



import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
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,
							    BeitForImageClassification,
							    BeitForMaskedImageModeling,
							    BeitForSemanticSegmentation,
							    BeitModel,
							)
							from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							import PIL
							from PIL import Image

							from transformers import BeitImageProcessor



class  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=1_00	,__lowerCamelCase=13	,__lowerCamelCase=30	,__lowerCamelCase=2	,__lowerCamelCase=3	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=32	,__lowerCamelCase=4	,__lowerCamelCase=4	,__lowerCamelCase=37	,__lowerCamelCase="gelu"	,__lowerCamelCase=0.1	,__lowerCamelCase=0.1	,__lowerCamelCase=10	,__lowerCamelCase=0.02	,__lowerCamelCase=3	,__lowerCamelCase=None	,__lowerCamelCase=[0, 1, 2, 3]	,)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  parent
						lowerCAmelCase__  :       int				=  1_00
						lowerCAmelCase__  :       Tuple				=  batch_size
						lowerCAmelCase__  :       Optional[int]				=  image_size
						lowerCAmelCase__  :       int				=  patch_size
						lowerCAmelCase__  :       List[str]				=  num_channels
						lowerCAmelCase__  :       Any				=  is_training
						lowerCAmelCase__  :       Tuple				=  use_labels
						lowerCAmelCase__  :       int				=  hidden_size
						lowerCAmelCase__  :       str				=  num_hidden_layers
						lowerCAmelCase__  :       Tuple				=  num_attention_heads
						lowerCAmelCase__  :       Tuple				=  intermediate_size
						lowerCAmelCase__  :       str				=  hidden_act
						lowerCAmelCase__  :       Optional[int]				=  hidden_dropout_prob
						lowerCAmelCase__  :       Optional[Any]				=  attention_probs_dropout_prob
						lowerCAmelCase__  :       Optional[int]				=  type_sequence_label_size
						lowerCAmelCase__  :       List[Any]				=  initializer_range
						lowerCAmelCase__  :       List[Any]				=  scope
						lowerCAmelCase__  :       Optional[Any]				=  out_indices
						lowerCAmelCase__  :       Optional[int]				=  num_labels

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



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

						lowerCAmelCase__  :       Tuple				=  None
						lowerCAmelCase__  :       Union[str, Any]				=  None
						if self.use_labels:
										lowerCAmelCase__  :       Union[str, Any]				=  ids_tensor([self.batch_size]	,self.type_sequence_label_size				)
										lowerCAmelCase__  :       List[Any]				=  ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels				)

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

						return config, pixel_values, labels, pixel_labels



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						return BeitConfig(
						    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=__lowerCamelCase	,initializer_range=self.initializer_range	,out_indices=self.out_indices	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  BeitModel(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase				)
						self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  BeitForMaskedImageModeling(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.seq_length - 1, self.vocab_size)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.type_sequence_label_size
						lowerCAmelCase__  :       int				=  BeitForImageClassification(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Optional[Any]				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.type_sequence_label_size)				)

						# test greyscale images
						lowerCAmelCase__  :       str				=  1
						lowerCAmelCase__  :       List[str]				=  BeitForImageClassification(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()

						lowerCAmelCase__  :       Optional[int]				=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
						lowerCAmelCase__  :       Any				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.type_sequence_label_size)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  self.num_labels
						lowerCAmelCase__  :       List[Any]				=  BeitForSemanticSegmentation(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       List[str]				=  model(__lowerCamelCase				)
						self.parent.assertEqual(
						    result.logits.shape	,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)
						lowerCAmelCase__  :       List[str]				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(
						    result.logits.shape	,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  self.prepare_config_and_inputs()
						lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__		,	lowerCAmelCase__  :       Union[str, Any]				=  config_and_inputs
						lowerCAmelCase__  :       Dict				=  {'''pixel_values''': pixel_values}
						return config, inputs_dict



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




		'''simple docstring'''



		snake_case_			=(
		    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		snake_case_			=(
		    {
		        """feature-extraction""": BeitModel,
		        """image-classification""": BeitForImageClassification,
		        """image-segmentation""": BeitForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		snake_case_			=False
		snake_case_			=False
		snake_case_			=False



		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  BeitModelTester(self				)
						lowerCAmelCase__  :       str				=  ConfigTester(self	,config_class=__lowerCamelCase	,has_text_modality=__lowerCamelCase	,hidden_size=37				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		@unittest.skip(reason='''BEiT does not use inputs_embeds'''				)
		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						pass



