





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       __lowerCAmelCase					:


	'''simple docstring'''





	def __init__(						self:						Dict,					lowerCamelCase_:						List[str],					lowerCamelCase_:						List[str]=13,					lowerCamelCase_:						int=7,					lowerCamelCase_:						Optional[int]=True,					lowerCamelCase_:						Union[str, Any]=True,					lowerCamelCase_:						int=True,					lowerCamelCase_:						Dict=True,					lowerCamelCase_:						List[str]=99,					lowerCamelCase_:						List[str]=32,					lowerCamelCase_:						str=2,					lowerCamelCase_:						Dict=4,					lowerCamelCase_:						List[Any]=37,					lowerCamelCase_:						List[Any]="gelu",					lowerCamelCase_:						Union[str, Any]=0.1,					lowerCamelCase_:						int=0.1,					lowerCamelCase_:						List[Any]=512,					lowerCamelCase_:						Any=16,					lowerCamelCase_:						Optional[Any]=2,					lowerCamelCase_:						List[str]=0.0_2,					lowerCamelCase_:						Tuple=3,					lowerCamelCase_:						int=4,					lowerCamelCase_:						Optional[Any]=None,					lowerCamelCase_:						Optional[int]=1000,					):
			lowercase__					:      List[str]      =		parent
			lowercase__					:      List[Any]      =		batch_size
			lowercase__					:      Tuple      =		seq_length
			lowercase__					:      Any      =		is_training
			lowercase__					:      List[str]      =		use_input_mask
			lowercase__					:      Any      =		use_token_type_ids
			lowercase__					:      Dict      =		use_labels
			lowercase__					:      List[str]      =		vocab_size
			lowercase__					:      Any      =		hidden_size
			lowercase__					:      List[Any]      =		num_hidden_layers
			lowercase__					:      Optional[int]      =		num_attention_heads
			lowercase__					:      Any      =		intermediate_size
			lowercase__					:      List[str]      =		hidden_act
			lowercase__					:      Union[str, Any]      =		hidden_dropout_prob
			lowercase__					:      str      =		attention_probs_dropout_prob
			lowercase__					:      str      =		max_position_embeddings
			lowercase__					:      Optional[int]      =		type_vocab_size
			lowercase__					:      Optional[int]      =		type_sequence_label_size
			lowercase__					:      List[Any]      =		initializer_range
			lowercase__					:      int      =		num_labels
			lowercase__					:      Union[str, Any]      =		num_choices
			lowercase__					:      Optional[Any]      =		scope
			lowercase__					:      List[str]      =		range_bbox

	def    snake_case__(						self:						Dict			):
			lowercase__					:      str      =		ids_tensor([self.batch_size, self.seq_length],					self.vocab_size			)

			# convert bbox to numpy since TF does not support item assignment
			lowercase__					:      List[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]:
									lowercase__					:      str      =		bbox[i, j, 3]
									lowercase__					:      Union[str, Any]      =		bbox[i, j, 1]
									lowercase__					:      str      =		t
							if bbox[i, j, 2] < bbox[i, j, 0]:
									lowercase__					:      Union[str, Any]      =		bbox[i, j, 2]
									lowercase__					:      List[Any]      =		bbox[i, j, 0]
									lowercase__					:      Tuple      =		t
			lowercase__					:      Tuple      =		tf.convert_to_tensor(lowerCamelCase_			)

			lowercase__					:      Dict      =		None
			if self.use_input_mask:
					lowercase__					:      int      =		random_attention_mask([self.batch_size, self.seq_length]			)

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

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

			lowercase__					:      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    snake_case__(						self:						Optional[int],					lowerCamelCase_:						str,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						str,					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						str			):
			lowercase__					:      Dict      =		TFLayoutLMModel(config=lowerCamelCase_			)

			lowercase__					:      int      =		model(lowerCamelCase_,					lowerCamelCase_,					attention_mask=lowerCamelCase_,					token_type_ids=lowerCamelCase_			)
			lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_,					token_type_ids=lowerCamelCase_			)
			lowercase__					:      Union[str, Any]      =		model(lowerCamelCase_,					lowerCamelCase_			)
			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    snake_case__(						self:						Dict,					lowerCamelCase_:						Any,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Any,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Union[str, Any]			):
			lowercase__					:      Dict      =		TFLayoutLMForMaskedLM(config=lowerCamelCase_			)

			lowercase__					:      Any      =		model(lowerCamelCase_,					lowerCamelCase_,					attention_mask=lowerCamelCase_,					token_type_ids=lowerCamelCase_,					labels=lowerCamelCase_			)
			self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length, self.vocab_size)			)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						Any,					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Dict,					lowerCamelCase_:						List[str]			):
			lowercase__					:      List[str]      =		self.num_labels
			lowercase__					:      Union[str, Any]      =		TFLayoutLMForSequenceClassification(config=lowerCamelCase_			)

			lowercase__					:      List[Any]      =		model(lowerCamelCase_,					lowerCamelCase_,					attention_mask=lowerCamelCase_,					token_type_ids=lowerCamelCase_			)
			self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)			)

	def    snake_case__(						self:						str,					lowerCamelCase_:						Optional[Any],					lowerCamelCase_:						List[str],					lowerCamelCase_:						List[str],					lowerCamelCase_:						List[Any],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						List[str],					lowerCamelCase_:						Union[str, Any],					lowerCamelCase_:						Optional[int]			):
			lowercase__					:      Union[str, Any]      =		self.num_labels
			lowercase__					:      Any      =		TFLayoutLMForTokenClassification(config=lowerCamelCase_			)

