

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 				a  :


						def __init__(       self						,					A_						,					A_=13						,					A_=7						,					A_=True						,					A_=True						,					A_=True						,					A_=True						,					A_=99						,					A_=32						,					A_=2						,					A_=4						,					A_=37						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=16						,					A_=2						,					A_=0.02						,					A_=3						,					A_=4						,					A_=None						,					A_=1000						,					):







										'''simple docstring'''


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


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[str] 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size	)

										# convert bbox to numpy since TF does not support item assignment
										_UpperCAmelCase       :		Optional[Any] 		=    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]:
																						_UpperCAmelCase       :		int 		=    bbox[i, j, 3]
																						_UpperCAmelCase       :		List[Any] 		=    bbox[i, j, 1]
																						_UpperCAmelCase       :		str 		=    t
																		if bbox[i, j, 2] < bbox[i, j, 0]:
																						_UpperCAmelCase       :		Any 		=    bbox[i, j, 2]
																						_UpperCAmelCase       :		List[Any] 		=    bbox[i, j, 0]
																						_UpperCAmelCase       :		Optional[int] 		=    t
										_UpperCAmelCase       :		Dict 		=    tf.convert_to_tensor(A_	)

										_UpperCAmelCase       :		List[str] 		=    None
										if self.use_input_mask:
														_UpperCAmelCase       :		Optional[int] 		=    random_attention_mask([self.batch_size, self.seq_length]	)

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

										_UpperCAmelCase       :		Optional[Any] 		=    None
										_UpperCAmelCase       :		Optional[Any] 		=    None
										_UpperCAmelCase       :		Tuple 		=    None
										if self.use_labels:
														_UpperCAmelCase       :		str 		=    ids_tensor([self.batch_size]						,					self.type_sequence_label_size	)
														_UpperCAmelCase       :		str 		=    ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels	)
														_UpperCAmelCase       :		Union[str, Any] 		=    ids_tensor([self.batch_size]						,					self.num_choices	)

										_UpperCAmelCase       :		List[Any] 		=    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        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    TFLayoutLMModel(config=A_	)

										_UpperCAmelCase       :		Any 		=    model(A_						,					A_						,					attention_mask=A_						,					token_type_ids=A_	)
										_UpperCAmelCase       :		Optional[int] 		=    model(A_						,					A_						,					token_type_ids=A_	)
										_UpperCAmelCase       :		str 		=    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        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    TFLayoutLMForMaskedLM(config=A_	)

										_UpperCAmelCase       :		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.vocab_size)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.num_labels
										_UpperCAmelCase       :		Optional[Any] 		=    TFLayoutLMForSequenceClassification(config=A_	)

										_UpperCAmelCase       :		Union[str, Any] 		=    model(A_						,					A_						,					attention_mask=A_						,					token_type_ids=A_	)
										self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)	)


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.num_labels
										_UpperCAmelCase       :		List[str] 		=    TFLayoutLMForTokenClassification(config=A_	)

										_UpperCAmelCase       :		Optional[int] 		=    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        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    TFLayoutLMForQuestionAnswering(config=A_	)

										_UpperCAmelCase       :		Dict 		=    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        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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







@require_tf
class 				a  (	UpperCAmelCase							, UpperCAmelCase							, unittest.TestCase				):
						_lowercase    						=			(
						    (
						        TFLayoutLMModel,
						        TFLayoutLMForMaskedLM,
						        TFLayoutLMForTokenClassification,
						        TFLayoutLMForSequenceClassification,
						        TFLayoutLMForQuestionAnswering,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowercase    						=			(
						    {
						        "feature-extraction": TFLayoutLMModel,
						        "fill-mask": TFLayoutLMForMaskedLM,
						        "text-classification": TFLayoutLMForSequenceClassification,
						        "token-classification": TFLayoutLMForTokenClassification,
						        "zero-shot": TFLayoutLMForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowercase    						=			False
						_lowercase    						=			True
						_lowercase    						=			1_0


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    TFLayoutLMModelTester(self	)
										_UpperCAmelCase       :		Union[str, Any] 		=    ConfigTester(self						,					config_class=A_						,					hidden_size=37	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										self.config_tester.run_common_tests()


