


"""simple docstring"""





from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
     TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     LayoutLMvaConfig,
     TFLayoutLMvaForQuestionAnswering,
     TFLayoutLMvaForSequenceClassification,
     TFLayoutLMvaForTokenClassification,
     TFLayoutLMvaModel,
 )

if is_vision_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor



class 						snake_case_:






     def __init__(		self     :    Tuple    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    List[Any]=2    , UpperCamelCase_     :    Dict=3    , UpperCamelCase_     :    List[str]=4    , UpperCamelCase_     :    Any=2    , UpperCamelCase_     :    List[str]=7    , UpperCamelCase_     :    Dict=True    , UpperCamelCase_     :    int=True    , UpperCamelCase_     :    int=True    , UpperCamelCase_     :    Union[str, Any]=True    , UpperCamelCase_     :    List[str]=9_9    , UpperCamelCase_     :    Optional[Any]=3_6    , UpperCamelCase_     :    Dict=2    , UpperCamelCase_     :    Optional[Any]=4    , UpperCamelCase_     :    Tuple=3_7    , UpperCamelCase_     :    str="gelu"    , UpperCamelCase_     :    Union[str, Any]=0.1    , UpperCamelCase_     :    Optional[int]=0.1    , UpperCamelCase_     :    Dict=5_1_2    , UpperCamelCase_     :    Dict=1_6    , UpperCamelCase_     :    List[Any]=2    , UpperCamelCase_     :    str=0.02    , UpperCamelCase_     :    Tuple=6    , UpperCamelCase_     :    List[Any]=6    , UpperCamelCase_     :    Optional[int]=3    , UpperCamelCase_     :    Any=4    , UpperCamelCase_     :    Union[str, Any]=None    , UpperCamelCase_     :    str=1_0_0_0    , ):
           lowerCAmelCase    :		Tuple				=      parent
           lowerCAmelCase    :		Dict				=      batch_size
           lowerCAmelCase    :		Optional[int]				=      num_channels
           lowerCAmelCase    :		List[Any]				=      image_size
           lowerCAmelCase    :		Optional[Any]				=      patch_size
           lowerCAmelCase    :		Union[str, Any]				=      is_training
           lowerCAmelCase    :		Optional[int]				=      use_input_mask
           lowerCAmelCase    :		List[Any]				=      use_token_type_ids
           lowerCAmelCase    :		Dict				=      use_labels
           lowerCAmelCase    :		str				=      vocab_size
           lowerCAmelCase    :		Any				=      hidden_size
           lowerCAmelCase    :		List[str]				=      num_hidden_layers
           lowerCAmelCase    :		str				=      num_attention_heads
           lowerCAmelCase    :		Optional[int]				=      intermediate_size
           lowerCAmelCase    :		Tuple				=      hidden_act
           lowerCAmelCase    :		Optional[Any]				=      hidden_dropout_prob
           lowerCAmelCase    :		Optional[int]				=      attention_probs_dropout_prob
           lowerCAmelCase    :		Union[str, Any]				=      max_position_embeddings
           lowerCAmelCase    :		int				=      type_vocab_size
           lowerCAmelCase    :		List[Any]				=      type_sequence_label_size
           lowerCAmelCase    :		List[Any]				=      initializer_range
           lowerCAmelCase    :		List[str]				=      coordinate_size
           lowerCAmelCase    :		Any				=      shape_size
           lowerCAmelCase    :		Tuple				=      num_labels
           lowerCAmelCase    :		Union[str, Any]				=      num_choices
           lowerCAmelCase    :		List[str]				=      scope
           lowerCAmelCase    :		Any				=      range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           lowerCAmelCase    :		Dict				=      text_seq_length
           lowerCAmelCase    :		Tuple				=      (image_size // patch_size) ** 2 + 1
           lowerCAmelCase    :		Tuple				=      self.text_seq_length + self.image_seq_length






