





import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
       from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import PoolFormerImageProcessor

class 			__A(    _a			):

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.config_class(**self.inputs_dict   )
            self.parent.assertTrue(hasattr(snake_case_,							'''hidden_sizes'''   )   )
            self.parent.assertTrue(hasattr(snake_case_,							'''num_encoder_blocks'''   )   )






class 			__A:

     def __init__(   self,							A,							A=13,							A=64,							A=3,							A=4,							A=[2, 2, 2, 2],							A=[8, 4, 2, 1],							A=[16, 32, 64, 128],							A=[1, 4, 8, 16],							A=False,							A=True,							A="gelu",							A=0.1,							A=0.02,							A=3,							A=None,							):





            """simple docstring"""

            _UpperCamelCase														=    parent
            _UpperCamelCase														=    batch_size
            _UpperCamelCase														=    image_size
            _UpperCamelCase														=    num_channels
            _UpperCamelCase														=    num_encoder_blocks
            _UpperCamelCase														=    sr_ratios
            _UpperCamelCase														=    depths
            _UpperCamelCase														=    hidden_sizes
            _UpperCamelCase														=    downsampling_rates
            _UpperCamelCase														=    is_training
            _UpperCamelCase														=    use_labels
            _UpperCamelCase														=    hidden_act
            _UpperCamelCase														=    hidden_dropout_prob
            _UpperCamelCase														=    initializer_range
            _UpperCamelCase														=    num_labels
            _UpperCamelCase														=    scope

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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.image_size, self.image_size],							self.num_labels   )

            _UpperCamelCase														=    PoolFormerConfig(
                image_size=self.image_size,							num_channels=self.num_channels,							num_encoder_blocks=self.num_encoder_blocks,							depths=self.depths,							hidden_sizes=self.hidden_sizes,							hidden_act=self.hidden_act,							hidden_dropout_prob=self.hidden_dropout_prob,							initializer_range=self.initializer_range,							)

            return config, pixel_values, labels

     def 						_UpperCamelCase  (   self,							A,							A,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    PoolFormerModel(config=snake_case_   )
            model.to(snake_case_   )
            model.eval()
            _UpperCamelCase														=    model(snake_case_   )
            _UpperCamelCase														=    _UpperCamelCase														=    self.image_size // 32.0
            self.parent.assertEqual(
                result.last_hidden_state.shape,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)   )



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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






@require_torch
class 			__A(    _a    ,      _a    ,      unittest.TestCase			):
     __A							=							(PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     __A							=							(
         {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     __A							=							False
     __A							=							False
     __A							=							False
     __A							=							False
     __A							=							False

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    PoolFormerModelTester(self   )
            _UpperCamelCase														=    PoolFormerConfigTester(self,							config_class=snake_case_   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            self.config_tester.run_common_tests()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*snake_case_   )

     @unittest.skip('''PoolFormer does not use inputs_embeds'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     @unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''   )
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            pass

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            def check_hidden_states_output(A,							A,							A   ):
                   _UpperCamelCase														=    model_class(snake_case_   )
                   model.to(snake_case_   )
                   model.eval()

                   with torch.no_grad():
                          _UpperCamelCase														=    model(**self._prepare_for_class(snake_case_,							snake_case_   )   )

                   _UpperCamelCase														=    outputs.hidden_states

                   _UpperCamelCase														=    self.model_tester.num_encoder_blocks
                   self.assertEqual(len(snake_case_   ),							snake_case_   )

                   # verify the first hidden states (first block)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-3:]   ),							[
                           self.model_tester.hidden_sizes[0],
                           self.model_tester.image_size // 4,
                           self.model_tester.image_size // 4,
                       ],							)

            _UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _UpperCamelCase														=    True
                   check_hidden_states_output(snake_case_,							snake_case_,							snake_case_   )

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

                   check_hidden_states_output(snake_case_,							snake_case_,							snake_case_   )

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            if not self.model_tester.is_training:
                   return