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		List[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		str 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[Any] 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*A_	)


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*A_	)


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_UpperCAmelCase       :		Any 		=    TFLayoutLMModel.from_pretrained(A_	)
														self.assertIsNotNone(A_	)





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







										'''simple docstring'''


										pass







def 							__SCREAMING_SNAKE_CASE							(				)   ->       int:
				# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
				# fmt: off
				_UpperCAmelCase       :		Any 		=    tf.convert_to_tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]] )  # noqa: E231
				_UpperCAmelCase       :		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
				_UpperCAmelCase       :		Dict 		=    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],[1000,1000,1000,1000]],[[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],[1000,1000,1000,1000]]] )  # noqa: E231
				_UpperCAmelCase       :		List[Any] 		=    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)
				_UpperCAmelCase       :		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 				a  (	unittest.TestCase				):


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"	)

										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Tuple 		=    prepare_layoutlm_batch_inputs()

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

										# test the sequence output on [0, :3, :3]
										_UpperCAmelCase       :		int 		=    tf.convert_to_tensor(
										    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]						,					)

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

										# test the pooled output on [1, :3]
										_UpperCAmelCase       :		Union[str, Any] 		=    tf.convert_to_tensor([-0.65_80, -0.02_14, 0.85_52]	)

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


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

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

										# forward pass
										_UpperCAmelCase       :		Optional[Any] 		=    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
										_UpperCAmelCase       :		Optional[int] 		=    outputs.loss
										_UpperCAmelCase       :		int 		=    (2,)
										self.assertEqual(loss.shape						,					A_	)

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


						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		Optional[int] 		=    TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"						,					num_labels=13	)

										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		Union[str, Any] 		=    prepare_layoutlm_batch_inputs()

										# forward pass
										_UpperCAmelCase       :		List[str] 		=    model(
										    input_ids=A_						,					bbox=A_						,					attention_mask=A_						,					token_type_ids=A_						,					labels=A_	)

										# test the shape of the logits
										_UpperCAmelCase       :		List[str] 		=    outputs.logits
										_UpperCAmelCase       :		Union[str, Any] 		=    tf.convert_to_tensor((2, 25, 13)	)
										self.assertEqual(logits.shape						,					A_	)





						@slow
						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


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

										_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase		,							_UpperCAmelCase       :		int 		=    prepare_layoutlm_batch_inputs()

										# forward pass
										_UpperCAmelCase       :		Dict 		=    model(input_ids=A_						,					bbox=A_						,					attention_mask=A_						,					token_type_ids=A_	)

										# test the shape of the logits
										_UpperCAmelCase       :		List[Any] 		=    tf.convert_to_tensor((2, 25)	)
										self.assertEqual(outputs.start_logits.shape						,					A_	)
										self.assertEqual(outputs.end_logits.shape						,					A_	)





from __future__ import annotations

from collections.abc import Callable

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Callable[[int | float], int | float]  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int | float  ,      lowerCAmelCase:  int = 100  ,      )   ->       float:
				_UpperCAmelCase       :		str 		=    x_start
				_UpperCAmelCase       :		Union[str, Any] 		=    fnc(lowerCAmelCase )
				_UpperCAmelCase       :		Tuple 		=    0.0
				for _ in range(lowerCAmelCase ):
								# Approximates small segments of curve as linear and solve
								# for trapezoidal area
								_UpperCAmelCase       :		Union[str, Any] 		=    (x_end - x_start) / steps + xa
								_UpperCAmelCase       :		Optional[Any] 		=    fnc(lowerCAmelCase )
								area += abs(fxa + fxa ) * (xa - xa) / 2
								# Increment step
								_UpperCAmelCase       :		Any 		=    xa
								_UpperCAmelCase       :		Optional[Any] 		=    fxa
				return area


if __name__ == "__main__":

					def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  List[str] )   ->       List[Any]:
									return x**3 + x**2

					print('f(x) = x^3 + x^2')
					print('The area between the curve, x = -5, x = 5 and the x axis is:')
					SCREAMING_SNAKE_CASE_        =      10
					while i <= 100000:
										print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
										i *= 10


