






'''simple docstring'''



import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 torch import nn

      from transformers import SwiftFormerForImageClassification, SwiftFormerModel
      from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import ViTImageProcessor
class UpperCAmelCase_       :




      """simple docstring"""




      def __init__(			self      :		List[str]  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		List[Any]=13  ,   UpperCAmelCase      :		Optional[int]=3  ,   UpperCAmelCase      :		Tuple=True  ,   UpperCAmelCase      :		Optional[int]=True  ,   UpperCAmelCase      :		Dict=0.1  ,   UpperCAmelCase      :		Optional[int]=0.1  ,   UpperCAmelCase      :		Optional[Any]=224  ,   UpperCAmelCase      :		str=1000  ,   UpperCAmelCase      :		List[Any]=[3, 3, 6, 4]  ,   UpperCAmelCase      :		Any=[48, 56, 112, 220]  ,   )	->	Union[str, Any]:


       '''simple docstring'''

       lowercase       : str						=parent
       lowercase       : str						=batch_size
       lowercase       : Any						=num_channels
       lowercase       : Union[str, Any]						=is_training
       lowercase       : Dict						=use_labels
       lowercase       : Tuple						=hidden_dropout_prob
       lowercase       : str						=attention_probs_dropout_prob
       lowercase       : int						=num_labels
       lowercase       : Optional[int]						=image_size
       lowercase       : Optional[Any]						=layer_depths
       lowercase       : List[str]						=embed_dims






      def 					A__   (			self      :		Any		)	->	int:


       '''simple docstring'''

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

       lowercase       : str						=None
       if self.use_labels:
        lowercase       : Tuple						=ids_tensor([self.batch_size]  ,   self.num_labels		)

       lowercase       : Tuple						=self.get_config()

       return config, pixel_values, labels






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


       '''simple docstring'''

       return SwiftFormerConfig(
           depths=self.layer_depths  ,   embed_dims=self.embed_dims  ,   mlp_ratio=4  ,   downsamples=[True, True, True, True]  ,   hidden_act='''gelu'''  ,   num_labels=self.num_labels  ,   down_patch_size=3  ,   down_stride=2  ,   down_pad=1  ,   drop_rate=0.0  ,   drop_path_rate=0.0  ,   use_layer_scale=UpperCAmelCase  ,   layer_scale_init_value=1e-5  ,   )






      def 					A__   (			self      :		int  ,   UpperCAmelCase      :		int  ,   UpperCAmelCase      :		str  ,   UpperCAmelCase      :		str		)	->	Tuple:


       '''simple docstring'''

       lowercase       : int						=SwiftFormerModel(config=UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()
       lowercase       : Optional[Any]						=model(UpperCAmelCase		)
       self.parent.assertEqual(result.last_hidden_state.shape  ,   (self.batch_size, self.embed_dims[-1], 7, 7)		)






      def 					A__   (			self      :		Tuple  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		List[str]  ,   UpperCAmelCase      :		Any		)	->	Optional[Any]:


       '''simple docstring'''

       lowercase       : List[str]						=self.num_labels
       lowercase       : int						=SwiftFormerForImageClassification(UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()
       lowercase       : Dict						=model(UpperCAmelCase  ,   labels=UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)		)

       lowercase       : Dict						=SwiftFormerForImageClassification(UpperCAmelCase		)
       model.to(UpperCAmelCase		)
       model.eval()

       lowercase       : int						=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
       lowercase       : str						=model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape  ,   (self.batch_size, self.num_labels)		)






      def 					A__   (			self      :		Tuple		)	->	Optional[Any]:


       '''simple docstring'''

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





@require_torch
class UpperCAmelCase_       (     __A					,							__A					,							unittest.TestCase					):




      """simple docstring"""




      UpperCamelCase_             =      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      UpperCamelCase_             =      (
          {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      UpperCamelCase_             =      False
      UpperCamelCase_             =      False
      UpperCamelCase_             =      False
      UpperCamelCase_             =      False
      UpperCamelCase_             =      False






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


       '''simple docstring'''

       lowercase       : Tuple						=SwiftFormerModelTester(self		)
       lowercase       : Optional[Any]						=ConfigTester(
           self  ,   config_class=UpperCAmelCase  ,   has_text_modality=UpperCAmelCase  ,   hidden_size=37  ,   num_attention_heads=12  ,   num_hidden_layers=12  ,   )






      def 					A__   (			self      :		Optional[Any]		)	->	Tuple:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      @unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''		)
      def 					A__   (			self      :		Optional[int]		)	->	Any:


       '''simple docstring'''

       pass






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


       '''simple docstring'''

       lowercase	,		lowercase       : Dict						=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowercase       : List[Any]						=model_class(UpperCAmelCase		)
        lowercase       : int						=model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(UpperCAmelCase  ,   nn.Linear		)		)






