
import unittest

from transformers import MarkupLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MarkupLMForQuestionAnswering,
						    MarkupLMForSequenceClassification,
						    MarkupLMForTokenClassification,
						    MarkupLMModel,
						)

# TODO check dependencies
from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer




class    __lowerCamelCase     :




				def __init__(							self       ,	UpperCAmelCase       ,	UpperCAmelCase=13       ,	UpperCAmelCase=7       ,	UpperCAmelCase=True       ,	UpperCAmelCase=True       ,	UpperCAmelCase=True       ,	UpperCAmelCase=True       ,	UpperCAmelCase=99       ,	UpperCAmelCase=32       ,	UpperCAmelCase=5       ,	UpperCAmelCase=4       ,	UpperCAmelCase=37       ,	UpperCAmelCase="gelu"       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=0.1       ,	UpperCAmelCase=512       ,	UpperCAmelCase=16       ,	UpperCAmelCase=2       ,	UpperCAmelCase=0.0_2       ,	UpperCAmelCase=3       ,	UpperCAmelCase=None       ,	UpperCAmelCase=20       ,	UpperCAmelCase=30       ,	UpperCAmelCase=2       ,	UpperCAmelCase=2       ,	UpperCAmelCase=10       ,	):
							lowerCamelCase_ 		=     parent
							lowerCamelCase_ 		=     batch_size
							lowerCamelCase_ 		=     seq_length
							lowerCamelCase_ 		=     is_training
							lowerCamelCase_ 		=     use_input_mask
							lowerCamelCase_ 		=     use_token_type_ids
							lowerCamelCase_ 		=     use_labels
							lowerCamelCase_ 		=     vocab_size
							lowerCamelCase_ 		=     hidden_size
							lowerCamelCase_ 		=     num_hidden_layers
							lowerCamelCase_ 		=     num_attention_heads
							lowerCamelCase_ 		=     intermediate_size
							lowerCamelCase_ 		=     hidden_act
							lowerCamelCase_ 		=     hidden_dropout_prob
							lowerCamelCase_ 		=     attention_probs_dropout_prob
							lowerCamelCase_ 		=     max_position_embeddings
							lowerCamelCase_ 		=     type_vocab_size
							lowerCamelCase_ 		=     type_sequence_label_size
							lowerCamelCase_ 		=     initializer_range
							lowerCamelCase_ 		=     num_labels
							lowerCamelCase_ 		=     scope
							lowerCamelCase_ 		=     max_xpath_tag_unit_embeddings
							lowerCamelCase_ 		=     max_xpath_subs_unit_embeddings
							lowerCamelCase_ 		=     tag_pad_id
							lowerCamelCase_ 		=     subs_pad_id
							lowerCamelCase_ 		=     max_depth




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     ids_tensor([self.batch_size, self.seq_length]       ,	self.vocab_size      )

							lowerCamelCase_ 		=     ids_tensor(
							    [self.batch_size, self.seq_length, self.max_depth]       ,	self.max_xpath_tag_unit_embeddings      )

							lowerCamelCase_ 		=     ids_tensor(
							    [self.batch_size, self.seq_length, self.max_depth]       ,	self.max_xpath_subs_unit_embeddings      )

							lowerCamelCase_ 		=     None
							if self.use_input_mask:
										lowerCamelCase_ 		=     ids_tensor([self.batch_size, self.seq_length]       ,	vocab_size=2      )

							lowerCamelCase_ 		=     None
							if self.use_token_type_ids:
										lowerCamelCase_ 		=     ids_tensor([self.batch_size, self.seq_length]       ,	self.type_vocab_size      )

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

							lowerCamelCase_ 		=     self.get_config()

							return (
							    config,
							    input_ids,
							    xpath_tags_seq,
							    xpath_subs_seq,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							)




				def 					UpperCAmelCase__	(							self      ):
							return MarkupLMConfig(
							    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       ,	max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings       ,	max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings       ,	tag_pad_id=self.tag_pad_id       ,	subs_pad_id=self.subs_pad_id       ,	max_depth=self.max_depth       ,	)




