




"""simple docstring"""

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 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Optional[int]=100       ,				_UpperCAmelCase			:			Tuple=13       ,				_UpperCAmelCase			:			Dict=30       ,				_UpperCAmelCase			:			str=2       ,				_UpperCAmelCase			:			int=3       ,				_UpperCAmelCase			:			Any=True       ,				_UpperCAmelCase			:			Any=True       ,				_UpperCAmelCase			:			Union[str, Any]=32       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			Tuple=4       ,				_UpperCAmelCase			:			str=37       ,				_UpperCAmelCase			:			List[str]="gelu"       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			List[str]=0.1       ,				_UpperCAmelCase			:			Union[str, Any]=10       ,				_UpperCAmelCase			:			int=0.02       ,				_UpperCAmelCase			:			List[str]=3       ,				_UpperCAmelCase			:			int=None       ,				_UpperCAmelCase			:			Union[str, Any]=[0, 1, 2, 3]       ,				):
       _A     =  parent
       _A     =  100
       _A     =  batch_size
       _A     =  image_size
       _A     =  patch_size
       _A     =  num_channels
       _A     =  is_training
       _A     =  use_labels
       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  type_sequence_label_size
       _A     =  initializer_range
       _A     =  scope
       _A     =  out_indices
       _A     =  num_labels

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

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

       _A     =  self.get_config()

       return config, pixel_values, labels, pixel_labels

 def      lowerCAmelCase_				(					self			:			Tuple						):
       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=_UpperCAmelCase       ,				initializer_range=self.initializer_range       ,				out_indices=self.out_indices       ,				)

 def      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			List[Any]						):
       _A     =  BeitModel(config=_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase						)
       self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  BeitForMaskedImageModeling(config=_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.seq_length - 1, self.vocab_size)						)

 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			Any						):
       _A     =  self.type_sequence_label_size
       _A     =  BeitForImageClassification(_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase       ,				labels=_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)						)

       # test greyscale images
       _A     =  1
       _A     =  BeitForImageClassification(_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()

       _A     =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
       _A     =  model(_UpperCAmelCase       ,				labels=_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.type_sequence_label_size)						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Optional[int]       ,				_UpperCAmelCase			:			Optional[Any]       ,				_UpperCAmelCase			:			Dict						):
       _A     =  self.num_labels
       _A     =  BeitForSemanticSegmentation(_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase						)
       self.parent.assertEqual(
           result.logits.shape       ,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
       _A     =  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			:			Tuple						):
       _A     =  self.prepare_config_and_inputs()
       _A  ,       _A  ,       _A  ,       _A     =  config_and_inputs
       _A     =  {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class 							lowercase_			(	__lowerCAmelCase     ,      __lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      (
     (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
     if is_torch_available()
     else ()
 )
 UpperCAmelCase       :	Union[str, Any]     =      (
     {
         '''feature-extraction''': BeitModel,
         '''image-classification''': BeitForImageClassification,
         '''image-segmentation''': BeitForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 UpperCAmelCase       :	Any     =      False
 UpperCAmelCase       :	Any     =      False
 UpperCAmelCase       :	List[str]     =      False

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  BeitModelTester(self						)
       _A     =  ConfigTester(self       ,				config_class=_UpperCAmelCase       ,				has_text_modality=_UpperCAmelCase       ,				hidden_size=37						)

 def      lowerCAmelCase_				(					self			:			Any						):
       self.config_tester.run_common_tests()

 @unittest.skip(reason='BEiT does not use inputs_embeds'						)
 def      lowerCAmelCase_				(					self			:			str						):
       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			:			Dict						):
       pass

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A  ,       _A     =  self.model_tester.prepare_config_and_inputs_for_common()

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

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A  ,       _A     =  self.model_tester.prepare_config_and_inputs_for_common()

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

             _A     =  ['pixel_values']
             self.assertListEqual(arg_names[:1]       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Any						):
       if not self.model_tester.is_training:
             return

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

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

             _A     =  model_class(_UpperCAmelCase						)
             model.to(_UpperCAmelCase						)
             model.train()
             _A     =  self._prepare_for_class(_UpperCAmelCase       ,				_UpperCAmelCase       ,				return_labels=_UpperCAmelCase						)
             _A     =  model(**_UpperCAmelCase						).loss
             loss.backward()

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A  ,       _A     =  self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       _A     =  False
       _A     =  True

