
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					try:
										lowercase							=						os.environ[key]
					except KeyError:
										# KEY isn't set, default to `default`.
										lowercase							=						default
					else:
										# KEY is set, convert it to True or False.
										try:
															lowercase							=						strtobool(lowerCAmelCase__		)
										except ValueError:
															# More values are supported, but let's keep the message simple.
															raise ValueError(f'If set, {key} must be yes or no.'		)
					return _value


lowercase__		:Tuple   =    parse_flag_from_env("RUN_SLOW", default=False)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skip('''Test was skipped'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(_run_slow_tests ,			'''test is slow'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(not torch.cuda.is_available() ,			'''test requires only a CPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.is_available() ,			'''test requires a GPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_xpu_available() ,			'''test requires a XPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_mps_available() ,			'''test requires a `mps` backend support in `torch`'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(
					    is_transformers_available() and is_datasets_available() ,			'''test requires the Hugging Face suite'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_bnb_available() ,			'''test requires the bitsandbytes library'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_tpu_available() ,			'''test requires TPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.device_count() == 1 ,			'''test requires a GPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.xpu.device_count() == 1 ,			'''test requires a XPU'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.cuda.device_count() > 1 ,			'''test requires multiple GPUs'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(torch.xpu.device_count() > 1 ,			'''test requires multiple XPUs'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_safetensors_available() ,			'''test requires safetensors'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_deepspeed_available() ,			'''test requires DeepSpeed'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_torch_version('''>=''' ,			'''1.12.0'''		) ,			'''test requires torch version >= 1.12.0'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__=None ,			lowerCAmelCase__=None		):
					'''simple docstring'''
					if test_case is None:
										return partial(lowerCAmelCase__ ,			version=lowerCAmelCase__		)
					return unittest.skipUnless(is_torch_version('''>=''' ,			lowerCAmelCase__		) ,			f'test requires torch version >= {version}'		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_tensorboard_available() ,			'''test requires Tensorboard'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_wandb_available() ,			'''test requires wandb'''		)(lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(is_comet_ml_available() ,			'''test requires comet_ml'''		)(lowerCAmelCase__		)


lowercase__		:Union[str, Any]   =    (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					return unittest.skipUnless(
					    _atleast_one_tracker_available ,			'''test requires at least one tracker to be available and for `comet_ml` to not be installed''' ,			)(lowerCAmelCase__		)







class 				lowercase		(					unittest.TestCase		):
							lowercase_       :       Dict       					=True




							@classmethod
							def  A__      (				cls):
												lowercase							=						tempfile.mkdtemp()




							@classmethod
							def  A__      (				cls):
												if os.path.exists(cls.tmpdir):
																	shutil.rmtree(cls.tmpdir)





							def  A__      (				self):
												if self.clear_on_setup:
																	for path in Path(self.tmpdir).glob('''**/*'''):
																						if path.is_file():
																											path.unlink()
																						elif path.is_dir():
																											shutil.rmtree(A__)







class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self):
												super().tearDown()
												# Reset the state of the AcceleratorState singleton.
												AcceleratorState._reset_state()
												PartialState._reset_state()







class 				lowercase		(					unittest.TestCase		):




							def  A__      (				self   ,A__):
												lowercase							=						mocks if isinstance(A__   ,(tuple, list)) else [mocks]
												for m in self.mocks:
																	m.start()
																	self.addCleanup(m.stop)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						AcceleratorState()
					lowercase							=						tensor[None].clone().to(state.device		)
					lowercase							=						gather(lowerCAmelCase__		).cpu()
					lowercase							=						tensor[0].cpu()
					for i in range(tensors.shape[0]		):
										if not torch.equal(tensors[i] ,			lowerCAmelCase__		):
															return False
					return True







class 				lowercase		:




							def __init__(				self   ,A__   ,A__   ,A__):
												lowercase							=						returncode
												lowercase							=						stdout
												lowercase							=						stderr



async def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__		):
					'''simple docstring'''
					while True:
										lowercase							=						await stream.readline()
										if line:
															callback(lowerCAmelCase__		)
										else:
															break



async def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None ,			lowerCAmelCase__=False ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					if echo:
										print('''\nRunning: ''' ,			''' '''.join(lowerCAmelCase__		)		)

					lowercase							=						await asyncio.create_subprocess_exec(
					    cmd[0] ,			*cmd[1:] ,			stdin=lowerCAmelCase__ ,			stdout=asyncio.subprocess.PIPE ,			stderr=asyncio.subprocess.PIPE ,			env=lowerCAmelCase__ ,			)

					# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
					# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
					#
					# If it starts hanging, will need to switch to the following code. The problem is that no data
					# will be seen until it's done and if it hangs for example there will be no debug info.
					# out, err = await p.communicate()
					# return _RunOutput(p.returncode, out, err)

					lowercase							=						[]
					lowercase							=						[]

					def tee(lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=""		):
										lowercase							=						line.decode('''utf-8'''		).rstrip()
										sink.append(lowerCAmelCase__		)
										if not quiet:
															print(lowerCAmelCase__ ,			lowerCAmelCase__ ,			file=lowerCAmelCase__		)

    # XXX: the timeout doesn't seem to make any difference here
					await asyncio.wait(
					    [
					        asyncio.create_task(_read_stream(p.stdout ,			lambda lowerCAmelCase__		: tee(lowerCAmelCase__ ,			lowerCAmelCase__ ,			sys.stdout ,			label='''stdout:'''		)		)		),
					        asyncio.create_task(_read_stream(p.stderr ,			lambda lowerCAmelCase__		: tee(lowerCAmelCase__ ,			lowerCAmelCase__ ,			sys.stderr ,			label='''stderr:'''		)		)		),
					    ] ,			timeout=lowerCAmelCase__ ,			)
					return _RunOutput(await p.wait() ,			lowerCAmelCase__ ,			lowerCAmelCase__		)



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=None ,			lowerCAmelCase__=None ,			lowerCAmelCase__=180 ,			lowerCAmelCase__=False ,			lowerCAmelCase__=True		):
					'''simple docstring'''
					lowercase							=						asyncio.get_event_loop()
					lowercase							=						loop.run_until_complete(
					    _stream_subprocess(lowerCAmelCase__ ,			env=lowerCAmelCase__ ,			stdin=lowerCAmelCase__ ,			timeout=lowerCAmelCase__ ,			quiet=lowerCAmelCase__ ,			echo=lowerCAmelCase__		)		)

					lowercase							=						''' '''.join(lowerCAmelCase__		)
					if result.returncode > 0:
										lowercase							=						'''\n'''.join(result.stderr		)
										raise RuntimeError(
										    f'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
										    f'The combined stderr from workers follows:\n{stderr}'		)

					return result







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							pass



def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					try:
										lowercase							=						subprocess.check_output(lowerCAmelCase__ ,			stderr=subprocess.STDOUT		)
										if return_stdout:
															if hasattr(lowerCAmelCase__ ,			'''decode'''		):
																				lowercase							=						output.decode('''utf-8'''		)
															return output
					except subprocess.CalledProcessError as e:
										raise SubprocessCallException(
										    f'Command `{" ".join(lowerCAmelCase__		)}` failed with the following error:\n\n{e.output.decode()}'		) from e






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 				lowercase		:




							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   ,):
												lowercase							=						parent
												lowercase							=						batch_size
												lowercase							=						num_channels
												lowercase							=						image_size
												lowercase							=						patch_size
												lowercase							=						text_seq_length
												lowercase							=						is_training
												lowercase							=						use_input_mask
												lowercase							=						use_token_type_ids
												lowercase							=						use_labels
												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						intermediate_size
												lowercase							=						hidden_act
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						type_sequence_label_size
												lowercase							=						initializer_range
												lowercase							=						coordinate_size
												lowercase							=						shape_size
												lowercase							=						num_labels
												lowercase							=						num_choices
												lowercase							=						scope
												lowercase							=						range_bbox

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




							def  A__      (				self):
												lowercase							=						ids_tensor([self.batch_size, self.text_seq_length]   ,self.vocab_size)

												lowercase							=						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]:
																											lowercase							=						bbox[i, j, 3]
																											lowercase							=						bbox[i, j, 1]
																											lowercase							=						t
																						if bbox[i, j, 2] < bbox[i, j, 0]:
																											lowercase							=						bbox[i, j, 2]
																											lowercase							=						bbox[i, j, 0]
																											lowercase							=						t

