

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 						__UpperCamelCase    :




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						int=13		,	lowerCAmelCase	:						Any=7		,	lowerCAmelCase	:						Any=True		,	lowerCAmelCase	:						int=True		,	lowerCAmelCase	:						str=True		,	lowerCAmelCase	:						str=True		,	lowerCAmelCase	:						str=99		,	lowerCAmelCase	:						Dict=32		,	lowerCAmelCase	:						str=2		,	lowerCAmelCase	:						str=4		,	lowerCAmelCase	:						Any=37		,	lowerCAmelCase	:						Any="gelu"		,	lowerCAmelCase	:						List[str]=0.1		,	lowerCAmelCase	:						Optional[Any]=0.1		,	lowerCAmelCase	:						Union[str, Any]=512		,	lowerCAmelCase	:						int=16		,	lowerCAmelCase	:						int=2		,	lowerCAmelCase	:						Optional[int]=0.02		,	lowerCAmelCase	:						Optional[Any]=3		,	lowerCAmelCase	:						Optional[Any]=4		,	lowerCAmelCase	:						List[str]=None		,	lowerCAmelCase	:						Tuple=1_000		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			parent
		UpperCAmelCase_       				=			batch_size
		UpperCAmelCase_       				=			seq_length
		UpperCAmelCase_       				=			is_training
		UpperCAmelCase_       				=			use_input_mask
		UpperCAmelCase_       				=			use_token_type_ids
		UpperCAmelCase_       				=			use_labels
		UpperCAmelCase_       				=			vocab_size
		UpperCAmelCase_       				=			hidden_size
		UpperCAmelCase_       				=			num_hidden_layers
		UpperCAmelCase_       				=			num_attention_heads
		UpperCAmelCase_       				=			intermediate_size
		UpperCAmelCase_       				=			hidden_act
		UpperCAmelCase_       				=			hidden_dropout_prob
		UpperCAmelCase_       				=			attention_probs_dropout_prob
		UpperCAmelCase_       				=			max_position_embeddings
		UpperCAmelCase_       				=			type_vocab_size
		UpperCAmelCase_       				=			type_sequence_label_size
		UpperCAmelCase_       				=			initializer_range
		UpperCAmelCase_       				=			num_labels
		UpperCAmelCase_       				=			num_choices
		UpperCAmelCase_       				=			scope
		UpperCAmelCase_       				=			range_bbox




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			ids_tensor([self.batch_size, self.seq_length]		,	self.vocab_size							)

		# convert bbox to numpy since TF does not support item assignment
		UpperCAmelCase_       				=			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_       				=			bbox[i, j, 3]
					UpperCAmelCase_       				=			bbox[i, j, 1]
					UpperCAmelCase_       				=			t
				if bbox[i, j, 2] < bbox[i, j, 0]:
					UpperCAmelCase_       				=			bbox[i, j, 2]
					UpperCAmelCase_       				=			bbox[i, j, 0]
					UpperCAmelCase_       				=			t
		UpperCAmelCase_       				=			tf.convert_to_tensor(lowerCAmelCase							)

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

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

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

		UpperCAmelCase_       				=			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       __A					(		self	:						Optional[int]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMModel(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)
		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)
		UpperCAmelCase_       				=			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       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForMaskedLM(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			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       __A					(		self	:						Dict		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						List[str]		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.num_labels
		UpperCAmelCase_       				=			TFLayoutLMForSequenceClassification(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			model(lowerCAmelCase		,	lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)
		self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels)							)




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						int		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.num_labels
		UpperCAmelCase_       				=			TFLayoutLMForTokenClassification(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			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       __A					(		self	:						List[str]		,	lowerCAmelCase	:						Dict		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Optional[Any]		,	lowerCAmelCase	:						Optional[int]		,	lowerCAmelCase	:						Union[str, Any]		,	lowerCAmelCase	:						Any		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForQuestionAnswering(config=lowerCAmelCase							)

		UpperCAmelCase_       				=			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       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.prepare_config_and_inputs()
		(
		    (
		    UpperCAmelCase_   
		)				,   (
		    UpperCAmelCase_   
		)				,   (
		    UpperCAmelCase_   
		)				,   (
		    UpperCAmelCase_   
		)				,   (
		    UpperCAmelCase_   
		)				,   (
		    UpperCAmelCase_   
		)				,   (
		    UpperCAmelCase_   
		)				,   (
		    UpperCAmelCase_   
		)				,      
		)       				=			config_and_inputs
		UpperCAmelCase_       				=			{
		    "input_ids": input_ids,
		    "bbox": bbox,
		    "token_type_ids": token_type_ids,
		    "attention_mask": input_mask,
		}
		return config, inputs_dict


