





import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
			    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    LayoutLMvaConfig,
			    LayoutLMvaForQuestionAnswering,
			    LayoutLMvaForSequenceClassification,
			    LayoutLMvaForTokenClassification,
			    LayoutLMvaModel,
			)
			from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
			from PIL import Image

			from transformers import LayoutLMvaImageProcessor






class       __A					:

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase=2 ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=9_9 ,						__lowerCAmelCase=3_6 ,						__lowerCAmelCase=3 ,						__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=6 ,						__lowerCAmelCase=6 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=4 ,						__lowerCAmelCase=None ,						__lowerCAmelCase=1_0_0_0 ,						):



				'''simple docstring'''







				lowerCamelCase__              =	parent
				lowerCamelCase__              =	batch_size
				lowerCamelCase__              =	num_channels
				lowerCamelCase__              =	image_size
				lowerCamelCase__              =	patch_size
				lowerCamelCase__              =	text_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__              =	coordinate_size
				lowerCamelCase__              =	shape_size
				lowerCamelCase__              =	num_labels
				lowerCamelCase__              =	num_choices
				lowerCamelCase__              =	scope
				lowerCamelCase__              =	range_bbox

				# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
				lowerCamelCase__              =	text_seq_length
				lowerCamelCase__              =	(image_size // patch_size) ** 2 + 1
				lowerCamelCase__              =	self.text_seq_length + self.image_seq_length





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	ids_tensor([self.batch_size, self.text_seq_length] ,						self.vocab_size							)

				lowerCamelCase__              =	ids_tensor([self.batch_size, self.text_seq_length, 4] ,						self.range_bbox							)
				# 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]:
							lowerCamelCase__              =	bbox[i, j, 3]
							lowerCamelCase__              =	bbox[i, j, 1]
							lowerCamelCase__              =	t
						if bbox[i, j, 2] < bbox[i, j, 0]:
							lowerCamelCase__              =	bbox[i, j, 2]
							lowerCamelCase__              =	bbox[i, j, 0]
							lowerCamelCase__              =	t

				lowerCamelCase__              =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

				lowerCamelCase__              =	None
				if self.use_input_mask:
					lowerCamelCase__              =	random_attention_mask([self.batch_size, self.text_seq_length]							)

				lowerCamelCase__              =	None
				if self.use_token_type_ids:
					lowerCamelCase__              =	ids_tensor([self.batch_size, self.text_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.text_seq_length] ,						self.num_labels							)

				lowerCamelCase__              =	LayoutLMvaConfig(
				    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 ,						coordinate_size=self.coordinate_size ,						shape_size=self.shape_size ,						input_size=self.image_size ,						patch_size=self.patch_size ,						)

				return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	LayoutLMvaModel(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()

				# text + image
				lowerCamelCase__              =	model(__lowerCAmelCase ,						pixel_values=__lowerCAmelCase							)
				lowerCamelCase__              =	model(
				    __lowerCAmelCase ,						bbox=__lowerCAmelCase ,						pixel_values=__lowerCAmelCase ,						attention_mask=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						bbox=__lowerCAmelCase ,						pixel_values=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	model(__lowerCAmelCase ,						bbox=__lowerCAmelCase ,						pixel_values=__lowerCAmelCase							)

				self.parent.assertEqual(result.last_hidden_state.shape ,						(self.batch_size, self.seq_length, self.hidden_size)							)

				# text only
				lowerCamelCase__              =	model(__lowerCAmelCase							)
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,						(self.batch_size, self.text_seq_length, self.hidden_size)							)

				# image only
				lowerCamelCase__              =	model(pixel_values=__lowerCAmelCase							)
				self.parent.assertEqual(
				    result.last_hidden_state.shape ,						(self.batch_size, self.image_seq_length, self.hidden_size)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.num_labels
				lowerCamelCase__              =	LayoutLMvaForSequenceClassification(__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__              =	model(
				    __lowerCAmelCase ,						bbox=__lowerCAmelCase ,						pixel_values=__lowerCAmelCase ,						attention_mask=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase ,						labels=__lowerCAmelCase ,						)
				self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	self.num_labels
				lowerCamelCase__              =	LayoutLMvaForTokenClassification(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__              =	model(
				    __lowerCAmelCase ,						bbox=__lowerCAmelCase ,						pixel_values=__lowerCAmelCase ,						attention_mask=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase ,						labels=__lowerCAmelCase ,						)
				self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.text_seq_length, self.num_labels)							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	LayoutLMvaForQuestionAnswering(config=__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)
				model.eval()
				lowerCamelCase__              =	model(
				    __lowerCAmelCase ,						bbox=__lowerCAmelCase ,						pixel_values=__lowerCAmelCase ,						attention_mask=__lowerCAmelCase ,						token_type_ids=__lowerCAmelCase ,						start_positions=__lowerCAmelCase ,						end_positions=__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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.prepare_config_and_inputs()
				(
				    (
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			(
				    lowerCamelCase__
				)       ,			
				)              =	config_and_inputs
				lowerCamelCase__              =	{
				    '''input_ids''': input_ids,
				    '''bbox''': bbox,
				    '''pixel_values''': pixel_values,
				    '''token_type_ids''': token_type_ids,
				    '''attention_mask''': input_mask,
				}
				return config, inputs_dict