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

            for model_class in self.all_model_classes:
                   if model_class in get_values(snake_case_   ):
                          continue
                   _UpperCamelCase														=    model_class(snake_case_   )
                   model.to(snake_case_   )
                   model.train()
                   _UpperCamelCase														=    self._prepare_for_class(snake_case_,							snake_case_,							return_labels=snake_case_   )
                   _UpperCamelCase														=    model(**snake_case_   ).loss
                   loss.backward()

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase							,		_UpperCamelCase														=    self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   _UpperCamelCase														=    model_class(snake_case_   )
                   _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],							snake_case_   )



     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   _UpperCamelCase														=    PoolFormerModel.from_pretrained(snake_case_   )
                   self.assertIsNotNone(snake_case_   )






def      SCREAMING_SNAKE_CASE      (   ):
       _UpperCamelCase														=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
       return image

@require_torch
class 			__A(    unittest.TestCase			):

     @slow
     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    PoolFormerImageProcessor()
            _UpperCamelCase														=    PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''   ).to(snake_case_   )

            _UpperCamelCase														=    image_processor(images=prepare_img(),							return_tensors='''pt'''   ).to(snake_case_   )

            # forward pass
            with torch.no_grad():
                   _UpperCamelCase														=    model(**snake_case_   )

            # verify the logits
            _UpperCamelCase														=    torch.Size((1, 1000)   )
            self.assertEqual(outputs.logits.shape,							snake_case_   )

            _UpperCamelCase														=    torch.tensor([-0.6_113, 0.1_685, -0.0_492]   ).to(snake_case_   )
            self.assertTrue(torch.allclose(outputs.logits[0, :3],							snake_case_,							atol=1E-4   )   )














"""simple docstring"""





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
  import torch
class  __lowerCamelCase     :

   def __init__(       self			,					snake_case_			,					snake_case_=14			,					snake_case_=7			,					snake_case_=True			,					snake_case_=True			,					snake_case_=False			,					snake_case_=True			,					snake_case_=99			,					snake_case_=32			,					snake_case_=4			,					snake_case_=4			,					snake_case_=[[["global", "local"], 2]]			,					snake_case_=37			,					snake_case_="gelu"			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=7			,					snake_case_=0.02			,					)		->							Tuple:
       UpperCamelCase__								=     parent
       UpperCamelCase__								=     batch_size
       UpperCamelCase__								=     seq_length
       UpperCamelCase__								=     is_training
       UpperCamelCase__								=     use_input_mask
       UpperCamelCase__								=     use_token_type_ids
       UpperCamelCase__								=     use_labels
       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     attention_types
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     window_size
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     None
       UpperCamelCase__								=     vocab_size - 1
       UpperCamelCase__								=     vocab_size - 1
       UpperCamelCase__								=     vocab_size - 1

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       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__								=     GPTNeoConfig(
           vocab_size=self.vocab_size			,					hidden_size=self.hidden_size			,					num_layers=self.num_hidden_layers			,					num_heads=self.num_attention_heads			,					max_position_embeddings=self.max_position_embeddings			,					use_cache=snake_case_			,					bos_token_id=self.bos_token_id			,					eos_token_id=self.eos_token_id			,					pad_token_id=self.pad_token_id			,					window_size=self.window_size			,					attention_types=self.attention_types			,					)

       return (config, input_ids, input_mask)

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       UpperCamelCase__								=     self.prepare_config_and_inputs()
       UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     config_and_inputs
       UpperCamelCase__								=     {'input_ids': input_ids, 'attention_mask': attention_mask}
       return config, inputs_dict

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )		->							Union[str, Any]:
       UpperCamelCase__								=     20
       UpperCamelCase__								=     model_class_name(snake_case_   )

       UpperCamelCase__								=     model.init_cache(input_ids.shape[0]			,					snake_case_   )
       UpperCamelCase__								=     jnp.ones((input_ids.shape[0], max_decoder_length)			,					dtype='i4'   )

       UpperCamelCase__								=     jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]			,					(input_ids.shape[0], input_ids.shape[-1] - 1)   )
       UpperCamelCase__								=     model(
           input_ids[:, :-1]			,					attention_mask=snake_case_			,					past_key_values=snake_case_			,					position_ids=snake_case_			,					)