		@require_torch_multi_gpu
		@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''				)
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    Dict:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       Any				=  self.model_tester.prepare_config_and_inputs_for_common()

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



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       str				=  self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCAmelCase__  :       Dict				=  ['''pixel_values''']
										self.assertListEqual(arg_names[:1]	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						if not self.model_tester.is_training:
										return

						lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[int]				=  self.model_tester.prepare_config_and_inputs_for_common()
						lowerCAmelCase__  :       List[str]				=  True

						for model_class in self.all_model_classes:
										# we don't test BeitForMaskedImageModeling
										if model_class in [*get_values(__lowerCamelCase				), BeitForMaskedImageModeling]:
														continue

										lowerCAmelCase__  :       int				=  model_class(__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.train()
										lowerCAmelCase__  :       List[str]				=  self._prepare_for_class(__lowerCamelCase	,__lowerCamelCase	,return_labels=__lowerCamelCase				)
										lowerCAmelCase__  :       Optional[Any]				=  model(**__lowerCamelCase				).loss
										loss.backward()



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       List[str]				=  self.model_tester.prepare_config_and_inputs_for_common()
						if not self.model_tester.is_training:
										return

						lowerCAmelCase__  :       int				=  False
						lowerCAmelCase__  :       Optional[Any]				=  True

						for model_class in self.all_model_classes:
										# we don't test BeitForMaskedImageModeling
										if (
										    model_class in [*get_values(__lowerCamelCase				), BeitForMaskedImageModeling]
										    or not model_class.supports_gradient_checkpointing
										):
														continue

										lowerCAmelCase__  :       Optional[int]				=  model_class(__lowerCamelCase				)
										model.gradient_checkpointing_enable()
										model.to(__lowerCamelCase				)
										model.train()
										lowerCAmelCase__  :       Any				=  self._prepare_for_class(__lowerCamelCase	,__lowerCamelCase	,return_labels=__lowerCamelCase				)
										lowerCAmelCase__  :       List[str]				=  model(**__lowerCamelCase				).loss
										loss.backward()



		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[int]				=  self.model_tester.prepare_config_and_inputs_for_common()

						lowerCAmelCase__  :       Optional[Any]				=  _config_zero_init(__lowerCamelCase				)
						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Any				=  model_class(config=__lowerCamelCase				)
										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"""	,)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__  :       Any				=  BeitModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Any				=  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[int]:

						"""simple docstring"""






						return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''				) if is_vision_available() else None



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''				).to(__lowerCamelCase				)

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

						# prepare bool_masked_pos
						lowerCAmelCase__  :       Union[str, Any]				=  torch.ones((1, 1_96)	,dtype=torch.bool				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       List[Any]				=  model(pixel_values=__lowerCamelCase	,bool_masked_pos=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  outputs.logits

						# verify the logits
						lowerCAmelCase__  :       List[Any]				=  torch.Size((1, 1_96, 81_92)				)
						self.assertEqual(logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  torch.tensor(
						    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]				).to(__lowerCamelCase				)

						self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]	,__lowerCamelCase	,atol=1e-2				)				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''				).to(__lowerCamelCase				)

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

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       List[str]				=  model(**__lowerCamelCase				)
						lowerCAmelCase__  :       int				=  outputs.logits

						# verify the logits
						lowerCAmelCase__  :       int				=  torch.Size((1, 10_00)				)
						self.assertEqual(logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  torch.tensor([-1.2385, -1.0987, -1.0108]				).to(__lowerCamelCase				)

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

						lowerCAmelCase__  :       Union[str, Any]				=  2_81
						self.assertEqual(logits.argmax(-1				).item()	,__lowerCamelCase				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''				).to(
						    __lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  self.default_image_processor
						lowerCAmelCase__  :       Optional[int]				=  prepare_img()
						lowerCAmelCase__  :       Optional[int]				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       Union[str, Any]				=  model(**__lowerCamelCase				)
						lowerCAmelCase__  :       Dict				=  outputs.logits

						# verify the logits
						lowerCAmelCase__  :       Optional[Any]				=  torch.Size((1, 2_18_41)				)
						self.assertEqual(logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  torch.tensor([1.6881, -0.2787, 0.5901]				).to(__lowerCamelCase				)

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

						lowerCAmelCase__  :       List[Any]				=  23_96
						self.assertEqual(logits.argmax(-1				).item()	,__lowerCamelCase				)



