




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 		UpperCamelCase__	:

    '''simple docstring'''



    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__=2     ,  UpperCamelCase__=3     ,  UpperCamelCase__=4     ,  UpperCamelCase__=2     ,  UpperCamelCase__=7     ,  UpperCamelCase__=True     ,  UpperCamelCase__=True     ,  UpperCamelCase__=True     ,  UpperCamelCase__=True     ,  UpperCamelCase__=99     ,  UpperCamelCase__=36     ,  UpperCamelCase__=2     ,  UpperCamelCase__=4     ,  UpperCamelCase__=37     ,  UpperCamelCase__="gelu"     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=0.1     ,  UpperCamelCase__=512     ,  UpperCamelCase__=16     ,  UpperCamelCase__=2     ,  UpperCamelCase__=0.02     ,  UpperCamelCase__=6     ,  UpperCamelCase__=6     ,  UpperCamelCase__=3     ,  UpperCamelCase__=4     ,  UpperCamelCase__=None     ,  UpperCamelCase__=1000     ,  )     ->      str:
           lowerCamelCase   :      int			 =					parent
           lowerCamelCase   :      Optional[Any]			 =					batch_size
           lowerCamelCase   :      Union[str, Any]			 =					num_channels
           lowerCamelCase   :      Dict			 =					image_size
           lowerCamelCase   :      Union[str, Any]			 =					patch_size
           lowerCamelCase   :      Dict			 =					is_training
           lowerCamelCase   :      List[str]			 =					use_input_mask
           lowerCamelCase   :      int			 =					use_token_type_ids
           lowerCamelCase   :      Dict			 =					use_labels
           lowerCamelCase   :      Union[str, Any]			 =					vocab_size
           lowerCamelCase   :      List[Any]			 =					hidden_size
           lowerCamelCase   :      Optional[int]			 =					num_hidden_layers
           lowerCamelCase   :      List[str]			 =					num_attention_heads
           lowerCamelCase   :      int			 =					intermediate_size
           lowerCamelCase   :      List[str]			 =					hidden_act
           lowerCamelCase   :      Tuple			 =					hidden_dropout_prob
           lowerCamelCase   :      Optional[int]			 =					attention_probs_dropout_prob
           lowerCamelCase   :      List[Any]			 =					max_position_embeddings
           lowerCamelCase   :      Dict			 =					type_vocab_size
           lowerCamelCase   :      str			 =					type_sequence_label_size
           lowerCamelCase   :      Tuple			 =					initializer_range
           lowerCamelCase   :      Dict			 =					coordinate_size
           lowerCamelCase   :      Tuple			 =					shape_size
           lowerCamelCase   :      List[Any]			 =					num_labels
           lowerCamelCase   :      Tuple			 =					num_choices
           lowerCamelCase   :      int			 =					scope
           lowerCamelCase   :      Optional[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   :      List[Any]			 =					(image_size // patch_size) ** 2 + 1
           lowerCamelCase   :      Union[str, Any]			 =					self.text_seq_length + self.image_seq_length







    def     _lowercase     (    self     )     ->      int:
           lowerCamelCase   :      Optional[int]			 =					ids_tensor([self.batch_size, self.text_seq_length]     ,  self.vocab_size     )

           lowerCamelCase   :      Dict			 =					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   :      Dict			 =					bbox[i, j, 3]
                                lowerCamelCase   :      Optional[int]			 =					bbox[i, j, 1]
                                lowerCamelCase   :      Any			 =					tmp_coordinate
                         if bbox[i, j, 2] < bbox[i, j, 0]:
                                lowerCamelCase   :      List[str]			 =					bbox[i, j, 2]
                                lowerCamelCase   :      Optional[int]			 =					bbox[i, j, 0]
                                lowerCamelCase   :      Tuple			 =					tmp_coordinate
           lowerCamelCase   :      Union[str, Any]			 =					tf.constant(UpperCamelCase__     )

           lowerCamelCase   :      Any			 =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

           lowerCamelCase   :      Optional[int]			 =					None
           if self.use_token_type_ids:
                  lowerCamelCase   :      List[Any]			 =					ids_tensor([self.batch_size, self.text_seq_length]     ,  self.type_vocab_size     )

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

           lowerCamelCase   :      Any			 =					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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[int]:
           lowerCamelCase   :      Optional[int]			 =					TFLayoutLMvaModel(config=UpperCamelCase__     )

