

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 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=2       ,							_a=3       ,							_a=4       ,							_a=2       ,							_a=7       ,							_a=True       ,							_a=True       ,							_a=True       ,							_a=True       ,							_a=9_9       ,							_a=3_6       ,							_a=3       ,							_a=4       ,							_a=3_7       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=5_1_2       ,							_a=1_6       ,							_a=2       ,							_a=0.02       ,							_a=6       ,							_a=6       ,							_a=3       ,							_a=4       ,							_a=None       ,							_a=1_0_0_0       ,							)    ->    str:
												_a	:    Dict         =		parent
												_a	:    Optional[Any]         =		batch_size
												_a	:    Optional[int]         =		num_channels
												_a	:    List[Any]         =		image_size
												_a	:    int         =		patch_size
												_a	:    Dict         =		text_seq_length
												_a	:    Optional[Any]         =		is_training
												_a	:    Union[str, Any]         =		use_input_mask
												_a	:    Union[str, Any]         =		use_token_type_ids
												_a	:    Dict         =		use_labels
												_a	:    Dict         =		vocab_size
												_a	:    List[str]         =		hidden_size
												_a	:    Union[str, Any]         =		num_hidden_layers
												_a	:    Union[str, Any]         =		num_attention_heads
												_a	:    Optional[int]         =		intermediate_size
												_a	:    Any         =		hidden_act
												_a	:    Tuple         =		hidden_dropout_prob
												_a	:    List[str]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Tuple         =		type_vocab_size
												_a	:    List[Any]         =		type_sequence_label_size
												_a	:    str         =		initializer_range
												_a	:    List[str]         =		coordinate_size
												_a	:    Union[str, Any]         =		shape_size
												_a	:    Tuple         =		num_labels
												_a	:    int         =		num_choices
												_a	:    Union[str, Any]         =		scope
												_a	:    List[str]         =		range_bbox

												# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
												_a	:    List[Any]         =		text_seq_length
												_a	:    Dict         =		(image_size // patch_size) ** 2 + 1
												_a	:    List[str]         =		self.text_seq_length + self.image_seq_length


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    List[Any]         =		ids_tensor([self.batch_size, self.text_seq_length]       ,							self.vocab_size       )

												_a	:    List[Any]         =		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]:
																											_a	:    Union[str, Any]         =		bbox[i, j, 3]
																											_a	:    List[Any]         =		bbox[i, j, 1]
																											_a	:    Dict         =		t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																											_a	:    List[str]         =		bbox[i, j, 2]
																											_a	:    List[Any]         =		bbox[i, j, 0]
																											_a	:    int         =		t

												_a	:    Any         =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

												_a	:    Any         =		None
												if self.use_input_mask:
																	_a	:    int         =		random_attention_mask([self.batch_size, self.text_seq_length]       )

												_a	:    Tuple         =		None
												if self.use_token_type_ids:
																	_a	:    List[Any]         =		ids_tensor([self.batch_size, self.text_seq_length]       ,							self.type_vocab_size       )

												_a	:    Dict         =		None
												_a	:    int         =		None
												if self.use_labels:
																	_a	:    Optional[int]         =		ids_tensor([self.batch_size]       ,							self.type_sequence_label_size       )
																	_a	:    Union[str, Any]         =		ids_tensor([self.batch_size, self.text_seq_length]       ,							self.num_labels       )

												_a	:    str         =		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 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    List[Any]:
												_a	:    Tuple         =		LayoutLMvaModel(config=_a       )
												model.to(_a       )
												model.eval()

												# text + image
												_a	:    Dict         =		model(_a       ,							pixel_values=_a       )
												_a	:    str         =		model(
												    _a       ,							bbox=_a       ,							pixel_values=_a       ,							attention_mask=_a       ,							token_type_ids=_a       )
												_a	:    Any         =		model(_a       ,							bbox=_a       ,							pixel_values=_a       ,							token_type_ids=_a       )
												_a	:    Dict         =		model(_a       ,							bbox=_a       ,							pixel_values=_a       )

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

												# text only
												_a	:    int         =		model(_a       )
												self.parent.assertEqual(
												    result.last_hidden_state.shape       ,							(self.batch_size, self.text_seq_length, self.hidden_size)       )

