




import inspect
import unittest

from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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_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 transformers import ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
  from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class        UpperCamelCase_					:

    '''simple docstring'''






    def __init__(    self   :			str      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Optional[Any]=13      ,				UpperCAmelCase__   :			List[str]=32      ,				UpperCAmelCase__   :			Any=3      ,				UpperCAmelCase__   :			List[Any]=4      ,				UpperCAmelCase__   :			List[Any]=[10, 20, 30, 40]      ,				UpperCAmelCase__   :			Dict=[2, 2, 3, 2]      ,				UpperCAmelCase__   :			List[str]=True      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Any=37      ,				UpperCAmelCase__   :			Union[str, Any]="gelu"      ,				UpperCAmelCase__   :			Optional[int]=10      ,				UpperCAmelCase__   :			Any=0.02      ,				UpperCAmelCase__   :			List[Any]=["stage2", "stage3", "stage4"]      ,				UpperCAmelCase__   :			List[str]=[2, 3, 4]      ,				UpperCAmelCase__   :			int=None      ,				)		->Tuple:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					image_size
           A__   				=					num_channels
           A__   				=					num_stages
           A__   				=					hidden_sizes
           A__   				=					depths
           A__   				=					is_training
           A__   				=					use_labels
           A__   				=					intermediate_size
           A__   				=					hidden_act
           A__   				=					num_labels
           A__   				=					initializer_range
           A__   				=					out_features
           A__   				=					out_indices
           A__   				=					scope


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





           '''simple docstring'''

           A__   				=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

           A__   				=					self.get_config()

           return config, pixel_values, labels


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->str:





           '''simple docstring'''

           return ConvNextVaConfig(
               num_channels=self.num_channels      ,				hidden_sizes=self.hidden_sizes      ,				depths=self.depths      ,				num_stages=self.num_stages      ,				hidden_act=self.hidden_act      ,				is_decoder=__lowerCamelCase      ,				initializer_range=self.initializer_range      ,				out_features=self.out_features      ,				out_indices=self.out_indices      ,				num_labels=self.num_labels      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			int      ,				UpperCAmelCase__   :			Dict)		->int:





           '''simple docstring'''

           A__   				=					ConvNextVaModel(config=__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           A__   				=					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 // 32, self.image_size // 32)      ,				)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			List[str])		->Dict:





           '''simple docstring'''

           A__   				=					ConvNextVaForImageClassification(__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           A__   				=					model(__lowerCamelCase      ,				labels=__lowerCamelCase)
           self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_labels))


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Tuple      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any])		->Tuple:





           '''simple docstring'''

           A__   				=					ConvNextVaBackbone(config=__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           A__   				=					model(__lowerCamelCase)

           # verify hidden states
           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
           A__   				=					None
           A__   				=					ConvNextVaBackbone(config=__lowerCamelCase)
           model.to(__lowerCamelCase)
           model.eval()
           A__   				=					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  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Dict:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()
           A__   				=					config_and_inputs
           A__   				=					{'''pixel_values''': pixel_values}
           return config, inputs_dict





    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Dict:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()
           A__   				=					config_and_inputs
           A__   				=					{'''pixel_values''': pixel_values, '''labels''': labels}
           return config, inputs_dict


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

    '''simple docstring'''






    UpperCAmelCase__													=   (
        (
            ConvNextVaModel,
            ConvNextVaForImageClassification,
            ConvNextVaBackbone,
        )
        if is_torch_available()
        else ()
    )
    UpperCAmelCase__													=   (
        {'''feature-extraction''': ConvNextVaModel, '''image-classification''': ConvNextVaForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[int]:





           '''simple docstring'''

           A__   				=					ConvNextVaModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=__lowerCamelCase      ,				has_text_modality=__lowerCamelCase      ,				hidden_size=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Optional[int]:





           '''simple docstring'''

           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  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''simple docstring'''

           return


    @unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[Any]:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Any:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int])		->Tuple:





           '''simple docstring'''

           pass


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Tuple:





           '''simple docstring'''

           if not self.model_tester.is_training:
                  return

           for model_class in self.all_model_classes:
                  A__   				=					self.model_tester.prepare_config_and_inputs_with_labels()
                  A__   				=					True

                  if model_class.__name__ in [
                      *get_values(__lowerCamelCase),
                      *get_values(__lowerCamelCase),
                  ]:
                         continue