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


       '''simple docstring'''

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

       for model_class in self.all_model_classes:
        lowercase       : List[Any]						=model_class(UpperCAmelCase		)
        lowercase       : Union[str, Any]						=inspect.signature(model.forward		)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase       : List[str]						=[*signature.parameters.keys()]

        lowercase       : Union[str, Any]						=['''pixel_values''']
        self.assertListEqual(arg_names[:1]  ,   UpperCAmelCase		)






      def 					A__   (			self      :		List[str]		)	->	Tuple:


       '''simple docstring'''

       lowercase       : str						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase		)






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


       '''simple docstring'''

       lowercase       : Tuple						=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase		)






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


       '''simple docstring'''

       for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase       : Any						=SwiftFormerModel.from_pretrained(UpperCAmelCase		)
        self.assertIsNotNone(UpperCAmelCase		)






      @unittest.skip(reason='''SwiftFormer does not output attentions'''		)
      def 					A__   (			self      :		List[str]		)	->	int:


       '''simple docstring'''

       pass






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


       '''simple docstring'''

       def check_hidden_states_output(UpperCAmelCase      :		Dict  ,   UpperCAmelCase      :		List[Any]  ,   UpperCAmelCase      :		List[str]		):
        lowercase       : Optional[int]						=model_class(UpperCAmelCase		)
        model.to(UpperCAmelCase		)
        model.eval()

        with torch.no_grad():
         lowercase       : Union[str, Any]						=model(**self._prepare_for_class(UpperCAmelCase  ,   UpperCAmelCase		)		)

        lowercase       : List[str]						=outputs.hidden_states

        lowercase       : Dict						=8
        self.assertEqual(len(UpperCAmelCase		)  ,   UpperCAmelCase		)  # TODO

        # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
        # with the width and height being successively divided by 2, after every 2 blocks
        for i in range(len(UpperCAmelCase		)		):
         self.assertEqual(
             hidden_states[i].shape  ,   torch.Size(
                 [
                     self.model_tester.batch_size,
                     self.model_tester.embed_dims[i // 2],
                     (self.model_tester.image_size // 4) // 2 ** (i // 2),
                     (self.model_tester.image_size // 4) // 2 ** (i // 2),
                 ]		)  ,   )

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

       for model_class in self.all_model_classes:
        lowercase       : Dict						=True
        check_hidden_states_output(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase       : str						=True

        check_hidden_states_output(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)






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


       '''simple docstring'''

       def _config_zero_init(UpperCAmelCase      :		Optional[Any]		):
        lowercase       : List[Any]						=copy.deepcopy(UpperCAmelCase		)
        for key in configs_no_init.__dict__.keys():
         if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
          setattr(UpperCAmelCase  ,   UpperCAmelCase  ,   1e-10		)
         if isinstance(getattr(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)  ,   UpperCAmelCase		):
          lowercase       : Dict						=_config_zero_init(getattr(UpperCAmelCase  ,   UpperCAmelCase		)		)
          setattr(UpperCAmelCase  ,   UpperCAmelCase  ,   UpperCAmelCase		)
        return configs_no_init

       lowercase	,		lowercase       : List[Any]						=self.model_tester.prepare_config_and_inputs_for_common()

       lowercase       : List[Any]						=_config_zero_init(UpperCAmelCase		)
       for model_class in self.all_model_classes:
        lowercase       : List[Any]						=model_class(config=UpperCAmelCase		)
        for name, param in model.named_parameters():
         if param.requires_grad:
          self.assertIn(
              ((param.data.mean() * 1e9) / 1e9).round().item()  ,   [0.0, 1.0]  ,   msg=f'Parameter {name} of model {model_class} seems not properly initialized'  ,   )






      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''		)
      def 					A__   (			self      :		int		)	->	Tuple:


       '''simple docstring'''

       pass





def 				lowercase_					(     )							->   Tuple:



 """simple docstring"""




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





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




      """simple docstring"""




      @cached_property
      def 					A__   (			self      :		List[str]		)	->	Union[str, Any]:


       '''simple docstring'''

       return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''		) if is_vision_available() else None






      @slow
      def 					A__   (			self      :		Dict		)	->	Tuple:


       '''simple docstring'''

       lowercase       : List[str]						=SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''		).to(UpperCAmelCase		)

       lowercase       : Optional[Any]						=self.default_image_processor
       lowercase       : Any						=prepare_img()
       lowercase       : Tuple						=image_processor(images=UpperCAmelCase  ,   return_tensors='''pt'''		).to(UpperCAmelCase		)