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

             _A     =  model_class(_UpperCAmelCase						)
             model.gradient_checkpointing_enable()
             model.to(_UpperCAmelCase						)
             model.train()
             _A     =  self._prepare_for_class(_UpperCAmelCase       ,				_UpperCAmelCase       ,				return_labels=_UpperCAmelCase						)
             _A     =  model(**_UpperCAmelCase						).loss
             loss.backward()

 def      lowerCAmelCase_				(					self			:			Any						):
       _A  ,       _A     =  self.model_tester.prepare_config_and_inputs_for_common()

       _A     =  _config_zero_init(_UpperCAmelCase						)
       for model_class in self.all_model_classes:
             _A     =  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'''       ,				)



 @slow
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =  BeitModel.from_pretrained(_UpperCAmelCase						)
             self.assertIsNotNone(_UpperCAmelCase						)






def 		_snake_case	(   )       ->   int:






      '''simple docstring'''


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







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



 '''simple docstring'''

 @cached_property
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'						) if is_vision_available() else None

 @slow
 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'						).to(_UpperCAmelCase						)

       _A     =  self.default_image_processor
       _A     =  prepare_img()
       _A     =  image_processor(images=_UpperCAmelCase       ,				return_tensors='pt'						).pixel_values.to(_UpperCAmelCase						)

       # prepare bool_masked_pos
       _A     =  torch.ones((1, 196)       ,				dtype=torch.bool						).to(_UpperCAmelCase						)

       # forward pass
       with torch.no_grad():
             _A     =  model(pixel_values=_UpperCAmelCase       ,				bool_masked_pos=_UpperCAmelCase						)
       _A     =  outputs.logits

       # verify the logits
       _A     =  torch.Size((1, 196, 8_192)						)
       self.assertEqual(logits.shape       ,				_UpperCAmelCase						)

       _A     =  torch.tensor(
           [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]						).to(_UpperCAmelCase						)

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

 @slow
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       _A     =  BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'						).to(_UpperCAmelCase						)

       _A     =  self.default_image_processor
       _A     =  prepare_img()
       _A     =  image_processor(images=_UpperCAmelCase       ,				return_tensors='pt'						).to(_UpperCAmelCase						)

       # forward pass
       with torch.no_grad():
             _A     =  model(**_UpperCAmelCase						)
       _A     =  outputs.logits

       # verify the logits
       _A     =  torch.Size((1, 1_000)						)
       self.assertEqual(logits.shape       ,				_UpperCAmelCase						)

       _A     =  torch.tensor([-1.2385, -1.0987, -1.0108]						).to(_UpperCAmelCase						)

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

       _A     =  281
       self.assertEqual(logits.argmax(-1						).item()       ,				_UpperCAmelCase						)

 @slow
 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'						).to(
           _UpperCAmelCase						)

       _A     =  self.default_image_processor
       _A     =  prepare_img()
       _A     =  image_processor(images=_UpperCAmelCase       ,				return_tensors='pt'						).to(_UpperCAmelCase						)

       # forward pass
       with torch.no_grad():
             _A     =  model(**_UpperCAmelCase						)
       _A     =  outputs.logits

       # verify the logits
       _A     =  torch.Size((1, 21_841)						)
       self.assertEqual(logits.shape       ,				_UpperCAmelCase						)

       _A     =  torch.tensor([1.6881, -0.2787, 0.5901]						).to(_UpperCAmelCase						)

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

       _A     =  2_396
       self.assertEqual(logits.argmax(-1						).item()       ,				_UpperCAmelCase						)

 @slow
 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'						)
       _A     =  model.to(_UpperCAmelCase						)

       _A     =  BeitImageProcessor(do_resize=_UpperCAmelCase       ,				size=640       ,				do_center_crop=_UpperCAmelCase						)

       _A     =  load_dataset('hf-internal-testing/fixtures_ade20k'       ,				split='test'						)
       _A     =  Image.open(ds[0]['file']						)
       _A     =  image_processor(images=_UpperCAmelCase       ,				return_tensors='pt'						).to(_UpperCAmelCase						)

       # forward pass
       with torch.no_grad():
             _A     =  model(**_UpperCAmelCase						)
       _A     =  outputs.logits

       # verify the logits
       _A     =  torch.Size((1, 150, 160, 160)						)
       self.assertEqual(logits.shape       ,				_UpperCAmelCase						)

       _A     =  version.parse(PIL.__version__						) < version.parse('9.0.0'						)

       if is_pillow_less_than_a:
             _A     =  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=_UpperCAmelCase       ,				)
       else:
             _A     =  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=_UpperCAmelCase       ,				)

