




"""simple docstring"""







import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
  from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor

class 							snake_case_ :







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				lowerCamelCase_=3					,				lowerCamelCase_=3_2					,				lowerCamelCase_=3					,				lowerCamelCase_=1_0					,				lowerCamelCase_=[1_0, 2_0, 3_0, 4_0]					,				lowerCamelCase_=[1, 1, 2, 1]					,				lowerCamelCase_=True					,				lowerCamelCase_=True					,				lowerCamelCase_="relu"					,				lowerCamelCase_=3					,				lowerCamelCase_=None					,				lowerCamelCase_=["stage2", "stage3", "stage4"]					,				lowerCamelCase_=[2, 3, 4]					,				)							->      Any:
       UpperCamelCase										=  parent
       UpperCamelCase										=  batch_size
       UpperCamelCase										=  image_size
       UpperCamelCase										=  num_channels
       UpperCamelCase										=  embeddings_size
       UpperCamelCase										=  hidden_sizes
       UpperCamelCase										=  depths
       UpperCamelCase										=  is_training
       UpperCamelCase										=  use_labels
       UpperCamelCase										=  hidden_act
       UpperCamelCase										=  num_labels
       UpperCamelCase										=  scope
       UpperCamelCase										=  len(lowerCamelCase_)
       UpperCamelCase										=  out_features
       UpperCamelCase										=  out_indices







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

       UpperCamelCase										=  None
       if self.use_labels:
           UpperCamelCase										=  ids_tensor([self.batch_size]					,				self.num_labels)

       UpperCamelCase										=  self.get_config()

       return config, pixel_values, labels







   def      UpperCAmelCase__ (		self)							->      Tuple:
       return ResNetConfig(
           num_channels=self.num_channels					,				embeddings_size=self.embeddings_size					,				hidden_sizes=self.hidden_sizes					,				depths=self.depths					,				hidden_act=self.hidden_act					,				num_labels=self.num_labels					,				out_features=self.out_features					,				out_indices=self.out_indices					,				)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Any:
       UpperCamelCase										=  ResNetModel(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_)
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape					,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,				)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Dict:
       UpperCamelCase										=  self.num_labels
       UpperCamelCase										=  ResNetForImageClassification(lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_					,				labels=lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels))







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      Dict:
       UpperCamelCase										=  ResNetBackbone(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps)					,				len(config.out_features))
       self.parent.assertListEqual(list(result.feature_maps[0].shape)					,				[self.batch_size, self.hidden_sizes[1], 4, 4])

       # verify channels
       self.parent.assertEqual(len(model.channels)					,				len(config.out_features))
       self.parent.assertListEqual(model.channels					,				config.hidden_sizes[1:])

       # verify backbone works with out_features=None
       UpperCamelCase										=  None
       UpperCamelCase										=  ResNetBackbone(config=lowerCamelCase_)
       model.to(lowerCamelCase_)
       model.eval()
       UpperCamelCase										=  model(lowerCamelCase_)

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps)					,				1)
       self.parent.assertListEqual(list(result.feature_maps[0].shape)					,				[self.batch_size, self.hidden_sizes[-1], 1, 1])

       # verify channels
       self.parent.assertEqual(len(model.channels)					,				1)
       self.parent.assertListEqual(model.channels					,				[config.hidden_sizes[-1]])







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  self.prepare_config_and_inputs()
       UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase										=  config_and_inputs
       UpperCamelCase										=  {'''pixel_values''': pixel_values}
       return config, inputs_dict

@require_torch
class 							snake_case_ (				lowerCamelCase_  ,   lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(
       (
           ResNetModel,
           ResNetForImageClassification,
           ResNetBackbone,
       )
       if is_torch_available()
       else ()
   )
   A_										=					(
       {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
       if is_torch_available()
       else {}
   )

   A_										=					True
   A_										=					False
   A_										=					False
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  ResNetModelTester(self)
       UpperCamelCase										=  ConfigTester(self					,				config_class=lowerCamelCase_					,				has_text_modality=lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      str:
       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       return







   @unittest.skip(reason='''ResNet does not use inputs_embeds''')
   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       pass







   @unittest.skip(reason='''ResNet does not support input and output embeddings''')
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       pass







   def      UpperCAmelCase__ (		self)							->      List[str]:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

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

           UpperCamelCase										=  ['''pixel_values''']
           self.assertListEqual(arg_names[:1]					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Any:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase										=  model_class(config=lowerCamelCase_)
           for name, module in model.named_modules():
               if isinstance(lowerCamelCase_					,				(nn.BatchNormad, nn.GroupNorm)):
                   self.assertTrue(
                       torch.all(module.weight == 1)					,				msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,				)
                   self.assertTrue(
                       torch.all(module.bias == 0)					,				msg=F'Parameter {name} of model {model_class} seems not properly initialized'					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       def check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_):
           UpperCamelCase										=  model_class(lowerCamelCase_)
           model.to(lowerCamelCase_)
           model.eval()

           with torch.no_grad():
               UpperCamelCase										=  model(**self._prepare_for_class(lowerCamelCase_					,				lowerCamelCase_))