		@slow
		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''				)
						lowerCAmelCase__  :       Dict				=  model.to(__lowerCamelCase				)

						lowerCAmelCase__  :       Union[str, Any]				=  BeitImageProcessor(do_resize=__lowerCamelCase	,size=6_40	,do_center_crop=__lowerCamelCase				)

						lowerCAmelCase__  :       Tuple				=  load_dataset('''hf-internal-testing/fixtures_ade20k'''	,split='''test'''				)
						lowerCAmelCase__  :       Optional[Any]				=  Image.open(ds[0]['''file''']				)
						lowerCAmelCase__  :       str				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       int				=  model(**__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  outputs.logits

						# verify the logits
						lowerCAmelCase__  :       Dict				=  torch.Size((1, 1_50, 1_60, 1_60)				)
						self.assertEqual(logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  version.parse(PIL.__version__				) < version.parse('''9.0.0'''				)

						if is_pillow_less_than_a:
										lowerCAmelCase__  :       Union[str, Any]				=  torch.tensor(
										    [
										        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
										        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
										        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
										    ]	,device=__lowerCamelCase	,)
						else:
										lowerCAmelCase__  :       Union[str, Any]				=  torch.tensor(
										    [
										        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
										        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
										        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
										    ]	,device=__lowerCamelCase	,)

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



		@slow
		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       str				=  BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''				)
						lowerCAmelCase__  :       Tuple				=  model.to(__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  BeitImageProcessor(do_resize=__lowerCamelCase	,size=6_40	,do_center_crop=__lowerCamelCase				)

						lowerCAmelCase__  :       Any				=  load_dataset('''hf-internal-testing/fixtures_ade20k'''	,split='''test'''				)
						lowerCAmelCase__  :       Optional[Any]				=  Image.open(ds[0]['''file''']				)
						lowerCAmelCase__  :       Tuple				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       Optional[int]				=  model(**__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  outputs.logits.detach().cpu()

						lowerCAmelCase__  :       str				=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase	,target_sizes=[(5_00, 3_00)]				)
						lowerCAmelCase__  :       Any				=  torch.Size((5_00, 3_00)				)
						self.assertEqual(segmentation[0].shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       Dict				=  image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  torch.Size((1_60, 1_60)				)
						self.assertEqual(segmentation[0].shape	,__lowerCamelCase				)






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

__snake_case       :	List[str]            ='tiny-wmt19-en-ru'

# Build

# borrowed from a test
__snake_case       :	Any            =[
    'l',
    'o',
    'w',
    'e',
    'r',
    's',
    't',
    'i',
    'd',
    'n',
    'w</w>',
    'r</w>',
    't</w>',
    'lo',
    'low',
    'er</w>',
    'low</w>',
    'lowest</w>',
    'newer</w>',
    'wider</w>',
    '<unk>',
]
__snake_case       :	Union[str, Any]            =dict(zip(vocab, range(len(vocab))))
__snake_case       :	List[Any]            =['l o 123', 'lo w 1456', 'e r</w> 1789', '']

with tempfile.TemporaryDirectory() as tmpdirname:
							__snake_case       :	Optional[Any]            =Path(tmpdirname)
							__snake_case       :	int            =build_dir / VOCAB_FILES_NAMES['src_vocab_file']
							__snake_case       :	str            =build_dir / VOCAB_FILES_NAMES['tgt_vocab_file']
							__snake_case       :	Optional[int]            =build_dir / VOCAB_FILES_NAMES['merges_file']
							with open(src_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(tgt_vocab_file, 'w') as fp:
														fp.write(json.dumps(vocab_tokens))
							with open(merges_file, 'w') as fp:
														fp.write('\n'.join(merges))

							__snake_case       :	Union[str, Any]            =FSMTTokenizer(
							    langs=['en', 'ru'],
							    src_vocab_size=len(vocab),
							    tgt_vocab_size=len(vocab),
							    src_vocab_file=src_vocab_file,
							    tgt_vocab_file=tgt_vocab_file,
							    merges_file=merges_file,
							)

__snake_case       :	Dict            =FSMTConfig(
    langs=['ru', 'en'],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

__snake_case       :	Optional[Any]            =FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")

# Test
__snake_case       :	Dict            =tokenizer(['Making tiny model'], return_tensors='pt')
__snake_case       :	Dict            =tiny_model(**batch)

print('test output:', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f"""Generated {mname_tiny}""")

# Upload
# transformers-cli upload tiny-wmt19-en-ru

