




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 						lowercase							:

     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=0.02		,     _a=6		,     _a=6		,     _a=3		,     _a=4		,     _a=None		,     _a=1000		,     )						->							int:
           _A							:     Tuple			=						parent
           _A							:     int			=						batch_size
           _A							:     Union[str, Any]			=						num_channels
           _A							:     int			=						image_size
           _A							:     Tuple			=						patch_size
           _A							:     Union[str, Any]			=						is_training
           _A							:     List[Any]			=						use_input_mask
           _A							:     Tuple			=						use_token_type_ids
           _A							:     Optional[int]			=						use_labels
           _A							:     Any			=						vocab_size
           _A							:     Union[str, Any]			=						hidden_size
           _A							:     Dict			=						num_hidden_layers
           _A							:     Dict			=						num_attention_heads
           _A							:     List[Any]			=						intermediate_size
           _A							:     Optional[Any]			=						hidden_act
           _A							:     Optional[Any]			=						hidden_dropout_prob
           _A							:     Dict			=						attention_probs_dropout_prob
           _A							:     Any			=						max_position_embeddings
           _A							:     int			=						type_vocab_size
           _A							:     Tuple			=						type_sequence_label_size
           _A							:     Optional[int]			=						initializer_range
           _A							:     List[Any]			=						coordinate_size
           _A							:     Dict			=						shape_size
           _A							:     Tuple			=						num_labels
           _A							:     str			=						num_choices
           _A							:     str			=						scope
           _A							:     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)
           _A							:     Union[str, Any]			=						text_seq_length
           _A							:     Optional[Any]			=						(image_size // patch_size) ** 2 + 1
           _A							:     List[str]			=						self.text_seq_length + self.image_seq_length

     def 					a__	(  self				)						->							str:
           _A							:     Union[str, Any]			=						ids_tensor([self.batch_size, self.text_seq_length]		,     self.vocab_size				)

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

           _A							:     Union[str, Any]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

           _A							:     List[Any]			=						None
           if self.use_input_mask:
                 _A							:     List[str]			=						random_attention_mask([self.batch_size, self.text_seq_length]				)

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

           _A							:     Union[str, Any]			=						None
           _A							:     Union[str, Any]			=						None
           if self.use_labels:
                 _A							:     int			=						ids_tensor([self.batch_size]		,     self.type_sequence_label_size				)
                 _A							:     List[Any]			=						ids_tensor([self.batch_size, self.text_seq_length]		,     self.num_labels				)

           _A							:     Optional[int]			=						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 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Optional[int]:
           _A							:     str			=						TFLayoutLMvaModel(config=_a				)

           # text + image
           _A							:     List[str]			=						model(_a		,     pixel_values=_a		,     training=_a				)
           _A							:     Union[str, Any]			=						model(
               _a		,     bbox=_a		,     pixel_values=_a		,     attention_mask=_a		,     token_type_ids=_a		,     training=_a		,     )
           _A							:     int			=						model(_a		,     bbox=_a		,     pixel_values=_a		,     training=_a				)

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

           # text only
           _A							:     Dict			=						model(_a		,     training=_a				)
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.text_seq_length, self.hidden_size)				)

           # image only
           _A							:     Union[str, Any]			=						model({"""pixel_values""": pixel_values}		,     training=_a				)
           self.parent.assertEqual(
               result.last_hidden_state.shape		,     (self.batch_size, self.image_seq_length, self.hidden_size)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						self.num_labels
           _A							:     List[str]			=						TFLayoutLMvaForSequenceClassification(config=_a				)
           _A							:     int			=						model(
               _a		,     bbox=_a		,     pixel_values=_a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a		,     training=_a		,     )
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Any:
           _A							:     str			=						self.num_labels
           _A							:     Any			=						TFLayoutLMvaForTokenClassification(config=_a				)
           _A							:     Union[str, Any]			=						model(
               _a		,     bbox=_a		,     pixel_values=_a		,     attention_mask=_a		,     token_type_ids=_a		,     labels=_a		,     training=_a		,     )
           self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.text_seq_length, self.num_labels)				)

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Optional[int]:
           _A							:     int			=						2
           _A							:     Dict			=						TFLayoutLMvaForQuestionAnswering(config=_a				)
           _A							:     Tuple			=						model(
               _a		,     bbox=_a		,     pixel_values=_a		,     attention_mask=_a		,     token_type_ids=_a		,     start_positions=_a		,     end_positions=_a		,     training=_a		,     )
           self.parent.assertEqual(result.start_logits.shape		,     (self.batch_size, self.seq_length)				)
           self.parent.assertEqual(result.end_logits.shape		,     (self.batch_size, self.seq_length)				)