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		List[str]				=      ids_tensor([self.batch_size, self.text_seq_length]    , self.vocab_size )

           lowerCAmelCase    :		Union[str, Any]				=      ids_tensor([self.batch_size, self.text_seq_length, 4]    , self.range_bbox )
           lowerCAmelCase    :		Optional[int]				=      bbox.numpy()
           # 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    :		List[str]				=      bbox[i, j, 3]
                             lowerCAmelCase    :		str				=      bbox[i, j, 1]
                             lowerCAmelCase    :		Dict				=      tmp_coordinate
                       if bbox[i, j, 2] < bbox[i, j, 0]:
                             lowerCAmelCase    :		int				=      bbox[i, j, 2]
                             lowerCAmelCase    :		Union[str, Any]				=      bbox[i, j, 0]
                             lowerCAmelCase    :		List[str]				=      tmp_coordinate
           lowerCAmelCase    :		Dict				=      tf.constant(UpperCamelCase_ )

           lowerCAmelCase    :		str				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

           lowerCAmelCase    :		Optional[int]				=      None
           if self.use_input_mask:
                 lowerCAmelCase    :		Any				=      random_attention_mask([self.batch_size, self.text_seq_length] )

           lowerCAmelCase    :		str				=      None
           if self.use_token_type_ids:
                 lowerCAmelCase    :		Any				=      ids_tensor([self.batch_size, self.text_seq_length]    , self.type_vocab_size )

           lowerCAmelCase    :		List[str]				=      None
           lowerCAmelCase    :		Optional[Any]				=      None
           if self.use_labels:
                 lowerCAmelCase    :		Tuple				=      ids_tensor([self.batch_size]    , self.type_sequence_label_size )
                 lowerCAmelCase    :		Any				=      ids_tensor([self.batch_size, self.text_seq_length]    , self.num_labels )

           lowerCAmelCase    :		List[str]				=      LayoutLMvaConfig(
               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    , coordinate_size=self.coordinate_size    , shape_size=self.shape_size    , input_size=self.image_size    , patch_size=self.patch_size    , )

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






     def    lowerCamelCase__   (		self     :    Dict    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Any    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    List[Any] ):
           lowerCAmelCase    :		List[Any]				=      TFLayoutLMvaModel(config=UpperCamelCase_ )

           # text + image
           lowerCAmelCase    :		List[Any]				=      model(UpperCamelCase_    , pixel_values=UpperCamelCase_    , training=UpperCamelCase_ )
           lowerCAmelCase    :		str				=      model(
               UpperCamelCase_    , bbox=UpperCamelCase_    , pixel_values=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , training=UpperCamelCase_    , )
           lowerCAmelCase    :		Dict				=      model(UpperCamelCase_    , bbox=UpperCamelCase_    , pixel_values=UpperCamelCase_    , training=UpperCamelCase_ )

           self.parent.assertEqual(result.last_hidden_state.shape    , (self.batch_size, self.seq_length, self.hidden_size) )

           # text only
           lowerCAmelCase    :		Optional[int]				=      model(UpperCamelCase_    , training=UpperCamelCase_ )
           self.parent.assertEqual(
               result.last_hidden_state.shape    , (self.batch_size, self.text_seq_length, self.hidden_size) )

           # image only
           lowerCAmelCase    :		int				=      model({'''pixel_values''': pixel_values}    , training=UpperCamelCase_ )
           self.parent.assertEqual(
               result.last_hidden_state.shape    , (self.batch_size, self.image_seq_length, self.hidden_size) )






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    str    , UpperCamelCase_     :    Optional[int] ):
           lowerCAmelCase    :		int				=      self.num_labels
           lowerCAmelCase    :		str				=      TFLayoutLMvaForSequenceClassification(config=UpperCamelCase_ )
           lowerCAmelCase    :		Dict				=      model(
               UpperCamelCase_    , bbox=UpperCamelCase_    , pixel_values=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , labels=UpperCamelCase_    , training=UpperCamelCase_    , )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.num_labels) )






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    List[str]    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    int    , UpperCamelCase_     :    int    , UpperCamelCase_     :    Union[str, Any] ):
           lowerCAmelCase    :		Union[str, Any]				=      self.num_labels
           lowerCAmelCase    :		Tuple				=      TFLayoutLMvaForTokenClassification(config=UpperCamelCase_ )
           lowerCAmelCase    :		List[Any]				=      model(
               UpperCamelCase_    , bbox=UpperCamelCase_    , pixel_values=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , labels=UpperCamelCase_    , training=UpperCamelCase_    , )
           self.parent.assertEqual(result.logits.shape    , (self.batch_size, self.text_seq_length, self.num_labels) )






