				_SCREAMING_SNAKE_CASE	)
      self.assertEqual(outputs.end_logits.shape ,				_SCREAMING_SNAKE_CASE	)








'''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]