                  A__   				=					model_class(__lowerCamelCase)
                  model.to(__lowerCamelCase)
                  model.train()
                  A__   				=					self._prepare_for_class(__lowerCamelCase      ,				__lowerCamelCase      ,				return_labels=__lowerCamelCase)
                  A__   				=					model(**__lowerCamelCase).loss
                  loss.backward()


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





           '''simple docstring'''

           if not self.model_tester.is_training:
                  return

           for model_class in self.all_model_classes:
                  A__   				=					self.model_tester.prepare_config_and_inputs_with_labels()
                  A__   				=					False
                  A__   				=					True

                  if (
                      model_class.__name__
                      in [*get_values(__lowerCamelCase), *get_values(__lowerCamelCase)]
                      or not model_class.supports_gradient_checkpointing
                  ):
                         continue

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Optional[int]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=					model_class(__lowerCamelCase)
                  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]      ,				__lowerCamelCase)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__lowerCamelCase)


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			List[str]      ,				UpperCAmelCase__   :			Dict):
                  A__   				=					model_class(__lowerCamelCase)
                  model.to(__lowerCamelCase)
                  model.eval()

                  with torch.no_grad():
                         A__   				=					model(**self._prepare_for_class(__lowerCamelCase      ,				__lowerCamelCase))

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

                  A__   				=					self.model_tester.num_stages
                  self.assertEqual(len(__lowerCamelCase)      ,				expected_num_stages + 1)

                  # ConvNextV2'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]      ,				)

           A__   				=					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  A__   				=					True
                  check_hidden_states_output(__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase)

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

                  check_hidden_states_output(__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase)


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





           '''simple docstring'''

           A__   				=					self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase)





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->Any:





           '''simple docstring'''

           for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					ConvNextVaModel.from_pretrained(__lowerCamelCase)
                  self.assertIsNotNone(__lowerCamelCase)







def SCREAMING_SNAKE_CASE       ( )     -> Dict:



       """simple docstring"""

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



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

    '''simple docstring'''






    @cached_property
    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->str:





           '''simple docstring'''

           return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''') if is_vision_available() else None





    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->str:





           '''simple docstring'''

           A__   				=					ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''').to(__lowerCamelCase)

           A__   				=					self.default_image_processor
           A__   				=					prepare_img()
           A__   				=					preprocessor(images=__lowerCamelCase      ,				return_tensors='''pt''').to(__lowerCamelCase)

           # forward pass
           with torch.no_grad():
                  A__   				=					model(**__lowerCamelCase)

           # verify the logits
           A__   				=					torch.Size((1, 1_000))
           self.assertEqual(outputs.logits.shape      ,				__lowerCamelCase)

           A__   				=					torch.tensor([0.9996, 0.1966, -0.4386]).to(__lowerCamelCase)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,				__lowerCamelCase      ,				atol=1e-4))








from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers.models.xglm.modeling_tf_xglm import (
      TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFXGLMForCausalLM,
      TFXGLMModel,
  )


@require_tf
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   XGLMConfig
    UpperCAmelCase__													=   {}
    UpperCAmelCase__													=   '''gelu'''


    def __init__(    self   :			str      ,				UpperCAmelCase__   :			Optional[Any]      ,				UpperCAmelCase__   :			Union[str, Any]=14      ,				UpperCAmelCase__   :			Tuple=7      ,				UpperCAmelCase__   :			List[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Dict=True      ,				UpperCAmelCase__   :			Any=99      ,				UpperCAmelCase__   :			Union[str, Any]=32      ,				UpperCAmelCase__   :			Any=2      ,				UpperCAmelCase__   :			Dict=4      ,				UpperCAmelCase__   :			List[str]=37      ,				UpperCAmelCase__   :			Union[str, Any]="gelu"      ,				UpperCAmelCase__   :			Tuple=0.1      ,				UpperCAmelCase__   :			Any=0.1      ,				UpperCAmelCase__   :			Any=512      ,				UpperCAmelCase__   :			List[Any]=0.02      ,				)		->str:





           '''simple docstring'''

           A__   				=					parent
           A__   				=					batch_size
           A__   				=					seq_length
           A__   				=					is_training
           A__   				=					use_input_mask
           A__   				=					use_labels
           A__   				=					vocab_size
           A__   				=					d_model
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					ffn_dim
           A__   				=					activation_function
           A__   				=					activation_dropout
           A__   				=					attention_dropout
           A__   				=					max_position_embeddings
           A__   				=					initializer_range
           A__   				=					None
           A__   				=					0
           A__   				=					2
           A__   				=					1


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Optional[int]:





           '''simple docstring'''

           return XGLMConfig.from_pretrained('''facebook/xglm-564M''')