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     MarkupLMModel(config=UpperCAmelCase      )
							model.to(UpperCAmelCase      )
							model.eval()
							print('''Configs:'''       ,	model.config.tag_pad_id       ,	model.config.subs_pad_id      )
							lowerCamelCase_ 		=     model(UpperCAmelCase       ,	attention_mask=UpperCAmelCase       ,	token_type_ids=UpperCAmelCase      )
							lowerCamelCase_ 		=     model(UpperCAmelCase       ,	token_type_ids=UpperCAmelCase      )
							lowerCamelCase_ 		=     model(UpperCAmelCase      )
							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 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     self.num_labels
							lowerCamelCase_ 		=     MarkupLMForSequenceClassification(UpperCAmelCase      )
							model.to(UpperCAmelCase      )
							model.eval()
							lowerCamelCase_ 		=     model(
							    UpperCAmelCase       ,	xpath_tags_seq=UpperCAmelCase       ,	xpath_subs_seq=UpperCAmelCase       ,	attention_mask=UpperCAmelCase       ,	token_type_ids=UpperCAmelCase       ,	labels=UpperCAmelCase       ,	)
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     self.num_labels
							lowerCamelCase_ 		=     MarkupLMForTokenClassification(config=UpperCAmelCase      )
							model.to(UpperCAmelCase      )
							model.eval()
							lowerCamelCase_ 		=     model(
							    UpperCAmelCase       ,	xpath_tags_seq=UpperCAmelCase       ,	xpath_subs_seq=UpperCAmelCase       ,	attention_mask=UpperCAmelCase       ,	token_type_ids=UpperCAmelCase       ,	labels=UpperCAmelCase       ,	)
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels)      )




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	):
							lowerCamelCase_ 		=     MarkupLMForQuestionAnswering(config=UpperCAmelCase      )
							model.to(UpperCAmelCase      )
							model.eval()
							lowerCamelCase_ 		=     model(
							    UpperCAmelCase       ,	xpath_tags_seq=UpperCAmelCase       ,	xpath_subs_seq=UpperCAmelCase       ,	attention_mask=UpperCAmelCase       ,	token_type_ids=UpperCAmelCase       ,	start_positions=UpperCAmelCase       ,	end_positions=UpperCAmelCase       ,	)
							self.parent.assertEqual(result.start_logits.shape       ,	(self.batch_size, self.seq_length)      )
							self.parent.assertEqual(result.end_logits.shape       ,	(self.batch_size, self.seq_length)      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.prepare_config_and_inputs()
							(
							    (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   (
							    lowerCamelCase_
							)		,   
							) 		=     config_and_inputs
							lowerCamelCase_ 		=     {
							    '''input_ids''': input_ids,
							    '''xpath_tags_seq''': xpath_tags_seq,
							    '''xpath_subs_seq''': xpath_subs_seq,
							    '''token_type_ids''': token_type_ids,
							    '''attention_mask''': input_mask,
							}
							return config, inputs_dict




@require_torch
class    __lowerCamelCase     (   _a	,  _a	,  unittest.TestCase ):
				a__:			List[Any]									=						(
				    (
				        MarkupLMModel,
				        MarkupLMForSequenceClassification,
				        MarkupLMForTokenClassification,
				        MarkupLMForQuestionAnswering,
				    )
				    if is_torch_available()
				    else None
				)
				a__:			List[Any]									=						(
				    {
				        'feature-extraction': MarkupLMModel,
				        'question-answering': MarkupLMForQuestionAnswering,
				        'text-classification': MarkupLMForSequenceClassification,
				        'token-classification': MarkupLMForTokenClassification,
				        'zero-shot': MarkupLMForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)




				def 					UpperCAmelCase__	(							self       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	UpperCAmelCase      ):
							# ValueError: Nodes must be of type `List[str]` (single pretokenized example), or `List[List[str]]`
							# (batch of pretokenized examples).
							return True




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     MarkupLMModelTester(self      )
							lowerCamelCase_ 		=     ConfigTester(self       ,	config_class=UpperCAmelCase       ,	hidden_size=37      )




				def 					UpperCAmelCase__	(							self      ):
							self.config_tester.run_common_tests()




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase      )