     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     int			=						self.prepare_config_and_inputs()
           ((_A)			,   (_A)			,   (_A)			,   (_A)			,   (_A)			,   (_A)			,   (_A)			,   (_A))							:     Any			=						config_and_inputs
           _A							:     List[Any]			=						{
               """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 						lowercase							(   UpperCamelCase__,UpperCamelCase__,unittest.TestCase		):
     _a							 =						(
         (
             TFLayoutLMvaModel,
             TFLayoutLMvaForQuestionAnswering,
             TFLayoutLMvaForSequenceClassification,
             TFLayoutLMvaForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     _a							 =						(
         {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
         if is_tf_available()
         else {}
     )

     _a							 =						False
     _a							 =						False
     _a							 =						False

     def 					a__	(  self		,     _a		,     _a		,     _a		,     _a		,     _a				)						->							Optional[int]:
           return True

     def 					a__	(  self		,     _a		,     _a		,     _a=False				)						->							dict:
           _A							:     int			=						copy.deepcopy(_a				)

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

           if return_labels:
                 if model_class in get_values(_a				):
                       _A							:     List[str]			=						tf.ones(self.model_tester.batch_size		,     dtype=tf.intaa				)
                 elif model_class in get_values(_a				):
                       _A							:     Dict			=						tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa				)
                       _A							:     List[Any]			=						tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa				)
                 elif model_class in get_values(_a				):
                       _A							:     str			=						tf.zeros(self.model_tester.batch_size		,     dtype=tf.intaa				)
                 elif model_class in get_values(_a				):
                       _A							:     Tuple			=						tf.zeros(
                           (self.model_tester.batch_size, self.model_tester.text_seq_length)		,     dtype=tf.intaa				)

           return inputs_dict

     def 					a__	(  self				)						->							List[str]:
           _A							:     List[str]			=						TFLayoutLMvaModelTester(self				)
           _A							:     Optional[Any]			=						ConfigTester(self		,     config_class=_a		,     hidden_size=37				)

     def 					a__	(  self				)						->							Optional[Any]:
           self.config_tester.run_common_tests()

     def 					a__	(  self				)						->							List[str]:
           _A			,   _A							:     Tuple			=						self.model_tester.prepare_config_and_inputs_for_common()
           for model_class in self.all_model_classes:
                 _A							:     Dict			=						model_class(_a				)
                 if getattr(_a		,     """hf_compute_loss"""		,     _a				):
                       # The number of elements in the loss should be the same as the number of elements in the label
                       _A							:     int			=						self._prepare_for_class(inputs_dict.copy()		,     _a		,     return_labels=_a				)
                       _A							:     int			=						prepared_for_class[
                           sorted(prepared_for_class.keys() - inputs_dict.keys()		,     reverse=_a				)[0]
                       ]
                       _A							:     int			=						added_label.shape.as_list()[:1]

                       # Test that model correctly compute the loss with kwargs
                       _A							:     List[str]			=						self._prepare_for_class(inputs_dict.copy()		,     _a		,     return_labels=_a				)
                       _A							:     Union[str, Any]			=						prepared_for_class.pop("""input_ids"""				)

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

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

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

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

                       for index, value in sorted_tuple_index_mapping:
                             _A							:     Tuple			=						prepared_for_class[value]

                       _A							:     int			=						tuple(_a				)

                       # Send to model
                       _A							:     List[str]			=						model(tuple_input[:-1]				)[0]

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

     def 					a__	(  self				)						->							Optional[int]:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     Optional[int]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(_a		,     _a		,     _a		,     _a		,     _a		,     _a				)

     def 					a__	(  self				)						->							Any:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                 _A							:     Optional[Any]			=						type
                 self.model_tester.create_and_check_model(_a		,     _a		,     _a		,     _a		,     _a		,     _a				)

     def 					a__	(  self				)						->							Tuple:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     List[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(
               _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)

     def 					a__	(  self				)						->							Tuple:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     List[Any]			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(
               _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)

     def 					a__	(  self				)						->							List[str]:
           (
               (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   (
               _A
           )			,   
           )							:     Dict			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(
               _a		,     _a		,     _a		,     _a		,     _a		,     _a		,     _a				)



     @slow
     def 					a__	(  self				)						->							str:
           for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A							:     Tuple			=						TFLayoutLMvaModel.from_pretrained(_a				)
                 self.assertIsNotNone(_a				)







def 				lowerCAmelCase_	(     ):
      _A							:     List[Any]			=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image







@require_tf
class 						lowercase							(   unittest.TestCase		):

     @cached_property
     def 					a__	(  self				)						->							Tuple:
           return LayoutLMvaImageProcessor(apply_ocr=_a				) if is_vision_available() else None



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

           _A							:     Tuple			=						self.default_image_processor
           _A							:     int			=						prepare_img()
           _A							:     List[Any]			=						image_processor(images=_a		,     return_tensors="""tf"""				).pixel_values

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

           # forward pass
           _A							:     Tuple			=						model(input_ids=_a		,     bbox=_a		,     pixel_values=_a		,     training=_a				)

           # verify the logits
           _A							:     int			=						(1, 199, 768)
           self.assertEqual(outputs.last_hidden_state.shape		,     _a				)

           _A							:     List[Any]			=						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]		,     _a		,     atol=1e-4				)				)


from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




