
"""simple docstring"""


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 		lowerCamelCase__					:





			'''simple docstring'''



			def __init__(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase=1_00     ,     __UpperCAmelCase=13     ,     __UpperCAmelCase=30     ,     __UpperCAmelCase=2     ,     __UpperCAmelCase=3     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=True     ,     __UpperCAmelCase=32     ,     __UpperCAmelCase=4     ,     __UpperCAmelCase=4     ,     __UpperCAmelCase=37     ,     __UpperCAmelCase="gelu"     ,     __UpperCAmelCase=0.1     ,     __UpperCAmelCase=0.1     ,     __UpperCAmelCase=10     ,     __UpperCAmelCase=0.0_2     ,     __UpperCAmelCase=3     ,     __UpperCAmelCase=None     ,     __UpperCAmelCase=[0, 1, 2, 3]     ,     ) ->     Tuple:
				_lowerCAmelCase								=parent
				_lowerCAmelCase								=1_00
				_lowerCAmelCase								=batch_size
				_lowerCAmelCase								=image_size
				_lowerCAmelCase								=patch_size
				_lowerCAmelCase								=num_channels
				_lowerCAmelCase								=is_training
				_lowerCAmelCase								=use_labels
				_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								=type_sequence_label_size
				_lowerCAmelCase								=initializer_range
				_lowerCAmelCase								=scope
				_lowerCAmelCase								=out_indices
				_lowerCAmelCase								=num_labels

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



			def 					_lowerCAmelCase		(							self  ) ->     Dict:
				_lowerCAmelCase								=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_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.image_size, self.image_size]     ,     self.num_labels  )

				_lowerCAmelCase								=self.get_config()

				return config, pixel_values, labels, pixel_labels



			def 					_lowerCAmelCase		(							self  ) ->     Any:
				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=__UpperCAmelCase     ,     initializer_range=self.initializer_range     ,     out_indices=self.out_indices     ,     )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Optional[int]:
				_lowerCAmelCase								=DataaVecVisionModel(config=__UpperCAmelCase  )
				model.to(__UpperCAmelCase  )
				model.eval()
				_lowerCAmelCase								=model(__UpperCAmelCase  )
				# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
				_lowerCAmelCase								=(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 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Dict:
				_lowerCAmelCase								=self.type_sequence_label_size
				_lowerCAmelCase								=DataaVecVisionForImageClassification(__UpperCAmelCase  )
				model.to(__UpperCAmelCase  )
				model.eval()
				_lowerCAmelCase								=model(__UpperCAmelCase     ,     labels=__UpperCAmelCase  )
				self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.type_sequence_label_size)  )



			def 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  ) ->     Any:
				_lowerCAmelCase								=self.num_labels
				_lowerCAmelCase								=DataaVecVisionForSemanticSegmentation(__UpperCAmelCase  )
				model.to(__UpperCAmelCase  )
				model.eval()
				_lowerCAmelCase								=model(__UpperCAmelCase  )
				self.parent.assertEqual(
				    result.logits.shape     ,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )
				_lowerCAmelCase								=model(__UpperCAmelCase     ,     labels=__UpperCAmelCase  )
				self.parent.assertEqual(
				    result.logits.shape     ,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)  )



			def 					_lowerCAmelCase		(							self  ) ->     Union[str, Any]:
				_lowerCAmelCase								=self.prepare_config_and_inputs()
				_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase								=config_and_inputs
				_lowerCAmelCase								={"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_torch
class 		lowerCamelCase__					(					__magic_name__ ,	__magic_name__ ,	unittest.TestCase				):





			'''simple docstring'''
			lowerCamelCase  					=		(
			    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase  					=		(
			    {
			        '''feature-extraction''': DataaVecVisionModel,
			        '''image-classification''': DataaVecVisionForImageClassification,
			        '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			lowerCamelCase  					=		False
			lowerCamelCase  					=		False
			lowerCamelCase  					=		False



			def 					_lowerCAmelCase		(							self  ) ->     Dict:
				_lowerCAmelCase								=DataaVecVisionModelTester(self  )
				_lowerCAmelCase								=ConfigTester(
				    self     ,     config_class=__UpperCAmelCase     ,     has_text_modality=__UpperCAmelCase     ,     hidden_size=37  )



			def 					_lowerCAmelCase		(							self  ) ->     Any:
				self.config_tester.run_common_tests()



			def 					_lowerCAmelCase		(							self  ) ->     int:
				# 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 					_lowerCAmelCase		(							self  ) ->     Optional[Any]:
				pass



			def 					_lowerCAmelCase		(							self  ) ->     int:
				_lowerCAmelCase						,							_lowerCAmelCase								=self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_lowerCAmelCase								=model_class(__UpperCAmelCase  )
					self.assertIsInstance(model.get_input_embeddings()     ,     (nn.Module)  )
					_lowerCAmelCase								=model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(__UpperCAmelCase     ,     nn.Linear  )  )



			def 					_lowerCAmelCase		(							self  ) ->     str:
				_lowerCAmelCase						,							_lowerCAmelCase								=self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					_lowerCAmelCase								=model_class(__UpperCAmelCase  )
					_lowerCAmelCase								=inspect.signature(model.forward  )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					_lowerCAmelCase								=[*signature.parameters.keys()]