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Tuple:





           '''simple docstring'''

           A__   				=					tf.clip_by_value(
               ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size)      ,				clip_value_min=0      ,				clip_value_max=3)

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

           A__   				=					self.get_config()

           A__   				=					floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,				2)

           return (
               config,
               input_ids,
               input_mask,
               head_mask,
           )


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Dict:





           '''simple docstring'''

           return XGLMConfig(
               vocab_size=self.vocab_size      ,				d_model=self.hidden_size      ,				num_layers=self.num_hidden_layers      ,				attention_heads=self.num_attention_heads      ,				ffn_dim=self.ffn_dim      ,				activation_function=self.activation_function      ,				activation_dropout=self.activation_dropout      ,				attention_dropout=self.attention_dropout      ,				max_position_embeddings=self.max_position_embeddings      ,				initializer_range=self.initializer_range      ,				use_cache=UpperCAmelCase__      ,				bos_token_id=self.bos_token_id      ,				eos_token_id=self.eos_token_id      ,				pad_token_id=self.pad_token_id      ,				return_dict=UpperCAmelCase__      ,				)





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           A__   				=					self.prepare_config_and_inputs()

           (
               (
               A__
           )				,							(
               A__
           )				,							(
               A__
           )				,							(
               A__
           )				,							
           )   				=					config_and_inputs

           A__   				=					{
               '''input_ids''': input_ids,
               '''head_mask''': head_mask,
           }

           return config, inputs_dict


@require_tf
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
    UpperCAmelCase__													=   (TFXGLMForCausalLM,) if is_tf_available() else ()
    UpperCAmelCase__													=   (
        {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
    )
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False
    UpperCAmelCase__													=   False


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





           '''simple docstring'''

           A__   				=					TFXGLMModelTester(self)
           A__   				=					ConfigTester(self      ,				config_class=UpperCAmelCase__      ,				n_embd=37)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->str:





           '''simple docstring'''

           self.config_tester.run_common_tests()


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





           '''simple docstring'''

           for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__   				=					TFXGLMModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)





    @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.''')
    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Optional[int]:





           '''simple docstring'''

           super().test_resize_token_embeddings()


@require_tf
class        UpperCamelCase_					(				unittest.TestCase  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[Any]=True)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					tf.convert_to_tensor([[2, 268, 9_865]]      ,				dtype=tf.intaa)  # The dog
           # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
           # fmt: off
           A__   				=					[2, 268, 9_865, 67, 11, 1_988, 57_252, 9_865, 5, 984, 67, 1_988, 213_838, 1_658, 53, 70_446, 33, 6_657, 278, 1_581]
           # fmt: on
           A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				num_beams=1)
           if verify_outputs:
                  self.assertListEqual(output_ids[0].numpy().tolist()      ,				UpperCAmelCase__)


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





           '''simple docstring'''

           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')
           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')

           tf.random.set_seed(0)
           A__   				=					tokenizer('''Today is a nice day and'''      ,				return_tensors='''tf''')
           A__   				=					tokenized.input_ids
           # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
           with tf.device(''':/CPU:0'''):
                  A__   				=					model.generate(UpperCAmelCase__      ,				do_sample=UpperCAmelCase__      ,				seed=[7, 0])
           A__   				=					tokenizer.decode(output_ids[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					(
               '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
           )
           self.assertEqual(UpperCAmelCase__      ,				UpperCAmelCase__)





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





           '''simple docstring'''

           A__   				=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M''')
           A__   				=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M''')

           A__   				=					'''left'''

           # use different length sentences to test batching
           A__   				=					[
               '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
               '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
               '''regardless of whether left padding is applied or not. When''',
               '''Hello, my dog is a little''',
           ]

           A__   				=					tokenizer(UpperCAmelCase__      ,				return_tensors='''tf'''      ,				padding=UpperCAmelCase__)
           A__   				=					inputs['''input_ids''']

           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				attention_mask=inputs['''attention_mask''']      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[0]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer(sentences[1]      ,				return_tensors='''tf''').input_ids
           A__   				=					model.generate(input_ids=UpperCAmelCase__      ,				max_new_tokens=12)

           A__   				=					tokenizer.batch_decode(UpperCAmelCase__      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_non_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)
           A__   				=					tokenizer.decode(output_padded[0]      ,				skip_special_tokens=UpperCAmelCase__)

           A__   				=					[
               '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
               '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
               '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
               '''a single''',
               '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
           ]
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				[non_padded_sentence, padded_sentence])


