
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 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=4				,				_UpperCamelCase=2				,				_UpperCamelCase=7				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=True				,				_UpperCamelCase=99				,				_UpperCamelCase=36				,				_UpperCamelCase=3				,				_UpperCamelCase=4				,				_UpperCamelCase=37				,				_UpperCamelCase="gelu"				,				_UpperCamelCase=0.1				,				_UpperCamelCase=0.1				,				_UpperCamelCase=512				,				_UpperCamelCase=16				,				_UpperCamelCase=2				,				_UpperCamelCase=0.02				,				_UpperCamelCase=6				,				_UpperCamelCase=6				,				_UpperCamelCase=3				,				_UpperCamelCase=4				,				_UpperCamelCase=None				,				_UpperCamelCase=1_000				,				)				->      List[Any]:
							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      __a						(					self     )				->      int:
							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      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[Any]:
							lowerCAmelCase_						=	LayoutLMvaModel(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()

							# text + image
							lowerCAmelCase_						=	model(_UpperCamelCase				,				pixel_values=_UpperCamelCase     )
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				bbox=_UpperCamelCase				,				pixel_values=_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase     )
							lowerCAmelCase_						=	model(_UpperCamelCase				,				bbox=_UpperCamelCase				,				pixel_values=_UpperCamelCase				,				token_type_ids=_UpperCamelCase     )
							lowerCAmelCase_						=	model(_UpperCamelCase				,				bbox=_UpperCamelCase				,				pixel_values=_UpperCamelCase     )

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

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

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




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Union[str, Any]:
							lowerCAmelCase_						=	self.num_labels
							lowerCAmelCase_						=	LayoutLMvaForSequenceClassification(_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				bbox=_UpperCamelCase				,				pixel_values=_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase				,				)
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.num_labels)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      List[str]:
							lowerCAmelCase_						=	self.num_labels
							lowerCAmelCase_						=	LayoutLMvaForTokenClassification(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				bbox=_UpperCamelCase				,				pixel_values=_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				labels=_UpperCamelCase				,				)
							self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.text_seq_length, self.num_labels)     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	LayoutLMvaForQuestionAnswering(config=_UpperCamelCase     )
							model.to(_UpperCamelCase     )
							model.eval()
							lowerCAmelCase_						=	model(
							    _UpperCamelCase				,				bbox=_UpperCamelCase				,				pixel_values=_UpperCamelCase				,				attention_mask=_UpperCamelCase				,				token_type_ids=_UpperCamelCase				,				start_positions=_UpperCamelCase				,				end_positions=_UpperCamelCase				,				)
							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     )				->      List[Any]:
							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 	_lowerCAmelCase					(	__a					,      __a					,      unittest.TestCase       ):
				_lowercase					=False
				_lowercase					=False
				_lowercase					=False

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




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      int:
							# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
							# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
							# the sequence dimension of the text embedding only.
							# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
							return True




				def      __a						(					self     )				->      Tuple:
							lowerCAmelCase_						=	LayoutLMvaModelTester(self     )
							lowerCAmelCase_						=	ConfigTester(self				,				config_class=_UpperCamelCase				,				hidden_size=37     )




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

							return inputs_dict




				def      __a						(					self     )				->      Tuple:
							self.config_tester.run_common_tests()




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_UpperCamelCase     )




				def      __a						(					self     )				->      Any:
							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(*_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase     )




				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase     )




				def      __a						(					self     )				->      List[Any]:
							lowerCAmelCase_						=	self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase     )




				@slow
				def      __a						(					self     )				->      Union[str, Any]:
							for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase_						=	LayoutLMvaModel.from_pretrained(_UpperCamelCase     )
										self.assertIsNotNone(_UpperCamelCase     )






def    lowerCamelCase__				(				):




			"""simple docstring"""


			lowerCAmelCase_						=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
			return image





@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):




				@cached_property
				def      __a						(					self     )				->      str:
							return LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase     ) if is_vision_available() else None




				@slow
				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"     ).to(_UpperCamelCase     )

							lowerCAmelCase_						=	self.default_image_processor
							lowerCAmelCase_						=	prepare_img()
							lowerCAmelCase_						=	image_processor(images=_UpperCamelCase				,				return_tensors="pt"     ).pixel_values.to(_UpperCamelCase     )

							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(_UpperCamelCase     )				,				bbox=bbox.to(_UpperCamelCase     )				,				pixel_values=pixel_values.to(_UpperCamelCase     )				,				)

							# verify the logits
							lowerCAmelCase_						=	torch.Size((1, 199, 768)     )
							self.assertEqual(outputs.last_hidden_state.shape				,				_UpperCamelCase     )

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

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





import requests

_A   =     "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="






def    lowerCamelCase__				(				__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	requests.get(_NEWS_API + bbc_news_api_key				).json()
			# each article in the list is a dict
			for i, article in enumerate(bbc_news_page["articles"]    ,							1				):
						print(F"""{i}.) {article['title']}"""				)


if __name__ == "__main__":
			fetch_bbc_news(bbc_news_api_key="<Your BBC News API key goes here>")