           UpperCamelCase										=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           UpperCamelCase										=  self.model_tester.num_stages
           self.assertEqual(len(lowerCamelCase_)					,				expected_num_stages + 1)

           # ResNet's feature maps are of shape (batch_size, num_channels, height, width)
           self.assertListEqual(
               list(hidden_states[0].shape[-2:])					,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]					,				)

       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase										=  ['''basic''', '''bottleneck''']
       for model_class in self.all_model_classes:
           for layer_type in layers_type:
               UpperCamelCase										=  layer_type
               UpperCamelCase										=  True
               check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCamelCase										=  True

               check_hidden_states_output(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   @unittest.skip(reason='''ResNet does not use feedforward chunking''')
   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       pass







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_)







   @slow
   def      UpperCAmelCase__ (		self)							->      str:
       for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase										=  ResNetModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)





def     __snake_case   (							):


    """simple docstring"""







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

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







   """simple docstring"""




   @cached_property
   def      UpperCAmelCase__ (		self)							->      Dict:
       return (
           AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
           if is_vision_available()
           else None
       )







   @slow
   def      UpperCAmelCase__ (		self)							->      Tuple:
       UpperCamelCase										=  ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowerCamelCase_)

       UpperCamelCase										=  self.default_image_processor
       UpperCamelCase										=  prepare_img()
       UpperCamelCase										=  image_processor(images=lowerCamelCase_					,				return_tensors='''pt''').to(lowerCamelCase_)

       # forward pass
       with torch.no_grad():
           UpperCamelCase										=  model(**lowerCamelCase_)

       # verify the logits
       UpperCamelCase										=  torch.Size((1, 1_0_0_0))
       self.assertEqual(outputs.logits.shape					,				lowerCamelCase_)

       UpperCamelCase										=  torch.tensor([-11.1069, -9.7877, -8.3777]).to(lowerCamelCase_)

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

@require_torch
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(ResNetBackbone,) if is_torch_available() else ()
   A_										=					False
   A_										=					ResNetConfig







   def      UpperCAmelCase__ (		self)							->      List[str]:
       UpperCamelCase										=  ResNetModelTester(self)





"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import CTRLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.ctrl.modeling_tf_ctrl import (
      TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFCTRLForSequenceClassification,
      TFCTRLLMHeadModel,
      TFCTRLModel,
  )

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   def __init__(		self					,				lowerCamelCase_					,				)							->      Any:
       UpperCamelCase										=  parent
       UpperCamelCase										=  1_3
       UpperCamelCase										=  7
       UpperCamelCase										=  True
       UpperCamelCase										=  True
       UpperCamelCase										=  True
       UpperCamelCase										=  True
       UpperCamelCase										=  True
       UpperCamelCase										=  9_9
       UpperCamelCase										=  3_2
       UpperCamelCase										=  2
       UpperCamelCase										=  4
       UpperCamelCase										=  3_7
       UpperCamelCase										=  '''gelu'''
       UpperCamelCase										=  0.1
       UpperCamelCase										=  0.1
       UpperCamelCase										=  5_1_2
       UpperCamelCase										=  1_6
       UpperCamelCase										=  2
       UpperCamelCase										=  0.02
       UpperCamelCase										=  3
       UpperCamelCase										=  4
       UpperCamelCase										=  None
       UpperCamelCase										=  self.vocab_size - 1







   def      UpperCAmelCase__ (		self)							->      int:
       UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]					,				self.vocab_size)

       UpperCamelCase										=  None
       if self.use_input_mask:
           UpperCamelCase										=  random_attention_mask([self.batch_size, self.seq_length])

       UpperCamelCase										=  None
       if self.use_token_type_ids:
           UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]					,				self.type_vocab_size)

       UpperCamelCase										=  None
       if self.use_mc_token_ids:
           UpperCamelCase										=  ids_tensor([self.batch_size, self.num_choices]					,				self.seq_length)

       UpperCamelCase										=  None
       UpperCamelCase										=  None
       UpperCamelCase										=  None
       if self.use_labels:
           UpperCamelCase										=  ids_tensor([self.batch_size]					,				self.type_sequence_label_size)
           UpperCamelCase										=  ids_tensor([self.batch_size, self.seq_length]					,				self.num_labels)
           UpperCamelCase										=  ids_tensor([self.batch_size]					,				self.num_choices)

       UpperCamelCase										=  CTRLConfig(
           vocab_size=self.vocab_size					,				n_embd=self.hidden_size					,				n_layer=self.num_hidden_layers					,				n_head=self.num_attention_heads					,				n_positions=self.max_position_embeddings					,				pad_token_id=self.pad_token_id					,				)

       UpperCamelCase										=  ids_tensor([self.num_hidden_layers, self.num_attention_heads]					,				2)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				*lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  TFCTRLModel(config=lowerCamelCase_)
       UpperCamelCase										=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       UpperCamelCase										=  model(lowerCamelCase_)

