

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_MAPPING,
							    DataaVecVisionForImageClassification,
							    DataaVecVisionForSemanticSegmentation,
							    DataaVecVisionModel,
							)
							from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import BeitImageProcessor







class 						lowercase     :


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case=1_00						,							__snake_case=13						,							__snake_case=30						,							__snake_case=2						,							__snake_case=3						,							__snake_case=True						,							__snake_case=True						,							__snake_case=32						,							__snake_case=4						,							__snake_case=4						,							__snake_case=37						,							__snake_case="gelu"						,							__snake_case=0.1						,							__snake_case=0.1						,							__snake_case=10						,							__snake_case=0.0_2						,							__snake_case=3						,							__snake_case=None						,							__snake_case=[0, 1, 2, 3]						,							):
			_UpperCamelCase :		Optional[Any]								=    parent
			_UpperCamelCase :		Optional[Any]								=    1_00
			_UpperCamelCase :		str								=    batch_size
			_UpperCamelCase :		Optional[int]								=    image_size
			_UpperCamelCase :		Any								=    patch_size
			_UpperCamelCase :		Optional[Any]								=    num_channels
			_UpperCamelCase :		Optional[int]								=    is_training
			_UpperCamelCase :		int								=    use_labels
			_UpperCamelCase :		Union[str, Any]								=    hidden_size
			_UpperCamelCase :		Optional[Any]								=    num_hidden_layers
			_UpperCamelCase :		str								=    num_attention_heads
			_UpperCamelCase :		Any								=    intermediate_size
			_UpperCamelCase :		Union[str, Any]								=    hidden_act
			_UpperCamelCase :		Union[str, Any]								=    hidden_dropout_prob
			_UpperCamelCase :		Tuple								=    attention_probs_dropout_prob
			_UpperCamelCase :		Any								=    type_sequence_label_size
			_UpperCamelCase :		Optional[Any]								=    initializer_range
			_UpperCamelCase :		str								=    scope
			_UpperCamelCase :		str								=    out_indices
			_UpperCamelCase :		Dict								=    num_labels

			# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
			_UpperCamelCase :		Any								=    (image_size // patch_size) ** 2
			_UpperCamelCase :		int								=    num_patches + 1


		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase :		int								=    None
			_UpperCamelCase :		Dict								=    None
			if self.use_labels:
				_UpperCamelCase :		List[str]								=    ids_tensor([self.batch_size]						,							self.type_sequence_label_size)
				_UpperCamelCase :		Tuple								=    ids_tensor([self.batch_size, self.image_size, self.image_size]						,							self.num_labels)

			_UpperCamelCase :		Optional[Any]								=    self.get_config()

			return config, pixel_values, labels, pixel_labels


		def 		A__       (					self):
			return DataaVecVisionConfig(
			    vocab_size=self.vocab_size						,							image_size=self.image_size						,							patch_size=self.patch_size						,							num_channels=self.num_channels						,							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						,							is_decoder=__snake_case						,							initializer_range=self.initializer_range						,							out_indices=self.out_indices						,							)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Optional[int]								=    DataaVecVisionModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase :		List[Any]								=    model(__snake_case)
			# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
			_UpperCamelCase :		Optional[Any]								=    (self.image_size // self.patch_size) ** 2
			self.parent.assertEqual(result.last_hidden_state.shape						,							(self.batch_size, num_patches + 1, self.hidden_size))


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		str								=    self.type_sequence_label_size
			_UpperCamelCase :		Union[str, Any]								=    DataaVecVisionForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase :		str								=    model(__snake_case						,							labels=__snake_case)
			self.parent.assertEqual(result.logits.shape						,							(self.batch_size, self.type_sequence_label_size))


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case):
			_UpperCamelCase :		Union[str, Any]								=    self.num_labels
			_UpperCamelCase :		Dict								=    DataaVecVisionForSemanticSegmentation(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase :		Dict								=    model(__snake_case)
			self.parent.assertEqual(
			    result.logits.shape						,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
			_UpperCamelCase :		Union[str, Any]								=    model(__snake_case						,							labels=__snake_case)
			self.parent.assertEqual(
			    result.logits.shape						,							(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))






		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    self.prepare_config_and_inputs()
			_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Union[str, Any]								=    config_and_inputs
			_UpperCamelCase :		Any								=    {'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class 						lowercase     (					_lowercase     ,				_lowercase     ,				unittest.TestCase				):


		"""simple docstring"""




		a__       							=	(
		    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		a__       							=	(
		    {
		        "feature-extraction": DataaVecVisionModel,
		        "image-classification": DataaVecVisionForImageClassification,
		        "image-segmentation": DataaVecVisionForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		a__       							=	False
		a__       							=	False
		a__       							=	False


		def 		A__       (					self):
			_UpperCamelCase :		int								=    DataaVecVisionModelTester(self)
			_UpperCamelCase :		Optional[Any]								=    ConfigTester(
			    self						,							config_class=__snake_case						,							has_text_modality=__snake_case						,							hidden_size=37)


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


		def 		A__       (					self):
			# Data2VecVision does not use inputs_embeds
			pass