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    int    , UpperCamelCase_     :    str    , UpperCamelCase_     :    int    , UpperCamelCase_     :    Union[str, Any]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    Optional[Any] ):
           lowerCAmelCase    :		Optional[int]				=      2
           lowerCAmelCase    :		int				=      TFLayoutLMvaForQuestionAnswering(config=UpperCamelCase_ )
           lowerCAmelCase    :		Tuple				=      model(
               UpperCamelCase_    , bbox=UpperCamelCase_    , pixel_values=UpperCamelCase_    , attention_mask=UpperCamelCase_    , token_type_ids=UpperCamelCase_    , start_positions=UpperCamelCase_    , end_positions=UpperCamelCase_    , training=UpperCamelCase_    , )
           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    lowerCamelCase__   (		self     :    Any ):
           lowerCAmelCase    :		Union[str, Any]				=      self.prepare_config_and_inputs()
           ((lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase),					(lowerCAmelCase))    :		Optional[Any]				=      config_and_inputs
           lowerCAmelCase    :		List[str]				=      {
               '''input_ids''': input_ids,
               '''bbox''': bbox,
               '''pixel_values''': pixel_values,
               '''token_type_ids''': token_type_ids,
               '''attention_mask''': input_mask,
           }
           return config, inputs_dict



@require_tf
class 						snake_case_(				a__							,    a__							,    unittest.TestCase	):
     __UpperCamelCase     =    (
         (
             TFLayoutLMvaModel,
             TFLayoutLMvaForQuestionAnswering,
             TFLayoutLMvaForSequenceClassification,
             TFLayoutLMvaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     __UpperCamelCase     =    (
         {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
         if is_tf_available()
         else {}
     )

     __UpperCamelCase     =    False
     __UpperCamelCase     =    False
     __UpperCamelCase     =    False






     def    lowerCamelCase__   (		self     :    int    , UpperCamelCase_     :    int    , UpperCamelCase_     :    Tuple    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    List[str] ):
           return True






     def    lowerCamelCase__   (		self     :    List[Any]    , UpperCamelCase_     :    Dict    , UpperCamelCase_     :    int    , UpperCamelCase_     :    Optional[Any]=False ):
           lowerCAmelCase    :		List[Any]				=      copy.deepcopy(UpperCamelCase_ )

           if model_class in get_values(UpperCamelCase_ ):
                 lowerCAmelCase    :		List[str]				=      {
                     k: tf.tile(tf.expand_dims(UpperCamelCase_    , 1 )    , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
                     if isinstance(UpperCamelCase_    , tf.Tensor ) and v.ndim > 0
                     else v
                     for k, v in inputs_dict.items()
                 }

           if return_labels:
                 if model_class in get_values(UpperCamelCase_ ):
                       lowerCAmelCase    :		List[Any]				=      tf.ones(self.model_tester.batch_size    , dtype=tf.intaa )
                 elif model_class in get_values(UpperCamelCase_ ):
                       lowerCAmelCase    :		Optional[int]				=      tf.zeros(self.model_tester.batch_size    , dtype=tf.intaa )
                       lowerCAmelCase    :		Union[str, Any]				=      tf.zeros(self.model_tester.batch_size    , dtype=tf.intaa )
                 elif model_class in get_values(UpperCamelCase_ ):
                       lowerCAmelCase    :		List[Any]				=      tf.zeros(self.model_tester.batch_size    , dtype=tf.intaa )
                 elif model_class in get_values(UpperCamelCase_ ):
                       lowerCAmelCase    :		Optional[Any]				=      tf.zeros(
                           (self.model_tester.batch_size, self.model_tester.text_seq_length)    , dtype=tf.intaa )

           return inputs_dict






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Any				=      TFLayoutLMvaModelTester(self )
           lowerCAmelCase    :		Tuple				=      ConfigTester(self    , config_class=UpperCamelCase_    , hidden_size=3_7 )






     def    lowerCamelCase__   (		self     :    Any ):
           self.config_tester.run_common_tests()






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 lowerCAmelCase    :		int				=      model_class(UpperCamelCase_ )
                 if getattr(UpperCamelCase_    , '''hf_compute_loss'''    , UpperCamelCase_ ):
                       # The number of elements in the loss should be the same as the number of elements in the label
                       lowerCAmelCase    :		List[Any]				=      self._prepare_for_class(inputs_dict.copy()    , UpperCamelCase_    , return_labels=UpperCamelCase_ )
                       lowerCAmelCase    :		Union[str, Any]				=      prepared_for_class[
                           sorted(prepared_for_class.keys() - inputs_dict.keys()    , reverse=UpperCamelCase_ )[0]
                       ]
                       lowerCAmelCase    :		int				=      added_label.shape.as_list()[:1]