@require_torch
class       __A					(					lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False
			lowerCAmelCase_							  =		False

			lowerCAmelCase_							  =		(
			    (
			        LayoutLMvaModel,
			        LayoutLMvaForSequenceClassification,
			        LayoutLMvaForTokenClassification,
			        LayoutLMvaForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCAmelCase_							  =		(
			    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
			    if is_torch_available()
			    else {}
			)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				return True





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	LayoutLMvaModelTester(self							)
				lowerCamelCase__              =	ConfigTester(self ,						config_class=__lowerCAmelCase ,						hidden_size=3_7							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase=False							):



				'''simple docstring'''







				lowerCamelCase__              =	copy.deepcopy(__lowerCAmelCase							)
				if model_class in get_values(__lowerCAmelCase							):
					lowerCamelCase__              =	{
					    k: v.unsqueeze(1							).expand(-1 ,						self.model_tester.num_choices ,						-1							).contiguous()
					    if isinstance(__lowerCAmelCase ,						torch.Tensor							) and v.ndim > 1
					    else v
					    for k, v in inputs_dict.items()
					}
				if return_labels:
					if model_class in get_values(__lowerCAmelCase							):
						lowerCamelCase__              =	torch.ones(self.model_tester.batch_size ,						dtype=torch.long ,						device=__lowerCAmelCase							)
					elif model_class in get_values(__lowerCAmelCase							):
						lowerCamelCase__              =	torch.zeros(
						    self.model_tester.batch_size ,						dtype=torch.long ,						device=__lowerCAmelCase							)
						lowerCamelCase__              =	torch.zeros(
						    self.model_tester.batch_size ,						dtype=torch.long ,						device=__lowerCAmelCase							)
					elif model_class in [
					    *get_values(__lowerCAmelCase							),
					]:
						lowerCamelCase__              =	torch.zeros(
						    self.model_tester.batch_size ,						dtype=torch.long ,						device=__lowerCAmelCase							)
					elif model_class in [
					    *get_values(__lowerCAmelCase							),
					]:
						lowerCamelCase__              =	torch.zeros(
						    (self.model_tester.batch_size, self.model_tester.text_seq_length) ,						dtype=torch.long ,						device=__lowerCAmelCase ,						)

				return inputs_dict





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				self.config_tester.run_common_tests()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					lowerCamelCase__              =	type
					self.model_tester.create_and_check_model(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase							)





			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase__              =	LayoutLMvaModel.from_pretrained(__lowerCAmelCase							)
					self.assertIsNotNone(__lowerCAmelCase							)





def lowerCAmelCase__()       ->   Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__              =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
	return image






@require_torch
class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			@cached_property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return LayoutLMvaImageProcessor(apply_ocr=__lowerCAmelCase							) if is_vision_available() else None





			@slow
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''							).to(__lowerCAmelCase							)

				lowerCamelCase__              =	self.default_image_processor
				lowerCamelCase__              =	prepare_img()
				lowerCamelCase__              =	image_processor(images=__lowerCAmelCase ,						return_tensors='''pt'''							).pixel_values.to(__lowerCAmelCase							)

				lowerCamelCase__              =	torch.tensor([[1, 2]]							)
				lowerCamelCase__              =	torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]							).unsqueeze(0							)

				# forward pass
				lowerCamelCase__              =	model(
				    input_ids=input_ids.to(__lowerCAmelCase							) ,						bbox=bbox.to(__lowerCAmelCase							) ,						pixel_values=pixel_values.to(__lowerCAmelCase							) ,						)

				# verify the logits
				lowerCamelCase__              =	torch.Size((1, 1_9_9, 7_6_8)							)
				self.assertEqual(outputs.last_hidden_state.shape ,						__lowerCAmelCase							)

				lowerCamelCase__              =	torch.tensor(
				    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]							).to(__lowerCAmelCase							)

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a						=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTNeoForCausalLM",
			    "GPTNeoForQuestionAnswering",
			    "GPTNeoForSequenceClassification",
			    "GPTNeoForTokenClassification",
			    "GPTNeoModel",
			    "GPTNeoPreTrainedModel",
			    "load_tf_weights_in_gpt_neo",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FlaxGPTNeoForCausalLM",
			    "FlaxGPTNeoModel",
			    "FlaxGPTNeoPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			_a						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