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

	def    snake_case__(						self:						Union[str, Any],					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						int,					lowerCamelCase_:						Optional[int],					lowerCamelCase_:						str,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						int,					lowerCamelCase_:						Dict,					lowerCamelCase_:						str			):
			lowercase__					:      int      =		TFLayoutLMForQuestionAnswering(config=lowerCamelCase_			)

			lowercase__					:      str      =		model(lowerCamelCase_,					lowerCamelCase_,					attention_mask=lowerCamelCase_,					token_type_ids=lowerCamelCase_			)
			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    snake_case__(						self:						Tuple			):
			lowercase__					:      Any      =		self.prepare_config_and_inputs()
			(
			    (
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				(
			    lowercase__
			)   ,				
			)					:      Tuple      =		config_and_inputs
			lowercase__					:      Tuple      =		{
			    'input_ids': input_ids,
			    'bbox': bbox,
			    'token_type_ids': token_type_ids,
			    'attention_mask': input_mask,
			}
			return config, inputs_dict


@require_tf
class       __lowerCAmelCase					(       _UpperCamelCase   ,    _UpperCamelCase   ,    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    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      int      =		TFLayoutLMModelTester(self			)
			lowercase__					:      Tuple      =		ConfigTester(self,					config_class=lowerCamelCase_,					hidden_size=37			)

	def    snake_case__(						self:						Optional[Any]			):
			self.config_tester.run_common_tests()

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      int      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase_			)

	def    snake_case__(						self:						int			):
			lowercase__					:      Optional[Any]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase_			)

	def    snake_case__(						self:						int			):
			lowercase__					:      List[str]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_			)

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      Optional[int]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Optional[int]      =		self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_			)

	@slow
	def    snake_case__(						self:						List[str]			):
			for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowercase__					:      int      =		TFLayoutLMModel.from_pretrained(lowerCamelCase_			)
					self.assertIsNotNone(lowerCamelCase_			)





	@unittest.skip('Onnx compliancy broke with TF 2.10'			)
	def    snake_case__(						self:						Optional[Any]			):
			pass


def       SCREAMING_SNAKE_CASE__  (       )   ->							List[Any]:




		'''simple docstring'''
		# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
		# fmt: off
		lowercase__					:      List[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
		lowercase__					:      Tuple      =		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
		lowercase__					:      int      =		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
		lowercase__					:      Dict      =		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)
		lowercase__					:      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       __lowerCAmelCase					(       unittest.TestCase      ):


	'''simple docstring'''





	@slow
	def    snake_case__(						self:						Union[str, Any]			):
			lowercase__					:      str      =		TFLayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'			)

			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      Dict      =		prepare_layoutlm_batch_inputs()

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

			# test the sequence output on [0, :3, :3]
			lowercase__					:      Optional[int]      =		tf.convert_to_tensor(
			    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]],					)

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

			# test the pooled output on [1, :3]
			lowercase__					:      str      =		tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]			)

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

	@slow
	def    snake_case__(						self:						Tuple			):
			# initialize model with randomly initialized sequence classification head
			lowercase__					:      List[str]      =		TFLayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased',					num_labels=2			)

			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      List[Any]      =		prepare_layoutlm_batch_inputs()

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

			# test whether we get a loss as a scalar
			lowercase__					:      str      =		outputs.loss
			lowercase__					:      int      =		(2,)
			self.assertEqual(loss.shape,					lowerCamelCase_			)

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

	@slow
	def    snake_case__(						self:						List[Any]			):
			# initialize model with randomly initialized token classification head
			lowercase__					:      Dict      =		TFLayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased',					num_labels=13			)

			lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__   ,				lowercase__					:      int      =		prepare_layoutlm_batch_inputs()

			# forward pass
			lowercase__					:      Any      =		model(
			    input_ids=lowerCamelCase_,					bbox=lowerCamelCase_,					attention_mask=lowerCamelCase_,					token_type_ids=lowerCamelCase_,					labels=lowerCamelCase_			)

			# test the shape of the logits
			lowercase__					:      Optional[int]      =		outputs.logits
			lowercase__					:      Optional[Any]      =		tf.convert_to_tensor((2, 25, 13)			)
			self.assertEqual(logits.shape,					lowerCamelCase_			)





	@slow
	def    snake_case__(						self:						Dict			):
			# initialize model with randomly initialized token classification head
			lowercase__					:      Optional[int]      =		TFLayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'			)

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

			# forward pass
			lowercase__					:      Optional[int]      =		model(input_ids=lowerCamelCase_,					bbox=lowerCamelCase_,					attention_mask=lowerCamelCase_,					token_type_ids=lowerCamelCase_			)

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









def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							Optional[int]:




		'''simple docstring'''
		if b == 0:
				return 1
		if (b % 2) == 0:
				return actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)
		else:
				return a * actual_power(_lowercase   ,    int(b / 2			)			) * actual_power(_lowercase   ,    int(b / 2			)			)


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : int   ,    _lowercase     : int			)   ->							float:




		'''simple docstring'''
		if b < 0:
				return 1 / actual_power(_lowercase   ,    _lowercase			)
		return actual_power(_lowercase   ,    _lowercase			)


if __name__ == "__main__":
			print(power(-2, -3))