				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase      )






def    lowercase	(  ):
			lowerCamelCase_ 		=     '''\n    <!DOCTYPE html>\n    <html>\n    <head>\n    <title>Page Title</title>\n    </head>\n    <body>\n\n    <h1>This is a Heading</h1>\n    <p>This is a paragraph.</p>\n\n    </body>\n    </html>\n    '''

			return html_string




@require_torch
class    __lowerCamelCase     (   unittest.TestCase ):




				@cached_property
				def 					UpperCAmelCase__	(							self      ):
							# TODO use from_pretrained here
							lowerCamelCase_ 		=     MarkupLMFeatureExtractor()
							lowerCamelCase_ 		=     MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''      )

							return MarkupLMProcessor(UpperCAmelCase       ,	UpperCAmelCase      )





				@slow
				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''      ).to(UpperCAmelCase      )

							lowerCamelCase_ 		=     self.default_processor

							lowerCamelCase_ 		=     processor(prepare_html_string()       ,	return_tensors='''pt'''      )
							lowerCamelCase_ 		=     inputs.to(UpperCAmelCase      )

							# forward pass
							with torch.no_grad():
										lowerCamelCase_ 		=     model(**UpperCAmelCase      )

							# verify the last hidden states
							lowerCamelCase_ 		=     torch.Size([1, 14, 768]      )
							self.assertEqual(outputs.last_hidden_state.shape       ,	UpperCAmelCase      )

							lowerCamelCase_ 		=     torch.tensor(
							    [[0.0_6_7_5, -0.0_0_5_2, 0.5_0_0_1], [-0.2_2_8_1, 0.0_8_0_2, 0.2_1_9_2], [-0.0_5_8_3, -0.3_3_1_1, 0.1_1_8_5]]      ).to(UpperCAmelCase      )

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





"""simple docstring"""


import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

A_						       =				0.25 + 0.0J
A_						       =				-0.4 + 0.6J
A_						       =				-0.1 + 0.651J
A_						       =				-2.0
A_						       =				56
A_						       =				2.0
A_						       =				666






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return numpy.exp(lowerCAmelCase__	) + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			return z_values * z_values + c_parameter






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     x.reshape((nb_pixels, 1)	)
			lowerCamelCase_ 		=     numpy.linspace(-window_size  ,lowerCAmelCase__  ,lowerCAmelCase__	)
			lowerCamelCase_ 		=     y.reshape((1, nb_pixels)	)
			return x + 1.0J * y






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__ = None  ,):
			lowerCamelCase_ 		=     z_a.astype('''complex64'''	)
			for _ in range(lowerCAmelCase__	):
						lowerCamelCase_ 		=     eval_function(lowerCAmelCase__  ,lowerCAmelCase__	)
						if infinity is not None:
									numpy.nan_to_num(lowerCAmelCase__  ,copy=lowerCAmelCase__  ,nan=lowerCAmelCase__	)
									lowerCamelCase_ 		=     infinity
			return z_n






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__  ,):
			lowerCamelCase_ 		=     (abs(lowerCAmelCase__	)).transpose()
			lowerCamelCase_ 		=     abs_z_final[::-1, :]
			pyplot.matshow(abs_z_final < escape_radius	)
			pyplot.title(f"Julia set of ${function_label}$, $c={function_params}$"	)
			pyplot.show()






def    lowercase	(  ):
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in multiply'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''invalid value encountered in multiply'''  ,)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in absolute'''	)
			warnings.filterwarnings(
			    '''ignore'''  ,category=lowerCAmelCase__  ,message='''overflow encountered in exp'''	)


if __name__ == "__main__":
						A_						       =				prepare_grid(window_size, nb_pixels)

						ignore_overflow_warnings()  # See file header for explanations

						A_						       =				24
						A_						       =				2 * abs(c_cauliflower) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_cauliflower,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

						A_						       =				64
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

						A_						       =				161
						A_						       =				2 * abs(c_polynomial_a) + 1
						A_						       =				iterate_function(
						    eval_quadratic_polynomial,
						    c_polynomial_a,
						    nb_iterations,
						    z_a,
						    infinity=1.1 * escape_radius,
						)
						show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

						A_						       =				12
						A_						       =				10000.0
						A_						       =				iterate_function(
						    eval_exponential,
						    c_exponential,
						    nb_iterations,
						    z_a + 2,
						    infinity=1.0e10,
						)
						show_results("""e^z+c""", c_exponential, escape_radius, z_final)