												# image only
												_a	:    str         =		model(pixel_values=_a       )
												self.parent.assertEqual(
												    result.last_hidden_state.shape       ,							(self.batch_size, self.image_seq_length, self.hidden_size)       )


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    str:
												_a	:    Dict         =		self.num_labels
												_a	:    List[Any]         =		LayoutLMvaForSequenceClassification(_a       )
												model.to(_a       )
												model.eval()
												_a	:    str         =		model(
												    _a       ,							bbox=_a       ,							pixel_values=_a       ,							attention_mask=_a       ,							token_type_ids=_a       ,							labels=_a       ,							)
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)       )


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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    List[Any]:
												_a	:    Union[str, Any]         =		LayoutLMvaForQuestionAnswering(config=_a       )
												model.to(_a       )
												model.eval()
												_a	:    Optional[int]         =		model(
												    _a       ,							bbox=_a       ,							pixel_values=_a       ,							attention_mask=_a       ,							token_type_ids=_a       ,							start_positions=_a       ,							end_positions=_a       ,							)
												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 				__lowercase				(			self       )    ->    int:
												_a	:    Any         =		self.prepare_config_and_inputs()
												(
												    (
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					
												)	:    Optional[Any]         =		config_and_inputs
												_a	:    List[str]         =		{
												    '''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 				UpperCAmelCase_		(			__lowercase				,       __lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						str	    =    False
							UpperCAmelCase__       :						Any	    =    False
							UpperCAmelCase__       :						int	    =    False

							UpperCAmelCase__       :						Optional[Any]	    =    (
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__       :						Any	    =    (
							    {"document-question-answering": LayoutLMvaForQuestionAnswering, "feature-extraction": LayoutLMvaModel}
							    if is_torch_available()
							    else {}
							)


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       ,							_a       ,							_a       )    ->    Union[str, Any]:
												# `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 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Optional[int]         =		LayoutLMvaModelTester(self       )
												_a	:    Any         =		ConfigTester(self       ,							config_class=_a       ,							hidden_size=3_7       )


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

												return inputs_dict


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												self.config_tester.run_common_tests()


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Tuple         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    Dict         =		self.model_tester.prepare_config_and_inputs()
												for type in ["absolute", "relative_key", "relative_key_query"]:
																	_a	:    int         =		type
																	self.model_tester.create_and_check_model(*_a       )


							def 				__lowercase				(			self       )    ->    str:
												_a	:    int         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*_a       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    Optional[Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*_a       )


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    Union[str, Any]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*_a       )




							@slow
							def 				__lowercase				(			self       )    ->    Optional[int]:
												for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a	:    Any         =		LayoutLMvaModel.from_pretrained(_a       )
																	self.assertIsNotNone(_a       )



def    __UpperCAmelCase (	)			->     int:

					"""simple docstring"""

					_a	:    Optional[int]         =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image







@require_torch
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							@cached_property
							def 				__lowercase				(			self       )    ->    Optional[Any]:
												return LayoutLMvaImageProcessor(apply_ocr=_a       ) if is_vision_available() else None




							@slow
							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    List[Any]         =		LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''       ).to(_a       )

												_a	:    Optional[int]         =		self.default_image_processor
												_a	:    List[Any]         =		prepare_img()
												_a	:    str         =		image_processor(images=_a       ,							return_tensors='''pt'''       ).pixel_values.to(_a       )

												_a	:    Optional[int]         =		torch.tensor([[1, 2]]       )
												_a	:    int         =		torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]       ).unsqueeze(0       )

												# forward pass
												_a	:    Tuple         =		model(
												    input_ids=input_ids.to(_a       )       ,							bbox=bbox.to(_a       )       ,							pixel_values=pixel_values.to(_a       )       ,							)

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

												_a	:    List[Any]         =		torch.tensor(
												    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]       ).to(_a       )

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






import requests

a__				=		'''YOUR API KEY'''



def    __UpperCAmelCase (	__a      :			str		,__a      :			str = giphy_api_key	)			->     list:

					"""simple docstring"""

					_a	:    Optional[Any]         =		'''+'''.join(query.split()	)
					_a	:    Union[str, Any]         =		F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
					_a	:    List[Any]         =		requests.get(__a	).json()['''data''']
					return [gif["url"] for gif in gifs]


if __name__ == "__main__":
			print('''\n'''.join(get_gifs('''space ship''')))