       UpperCamelCase__								=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,					dtype='i4'   )
       UpperCamelCase__								=     model(
           input_ids[:, -1:]			,					attention_mask=snake_case_			,					past_key_values=outputs_cache.past_key_values			,					position_ids=snake_case_			,					)

       UpperCamelCase__								=     model(snake_case_   )

       UpperCamelCase__								=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
       self.parent.assertTrue(diff < 1E-3			,					msg=F'Max diff is {diff}'   )







   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )		->							List[str]:
       UpperCamelCase__								=     20
       UpperCamelCase__								=     model_class_name(snake_case_   )

       UpperCamelCase__								=     jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])   )]			,					axis=-1			,					)

       UpperCamelCase__								=     model.init_cache(input_ids.shape[0]			,					snake_case_   )
       UpperCamelCase__								=     jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]			,					(input_ids.shape[0], input_ids.shape[-1] - 1)   )

       UpperCamelCase__								=     model(
           input_ids[:, :-1]			,					attention_mask=snake_case_			,					past_key_values=snake_case_			,					position_ids=snake_case_			,					)
       UpperCamelCase__								=     jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]			,					dtype='i4'   )
       UpperCamelCase__								=     model(
           input_ids[:, -1:]			,					past_key_values=outputs_cache.past_key_values			,					attention_mask=snake_case_			,					position_ids=snake_case_			,					)

       UpperCamelCase__								=     model(snake_case_			,					attention_mask=snake_case_   )

       UpperCamelCase__								=     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
       self.parent.assertTrue(diff < 1E-3			,					msg=F'Max diff is {diff}'   )







@require_flax
class  __lowerCamelCase     (	_a				,						_a				,						unittest.TestCase	):
   a   :					int			    =(FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
   a   :					Optional[Any]			    =(FlaxGPTNeoForCausalLM,) if is_flax_available() else ()

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__								=     FlaxGPTNeoModelTester(self   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               snake_case_			,					snake_case_			,					snake_case_			,					snake_case_   )

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       UpperCamelCase__								=     GPTaTokenizer.from_pretrained('gpt2'			,					pad_token='<|endoftext|>'			,					padding_side='left'   )
       UpperCamelCase__								=     tokenizer(['Hello this is a long string', 'Hey']			,					return_tensors='np'			,					padding=snake_case_			,					truncation=snake_case_   )

       UpperCamelCase__								=     FlaxGPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-125M'   )
       UpperCamelCase__								=     False
       UpperCamelCase__								=     model.config.eos_token_id

       UpperCamelCase__								=     jax.jit(model.generate   )

       UpperCamelCase__								=     jit_generate(
           inputs['input_ids']			,					attention_mask=inputs['attention_mask']			,					pad_token_id=tokenizer.pad_token_id   ).sequences

       UpperCamelCase__								=     tokenizer.batch_decode(snake_case_			,					skip_special_tokens=snake_case_   )

       UpperCamelCase__								=     [
           'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
           'Hey, I\'m a little late to the party. I\'m going to',
       ]

       self.assertListEqual(snake_case_			,					snake_case_   )

   @is_pt_flax_cross_test
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__   ):
               # prepare inputs
               UpperCamelCase__								=     self._prepare_for_class(snake_case_			,					snake_case_   )
               UpperCamelCase__								=     {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

               # load corresponding PyTorch class
               UpperCamelCase__								=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
               UpperCamelCase__								=     getattr(snake_case_			,					snake_case_   )

               UpperCamelCase__							,    UpperCamelCase__								=     pt_inputs['input_ids'].shape
               UpperCamelCase__								=     np.random.randint(0			,					seq_length - 1			,					size=(batch_size,)   )
               for batch_idx, start_index in enumerate(snake_case_   ):
                   UpperCamelCase__								=     0
                   UpperCamelCase__								=     1
                   UpperCamelCase__								=     0
                   UpperCamelCase__								=     1
               UpperCamelCase__								=     pt_model_class(snake_case_   ).eval()
               UpperCamelCase__								=     model_class(snake_case_			,					dtype=jnp.floataa   )