       UpperCamelCase										=  [input_ids, None, input_mask]  # None is the input for 'past'
       UpperCamelCase										=  model(lowerCamelCase_)

       UpperCamelCase										=  model(lowerCamelCase_)

       self.parent.assertEqual(result.last_hidden_state.shape					,				(self.batch_size, self.seq_length, self.hidden_size))







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				*lowerCamelCase_)							->      int:
       UpperCamelCase										=  TFCTRLLMHeadModel(config=lowerCamelCase_)
       UpperCamelCase										=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
       UpperCamelCase										=  model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.seq_length, self.vocab_size))







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				*lowerCamelCase_)							->      List[str]:
       UpperCamelCase										=  self.num_labels
       UpperCamelCase										=  ids_tensor([self.batch_size]					,				self.type_sequence_label_size)
       UpperCamelCase										=  {
           '''input_ids''': input_ids,
           '''token_type_ids''': token_type_ids,
           '''labels''': sequence_labels,
       }
       UpperCamelCase										=  TFCTRLForSequenceClassification(lowerCamelCase_)
       UpperCamelCase										=  model(lowerCamelCase_)
       self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.num_labels))







   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  self.prepare_config_and_inputs()

       (
           (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  (
           UpperCamelCase
       )					,  
       )										=  config_and_inputs

       UpperCamelCase										=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict

@require_tf
class 							snake_case_ (				lowerCamelCase_  ,   lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
   A_										=					(TFCTRLLMHeadModel,) if is_tf_available() else ()
   A_										=					(
       {
           '''feature-extraction''': TFCTRLModel,
           '''text-classification''': TFCTRLForSequenceClassification,
           '''text-generation''': TFCTRLLMHeadModel,
           '''zero-shot''': TFCTRLForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)							->      int:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
           # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
           # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
           # config could not be created.
           return True

       return False







   def      UpperCAmelCase__ (		self)							->      int:
       UpperCamelCase										=  TFCTRLModelTester(self)
       UpperCamelCase										=  ConfigTester(self					,				config_class=lowerCamelCase_					,				n_embd=3_7)







   def      UpperCAmelCase__ (		self)							->      int:
       self.config_tester.run_common_tests()







   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_model(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_lm_head(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_ctrl_for_sequence_classification(*lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[str]:
       UpperCamelCase					,  UpperCamelCase										=  self.model_tester.prepare_config_and_inputs_for_common()
       UpperCamelCase										=  [TFCTRLLMHeadModel]
       UpperCamelCase										=  [TFCTRLForSequenceClassification]

       for model_class in self.all_model_classes:
           UpperCamelCase										=  model_class(lowerCamelCase_)
           model.build()  # may be needed for the get_bias() call below
           assert isinstance(model.get_input_embeddings()					,				tf.keras.layers.Layer)

           if model_class in list_lm_models:
               UpperCamelCase										=  model.get_output_embeddings()
               assert isinstance(lowerCamelCase_					,				tf.keras.layers.Layer)
               UpperCamelCase										=  model.get_bias()
               assert isinstance(lowerCamelCase_					,				lowerCamelCase_)
               for k, v in name.items():
                   assert isinstance(lowerCamelCase_					,				tf.Variable)
           elif model_class in list_other_models_with_output_ebd:
               UpperCamelCase										=  model.get_output_embeddings()
               assert isinstance(lowerCamelCase_					,				tf.keras.layers.Layer)
               UpperCamelCase										=  model.get_bias()
               assert name is None
           else:
               UpperCamelCase										=  model.get_output_embeddings()
               assert x is None
               UpperCamelCase										=  model.get_bias()
               assert name is None







   @slow
   def      UpperCAmelCase__ (		self)							->      List[Any]:
       for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase										=  TFCTRLModel.from_pretrained(lowerCamelCase_)
           self.assertIsNotNone(lowerCamelCase_)

@require_tf
class 							snake_case_ (				unittest.TestCase ):







   """simple docstring"""




   @slow
   def      UpperCAmelCase__ (		self)							->      str:
       UpperCamelCase										=  TFCTRLLMHeadModel.from_pretrained('''ctrl''')
       UpperCamelCase										=  tf.convert_to_tensor([[1_1_8_5_9, 0, 1_6_1_1, 8]]					,				dtype=tf.intaa)  # Legal the president is
       UpperCamelCase										=  [
           1_1_8_5_9,
           0,
           1_6_1_1,
           8,
           5,
           1_5_0,
           2_6_4_4_9,
           2,
           1_9,
           3_4_8,
           4_6_9,
           3,
           2_5_9_5,
           4_8,
           2_0_7_4_0,
           2_4_6_5_3_3,
           2_4_6_5_3_3,
           1_9,
           3_0,
           5,
       ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

       UpperCamelCase										=  model.generate(lowerCamelCase_					,				do_sample=lowerCamelCase_)
       self.assertListEqual(output_ids[0].numpy().tolist()					,				lowerCamelCase_)