                       # Test that model correctly compute the loss with kwargs
                       lowerCAmelCase    :		Dict				=      self._prepare_for_class(inputs_dict.copy()    , UpperCamelCase_    , return_labels=UpperCamelCase_ )
                       lowerCAmelCase    :		List[str]				=      prepared_for_class.pop('''input_ids''' )

                       lowerCAmelCase    :		Optional[Any]				=      model(UpperCamelCase_    , **UpperCamelCase_ )[0]
                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

                       # Test that model correctly compute the loss when we mask some positions
                       lowerCAmelCase    :		Optional[int]				=      self._prepare_for_class(inputs_dict.copy()    , UpperCamelCase_    , return_labels=UpperCamelCase_ )
                       lowerCAmelCase    :		Union[str, Any]				=      prepared_for_class.pop('''input_ids''' )
                       if "labels" in prepared_for_class:
                             lowerCAmelCase    :		List[Any]				=      prepared_for_class['''labels'''].numpy()
                             if len(labels.shape ) > 1 and labels.shape[1] != 1:
                                   lowerCAmelCase    :		List[str]				=      -1_0_0
                                   lowerCAmelCase    :		Tuple				=      tf.convert_to_tensor(UpperCamelCase_ )
                                   lowerCAmelCase    :		Optional[Any]				=      model(UpperCamelCase_    , **UpperCamelCase_ )[0]
                                   self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
                                   self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )

                # Test that model correctly compute the loss with a dict
                       lowerCAmelCase    :		Optional[int]				=      self._prepare_for_class(inputs_dict.copy()    , UpperCamelCase_    , return_labels=UpperCamelCase_ )
                       lowerCAmelCase    :		str				=      model(UpperCamelCase_ )[0]
                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )

                       # Test that model correctly compute the loss with a tuple
                       lowerCAmelCase    :		List[Any]				=      self._prepare_for_class(inputs_dict.copy()    , UpperCamelCase_    , return_labels=UpperCamelCase_ )

                       # Get keys that were added with the _prepare_for_class function
                       lowerCAmelCase    :		List[str]				=      prepared_for_class.keys() - inputs_dict.keys()
                       lowerCAmelCase    :		Dict				=      inspect.signature(model.call ).parameters
                       lowerCAmelCase    :		Optional[int]				=      list(signature.keys() )

                       # Create a dictionary holding the location of the tensors in the tuple
                       lowerCAmelCase    :		List[str]				=      {0: '''input_ids'''}
                       for label_key in label_keys:
                             lowerCAmelCase    :		str				=      signature_names.index(UpperCamelCase_ )
                             lowerCAmelCase    :		Any				=      label_key
                       lowerCAmelCase    :		Union[str, Any]				=      sorted(tuple_index_mapping.items() )
                       # Initialize a list with their default values, update the values and convert to a tuple
                       lowerCAmelCase    :		List[Any]				=      []

                       for name in signature_names:
                             if name != "kwargs":
                                   list_input.append(signature[name].default )

                       for index, value in sorted_tuple_index_mapping:
                             lowerCAmelCase    :		Any				=      prepared_for_class[value]

                       lowerCAmelCase    :		Optional[Any]				=      tuple(UpperCamelCase_ )

                       # Send to model
                       lowerCAmelCase    :		Dict				=      model(tuple_input[:-1] )[0]

                       self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )






     def    lowerCamelCase__   (		self     :    Union[str, Any] ):
           (
               (
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					
           )    :		Union[str, Any]				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[str] ):
           (
               (
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					
           )    :		int				=      self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 lowerCAmelCase    :		List[Any]				=      type
                 self.model_tester.create_and_check_model(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           (
               (
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					
           )    :		Any				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    List[str] ):
           (
               (
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					
           )    :		int				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     def    lowerCamelCase__   (		self     :    int ):
           (
               (
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					(
               lowerCAmelCase
           ),					
           )    :		str				=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(
               UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )






     @slow
     def    lowerCamelCase__   (		self     :    str ):
           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCAmelCase    :		Tuple				=      TFLayoutLMvaModel.from_pretrained(UpperCamelCase_ )
                 self.assertIsNotNone(UpperCamelCase_ )



def    _snake_case  (  ):
      lowerCAmelCase    :		Optional[int]				=      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
      return image






@require_tf
class 						snake_case_(				unittest.TestCase	):






