






"""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 					_UpperCAmelCase       :




					def __init__(				self     :						Any    ,						A     :						int    ,						A     :						Optional[Any]=2    ,						A     :						int=3    ,						A     :						Optional[Any]=4    ,						A     :						Union[str, Any]=2    ,						A     :						Dict=7    ,						A     :						Any=True    ,						A     :						List[Any]=True    ,						A     :						Any=True    ,						A     :						str=True    ,						A     :						Union[str, Any]=99    ,						A     :						Tuple=36    ,						A     :						Optional[int]=2    ,						A     :						Union[str, Any]=4    ,						A     :						Any=37    ,						A     :						Tuple="gelu"    ,						A     :						Dict=0.1    ,						A     :						Optional[Any]=0.1    ,						A     :						List[Any]=5_12    ,						A     :						List[str]=16    ,						A     :						Optional[Any]=2    ,						A     :						Any=0.02    ,						A     :						Optional[int]=6    ,						A     :						int=6    ,						A     :						Optional[int]=3    ,						A     :						str=4    ,						A     :						Tuple=None    ,						A     :						int=10_00    ,						)  ->  Optional[Any]:
							lowercase_ : Dict							       =   parent
							lowercase_ : Dict							       =   batch_size
							lowercase_ : Optional[Any]							       =   num_channels
							lowercase_ : List[str]							       =   image_size
							lowercase_ : str							       =   patch_size
							lowercase_ : Optional[int]							       =   is_training
							lowercase_ : str							       =   use_input_mask
							lowercase_ : Dict							       =   use_token_type_ids
							lowercase_ : int							       =   use_labels
							lowercase_ : Tuple							       =   vocab_size
							lowercase_ : List[Any]							       =   hidden_size
							lowercase_ : str							       =   num_hidden_layers
							lowercase_ : Any							       =   num_attention_heads
							lowercase_ : List[str]							       =   intermediate_size
							lowercase_ : Optional[Any]							       =   hidden_act
							lowercase_ : Tuple							       =   hidden_dropout_prob
							lowercase_ : Optional[Any]							       =   attention_probs_dropout_prob
							lowercase_ : List[Any]							       =   max_position_embeddings
							lowercase_ : str							       =   type_vocab_size
							lowercase_ : str							       =   type_sequence_label_size
							lowercase_ : List[Any]							       =   initializer_range
							lowercase_ : Dict							       =   coordinate_size
							lowercase_ : List[str]							       =   shape_size
							lowercase_ : int							       =   num_labels
							lowercase_ : Union[str, Any]							       =   num_choices
							lowercase_ : Union[str, Any]							       =   scope
							lowercase_ : Optional[int]							       =   range_bbox

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




					def   A  (				self     :						Optional[Any]   )  ->  Optional[Any]:
							lowercase_ : Optional[Any]							       =   ids_tensor([self.batch_size, self.text_seq_length]    ,						self.vocab_size   )

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

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

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

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

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

							lowercase_ : Optional[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   A  (				self     :						Optional[Any]    ,						A     :						Optional[int]    ,						A     :						Optional[Any]    ,						A     :						Tuple    ,						A     :						str    ,						A     :						Optional[int]    ,						A     :						str   )  ->  Any:
							lowercase_ : Tuple							       =   TFLayoutLMvaModel(config=A   )

							# text + image
							lowercase_ : Optional[Any]							       =   model(A    ,						pixel_values=A    ,						training=A   )
							lowercase_ : Optional[Any]							       =   model(
							    A    ,						bbox=A    ,						pixel_values=A    ,						attention_mask=A    ,						token_type_ids=A    ,						training=A    ,						)
							lowercase_ : Any							       =   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
							lowercase_ : Tuple							       =   model(A    ,						training=A   )
							self.parent.assertEqual(
							    result.last_hidden_state.shape    ,						(self.batch_size, self.text_seq_length, self.hidden_size)   )

