
"""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 						SCREAMING_SNAKE_CASE_ :




		"""simple docstring"""





		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__=1_3							,  lowerCAmelCase__=7							,  lowerCAmelCase__=True							,  lowerCAmelCase__=True							,  lowerCAmelCase__=True							,  lowerCAmelCase__=True							,  lowerCAmelCase__=9_9							,  lowerCAmelCase__=3_2							,  lowerCAmelCase__=2							,  lowerCAmelCase__=4							,  lowerCAmelCase__=3_7							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=0.1							,  lowerCAmelCase__=5_1_2							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2							,  lowerCAmelCase__=0.02							,  lowerCAmelCase__=3							,  lowerCAmelCase__=4							,  lowerCAmelCase__=None							,  lowerCAmelCase__=1_0_0_0							,  ):
				__SCREAMING_SNAKE_CASE            = parent
				__SCREAMING_SNAKE_CASE            = batch_size
				__SCREAMING_SNAKE_CASE            = seq_length
				__SCREAMING_SNAKE_CASE            = is_training
				__SCREAMING_SNAKE_CASE            = use_input_mask
				__SCREAMING_SNAKE_CASE            = use_token_type_ids
				__SCREAMING_SNAKE_CASE            = use_labels
				__SCREAMING_SNAKE_CASE            = vocab_size
				__SCREAMING_SNAKE_CASE            = hidden_size
				__SCREAMING_SNAKE_CASE            = num_hidden_layers
				__SCREAMING_SNAKE_CASE            = num_attention_heads
				__SCREAMING_SNAKE_CASE            = intermediate_size
				__SCREAMING_SNAKE_CASE            = hidden_act
				__SCREAMING_SNAKE_CASE            = hidden_dropout_prob
				__SCREAMING_SNAKE_CASE            = attention_probs_dropout_prob
				__SCREAMING_SNAKE_CASE            = max_position_embeddings
				__SCREAMING_SNAKE_CASE            = type_vocab_size
				__SCREAMING_SNAKE_CASE            = type_sequence_label_size
				__SCREAMING_SNAKE_CASE            = initializer_range
				__SCREAMING_SNAKE_CASE            = num_labels
				__SCREAMING_SNAKE_CASE            = num_choices
				__SCREAMING_SNAKE_CASE            = scope
				__SCREAMING_SNAKE_CASE            = range_bbox




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = 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            = 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            = bbox[i, j, 3]
										__SCREAMING_SNAKE_CASE            = bbox[i, j, 1]
										__SCREAMING_SNAKE_CASE            = t
								if bbox[i, j, 2] < bbox[i, j, 0]:
										__SCREAMING_SNAKE_CASE            = bbox[i, j, 2]
										__SCREAMING_SNAKE_CASE            = bbox[i, j, 0]
										__SCREAMING_SNAKE_CASE            = t
				__SCREAMING_SNAKE_CASE            = tf.convert_to_tensor(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = None
				if self.use_input_mask:
						__SCREAMING_SNAKE_CASE            = random_attention_mask([self.batch_size, self.seq_length])

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

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

				__SCREAMING_SNAKE_CASE            = 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							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = TFLayoutLMModel(config=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = model(lowerCAmelCase__							,  lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = 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							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = TFLayoutLMForMaskedLM(config=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = 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							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.num_labels
				__SCREAMING_SNAKE_CASE            = TFLayoutLMForSequenceClassification(config=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = 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							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = self.num_labels
				__SCREAMING_SNAKE_CASE            = TFLayoutLMForTokenClassification(config=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = 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							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__):
				__SCREAMING_SNAKE_CASE            = TFLayoutLMForQuestionAnswering(config=lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE            = 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):
				__SCREAMING_SNAKE_CASE            = 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
				)       ,
				)            = config_and_inputs
				__SCREAMING_SNAKE_CASE            = {
				    """input_ids""": input_ids,
				    """bbox""": bbox,
				    """token_type_ids""": token_type_ids,
				    """attention_mask""": input_mask,
				}
				return config, inputs_dict



@require_tf
class 						SCREAMING_SNAKE_CASE_ (						__a     ,				__a     ,				unittest.TestCase ):




		"""simple docstring"""





