"""simple docstring"""





from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
			import tensorflow as tf

			from transformers.models.layoutlm.modeling_tf_layoutlm import (
			    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
			    TFLayoutLMForMaskedLM,
			    TFLayoutLMForQuestionAnswering,
			    TFLayoutLMForSequenceClassification,
			    TFLayoutLMForTokenClassification,
			    TFLayoutLMModel,
			)




class  _snake_case		:







				"""simple docstring"""






				def __init__(      self							:							Union[str, Any]      ,    _A							:							Optional[int]      ,    _A							:							Tuple=1_3      ,    _A							:							Tuple=7      ,    _A							:							List[Any]=True      ,    _A							:							Optional[Any]=True      ,    _A							:							List[Any]=True      ,    _A							:							int=True      ,    _A							:							List[str]=9_9      ,    _A							:							List[str]=3_2      ,    _A							:							Optional[int]=2      ,    _A							:							Any=4      ,    _A							:							List[Any]=3_7      ,    _A							:							Any="gelu"      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							Any=0.1      ,    _A							:							List[str]=5_1_2      ,    _A							:							int=1_6      ,    _A							:							Optional[Any]=2      ,    _A							:							Dict=0.02      ,    _A							:							List[Any]=3      ,    _A							:							List[str]=4      ,    _A							:							Optional[Any]=None      ,    _A							:							List[Any]=1_0_0_0      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    parent
											_SCREAMING_SNAKE_CASE		:						Any			  =    batch_size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    seq_length
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    is_training
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    use_input_mask
											_SCREAMING_SNAKE_CASE		:						Dict			  =    use_token_type_ids
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    use_labels
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						str			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Dict			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						int			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						int			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Any			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    max_position_embeddings
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    type_vocab_size
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    type_sequence_label_size
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    initializer_range
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    num_labels
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    num_choices
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    scope
											_SCREAMING_SNAKE_CASE		:						Any			  =    range_bbox





				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.vocab_size)

											# convert bbox to numpy since TF does not support item assignment
											_SCREAMING_SNAKE_CASE		:						int			  =    ids_tensor([self.batch_size, self.seq_length, 4]      ,    self.range_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]:
																																_SCREAMING_SNAKE_CASE		:						List[Any]			  =    bbox[i, j, 3]
																																_SCREAMING_SNAKE_CASE		:						Dict			  =    bbox[i, j, 1]
																																_SCREAMING_SNAKE_CASE		:						Any			  =    t
																									if bbox[i, j, 2] < bbox[i, j, 0]:
																																_SCREAMING_SNAKE_CASE		:						Any			  =    bbox[i, j, 2]
																																_SCREAMING_SNAKE_CASE		:						List[str]			  =    bbox[i, j, 0]
																																_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    t
											_SCREAMING_SNAKE_CASE		:						str			  =    tf.convert_to_tensor(_A)

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

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    None
											if self.use_token_type_ids:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.type_vocab_size)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    None
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    None
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    None
											if self.use_labels:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    ids_tensor([self.batch_size]      ,    self.type_sequence_label_size)
																		_SCREAMING_SNAKE_CASE		:						Any			  =    ids_tensor([self.batch_size, self.seq_length]      ,    self.num_labels)
																		_SCREAMING_SNAKE_CASE		:						Any			  =    ids_tensor([self.batch_size]      ,    self.num_choices)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    LayoutLMConfig(
											    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      ,    )

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





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							Optional[int]      ,    _A							:							List[str]      ,    _A							:							Optional[int]      ,    _A							:							str      ,    _A							:							Optional[int]      ,    _A							:							Dict      ,    _A							:							int      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    TFLayoutLMModel(config=_A)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(_A      ,    _A      ,    attention_mask=_A      ,    token_type_ids=_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    model(_A      ,    _A      ,    token_type_ids=_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    model(_A      ,    _A)
											self.parent.assertEqual(result.last_hidden_state.shape      ,    (self.batch_size, self.seq_length, self.hidden_size))
											self.parent.assertEqual(result.pooler_output.shape      ,    (self.batch_size, self.hidden_size))





				def 		_lowerCAmelCase		(      self							:							Optional[int]      ,    _A							:							Optional[int]      ,    _A							:							str      ,    _A							:							Tuple      ,    _A							:							Optional[int]      ,    _A							:							Dict      ,    _A							:							List[Any]      ,    _A							:							Tuple      ,    _A							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TFLayoutLMForMaskedLM(config=_A)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    model(_A      ,    _A      ,    attention_mask=_A      ,    token_type_ids=_A      ,    labels=_A)
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.vocab_size))