							# image only
							lowercase_ : 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     :						Union[str, Any]    ,						A     :						str    ,						A     :						Optional[int]    ,						A     :						Optional[Any]    ,						A     :						str    ,						A     :						List[str]    ,						A     :						List[str]    ,						A     :						Tuple   )  ->  Dict:
							lowercase_ : int							       =   self.num_labels
							lowercase_ : Union[str, Any]							       =   TFLayoutLMvaForSequenceClassification(config=A   )
							lowercase_ : 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.num_labels)   )




					def   A  (				self     :						Dict    ,						A     :						int    ,						A     :						int    ,						A     :						Union[str, Any]    ,						A     :						Any    ,						A     :						Optional[int]    ,						A     :						int    ,						A     :						int   )  ->  List[Any]:
							lowercase_ : Any							       =   self.num_labels
							lowercase_ : str							       =   TFLayoutLMvaForTokenClassification(config=A   )
							lowercase_ : Tuple							       =   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     :						Optional[Any]    ,						A     :						Dict    ,						A     :						List[Any]    ,						A     :						Dict    ,						A     :						Tuple    ,						A     :						Union[str, Any]    ,						A     :						Union[str, Any]    ,						A     :						int   )  ->  Tuple:
							lowercase_ : Union[str, Any]							       =   2
							lowercase_ : Dict							       =   TFLayoutLMvaForQuestionAnswering(config=A   )
							lowercase_ : Optional[Any]							       =   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     :						Any   )  ->  Any:
							lowercase_ : Optional[int]							       =   self.prepare_config_and_inputs()
							((lowercase_)       ,					(lowercase_)       ,					(lowercase_)       ,					(lowercase_)       ,					(lowercase_)       ,					(lowercase_)       ,					(lowercase_)       ,					(lowercase_)) : List[Any]							       =   config_and_inputs
							lowercase_ : 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 					_UpperCAmelCase       ( _A   ,						_A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				int							    =   (
					    (
					        TFLayoutLMvaModel,
					        TFLayoutLMvaForQuestionAnswering,
					        TFLayoutLMvaForSequenceClassification,
					        TFLayoutLMvaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					SCREAMING_SNAKE_CASE_		:				str							    =   (
					    {"document-question-answering": TFLayoutLMvaForQuestionAnswering, "feature-extraction": TFLayoutLMvaModel}
					    if is_tf_available()
					    else {}
					)

					SCREAMING_SNAKE_CASE_		:				int							    =   False
					SCREAMING_SNAKE_CASE_		:				Optional[int]							    =   False
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   False




					def   A  (				self     :						List[Any]    ,						A     :						Tuple    ,						A     :						Optional[int]    ,						A     :						Dict    ,						A     :						Tuple    ,						A     :						List[str]   )  ->  List[Any]:
							return True




					def   A  (				self     :						Tuple    ,						A     :						List[str]    ,						A     :						Any    ,						A     :						List[Any]=False   )  ->  dict:
							lowercase_ : int							       =   copy.deepcopy(A   )

							if model_class in get_values(A   ):
									lowercase_ : List[str]							       =   {
									    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   ):
											lowercase_ : int							       =   tf.ones(self.model_tester.batch_size    ,						dtype=tf.intaa   )
									elif model_class in get_values(A   ):
											lowercase_ : str							       =   tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa   )
											lowercase_ : Optional[int]							       =   tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa   )
									elif model_class in get_values(A   ):
											lowercase_ : Dict							       =   tf.zeros(self.model_tester.batch_size    ,						dtype=tf.intaa   )
									elif model_class in get_values(A   ):
											lowercase_ : Optional[int]							       =   tf.zeros(
											    (self.model_tester.batch_size, self.model_tester.text_seq_length)    ,						dtype=tf.intaa   )

							return inputs_dict




					def   A  (				self     :						Optional[int]   )  ->  List[str]:
							lowercase_ : Union[str, Any]							       =   TFLayoutLMvaModelTester(self   )
							lowercase_ : List[Any]							       =   ConfigTester(self    ,						config_class=A    ,						hidden_size=37   )




					def   A  (				self     :						str   )  ->  Any:
							self.config_tester.run_common_tests()




					def   A  (				self     :						Dict   )  ->  Optional[int]:
							lowercase_       ,					lowercase_ : Tuple							       =   self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
									lowercase_ : Any							       =   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
											lowercase_ : Optional[Any]							       =   self._prepare_for_class(inputs_dict.copy()    ,						A    ,						return_labels=A   )
											lowercase_ : int							       =   prepared_for_class[
											    sorted(prepared_for_class.keys() - inputs_dict.keys()    ,						reverse=A   )[0]
											]
											lowercase_ : Any							       =   added_label.shape.as_list()[:1]