               UpperCamelCase__								=     convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,					snake_case_   )
               UpperCamelCase__								=     fx_state

               with torch.no_grad():
                   UpperCamelCase__								=     pt_model(**snake_case_   ).to_tuple()

               UpperCamelCase__								=     fx_model(**snake_case_   ).to_tuple()
               self.assertEqual(len(snake_case_   )			,					len(snake_case_   )			,					'Output lengths differ between Flax and PyTorch'   )
               for fx_output, pt_output in zip(snake_case_			,					snake_case_   ):
                   self.assert_almost_equals(fx_output[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2   )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   pt_model.save_pretrained(snake_case_   )
                   UpperCamelCase__								=     model_class.from_pretrained(snake_case_			,					from_pt=snake_case_   )

               UpperCamelCase__								=     fx_model_loaded(**snake_case_   ).to_tuple()
               self.assertEqual(
                   len(snake_case_   )			,					len(snake_case_   )			,					'Output lengths differ between Flax and PyTorch'   )
               for fx_output_loaded, pt_output in zip(snake_case_			,					snake_case_   ):
                   self.assert_almost_equals(fx_output_loaded[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2   )

   @is_pt_flax_cross_test
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       UpperCamelCase__							,    UpperCamelCase__								=     self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__   ):
               # prepare inputs
               UpperCamelCase__								=     self._prepare_for_class(snake_case_			,					snake_case_   )
               UpperCamelCase__								=     {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

               # load corresponding PyTorch class
               UpperCamelCase__								=     model_class.__name__[4:]  # Skip the "Flax" at the beginning
               UpperCamelCase__								=     getattr(snake_case_			,					snake_case_   )

               UpperCamelCase__								=     pt_model_class(snake_case_   ).eval()
               UpperCamelCase__								=     model_class(snake_case_			,					dtype=jnp.floataa   )

               UpperCamelCase__								=     load_flax_weights_in_pytorch_model(snake_case_			,					fx_model.params   )
               UpperCamelCase__							,    UpperCamelCase__								=     pt_inputs['input_ids'].shape
               UpperCamelCase__								=     np.random.randint(0			,					seq_length - 1			,					size=(batch_size,)   )
               for batch_idx, start_index in enumerate(snake_case_   ):
                   UpperCamelCase__								=     0
                   UpperCamelCase__								=     1
                   UpperCamelCase__								=     0
                   UpperCamelCase__								=     1

               # make sure weights are tied in PyTorch
               pt_model.tie_weights()

               with torch.no_grad():
                   UpperCamelCase__								=     pt_model(**snake_case_   ).to_tuple()

               UpperCamelCase__								=     fx_model(**snake_case_   ).to_tuple()
               self.assertEqual(len(snake_case_   )			,					len(snake_case_   )			,					'Output lengths differ between Flax and PyTorch'   )
               for fx_output, pt_output in zip(snake_case_			,					snake_case_   ):
                   self.assert_almost_equals(fx_output[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2   )

               with tempfile.TemporaryDirectory() as tmpdirname:
                   fx_model.save_pretrained(snake_case_   )
                   UpperCamelCase__								=     pt_model_class.from_pretrained(snake_case_			,					from_flax=snake_case_   )

               with torch.no_grad():
                   UpperCamelCase__								=     pt_model_loaded(**snake_case_   ).to_tuple()

               self.assertEqual(
                   len(snake_case_   )			,					len(snake_case_   )			,					'Output lengths differ between Flax and PyTorch'   )
               for fx_output, pt_output in zip(snake_case_			,					snake_case_   ):
                   self.assert_almost_equals(fx_output[:, -1]			,					pt_output[:, -1].numpy()			,					4E-2   )







   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[int]:
       for model_class_name in self.all_model_classes:
           UpperCamelCase__								=     model_class_name.from_pretrained('EleutherAI/gpt-neo-125M'   )
           UpperCamelCase__								=     model(np.ones((1, 1)   )   )
           self.assertIsNotNone(snake_case_   )