       # forward pass
       with torch.no_grad():
        lowercase       : List[str]						=model(**UpperCAmelCase		)

       # verify the logits
       lowercase       : Tuple						=torch.Size((1, 1000)		)
       self.assertEqual(outputs.logits.shape  ,   UpperCAmelCase		)

       lowercase       : Any						=torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]		).to(UpperCAmelCase		)
       self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,   UpperCAmelCase  ,   atol=1e-4		)		)










"""simple docstring"""




import random
import unittest

import numpy as np

import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax


if is_flax_available():
		import os

		import jax.numpy as jnp
		from jax import jit

		from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
		from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model

		a		:					Optional[Any]	      =     '''0.12'''  # assumed parallelism: 8


if is_torch_available():
		import torch






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       Any       ,			_lowercase		:       Union[str, Any]=None   )					->Union[str, Any]:






	'''simple docstring'''






	if rng is None:
		a :						Dict      =							random.Random()

	a :						Any      =							1
	for dim in shape:
		total_dims *= dim

	a :						Dict      =							[]
	for _ in range(_lowercase   ):
		values.append(rng.randint(0       ,			vocab_size - 1   )   )

	a :						Dict      =							np.array(_lowercase       ,			dtype=jnp.intaa   ).reshape(_lowercase   )

	return output






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[str]       ,			_lowercase		:       int=None   )					->Optional[Any]:






	'''simple docstring'''






	a :						str      =							ids_tensor(_lowercase       ,			vocab_size=2       ,			rng=_lowercase   )
	# make sure that at least one token is attended to for each batch
	a :						Union[str, Any]      =							1
	return attn_mask


@require_flax
class 			__UpperCamelCase							:
							lowerCamelCase  :   Dict    =None
							lowerCamelCase  :   List[Any]    =()






							def        __a  (  self )      ->					Union[str, Any]:
								a,			a :						List[Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

								# cut to half length & take max batch_size 3
								a :						Any      =							2
								a :						List[Any]      =							inputs["input_ids"].shape[-1] // 2
								a :						Union[str, Any]      =							inputs["input_ids"][:max_batch_size, :sequence_length]

								a :						Optional[Any]      =							jnp.ones_like(lowerCAmelCase__ )
								a :						Any      =							attention_mask[:max_batch_size, :sequence_length]

								# generate max 5 tokens
								a :						int      =							input_ids.shape[-1] + 5
								if config.eos_token_id is not None and config.pad_token_id is None:
									# hack to allow generate for models such as GPT2 as is done in `generate()`
									a :						List[str]      =							config.eos_token_id
								return config, input_ids, attention_mask, max_length






							@is_pt_flax_cross_test
							def        __a  (  self )      ->					List[Any]:
								a,			a,			a,			a :						int      =							self._get_input_ids_and_config()
								a :						Dict      =							False
								a :						Tuple      =							max_length
								a :						List[Any]      =							0

								for model_class in self.all_generative_model_classes:
									a :						List[str]      =							model_class(lowerCAmelCase__ )

									a :						int      =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
									a :						List[Any]      =							getattr(lowerCAmelCase__ ,   lowerCAmelCase__ )
									a :						Any      =							pt_model_class(lowerCAmelCase__ ).eval()
									a :						int      =							load_flax_weights_in_pytorch_model(lowerCAmelCase__ ,   flax_model.params )

									a :						Optional[Any]      =							flax_model.generate(lowerCAmelCase__ ).sequences
									a :						Tuple      =							pt_model.generate(torch.tensor(lowerCAmelCase__ ,   dtype=torch.long ) )

									if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
										a :						List[str]      =							flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]

									self.assertListEqual(pt_generation_outputs.numpy().tolist() ,   flax_generation_outputs.tolist() )






							def        __a  (  self )      ->					Optional[Any]:
								a,			a,			a,			a :						Optional[Any]      =							self._get_input_ids_and_config()
								a :						List[str]      =							False
								a :						Tuple      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Dict      =							model_class(lowerCAmelCase__ )

									a :						Union[str, Any]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Dict      =							jit(model.generate )
									a :						int      =							jit_generate(lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )






							def        __a  (  self )      ->					Any:
								a,			a,			a,			a :						List[Any]      =							self._get_input_ids_and_config()
								a :						Dict      =							True
								a :						Any      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						int      =							model_class(lowerCAmelCase__ )

									a :						Optional[int]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						List[Any]      =							jit_generate(lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )






							def        __a  (  self )      ->					Dict:
								a,			a,			a,			a :						str      =							self._get_input_ids_and_config()
								a :						str      =							False
								a :						Union[str, Any]      =							max_length
								a :						Optional[int]      =							2

								for model_class in self.all_generative_model_classes:
									a :						List[Any]      =							model_class(lowerCAmelCase__ )

									a :						Optional[Any]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Union[str, Any]      =							jit(model.generate )
									a :						Optional[int]      =							jit_generate(lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )






							def        __a  (  self )      ->					Optional[Any]:
								a,			a,			a,			a :						Optional[Any]      =							self._get_input_ids_and_config()
								a :						Optional[Any]      =							False
								a :						Tuple      =							max_length
								a :						List[Any]      =							2
								a :						Any      =							2

								for model_class in self.all_generative_model_classes:
									a :						Any      =							model_class(lowerCAmelCase__ )

									a :						List[str]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[0] ,   input_ids.shape[0] * config.num_return_sequences )






