




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       a__	:







     def __init__(     self  ,		A  ,		A=2  ,		A=3  ,		A=4  ,		A=2  ,		A=7  ,		A=True  ,		A=True  ,		A=True  ,		A=True  ,		A=99  ,		A=36  ,		A=2  ,		A=4  ,		A=37  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=512  ,		A=16  ,		A=2  ,		A=0.0_2  ,		A=6  ,		A=6  ,		A=3  ,		A=4  ,		A=None  ,		A=1000  ,		)  ->   str:



          '''simple docstring'''



          a     =				parent
          a     =				batch_size
          a     =				num_channels
          a     =				image_size
          a     =				patch_size
          a     =				is_training
          a     =				use_input_mask
          a     =				use_token_type_ids
          a     =				use_labels
          a     =				vocab_size
          a     =				hidden_size
          a     =				num_hidden_layers
          a     =				num_attention_heads
          a     =				intermediate_size
          a     =				hidden_act
          a     =				hidden_dropout_prob
          a     =				attention_probs_dropout_prob
          a     =				max_position_embeddings
          a     =				type_vocab_size
          a     =				type_sequence_label_size
          a     =				initializer_range
          a     =				coordinate_size
          a     =				shape_size
          a     =				num_labels
          a     =				num_choices
          a     =				scope
          a     =				range_bbox

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







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				ids_tensor([self.batch_size, self.text_seq_length]  ,		self.vocab_size			)

          a     =				ids_tensor([self.batch_size, self.text_seq_length, 4]  ,		self.range_bbox			)
          a     =				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]:
                         a     =				bbox[i, j, 3]
                         a     =				bbox[i, j, 1]
                         a     =				tmp_coordinate
                    if bbox[i, j, 2] < bbox[i, j, 0]:
                         a     =				bbox[i, j, 2]
                         a     =				bbox[i, j, 0]
                         a     =				tmp_coordinate
          a     =				tf.constant(snake_case__			)

          a     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

          a     =				None
          if self.use_input_mask:
               a     =				random_attention_mask([self.batch_size, self.text_seq_length]			)

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

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

          a     =				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  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				TFLayoutLMvaModel(config=snake_case__			)

          # text + image
          a     =				model(snake_case__  ,		pixel_values=snake_case__  ,		training=snake_case__			)
          a     =				model(
              snake_case__  ,		bbox=snake_case__  ,		pixel_values=snake_case__  ,		attention_mask=snake_case__  ,		token_type_ids=snake_case__  ,		training=snake_case__  ,		)
          a     =				model(snake_case__  ,		bbox=snake_case__  ,		pixel_values=snake_case__  ,		training=snake_case__			)

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

          # text only
          a     =				model(snake_case__  ,		training=snake_case__			)
          self.parent.assertEqual(
              result.last_hidden_state.shape  ,		(self.batch_size, self.text_seq_length, self.hidden_size)			)

          # image only
          a     =				model({"pixel_values": pixel_values}  ,		training=snake_case__			)
          self.parent.assertEqual(
              result.last_hidden_state.shape  ,		(self.batch_size, self.image_seq_length, self.hidden_size)			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.num_labels
          a     =				TFLayoutLMvaForSequenceClassification(config=snake_case__			)
          a     =				model(
              snake_case__  ,		bbox=snake_case__  ,		pixel_values=snake_case__  ,		attention_mask=snake_case__  ,		token_type_ids=snake_case__  ,		labels=snake_case__  ,		training=snake_case__  ,		)
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.num_labels)			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   Any:



          '''simple docstring'''



          a     =				self.num_labels
          a     =				TFLayoutLMvaForTokenClassification(config=snake_case__			)
          a     =				model(
              snake_case__  ,		bbox=snake_case__  ,		pixel_values=snake_case__  ,		attention_mask=snake_case__  ,		token_type_ids=snake_case__  ,		labels=snake_case__  ,		training=snake_case__  ,		)
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.text_seq_length, self.num_labels)			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				2
          a     =				TFLayoutLMvaForQuestionAnswering(config=snake_case__			)
          a     =				model(
              snake_case__  ,		bbox=snake_case__  ,		pixel_values=snake_case__  ,		attention_mask=snake_case__  ,		token_type_ids=snake_case__  ,		start_positions=snake_case__  ,		end_positions=snake_case__  ,		training=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       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.prepare_config_and_inputs()
          (a)     =				config_and_inputs
          a     =				{
              '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       a__	( SCREAMING_SNAKE_CASE_       , SCREAMING_SNAKE_CASE_       , unittest.TestCase				):
     a		:      Optional[int]		 =     (
         (
             TFLayoutLMvaModel,
             TFLayoutLMvaForQuestionAnswering,
             TFLayoutLMvaForSequenceClassification,
             TFLayoutLMvaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     a		:      int		 =     (
         {"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
         if is_tf_available()
         else {}
     )

     a		:      Any		 =     False
     a		:      Any		 =     False
     a		:      List[Any]		 =     False