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

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

												lowercase							=						None
												if self.use_token_type_ids:
																	lowercase							=						ids_tensor([self.batch_size, self.text_seq_length]   ,self.type_vocab_size)

												lowercase							=						None
												lowercase							=						None
												if self.use_labels:
																	lowercase							=						ids_tensor([self.batch_size]   ,self.type_sequence_label_size)
																	lowercase							=						ids_tensor([self.batch_size, self.text_seq_length]   ,self.num_labels)

												lowercase							=						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   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						LayoutLMvaModel(config=A__)
												model.to(A__)
												model.eval()

												# text + image
												lowercase							=						model(A__   ,pixel_values=A__)
												lowercase							=						model(
												    A__   ,bbox=A__   ,pixel_values=A__   ,attention_mask=A__   ,token_type_ids=A__)
												lowercase							=						model(A__   ,bbox=A__   ,pixel_values=A__   ,token_type_ids=A__)
												lowercase							=						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
												lowercase							=						model(A__)
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,(self.batch_size, self.text_seq_length, self.hidden_size))

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




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						self.num_labels
												lowercase							=						LayoutLMvaForSequenceClassification(A__)
												model.to(A__)
												model.eval()
												lowercase							=						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  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						self.num_labels
												lowercase							=						LayoutLMvaForTokenClassification(config=A__)
												model.to(A__)
												model.eval()
												lowercase							=						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  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__   ,A__):
												lowercase							=						LayoutLMvaForQuestionAnswering(config=A__)
												model.to(A__)
												model.eval()
												lowercase							=						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  A__      (				self):
												lowercase							=						self.prepare_config_and_inputs()
												(
												    (
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						(
												    lowercase
												)	,						
												)							=						config_and_inputs
												lowercase							=						{
												    '''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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Dict       					=False
							lowercase_       :       Any       					=False
							lowercase_       :       Tuple       					=False

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




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__   ,A__):
												# `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):
												lowercase							=						LayoutLMvaModelTester(self)
												lowercase							=						ConfigTester(self   ,config_class=A__   ,hidden_size=3_7)




							def  A__      (				self   ,A__   ,A__   ,A__=False):
												lowercase							=						copy.deepcopy(A__)
												if model_class in get_values(A__):
																	lowercase							=						{
																	    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__):
																						lowercase							=						torch.ones(self.model_tester.batch_size   ,dtype=torch.long   ,device=A__)
																	elif model_class in get_values(A__):
																						lowercase							=						torch.zeros(
																						    self.model_tester.batch_size   ,dtype=torch.long   ,device=A__)
																						lowercase							=						torch.zeros(
																						    self.model_tester.batch_size   ,dtype=torch.long   ,device=A__)
																	elif model_class in [
																	    *get_values(A__),
																	]:
																						lowercase							=						torch.zeros(
																						    self.model_tester.batch_size   ,dtype=torch.long   ,device=A__)
																	elif model_class in [
																	    *get_values(A__),
																	]:
																						lowercase							=						torch.zeros(
																						    (self.model_tester.batch_size, self.model_tester.text_seq_length)   ,dtype=torch.long   ,device=A__   ,)

												return inputs_dict




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




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




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




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_sequence_classification(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_token_classification(*A__)




							def  A__      (				self):
												lowercase							=						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_question_answering(*A__)





							@slow
							def  A__      (				self):
												for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase							=						LayoutLMvaModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
					return image







@require_torch
class 				lowercase		(					unittest.TestCase		):




							@cached_property
							def  A__      (				self):
												return LayoutLMvaImageProcessor(apply_ocr=A__) if is_vision_available() else None





							@slow
							def  A__      (				self):
												lowercase							=						LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base''').to(A__)

												lowercase							=						self.default_image_processor
												lowercase							=						prepare_img()
												lowercase							=						image_processor(images=A__   ,return_tensors='''pt''').pixel_values.to(A__)

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

												# forward pass
												lowercase							=						model(
												    input_ids=input_ids.to(A__)   ,bbox=bbox.to(A__)   ,pixel_values=pixel_values.to(A__)   ,)

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

												lowercase							=						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))