		@require_torch_multi_gpu
		@unittest.skip(
		    reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`')
		def 		A__       (					self):
			pass


		def 		A__       (					self):
			_UpperCamelCase						,		_UpperCamelCase :		Optional[int]								=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase :		Optional[int]								=    model_class(__snake_case)
				self.assertIsInstance(model.get_input_embeddings()						,							(nn.Module))
				_UpperCamelCase :		Tuple								=    model.get_output_embeddings()
				self.assertTrue(x is None or isinstance(__snake_case						,							nn.Linear))


		def 		A__       (					self):
			_UpperCamelCase						,		_UpperCamelCase :		Dict								=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
				_UpperCamelCase :		List[Any]								=    model_class(__snake_case)
				_UpperCamelCase :		str								=    inspect.signature(model.forward)
				# signature.parameters is an OrderedDict => so arg_names order is deterministic
				_UpperCamelCase :		int								=    [*signature.parameters.keys()]

				_UpperCamelCase :		int								=    ['pixel_values']
				self.assertListEqual(arg_names[:1]						,							__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_segmentation(*__snake_case)


		def 		A__       (					self):
			if not self.model_tester.is_training:
				return

			_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    self.model_tester.prepare_config_and_inputs_for_common()
			_UpperCamelCase :		Tuple								=    True

			for model_class in self.all_model_classes:
				if model_class in [*get_values(__snake_case)]:
					continue

				_UpperCamelCase :		Union[str, Any]								=    model_class(__snake_case)
				model.to(__snake_case)
				model.train()
				_UpperCamelCase :		Optional[Any]								=    self._prepare_for_class(__snake_case						,							__snake_case						,							return_labels=__snake_case)
				_UpperCamelCase :		str								=    model(**__snake_case).loss
				loss.backward()


		def 		A__       (					self):
			_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    self.model_tester.prepare_config_and_inputs_for_common()
			if not self.model_tester.is_training:
				return

			_UpperCamelCase :		Union[str, Any]								=    False
			_UpperCamelCase :		Union[str, Any]								=    True

			for model_class in self.all_model_classes:
				if model_class in [*get_values(__snake_case)] or not model_class.supports_gradient_checkpointing:
					continue
				# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
				# this can then be incorporated into _prepare_for_class in test_modeling_common.py
				elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
					_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Any								=    inputs_dict['pixel_values'].shape
					_UpperCamelCase :		List[str]								=    torch.zeros(
					    [self.model_tester.batch_size, height, width]						,							device=__snake_case).long()
				_UpperCamelCase :		Optional[Any]								=    model_class(__snake_case)
				model.gradient_checkpointing_enable()
				model.to(__snake_case)
				model.train()
				_UpperCamelCase :		Dict								=    self._prepare_for_class(__snake_case						,							__snake_case						,							return_labels=__snake_case)
				_UpperCamelCase :		Optional[Any]								=    model(**__snake_case).loss
				loss.backward()


		def 		A__       (					self):
			_UpperCamelCase						,		_UpperCamelCase :		int								=    self.model_tester.prepare_config_and_inputs_for_common()

			_UpperCamelCase :		Union[str, Any]								=    _config_zero_init(__snake_case)
			for model_class in self.all_model_classes:
				_UpperCamelCase :		Union[str, Any]								=    model_class(config=__snake_case)
				for name, param in model.named_parameters():
					# we skip lambda parameters as these require special initial values
					# determined by config.layer_scale_init_value
					if "lambda" in name:
						continue
					if param.requires_grad:
						self.assertIn(
						    ((param.data.mean() * 1e9).round() / 1e9).item()						,							[0.0, 1.0]						,							msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''						,							)


		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case=2e-4						,							__snake_case="outputs"						,							__snake_case=None):
			# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
			super().check_pt_tf_outputs(__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		List[Any]								=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)






		@slow
		def 		A__       (					self):
			for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase :		Any								=    DataaVecVisionModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






def        lowerCamelCase_  (				)    ->					Tuple:




	'''simple docstring'''




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







@require_torch
@require_vision
class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		@cached_property
		def 		A__       (					self):
			return (
			    BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k') if is_vision_available() else None
			)






		@slow
		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k').to(
			    __snake_case)

			_UpperCamelCase :		Any								=    self.default_image_processor
			_UpperCamelCase :		int								=    prepare_img()
			_UpperCamelCase :		Tuple								=    image_processor(images=__snake_case						,							return_tensors='pt').to(__snake_case)

			# forward pass
			with torch.no_grad():
				_UpperCamelCase :		int								=    model(**__snake_case)
			_UpperCamelCase :		str								=    outputs.logits

			# verify the logits
			_UpperCamelCase :		List[str]								=    torch.Size((1, 10_00))
			self.assertEqual(logits.shape						,							__snake_case)

			_UpperCamelCase :		Tuple								=    torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]).to(__snake_case)

			self.assertTrue(torch.allclose(logits[0, :3]						,							__snake_case						,							atol=1e-4))

			_UpperCamelCase :		Union[str, Any]								=    [model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
			self.assertEqual(logits[0].topk(2).indices.cpu().tolist()						,							__snake_case)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__						=		{
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['LayoutLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'LayoutLMForMaskedLM',
							    'LayoutLMForSequenceClassification',
							    'LayoutLMForTokenClassification',
							    'LayoutLMForQuestionAnswering',
							    'LayoutLMModel',
							    'LayoutLMPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFLayoutLMForMaskedLM',
							    'TFLayoutLMForSequenceClassification',
							    'TFLayoutLMForTokenClassification',
							    'TFLayoutLMForQuestionAnswering',
							    'TFLayoutLMMainLayer',
							    'TFLayoutLMModel',
							    'TFLayoutLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase__						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