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

											lowercase_ : Optional[Any]							       =   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
											lowercase_ : str							       =   self._prepare_for_class(inputs_dict.copy()    ,						A    ,						return_labels=A   )
											lowercase_ : Union[str, Any]							       =   prepared_for_class.pop('''input_ids'''   )
											if "labels" in prepared_for_class:
													lowercase_ : List[Any]							       =   prepared_for_class['''labels'''].numpy()
													if len(labels.shape   ) > 1 and labels.shape[1] != 1:
															lowercase_ : Dict							       =   -1_00
															lowercase_ : Tuple							       =   tf.convert_to_tensor(A   )
															lowercase_ : Union[str, 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
											lowercase_ : List[str]							       =   self._prepare_for_class(inputs_dict.copy()    ,						A    ,						return_labels=A   )
											lowercase_ : Tuple							       =   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
											lowercase_ : str							       =   self._prepare_for_class(inputs_dict.copy()    ,						A    ,						return_labels=A   )

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

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

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

											for index, value in sorted_tuple_index_mapping:
													lowercase_ : int							       =   prepared_for_class[value]

											lowercase_ : Optional[int]							       =   tuple(A   )

											# Send to model
											lowercase_ : 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     :						Tuple   )  ->  Any:
							(
							    (
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					
							) : 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     :						str   )  ->  Optional[int]:
							(
							    (
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					
							) : Any							       =   self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
									lowercase_ : Optional[int]							       =   type
									self.model_tester.create_and_check_model(A    ,						A    ,						A    ,						A    ,						A    ,						A   )




					def   A  (				self     :						Tuple   )  ->  int:
							(
							    (
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					
							) : 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   )  ->  Union[str, Any]:
							(
							    (
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					
							) : Tuple							       =   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[Any]   )  ->  Optional[Any]:
							(
							    (
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					(
							    lowercase_
							)       ,					
							) : List[Any]							       =   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     :						Union[str, Any]   )  ->  str:
							for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_ : Optional[Any]							       =   TFLayoutLMvaModel.from_pretrained(A   )
									self.assertIsNotNone(A   )



def 						lowercase    (				):
		lowercase_ : Any							       =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image






@require_tf
class 					_UpperCAmelCase       ( unittest.TestCase							):




					@cached_property
					def   A  (				self     :						Dict   )  ->  Dict:
							return LayoutLMvaImageProcessor(apply_ocr=A   ) if is_vision_available() else None






					@slow
					def   A  (				self     :						Union[str, Any]   )  ->  Union[str, Any]:
							lowercase_ : List[str]							       =   TFLayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''   )

							lowercase_ : List[Any]							       =   self.default_image_processor
							lowercase_ : Optional[Any]							       =   prepare_img()
							lowercase_ : Dict							       =   image_processor(images=A    ,						return_tensors='''tf'''   ).pixel_values

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

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

							# verify the logits
							lowercase_ : List[Any]							       =   (1, 1_99, 7_68)
							self.assertEqual(outputs.last_hidden_state.shape    ,						A   )

							lowercase_ : 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

from math import pi, sqrt


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):

						if inductance <= 0:
												raise ValueError('''Inductance cannot be 0 or negative'''       )

						elif capacitance <= 0:
												raise ValueError('''Capacitance cannot be 0 or negative'''       )

						else:
												return (
												    "Resonant frequency",
												    float(1 / (2 * pi * (sqrt(inductance * capacitance       )))       ),
												)


if __name__ == "__main__":
					import doctest

					doctest.testmod()