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



 @slow
 def      lowerCAmelCase_				(					self			:			List[Any]						):
       _A     =  BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'						)
       _A     =  model.to(_UpperCAmelCase						)

       _A     =  BeitImageProcessor(do_resize=_UpperCAmelCase       ,				size=640       ,				do_center_crop=_UpperCAmelCase						)

       _A     =  load_dataset('hf-internal-testing/fixtures_ade20k'       ,				split='test'						)
       _A     =  Image.open(ds[0]['file']						)
       _A     =  image_processor(images=_UpperCAmelCase       ,				return_tensors='pt'						).to(_UpperCAmelCase						)

       # forward pass
       with torch.no_grad():
             _A     =  model(**_UpperCAmelCase						)

       _A     =  outputs.logits.detach().cpu()

       _A     =  image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase       ,				target_sizes=[(500, 300)]						)
       _A     =  torch.Size((500, 300)						)
       self.assertEqual(segmentation[0].shape       ,				_UpperCAmelCase						)

       _A     =  image_processor.post_process_semantic_segmentation(outputs=_UpperCAmelCase						)
       _A     =  torch.Size((160, 160)						)
       self.assertEqual(segmentation[0].shape       ,				_UpperCAmelCase						)






'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class       __magic_name__		:
							lowerCAmelCase			:       Optional[Any]  							=   MBartConfig
							lowerCAmelCase			:       str  							=   {}
							lowerCAmelCase			:       Dict  							=   'gelu'







							def __init__(				self   :			List[str]      ,_UpperCAmelCase   :			List[str]      ,_UpperCAmelCase   :			Tuple=13      ,_UpperCAmelCase   :			str=7      ,_UpperCAmelCase   :			Dict=True      ,_UpperCAmelCase   :			Optional[int]=False      ,_UpperCAmelCase   :			List[str]=99      ,_UpperCAmelCase   :			str=32      ,_UpperCAmelCase   :			List[str]=2      ,_UpperCAmelCase   :			Optional[int]=4      ,_UpperCAmelCase   :			str=37      ,_UpperCAmelCase   :			Union[str, Any]=0.1      ,_UpperCAmelCase   :			str=0.1      ,_UpperCAmelCase   :			Dict=20      ,_UpperCAmelCase   :			Optional[Any]=2      ,_UpperCAmelCase   :			Union[str, Any]=1      ,_UpperCAmelCase   :			List[str]=0      ,):
									_a	: Any								=				parent
									_a	: List[Any]								=				batch_size
									_a	: Any								=				seq_length
									_a	: List[str]								=				is_training
									_a	: str								=				use_labels
									_a	: List[Any]								=				vocab_size
									_a	: List[str]								=				hidden_size
									_a	: Union[str, Any]								=				num_hidden_layers
									_a	: Optional[Any]								=				num_attention_heads
									_a	: Optional[int]								=				intermediate_size
									_a	: Any								=				hidden_dropout_prob
									_a	: int								=				attention_probs_dropout_prob
									_a	: Union[str, Any]								=				max_position_embeddings
									_a	: Union[str, Any]								=				eos_token_id
									_a	: int								=				pad_token_id
									_a	: Union[str, Any]								=				bos_token_id







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: str								=				ids_tensor([self.batch_size, self.seq_length - 1]      ,self.vocab_size						)
									_a	: List[str]								=				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)      ,1						)
									_a	: str								=				tf.concat([input_ids, eos_tensor]      ,axis=1						)

									_a	: Dict								=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size						)

									_a	: List[str]								=				self.config_cls(
									    vocab_size=self.vocab_size      ,d_model=self.hidden_size      ,encoder_layers=self.num_hidden_layers      ,decoder_layers=self.num_hidden_layers      ,encoder_attention_heads=self.num_attention_heads      ,decoder_attention_heads=self.num_attention_heads      ,encoder_ffn_dim=self.intermediate_size      ,decoder_ffn_dim=self.intermediate_size      ,dropout=self.hidden_dropout_prob      ,attention_dropout=self.attention_probs_dropout_prob      ,max_position_embeddings=self.max_position_embeddings      ,eos_token_ids=[2]      ,bos_token_id=self.bos_token_id      ,pad_token_id=self.pad_token_id      ,decoder_start_token_id=self.pad_token_id      ,**self.config_updates      ,)
									_a	: Optional[int]								=				prepare_mbart_inputs_dict(_UpperCAmelCase      ,_UpperCAmelCase      ,_UpperCAmelCase						)
									return config, inputs_dict