     @cached_property
     def    lowerCamelCase__   (		self     :    Tuple ):
           return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase_ ) if is_vision_available() else None






     @slow
     def    lowerCamelCase__   (		self     :    List[Any] ):
           lowerCAmelCase    :		Any				=      TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''' )

           lowerCAmelCase    :		Optional[Any]				=      self.default_image_processor
           lowerCAmelCase    :		Any				=      prepare_img()
           lowerCAmelCase    :		Optional[Any]				=      image_processor(images=UpperCamelCase_    , return_tensors='''tf''' ).pixel_values

           lowerCAmelCase    :		List[Any]				=      tf.constant([[1, 2]] )
           lowerCAmelCase    :		Optional[Any]				=      tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] )    , axis=0 )

           # forward pass
           lowerCAmelCase    :		str				=      model(input_ids=UpperCamelCase_    , bbox=UpperCamelCase_    , pixel_values=UpperCamelCase_    , training=UpperCamelCase_ )

           # verify the logits
           lowerCAmelCase    :		Any				=      (1, 1_9_9, 7_6_8)
           self.assertEqual(outputs.last_hidden_state.shape    , UpperCamelCase_ )

           lowerCAmelCase    :		Tuple				=      tf.constant(
               [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]] )

           self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]    , UpperCamelCase_    , atol=1E-4 ) )






"""simple docstring"""





import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


snake_case__						:   List[Any]			 = '''bart'''
snake_case__						:   Union[str, Any]			 = True


@st.cache(allow_output_mutation=_snake_case	)
def    _snake_case  (  ):
      if LOAD_DENSE_INDEX:
            lowerCAmelCase    :		Dict				=      AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''	)
            lowerCAmelCase    :		List[str]				=      AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''	).to('''cuda:0'''	)
            lowerCAmelCase    :		Optional[int]				=      qar_model.eval()
      else:
            lowerCAmelCase,					lowerCAmelCase    :		int				=      (None, None)
      if MODEL_TYPE == "bart":
            lowerCAmelCase    :		Tuple				=      AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''	)
            lowerCAmelCase    :		Tuple				=      AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''	).to('''cuda:0'''	)
            lowerCAmelCase    :		Optional[Any]				=      torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''	)
            sas_model.load_state_dict(save_dict['''model''']	)
            lowerCAmelCase    :		Any				=      sas_model.eval()
      else:
            lowerCAmelCase,					lowerCAmelCase    :		Any				=      make_qa_sas_model(
                model_name='''t5-small'''       ,      from_file='''seq2seq_models/eli5_t5_model_1024_4.pth'''       ,      device='''cuda:0'''	)
      return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)


@st.cache(allow_output_mutation=_snake_case	)
def    _snake_case  (  ):
      if LOAD_DENSE_INDEX:
            lowerCAmelCase    :		List[str]				=      faiss.StandardGpuResources()
            lowerCAmelCase    :		Optional[Any]				=      datasets.load_dataset(path='''wiki_snippets'''       ,      name='''wiki40b_en_100_0'''	)['''train''']
            lowerCAmelCase    :		List[Any]				=      np.memmap(
                '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'''       ,      dtype='''float32'''       ,      mode='''r'''       ,      shape=(wikiaab_passages.num_rows, 128)       ,      )
            lowerCAmelCase    :		Union[str, Any]				=      faiss.IndexFlatIP(128	)
            lowerCAmelCase    :		int				=      faiss.index_cpu_to_gpu(_snake_case       ,      1       ,      _snake_case	)
            wikiaab_gpu_index_flat.add(_snake_case	)  # TODO fix for larger GPU
      else:
            lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      (None, None)
      lowerCAmelCase    :		int				=      Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]	)
      return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


@st.cache(allow_output_mutation=_snake_case	)
def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datasets.load_dataset('''eli5'''       ,      name='''LFQA_reddit'''	)
      lowerCAmelCase    :		Any				=      elia['''train_eli5''']
      lowerCAmelCase    :		int				=      np.memmap(
          '''eli5_questions_reps.dat'''       ,      dtype='''float32'''       ,      mode='''r'''       ,      shape=(elia_train.num_rows, 128)	)
      lowerCAmelCase    :		Tuple				=      faiss.IndexFlatIP(128	)
      eli5_train_q_index.add(_snake_case	)
      return (elia_train, eli5_train_q_index)


snake_case__ ,   snake_case__ ,   snake_case__						:   Optional[Any]			 = load_indexes()
snake_case__ ,   snake_case__ ,   snake_case__ ,   snake_case__						:   str			 = load_models()
snake_case__ ,   snake_case__						:   Union[str, Any]			 = load_train_data()