           # text + image
           lowerCamelCase   :      Any			 =					model(UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  training=UpperCamelCase__     )
           lowerCamelCase   :      Optional[int]			 =					model(
               UpperCamelCase__     ,  bbox=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  attention_mask=UpperCamelCase__     ,  token_type_ids=UpperCamelCase__     ,  training=UpperCamelCase__     ,  )
           lowerCamelCase   :      Any			 =					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   :      Dict			 =					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   :      Union[str, Any]			 =					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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[Any]:
           lowerCamelCase   :      str			 =					self.num_labels
           lowerCamelCase   :      Optional[Any]			 =					TFLayoutLMvaForSequenceClassification(config=UpperCamelCase__     )
           lowerCamelCase   :      str			 =					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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[str]:
           lowerCamelCase   :      int			 =					self.num_labels
           lowerCamelCase   :      str			 =					TFLayoutLMvaForTokenClassification(config=UpperCamelCase__     )
           lowerCamelCase   :      Tuple			 =					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     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      Optional[Any]:
           lowerCamelCase   :      Union[str, Any]			 =					2
           lowerCamelCase   :      Dict			 =					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     _lowercase     (    self     )     ->      str:
           lowerCamelCase   :      List[Any]			 =					self.prepare_config_and_inputs()
           ((lowerCamelCase) ,      (lowerCamelCase) ,      (lowerCamelCase) ,      (lowerCamelCase) ,      (lowerCamelCase) ,      (lowerCamelCase) ,      (lowerCamelCase) ,      (lowerCamelCase))   :      List[str]			 =					config_and_inputs
           lowerCamelCase   :      Dict			 =					{
               "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 		UpperCamelCase__	(lowerCAmelCase__    ,				lowerCAmelCase__    ,				unittest.TestCase     ):

    '''simple docstring'''



    lowerCamelCase_      :	Union[str, Any]	     =      (
        (
            TFLayoutLMvaModel,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    lowerCamelCase_      :	Dict	     =      (
        {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
        if is_tf_available()
        else {}
    )

    lowerCamelCase_      :	int	     =      False
    lowerCamelCase_      :	int	     =      False
    lowerCamelCase_      :	str	     =      False







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )     ->      List[Any]:
           return True







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__=False     )     ->      dict:
           lowerCamelCase   :      Optional[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   :      Dict			 =					tf.ones(self.model_tester.batch_size     ,  dtype=tf.intaa     )
                  elif model_class in get_values(UpperCamelCase__     ):
                         lowerCamelCase   :      Dict			 =					tf.zeros(self.model_tester.batch_size     ,  dtype=tf.intaa     )
                         lowerCamelCase   :      Optional[Any]			 =					tf.zeros(self.model_tester.batch_size     ,  dtype=tf.intaa     )
                  elif model_class in get_values(UpperCamelCase__     ):
                         lowerCamelCase   :      Dict			 =					tf.zeros(self.model_tester.batch_size     ,  dtype=tf.intaa     )
                  elif model_class in get_values(UpperCamelCase__     ):
                         lowerCamelCase   :      Union[str, Any]			 =					tf.zeros(
                             (self.model_tester.batch_size, self.model_tester.text_seq_length)     ,  dtype=tf.intaa     )

           return inputs_dict







    def     _lowercase     (    self     )     ->      Optional[Any]:
           lowerCamelCase   :      Dict			 =					TFLayoutLMvaModelTester(self     )
           lowerCamelCase   :      List[Any]			 =					ConfigTester(self     ,  config_class=UpperCamelCase__     ,  hidden_size=37     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           self.config_tester.run_common_tests()







    def     _lowercase     (    self     )     ->      Optional[int]:
           lowerCamelCase ,      lowerCamelCase   :      Dict			 =					self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                  lowerCamelCase   :      List[Any]			 =					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   :      Optional[int]			 =					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   :      Optional[Any]			 =					added_label.shape.as_list()[:1]

                         # Test that model correctly compute the loss with kwargs
                         lowerCamelCase   :      int			 =					self._prepare_for_class(inputs_dict.copy()     ,  UpperCamelCase__     ,  return_labels=UpperCamelCase__     )
                         lowerCamelCase   :      Dict			 =					prepared_for_class.pop("input_ids"     )