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A  ,		A  ,		A			)  ->   Any:



          '''simple docstring'''



          return True







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A=False			)  ->   str:



          '''simple docstring'''



          a     =				copy.deepcopy(snake_case__			)

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

          if return_labels:
               if model_class in get_values(snake_case__			):
                    a     =				tf.ones(self.model_tester.batch_size  ,		dtype=tf.intaa			)
               elif model_class in get_values(snake_case__			):
                    a     =				tf.zeros(self.model_tester.batch_size  ,		dtype=tf.intaa			)
                    a     =				tf.zeros(self.model_tester.batch_size  ,		dtype=tf.intaa			)
               elif model_class in get_values(snake_case__			):
                    a     =				tf.zeros(self.model_tester.batch_size  ,		dtype=tf.intaa			)
               elif model_class in get_values(snake_case__			):
                    a     =				tf.zeros(
                        (self.model_tester.batch_size, self.model_tester.text_seq_length)  ,		dtype=tf.intaa			)

          return inputs_dict







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				TFLayoutLMvaModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=snake_case__  ,		hidden_size=37			)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
               a     =				model_class(snake_case__			)
               if getattr(snake_case__  ,		"hf_compute_loss"  ,		snake_case__			):
                    # The number of elements in the loss should be the same as the number of elements in the label
                    a     =				self._prepare_for_class(inputs_dict.copy()  ,		snake_case__  ,		return_labels=snake_case__			)
                    a     =				prepared_for_class[
                        sorted(prepared_for_class.keys() - inputs_dict.keys()  ,		reverse=snake_case__			)[0]
                    ]
                    a     =				added_label.shape.as_list()[:1]

                    # Test that model correctly compute the loss with kwargs
                    a     =				self._prepare_for_class(inputs_dict.copy()  ,		snake_case__  ,		return_labels=snake_case__			)
                    a     =				prepared_for_class.pop("input_ids"			)

                    a     =				model(snake_case__  ,		**snake_case__			)[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
                    a     =				self._prepare_for_class(inputs_dict.copy()  ,		snake_case__  ,		return_labels=snake_case__			)
                    a     =				prepared_for_class.pop("input_ids"			)
                    if "labels" in prepared_for_class:
                         a     =				prepared_for_class['labels'].numpy()
                         if len(labels.shape			) > 1 and labels.shape[1] != 1:
                              a     =				-100
                              a     =				tf.convert_to_tensor(snake_case__			)
                              a     =				model(snake_case__  ,		**snake_case__			)[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
                    a     =				self._prepare_for_class(inputs_dict.copy()  ,		snake_case__  ,		return_labels=snake_case__			)
                    a     =				model(snake_case__			)[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
                    a     =				self._prepare_for_class(inputs_dict.copy()  ,		snake_case__  ,		return_labels=snake_case__			)

                    # Get keys that were added with the _prepare_for_class function
                    a     =				prepared_for_class.keys() - inputs_dict.keys()
                    a     =				inspect.signature(model.call			).parameters
                    a     =				list(signature.keys()			)

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

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

                    for index, value in sorted_tuple_index_mapping:
                         a     =				prepared_for_class[value]

                    a     =				tuple(snake_case__			)

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

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







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          (
              a       
          )     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__			)







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          (
              a       
          )     =				self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
               a     =				type
               self.model_tester.create_and_check_model(snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          (
              a       
          )     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(
              snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          (
              a       
          )     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(
              snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



          (
              a       
          )     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(
              snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__  ,		snake_case__			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               a     =				TFLayoutLMvaModel.from_pretrained(snake_case__			)
               self.assertIsNotNone(snake_case__			)







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





@require_tf
class       a__	( unittest.TestCase				):







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          return LayoutLMvaImageProcessor(apply_ocr=snake_case__			) if is_vision_available() else None







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a     =				TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"			)

          a     =				self.default_image_processor
          a     =				prepare_img()
          a     =				image_processor(images=snake_case__  ,		return_tensors="tf"			).pixel_values

          a     =				tf.constant([[1, 2]]			)
          a     =				tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]			)  ,		axis=0			)

          # forward pass
          a     =				model(input_ids=snake_case__  ,		bbox=snake_case__  ,		pixel_values=snake_case__  ,		training=snake_case__			)

          # verify the logits
          a     =				(1, 199, 768)
          self.assertEqual(outputs.last_hidden_state.shape  ,		snake_case__			)

          a     =				tf.constant(
              [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]			)

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











def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     return 1 if digit in (0, 1) else (digit * factorial(digit - 1))

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					bool:
     a     =				0
     a     =				number
     while duplicate > 0:
          a  ,				a     =				divmod(__UpperCamelCase		, 10)
          fact_sum += factorial(__UpperCamelCase)
     return fact_sum == number


if __name__ == "__main__":
   print("Program to check whether a number is a Krisnamurthy Number or not.")
   lowercase__				:      str	   = int(input("Enter number: ").strip())
   print(
       F'{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'
   )