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Dict=10	):
      lowerCAmelCase    :		Tuple				=      embed_questions_for_retrieval([question]       ,      _snake_case       ,      _snake_case	)
      lowerCAmelCase,					lowerCAmelCase    :		Any				=      eli5_train_q_index.search(_snake_case       ,      _snake_case	)
      lowerCAmelCase    :		str				=      [elia_train[int(_snake_case	)] for i in I[0]]
      return nn_examples


def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	str="wiki40b"       ,      _snake_case :	List[str]="dense"       ,      _snake_case :	Union[str, Any]=10	):
      if source == "none":
            lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      (''' <P> '''.join(['''''' for _ in range(11	)]	).strip(), [])
      else:
            if method == "dense":
                  lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      query_qa_dense_index(
                      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case       ,      _snake_case	)
            else:
                  lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      query_es_index(
                      _snake_case       ,      _snake_case       ,      index_name='''english_wiki40b_snippets_100w'''       ,      n_results=_snake_case       ,      )
      lowerCAmelCase    :		int				=      [
          (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
      ]
      lowerCAmelCase    :		Any				=      '''question: {} context: {}'''.format(_snake_case       ,      _snake_case	)
      return question_doc, support_list


@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _snake_case	: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _snake_case	: None),
    }	)
def    _snake_case  (  _snake_case :	str       ,      _snake_case :	Dict       ,      _snake_case :	Dict       ,      _snake_case :	List[Any]=64       ,      _snake_case :	int=256       ,      _snake_case :	List[str]=False       ,      _snake_case :	Any=2       ,      _snake_case :	List[Any]=0.95       ,      _snake_case :	Tuple=0.8	):
      with torch.no_grad():
            lowerCAmelCase    :		Union[str, Any]				=      qa_sas_generate(
                _snake_case       ,      _snake_case       ,      _snake_case       ,      num_answers=1       ,      num_beams=_snake_case       ,      min_len=_snake_case       ,      max_len=_snake_case       ,      do_sample=_snake_case       ,      temp=_snake_case       ,      top_p=_snake_case       ,      top_k=_snake_case       ,      max_input_length=1024       ,      device='''cuda:0'''       ,      )[0]
      return (answer, support_list)


st.title('''Long Form Question Answering with ELI5''')

# Start sidebar
snake_case__						:   Dict			 = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
snake_case__						:   Tuple			 = '''
<html>
  <head>
    <style>
      .img-container {
        padding-left: 90px;
        padding-right: 90px;
        padding-top: 50px;
        padding-bottom: 50px;
        background-color: #f0f3f9;
      }
    </style>
  </head>
  <body>
    <span class="img-container"> <!-- Inline parent element -->
      %s
    </span>
  </body>
</html>
''' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
snake_case__						:   List[Any]			 = '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)

snake_case__						:   str			 = [
    '''Answer the question''',
    '''View the retrieved document only''',
    '''View the most similar ELI5 question and answer''',
    '''Show me everything, please!''',
]
snake_case__						:   List[Any]			 = st.sidebar.checkbox('''Demo options''')
if demo_options:
 snake_case__						:   Tuple			 = st.sidebar.selectbox(
     '''''',
     action_list,
     index=3,
 )
 snake_case__						:   List[Any]			 = action_list.index(action_st)
 snake_case__						:   List[str]			 = st.sidebar.selectbox(
     '''''',
     ['''Show full text of passages''', '''Show passage section titles'''],
     index=0,
 )
 snake_case__						:   List[Any]			 = show_type == '''Show full text of passages'''
else:
 snake_case__						:   Tuple			 = 3
 snake_case__						:   List[Any]			 = True

snake_case__						:   List[str]			 = st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
 snake_case__						:   str			 = '''
    ### Information retriever options

    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
    '''
 st.sidebar.markdown(retriever_info)
 snake_case__						:   Union[str, Any]			 = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
 snake_case__						:   Union[str, Any]			 = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
 snake_case__						:   List[Any]			 = '''wiki40b'''
 snake_case__						:   Union[str, Any]			 = '''dense'''

snake_case__						:   int			 = '''beam'''
snake_case__						:   str			 = 2
snake_case__						:   Dict			 = 64
snake_case__						:   List[str]			 = 256
snake_case__						:   Dict			 = None
snake_case__						:   List[str]			 = None
snake_case__						:   List[str]			 = st.sidebar.checkbox('''Generation options''')
if generate_options:
 snake_case__						:   List[Any]			 = '''
    ### Answer generation options