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						List[str]      =							self._get_input_ids_and_config()
								a :						Optional[int]      =							True
								a :						Dict      =							max_length
								a :						Dict      =							0.8
								a :						Any      =							10
								a :						Union[str, Any]      =							0.3
								a :						List[Any]      =							1
								a :						Optional[int]      =							8
								a :						List[Any]      =							9

								for model_class in self.all_generative_model_classes:
									a :						Tuple      =							model_class(lowerCAmelCase__ )

									a :						Any      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Tuple      =							jit(model.generate )
									a :						Dict      =							jit_generate(lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						Dict      =							self._get_input_ids_and_config()
								a :						Dict      =							max_length
								a :						List[str]      =							1
								a :						List[str]      =							8
								a :						List[str]      =							9

								for model_class in self.all_generative_model_classes:
									a :						Dict      =							model_class(lowerCAmelCase__ )

									a :						str      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[str]      =							jit(model.generate )
									a :						Union[str, Any]      =							jit_generate(lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )






							def        __a  (  self )      ->					int:
								a,			a,			a,			a :						Tuple      =							self._get_input_ids_and_config()
								a :						List[Any]      =							max_length
								a :						Dict      =							2
								a :						List[Any]      =							1
								a :						List[str]      =							8
								a :						Any      =							9

								for model_class in self.all_generative_model_classes:
									a :						Optional[int]      =							model_class(lowerCAmelCase__ )

									a :						List[str]      =							model.generate(lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						int      =							jit(model.generate )
									a :						Optional[int]      =							jit_generate(lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )






							def        __a  (  self )      ->					Optional[int]:
								a,			a,			a,			a :						List[str]      =							self._get_input_ids_and_config()

								# pad attention mask on the left
								a :						str      =							attention_mask.at[(0, 0)].set(0 )

								a :						Any      =							False
								a :						Union[str, Any]      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Union[str, Any]      =							model_class(lowerCAmelCase__ )

									a :						Union[str, Any]      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						int      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )






							def        __a  (  self )      ->					List[str]:
								a,			a,			a,			a :						Union[str, Any]      =							self._get_input_ids_and_config()

								# pad attention mask on the left
								a :						Any      =							attention_mask.at[(0, 0)].set(0 )

								a :						Dict      =							True
								a :						List[Any]      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						List[str]      =							model_class(lowerCAmelCase__ )

									a :						int      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						Any      =							jit(model.generate )
									a :						List[str]      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )






							def        __a  (  self )      ->					str:
								a,			a,			a,			a :						Dict      =							self._get_input_ids_and_config()

								# pad attention mask on the left
								a :						Union[str, Any]      =							attention_mask.at[(0, 0)].set(0 )

								a :						int      =							2
								a :						Any      =							max_length

								for model_class in self.all_generative_model_classes:
									a :						Optional[Any]      =							model_class(lowerCAmelCase__ )

									a :						List[Any]      =							model.generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences
									self.assertEqual(generation_outputs.shape[-1] ,   lowerCAmelCase__ )

									a :						List[Any]      =							jit(model.generate )
									a :						Dict      =							jit_generate(lowerCAmelCase__ ,   attention_mask=lowerCAmelCase__ ).sequences

									self.assertListEqual(generation_outputs.tolist() ,   jit_generation_outputs.tolist() )


@require_flax
class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					List[Any]:
								a :						Any      =							AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
								a :						Tuple      =							FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )

								a :						int      =							"Hello world"
								a :						int      =							tokenizer(lowerCAmelCase__ ,   return_tensors="np" ).input_ids

								# typos are quickly detected (the correct argument is `do_sample`)
								with self.assertRaisesRegex(lowerCAmelCase__ ,   "do_samples" ):
									model.generate(lowerCAmelCase__ ,   do_samples=lowerCAmelCase__ )

								# arbitrary arguments that will not be used anywhere are also not accepted
								with self.assertRaisesRegex(lowerCAmelCase__ ,   "foo" ):
									a :						str      =							{"foo": "bar"}
									model.generate(lowerCAmelCase__ ,   **lowerCAmelCase__ )