@require_tf
class 						__UpperCamelCase    (					lowercase     ,     lowercase     ,     unittest.TestCase						):
	SCREAMING_SNAKE_CASE__    				=     (
	    (
	        TFLayoutLMModel,
	        TFLayoutLMForMaskedLM,
	        TFLayoutLMForTokenClassification,
	        TFLayoutLMForSequenceClassification,
	        TFLayoutLMForQuestionAnswering,
	    )
	    if is_tf_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE__    				=     (
	    {
	        'feature-extraction': TFLayoutLMModel,
	        'fill-mask': TFLayoutLMForMaskedLM,
	        'text-classification': TFLayoutLMForSequenceClassification,
	        'token-classification': TFLayoutLMForTokenClassification,
	        'zero-shot': TFLayoutLMForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	SCREAMING_SNAKE_CASE__    				=     False
	SCREAMING_SNAKE_CASE__    				=     True
	SCREAMING_SNAKE_CASE__    				=     10




	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMModelTester(self							)
		UpperCAmelCase_       				=			ConfigTester(self		,	config_class=lowerCAmelCase		,	hidden_size=37							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		self.config_tester.run_common_tests()




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase							)




	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase							)




	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase							)




	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase							)




	@slow
	def       __A					(		self	:						Tuple							):


		'''simple docstring'''







		for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			UpperCAmelCase_       				=			TFLayoutLMModel.from_pretrained(lowerCAmelCase							)
			self.assertIsNotNone(lowerCAmelCase							)




	@unittest.skip("Onnx compliancy broke with TF 2.10"							)
	def       __A					(		self	:						Optional[int]							):


		'''simple docstring'''







		pass



def       __lowerCAmelCase      (						):
	# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
	# fmt: off
	UpperCAmelCase_       				=			tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]    )  # noqa: E231
	UpperCAmelCase_       				=			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_       				=			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_       				=			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_       				=			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 						__UpperCamelCase    (					unittest.TestCase						):




	@slow
	def       __A					(		self	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"							)

		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			prepare_layoutlm_batch_inputs()

		# forward pass
		UpperCAmelCase_       				=			model(input_ids=lowerCAmelCase		,	bbox=lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)

		# test the sequence output on [0, :3, :3]
		UpperCAmelCase_       				=			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]		,	lowerCAmelCase		,	atol=1e-3							)							)

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

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




	@slow
	def       __A					(		self	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,	num_labels=2							)

		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			prepare_layoutlm_batch_inputs()

		# forward pass
		UpperCAmelCase_       				=			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
		UpperCAmelCase_       				=			outputs.loss
		UpperCAmelCase_       				=			(2,)
		self.assertEqual(loss.shape		,	lowerCAmelCase							)

		# test the shape of the logits
		UpperCAmelCase_       				=			outputs.logits
		UpperCAmelCase_       				=			(2, 2)
		self.assertEqual(logits.shape		,	lowerCAmelCase							)




	@slow
	def       __A					(		self	:						int							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"		,	num_labels=13							)

		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			prepare_layoutlm_batch_inputs()

		# forward pass
		UpperCAmelCase_       				=			model(
		    input_ids=lowerCAmelCase		,	bbox=lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase		,	labels=lowerCAmelCase							)

		# test the shape of the logits
		UpperCAmelCase_       				=			outputs.logits
		UpperCAmelCase_       				=			tf.convert_to_tensor((2, 25, 13)							)
		self.assertEqual(logits.shape		,	lowerCAmelCase							)




	@slow
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"							)

		UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			prepare_layoutlm_batch_inputs()

		# forward pass
		UpperCAmelCase_       				=			model(input_ids=lowerCAmelCase		,	bbox=lowerCAmelCase		,	attention_mask=lowerCAmelCase		,	token_type_ids=lowerCAmelCase							)

		# test the shape of the logits
		UpperCAmelCase_       				=			tf.convert_to_tensor((2, 25)							)
		self.assertEqual(outputs.start_logits.shape		,	lowerCAmelCase							)
		self.assertEqual(outputs.end_logits.shape		,	lowerCAmelCase							)


from .imports import is_tqdm_available


if is_tqdm_available():
				from tqdm.auto import tqdm as _tqdm

from ..state import PartialState



def       __lowerCAmelCase      (						A = True       ,     *A       ,     **A    ):
	if not is_tqdm_available():
		raise ImportError("Accelerate's `tqdm` module requires `tqdm` to be installed. Please run `pip install tqdm`."    )
	UpperCAmelCase_       				=			False
	if main_process_only:
		UpperCAmelCase_       				=			PartialState().local_process_index == 0
	return _tqdm(*A       ,     **A       ,     disable=A    )