                         lowerCamelCase   :      Optional[int]			 =					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   :      List[Any]			 =					self._prepare_for_class(inputs_dict.copy()     ,  UpperCamelCase__     ,  return_labels=UpperCamelCase__     )
                         lowerCamelCase   :      Dict			 =					prepared_for_class.pop("input_ids"     )
                         if "labels" in prepared_for_class:
                                lowerCamelCase   :      List[str]			 =					prepared_for_class["labels"].numpy()
                                if len(labels.shape     ) > 1 and labels.shape[1] != 1:
                                       lowerCamelCase   :      Optional[int]			 =					-100
                                       lowerCamelCase   :      Any			 =					tf.convert_to_tensor(UpperCamelCase__     )
                                       lowerCamelCase   :      Dict			 =					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   :      str			 =					self._prepare_for_class(inputs_dict.copy()     ,  UpperCamelCase__     ,  return_labels=UpperCamelCase__     )
                         lowerCamelCase   :      Optional[int]			 =					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   :      Optional[Any]			 =					self._prepare_for_class(inputs_dict.copy()     ,  UpperCamelCase__     ,  return_labels=UpperCamelCase__     )

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

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

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

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

                         lowerCamelCase   :      Any			 =					tuple(UpperCamelCase__     )

                         # Send to model
                         lowerCamelCase   :      List[Any]			 =					model(tuple_input[:-1]     )[0]

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







    def     _lowercase     (    self     )     ->      Optional[Any]:
           (
               (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      
           )   :      Optional[Any]			 =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      List[str]:
           (
               (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      
           )   :      List[str]			 =					self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                  lowerCamelCase   :      List[str]			 =					type
                  self.model_tester.create_and_check_model(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Optional[int]:
           (
               (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      
           )   :      Tuple			 =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Any:
           (
               (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      
           )   :      str			 =					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     )







    def     _lowercase     (    self     )     ->      Union[str, Any]:
           (
               (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      (
               lowerCamelCase
           ) ,      
           )   :      Optional[int]			 =					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     _lowercase     (    self     )     ->      Optional[int]:
           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCamelCase   :      Dict			 =					TFLayoutLMvaModel.from_pretrained(UpperCamelCase__     )
                  self.assertIsNotNone(UpperCamelCase__     )




def      A      (      )	-> int:
       lowerCamelCase   :      Optional[Any]			 =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
       return image




@require_tf
class 		UpperCamelCase__	(unittest.TestCase     ):

    '''simple docstring'''



    @cached_property
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           return LayoutLMvaImageProcessor(apply_ocr=UpperCamelCase__     ) if is_vision_available() else None







    @slow
    def     _lowercase     (    self     )     ->      Union[str, Any]:
           lowerCamelCase   :      Union[str, Any]			 =					TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"     )

           lowerCamelCase   :      Any			 =					self.default_image_processor
           lowerCamelCase   :      Optional[Any]			 =					prepare_img()
           lowerCamelCase   :      Dict			 =					image_processor(images=UpperCamelCase__     ,  return_tensors="tf"     ).pixel_values

           lowerCamelCase   :      Union[str, Any]			 =					tf.constant([[1, 2]]     )
           lowerCamelCase   :      Dict			 =					tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]     )     ,  axis=0     )

           # forward pass
           lowerCamelCase   :      List[Any]			 =					model(input_ids=UpperCamelCase__     ,  bbox=UpperCamelCase__     ,  pixel_values=UpperCamelCase__     ,  training=UpperCamelCase__     )

           # verify the logits
           lowerCamelCase   :      Dict			 =					(1, 199, 768)
           self.assertEqual(outputs.last_hidden_state.shape     ,  UpperCamelCase__     )

           lowerCamelCase   :      Dict			 =					tf.constant(
               [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]     )

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











'''simple docstring'''





import string
from math import logaa





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    List[str]    						=      document.translate(
          str.maketrans(""""""     ,   """"""     ,   string.punctuation		)		).replace("""\n"""     ,   """"""		)
      snake_case__  :    List[str]    						=      document_without_punctuation.split(""" """		)  # word tokenization
      return len([word for word in tokenize_document if word.lower() == term.lower()]		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> tuple[int, int]:
      snake_case__  :    Dict    						=      corpus.lower().translate(
          str.maketrans(""""""     ,   """"""     ,   string.punctuation		)		)  # strip all punctuation and replace it with ''
      snake_case__  :    Any    						=      corpus_without_punctuation.split("""\n"""		)
      snake_case__  :    int    						=      term.lower()
      return (len([doc for doc in docs if term in doc]		), len(_lowerCAmelCase		))





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> float:
      if smoothing:
            if n == 0:
                  raise ValueError("""log10(0) is undefined."""		)
            return round(1 + logaa(n / (1 + df)		)     ,   3		)

      if df == 0:
            raise ZeroDivisionError("""df must be > 0"""		)
      elif n == 0:
            raise ValueError("""log10(0) is undefined."""		)
      return round(logaa(n / df		)     ,   3		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> float:
      return round(tf * idf     ,   3		)