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							Dict      ,    _A							:							Dict      ,    _A							:							Dict      ,    _A							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							Optional[Any]      ,    _A							:							Union[str, Any]      ,    _A							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.num_labels
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TFLayoutLMForSequenceClassification(config=_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(_A      ,    _A      ,    attention_mask=_A      ,    token_type_ids=_A)
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.num_labels))





				def 		_lowerCAmelCase		(      self							:							Dict      ,    _A							:							List[str]      ,    _A							:							Dict      ,    _A							:							str      ,    _A							:							str      ,    _A							:							Optional[int]      ,    _A							:							Dict      ,    _A							:							Optional[Any]      ,    _A							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						int			  =    self.num_labels
											_SCREAMING_SNAKE_CASE		:						Dict			  =    TFLayoutLMForTokenClassification(config=_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(_A      ,    _A      ,    attention_mask=_A      ,    token_type_ids=_A      ,    labels=_A)
											self.parent.assertEqual(result.logits.shape      ,    (self.batch_size, self.seq_length, self.num_labels))





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							List[str]      ,    _A							:							Any      ,    _A							:							List[Any]      ,    _A							:							List[Any]      ,    _A							:							Any      ,    _A							:							Any      ,    _A							:							Optional[int]      ,    _A							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TFLayoutLMForQuestionAnswering(config=_A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    model(_A      ,    _A      ,    attention_mask=_A      ,    token_type_ids=_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 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.prepare_config_and_inputs()
											(
											    (
											    _SCREAMING_SNAKE_CASE
											)  ,				(
											    _SCREAMING_SNAKE_CASE
											)  ,				(
											    _SCREAMING_SNAKE_CASE
											)  ,				(
											    _SCREAMING_SNAKE_CASE
											)  ,				(
											    _SCREAMING_SNAKE_CASE
											)  ,				(
											    _SCREAMING_SNAKE_CASE
											)  ,				(
											    _SCREAMING_SNAKE_CASE
											)  ,				(
											    _SCREAMING_SNAKE_CASE
											)  ,				
											)		:						List[str]			  =    config_and_inputs
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {
											    """input_ids""": input_ids,
											    """bbox""": bbox,
											    """token_type_ids""": token_type_ids,
											    """attention_mask""": input_mask,
											}
											return config, inputs_dict




@require_tf
class  _snake_case		(    __snake_case   ,       __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						(
				    (
				        TFLayoutLMModel,
				        TFLayoutLMForMaskedLM,
				        TFLayoutLMForTokenClassification,
				        TFLayoutLMForSequenceClassification,
				        TFLayoutLMForQuestionAnswering,
				    )
				    if is_tf_available()
				    else ()
				)
				a			=						(
				    {
				        "feature-extraction": TFLayoutLMModel,
				        "fill-mask": TFLayoutLMForMaskedLM,
				        "text-classification": TFLayoutLMForSequenceClassification,
				        "token-classification": TFLayoutLMForTokenClassification,
				        "zero-shot": TFLayoutLMForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				a			=						False
				a			=						True
				a			=						10





				def 		_lowerCAmelCase		(      self							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    TFLayoutLMModelTester(self)
											_SCREAMING_SNAKE_CASE		:						str			  =    ConfigTester(self      ,    config_class=_A      ,    hidden_size=3_7)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											self.config_tester.run_common_tests()





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_A)





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_A)





				@slow
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_SCREAMING_SNAKE_CASE		:						Dict			  =    TFLayoutLMModel.from_pretrained(_A)
																		self.assertIsNotNone(_A)