    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
    **beam** search, or **sample** from the decoder\'s output probabilities.
    '''
 st.sidebar.markdown(generate_info)
 snake_case__						:   List[str]			 = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
 snake_case__						:   List[str]			 = st.sidebar.slider(
     '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 snake_case__						:   Optional[Any]			 = st.sidebar.slider(
     '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
 )
 if sampled == "beam":
  snake_case__						:   Dict			 = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  snake_case__						:   int			 = st.sidebar.slider(
      '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
  )
  snake_case__						:   int			 = st.sidebar.slider(
      '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
  )
  snake_case__						:   List[str]			 = None

# start main text
snake_case__						:   str			 = [
    '''<MY QUESTION>''',
    '''How do people make chocolate?''',
    '''Why do we get a fever when we are sick?''',
    '''How can different animals perceive different colors?''',
    '''What is natural language processing?''',
    '''What\'s the best way to treat a sunburn?''',
    '''What exactly are vitamins ?''',
    '''How does nuclear energy provide electricity?''',
    '''What\'s the difference between viruses and bacteria?''',
    '''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
    '''Why do people like drinking coffee even though it tastes so bad?''',
    '''What happens when wine ages? How does it make the wine taste better?''',
    '''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
    '''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
    '''How does New Zealand have so many large bird predators?''',
]
snake_case__						:   Union[str, Any]			 = st.selectbox(
    '''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
 snake_case__						:   Optional[Any]			 = st.text_input('''Enter your question here:''', '''''')
else:
 snake_case__						:   int			 = question_s

if st.button('''Show me!'''):
 if action in [0, 1, 3]:
  if index_type == "mixed":
   snake_case__ ,   snake_case__						:   str			 = make_support(question, source=wiki_source, method='''dense''', n_results=10)
   snake_case__ ,   snake_case__						:   Tuple			 = make_support(question, source=wiki_source, method='''sparse''', n_results=10)
   snake_case__						:   int			 = []
   for res_d, res_s in zip(support_list_dense, support_list_sparse):
    if tuple(res_d) not in support_list:
     support_list += [tuple(res_d)]
    if tuple(res_s) not in support_list:
     support_list += [tuple(res_s)]
   snake_case__						:   List[str]			 = support_list[:10]
   snake_case__						:   int			 = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
  else:
   snake_case__ ,   snake_case__						:   Union[str, Any]			 = make_support(question, source=wiki_source, method=index_type, n_results=10)
 if action in [0, 3]:
  snake_case__ ,   snake_case__						:   List[str]			 = answer_question(
      question_doc,
      sas_model,
      sas_tokenizer,
      min_len=min_len,
      max_len=int(max_len),
      sampling=(sampled == '''sampled'''),
      n_beams=n_beams,
      top_p=top_p,
      temp=temp,
  )
  st.markdown('''### The model generated answer is:''')
  st.write(answer)
 if action in [0, 1, 3] and wiki_source != "none":
  st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
  for i, res in enumerate(support_list):
   snake_case__						:   int			 = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
   snake_case__						:   List[Any]			 = res[1].strip()
   if sec_titles == "":
    snake_case__						:   Tuple			 = '''[{}]({})'''.format(res[0], wiki_url)
   else:
    snake_case__						:   Optional[int]			 = sec_titles.split(''' & ''')
    snake_case__						:   Optional[Any]			 = ''' & '''.join(
        ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
    )
   st.markdown(
       '''{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'''.format(i + 1, res[0], sections),
       unsafe_allow_html=True,
   )
   if show_passages:
    st.write(
        '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
    )
 if action in [2, 3]:
  snake_case__						:   int			 = find_nearest_training(question)
  snake_case__						:   List[Any]			 = nn_train_list[0]
  st.markdown(
      '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
  )
  snake_case__						:   Dict			 = [
      '''{}. {}'''.format(i + 1, '''  \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
      for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
      if i == 0 or sc > 2
  ]
  st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))


snake_case__						:   Any			 = '''
---

**Disclaimer**

*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''






st.sidebar.markdown(disclaimer, unsafe_allow_html=True)