					_lowerCAmelCase								=["""pixel_values"""]
					self.assertListEqual(arg_names[:1]     ,     __UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self  ) ->     int:
				_lowerCAmelCase								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self  ) ->     Dict:
				_lowerCAmelCase								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_segmentation(*__UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self  ) ->     Union[str, Any]:
				if not self.model_tester.is_training:
					return

				_lowerCAmelCase						,							_lowerCAmelCase								=self.model_tester.prepare_config_and_inputs_for_common()
				_lowerCAmelCase								=True

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

					_lowerCAmelCase								=model_class(__UpperCAmelCase  )
					model.to(__UpperCAmelCase  )
					model.train()
					_lowerCAmelCase								=self._prepare_for_class(__UpperCAmelCase     ,     __UpperCAmelCase     ,     return_labels=__UpperCAmelCase  )
					_lowerCAmelCase								=model(**__UpperCAmelCase  ).loss
					loss.backward()



			def 					_lowerCAmelCase		(							self  ) ->     List[Any]:
				_lowerCAmelCase						,							_lowerCAmelCase								=self.model_tester.prepare_config_and_inputs_for_common()
				if not self.model_tester.is_training:
					return

				_lowerCAmelCase								=False
				_lowerCAmelCase								=True

				for model_class in self.all_model_classes:
					if model_class in [*get_values(__UpperCAmelCase  )] 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":
						_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase						,							_lowerCAmelCase								=inputs_dict["""pixel_values"""].shape
						_lowerCAmelCase								=torch.zeros(
						    [self.model_tester.batch_size, height, width]     ,     device=__UpperCAmelCase  ).long()
					_lowerCAmelCase								=model_class(__UpperCAmelCase  )
					model.gradient_checkpointing_enable()
					model.to(__UpperCAmelCase  )
					model.train()
					_lowerCAmelCase								=self._prepare_for_class(__UpperCAmelCase     ,     __UpperCAmelCase     ,     return_labels=__UpperCAmelCase  )
					_lowerCAmelCase								=model(**__UpperCAmelCase  ).loss
					loss.backward()



			def 					_lowerCAmelCase		(							self  ) ->     Dict:
				_lowerCAmelCase						,							_lowerCAmelCase								=self.model_tester.prepare_config_and_inputs_for_common()

				_lowerCAmelCase								=_config_zero_init(__UpperCAmelCase  )
				for model_class in self.all_model_classes:
					_lowerCAmelCase								=model_class(config=__UpperCAmelCase  )
					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 					_lowerCAmelCase		(							self     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase=2e-4     ,     __UpperCAmelCase="outputs"     ,     __UpperCAmelCase=None  ) ->     Dict:
				# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
				super().check_pt_tf_outputs(__UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase     ,     __UpperCAmelCase  )



			def 					_lowerCAmelCase		(							self  ) ->     Dict:
				_lowerCAmelCase								=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase  )



			@slow
			def 					_lowerCAmelCase		(							self  ) ->     Tuple:
				for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					_lowerCAmelCase								=DataaVecVisionModel.from_pretrained(__UpperCAmelCase  )
					self.assertIsNotNone(__UpperCAmelCase  )







def     _lowerCamelCase()				-> List[str]:
	_lowerCAmelCase								=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
	return image







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





			'''simple docstring'''



			@cached_property
			def 					_lowerCAmelCase		(							self  ) ->     Optional[Any]:
				return (
				    BeitImageProcessor.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ) if is_vision_available() else None
				)



			@slow
			def 					_lowerCAmelCase		(							self  ) ->     List[Any]:
				_lowerCAmelCase								=DataaVecVisionForImageClassification.from_pretrained("""facebook/data2vec-vision-base-ft1k"""  ).to(
				    __UpperCAmelCase  )

				_lowerCAmelCase								=self.default_image_processor
				_lowerCAmelCase								=prepare_img()
				_lowerCAmelCase								=image_processor(images=__UpperCAmelCase     ,     return_tensors="""pt"""  ).to(__UpperCAmelCase  )

				# forward pass
				with torch.no_grad():
					_lowerCAmelCase								=model(**__UpperCAmelCase  )
				_lowerCAmelCase								=outputs.logits

				# verify the logits
				_lowerCAmelCase								=torch.Size((1, 10_00)  )
				self.assertEqual(logits.shape     ,     __UpperCAmelCase  )

				_lowerCAmelCase								=torch.tensor([0.3_2_7_7, -0.1_3_9_5, 0.0_9_1_1]  ).to(__UpperCAmelCase  )

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

				_lowerCAmelCase								=[model.config.labelaid[i] for i in ["""remote control, remote""", """tabby, tabby cat"""]]
				self.assertEqual(logits[0].topk(2  ).indices.cpu().tolist()     ,     __UpperCAmelCase  )





"""simple docstring"""


from typing import TYPE_CHECKING

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


__A       						=       {'configuration_bert_generation': ['BertGenerationConfig']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       ['BertGenerationTokenizer']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A       						=       [
			    'BertGenerationDecoder',
			    'BertGenerationEncoder',
			    'BertGenerationPreTrainedModel',
			    'load_tf_weights_in_bert_generation',
			]


if TYPE_CHECKING:
			from .configuration_bert_generation import BertGenerationConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_bert_generation import BertGenerationTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_bert_generation import (
						    BertGenerationDecoder,
						    BertGenerationEncoder,
						    BertGenerationPreTrainedModel,
						    load_tf_weights_in_bert_generation,
						)

else:
			import sys

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