				@unittest.skip("""Onnx compliancy broke with TF 2.10""")
				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											pass



def      lowerCamelCase_()->    Union[str, Any]:
							# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
							# fmt: off
							_SCREAMING_SNAKE_CASE		:						str			  =    tf.convert_to_tensor([[101,1_019,1_014,1_016,1_037,12_849,4_747,1_004,14_246,2_278,5_439,4_524,5_002,2_930,2_193,2_930,4_341,3_208,1_005,1_055,2_171,2_848,11_300,3_531,102],[101,4_070,4_034,7_020,1_024,3_058,1_015,1_013,2_861,1_013,6_070,19_274,2_772,6_205,27_814,16_147,16_147,4_343,2_047,10_283,10_969,14_389,1_012,2_338,102]]  )  # noqa: E231
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]  )  # noqa: E231
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1_000,1_000,1_000,1_000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1_000,1_000,1_000,1_000]]]  )  # noqa: E231
							_SCREAMING_SNAKE_CASE		:						str			  =    tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]  )  # noqa: E231
							# these are sequence labels (i.e. at the token level)
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]  )  # noqa: E231
							# fmt: on

							return input_ids, attention_mask, bbox, token_type_ids, labels




@require_tf
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""")

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    prepare_layoutlm_batch_inputs()

											# forward pass
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    model(input_ids=_A      ,    bbox=_A      ,    attention_mask=_A      ,    token_type_ids=_A)

											# test the sequence output on [0, :3, :3]
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tf.convert_to_tensor(
											    [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]      ,    )

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

											# test the pooled output on [1, :3]
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tf.convert_to_tensor([-0.6_580, -0.0_214, 0.8_552])

											self.assertTrue(np.allclose(outputs.pooler_output[1, :3]      ,    _A      ,    atol=1e-3))





				@slow
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TFLayoutLMForSequenceClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,    num_labels=2)

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						str			  =    prepare_layoutlm_batch_inputs()

											# forward pass
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(
											    input_ids=_A      ,    bbox=_A      ,    attention_mask=_A      ,    token_type_ids=_A      ,    labels=tf.convert_to_tensor([1, 1])      ,    )

											# test whether we get a loss as a scalar
											_SCREAMING_SNAKE_CASE		:						str			  =    outputs.loss
											_SCREAMING_SNAKE_CASE		:						Dict			  =    (2,)
											self.assertEqual(loss.shape      ,    _A)

											# test the shape of the logits
											_SCREAMING_SNAKE_CASE		:						Any			  =    outputs.logits
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    (2, 2)
											self.assertEqual(logits.shape      ,    _A)





				@slow
				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TFLayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,    num_labels=1_3)

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Tuple			  =    prepare_layoutlm_batch_inputs()

											# forward pass
											_SCREAMING_SNAKE_CASE		:						int			  =    model(
											    input_ids=_A      ,    bbox=_A      ,    attention_mask=_A      ,    token_type_ids=_A      ,    labels=_A)

											# test the shape of the logits
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tf.convert_to_tensor((2, 2_5, 1_3))
											self.assertEqual(logits.shape      ,    _A)





				@slow
				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""")

											_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    prepare_layoutlm_batch_inputs()

											# forward pass
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    model(input_ids=_A      ,    bbox=_A      ,    attention_mask=_A      ,    token_type_ids=_A)

											# test the shape of the logits
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tf.convert_to_tensor((2, 2_5))
											self.assertEqual(outputs.start_logits.shape      ,    _A)
											self.assertEqual(outputs.end_logits.shape      ,    _A)



"""simple docstring"""





from typing import Any

import numpy as np



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    bool:
							return np.array_equal(__SCREAMING_SNAKE_CASE     ,	matrix.conjugate().T  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Any:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    v.conjugate().T
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    v_star.dot(__SCREAMING_SNAKE_CASE  )
							assert isinstance(__SCREAMING_SNAKE_CASE     ,	np.ndarray  )
							return (v_star_dot.dot(__SCREAMING_SNAKE_CASE  )) / (v_star.dot(__SCREAMING_SNAKE_CASE  ))



def      lowerCamelCase_()->    None:
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]  )
							_SCREAMING_SNAKE_CASE		:						int			  =    np.array([[1], [2], [3]]  )
							assert is_hermitian(__SCREAMING_SNAKE_CASE  ), F"""{a} is not hermitian."""
							print(rayleigh_quotient(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )  )

							_SCREAMING_SNAKE_CASE		:						int			  =    np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]  )
							assert is_hermitian(__SCREAMING_SNAKE_CASE  ), F"""{a} is not hermitian."""
							assert rayleigh_quotient(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  ) == float(3  )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			tests()