		__lowercase							:  int						   =		(
		    (
		        TFLayoutLMModel,
		        TFLayoutLMForMaskedLM,
		        TFLayoutLMForTokenClassification,
		        TFLayoutLMForSequenceClassification,
		        TFLayoutLMForQuestionAnswering,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowercase							:  str						   =		(
		    {
		        '''feature-extraction''': TFLayoutLMModel,
		        '''fill-mask''': TFLayoutLMForMaskedLM,
		        '''text-classification''': TFLayoutLMForSequenceClassification,
		        '''token-classification''': TFLayoutLMForTokenClassification,
		        '''zero-shot''': TFLayoutLMForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		__lowercase							:  Tuple						   =		False
		__lowercase							:  int						   =		True
		__lowercase							:  Any						   =		10




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFLayoutLMModelTester(self)
				__SCREAMING_SNAKE_CASE            = ConfigTester(self							,  config_class=lowerCAmelCase__							,  hidden_size=3_7)




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




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__)




		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__)




		@slow
		def 	snake_case_     (     self):
				for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__SCREAMING_SNAKE_CASE            = TFLayoutLMModel.from_pretrained(lowerCAmelCase__)
						self.assertIsNotNone(lowerCAmelCase__)




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



def 						_lowerCAmelCase				(				):
		# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
		# fmt: off
		__SCREAMING_SNAKE_CASE            = 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
		__SCREAMING_SNAKE_CASE            = 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            = 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
		__SCREAMING_SNAKE_CASE            = 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            = 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 						SCREAMING_SNAKE_CASE_ (						unittest.TestCase ):




		"""simple docstring"""





		@slow
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = TFLayoutLMModel.from_pretrained("""microsoft/layoutlm-base-uncased""")

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = prepare_layoutlm_batch_inputs()

				# forward pass
				__SCREAMING_SNAKE_CASE            = model(input_ids=lowerCAmelCase__							,  bbox=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__)

				# test the sequence output on [0, :3, :3]
				__SCREAMING_SNAKE_CASE            = 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]							,  lowerCAmelCase__							,  atol=1E-3))

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

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




		@slow
		def 	snake_case_     (     self):
				# initialize model with randomly initialized sequence classification head
				__SCREAMING_SNAKE_CASE            = 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            = prepare_layoutlm_batch_inputs()

				# forward pass
				__SCREAMING_SNAKE_CASE            = 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
				__SCREAMING_SNAKE_CASE            = outputs.loss
				__SCREAMING_SNAKE_CASE            = (2,)
				self.assertEqual(loss.shape							,  lowerCAmelCase__)

				# test the shape of the logits
				__SCREAMING_SNAKE_CASE            = outputs.logits
				__SCREAMING_SNAKE_CASE            = (2, 2)
				self.assertEqual(logits.shape							,  lowerCAmelCase__)




		@slow
		def 	snake_case_     (     self):
				# initialize model with randomly initialized token classification head
				__SCREAMING_SNAKE_CASE            = 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            = prepare_layoutlm_batch_inputs()

				# forward pass
				__SCREAMING_SNAKE_CASE            = model(
				    input_ids=lowerCAmelCase__							,  bbox=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__							,  labels=lowerCAmelCase__)

				# test the shape of the logits
				__SCREAMING_SNAKE_CASE            = outputs.logits
				__SCREAMING_SNAKE_CASE            = tf.convert_to_tensor((2, 2_5, 1_3))
				self.assertEqual(logits.shape							,  lowerCAmelCase__)




		@slow
		def 	snake_case_     (     self):
				# initialize model with randomly initialized token classification head
				__SCREAMING_SNAKE_CASE            = TFLayoutLMForQuestionAnswering.from_pretrained("""microsoft/layoutlm-base-uncased""")

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = prepare_layoutlm_batch_inputs()

				# forward pass
				__SCREAMING_SNAKE_CASE            = model(input_ids=lowerCAmelCase__							,  bbox=lowerCAmelCase__							,  attention_mask=lowerCAmelCase__							,  token_type_ids=lowerCAmelCase__)

				# test the shape of the logits
				__SCREAMING_SNAKE_CASE            = tf.convert_to_tensor((2, 2_5))
				self.assertEqual(outputs.start_logits.shape							,  lowerCAmelCase__)
				self.assertEqual(outputs.end_logits.shape							,  lowerCAmelCase__)






"""simple docstring"""





from ..utils import DummyObject, requires_backends
class 						SCREAMING_SNAKE_CASE_ (						metaclass=__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		['''keras_nlp''']




		def __init__(     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				requires_backends(self							,  ["""keras_nlp"""])