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Optional[int]						):
									_a	: str								=				TFMBartModel(config=_UpperCAmelCase						).get_decoder()
									_a	: List[Any]								=				inputs_dict['input_ids']

									_a	: Optional[Any]								=				input_ids[:1, :]
									_a	: Tuple								=				inputs_dict['attention_mask'][:1, :]
									_a	: List[Any]								=				inputs_dict['head_mask']
									_a	: str								=				1

									# first forward pass
									_a	: Union[str, Any]								=				model(_UpperCAmelCase      ,attention_mask=_UpperCAmelCase      ,head_mask=_UpperCAmelCase      ,use_cache=_UpperCAmelCase						)

									_a			,   _a	: Optional[int]								=				outputs.to_tuple()
									_a	: Union[str, Any]								=				past_key_values[1]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     lowerCAmelCase_=None		,     )     -> Union[str, Any]:
		if attention_mask is None:
				_a	: List[Any]								=				tf.cast(tf.math.not_equal(lowerCAmelCase_		,     config.pad_token_id    )		,     tf.inta    )
		if decoder_attention_mask is None:
				_a	: Any								=				tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape		,     dtype=tf.inta    ),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,     config.pad_token_id    )		,     tf.inta    ),
				    ]		,     axis=-1		,     )
		if head_mask is None:
				_a	: int								=				tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
		if decoder_head_mask is None:
				_a	: Any								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		if cross_attn_head_mask is None:
				_a	: int								=				tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}

@require_tf
class       __magic_name__		(       _UpperCamelCase     ,		_UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       List[str]  							=   (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							lowerCAmelCase			:       Optional[Any]  							=   (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							lowerCAmelCase			:       Dict  							=   (
							    {
							        'conversational': TFMBartForConditionalGeneration,
							        'feature-extraction': TFMBartModel,
							        'summarization': TFMBartForConditionalGeneration,
							        'text2text-generation': TFMBartForConditionalGeneration,
							        'translation': TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							lowerCAmelCase			:       Dict  							=   True
							lowerCAmelCase			:       List[str]  							=   False
							lowerCAmelCase			:       str  							=   False







							def 				__lowercase     (				self   :			int      ,_UpperCAmelCase   :			Dict      ,_UpperCAmelCase   :			str      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			Union[str, Any]      ,_UpperCAmelCase   :			int						):
									if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
											# Exception encountered when calling layer '...'
											return True

									return False







							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				TFMBartModelTester(self						)
									_a	: Dict								=				ConfigTester(self      ,config_class=_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Optional[int]						):
									self.config_tester.run_common_tests()







							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: List[Any]								=				self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase						)

@require_sentencepiece
@require_tokenizers
@require_tf
class       __magic_name__		(       unittest.TestCase  ):
							lowerCAmelCase			:       List[Any]  							=   [
							    ' UN Chief Says There Is No Military Solution in Syria',
							]
							lowerCAmelCase			:       Dict  							=   [
							    'Şeful ONU declară că nu există o soluţie militară în Siria',
							]
							lowerCAmelCase			:       Tuple  							=   'facebook/mbart-large-en-ro'







							@cached_property
							def 				__lowercase     (				self   :			Optional[int]						):
									return AutoTokenizer.from_pretrained(self.model_name						)







							@cached_property
							def 				__lowercase     (				self   :			Optional[int]						):
									_a	: Any								=				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
									return model







							def 				__lowercase     (				self   :			Optional[int]      ,**_UpperCAmelCase   :			Dict						):
									_a	: List[Any]								=				self.translate_src_text(**_UpperCAmelCase						)
									self.assertListEqual(self.expected_text      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Tuple      ,**_UpperCAmelCase   :			str						):
									_a	: List[Any]								=				self.tokenizer(self.src_text      ,**_UpperCAmelCase      ,return_tensors='tf'						)
									_a	: Any								=				self.model.generate(
									    model_inputs.input_ids      ,attention_mask=model_inputs.attention_mask      ,num_beams=2						)
									_a	: Tuple								=				self.tokenizer.batch_decode(_UpperCAmelCase      ,skip_special_tokens=_UpperCAmelCase						)
									return generated_words







							@slow
							def 				__lowercase     (				self   :			Tuple						):
									self._assert_generated_batch_equal_expected()




