





from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)

@flax.struct.dataclass
class 		UpperCamelCase__			(				SCREAMING_SNAKE_CASE_       ):



     '''simple docstring'''

     __snake_case   :     Any		     =      42
     __snake_case   :     Tuple		     =      42

class 		UpperCamelCase__			(				nn.Module       ):



     '''simple docstring'''

     __snake_case   :     Optional[int]		     =      42
     __snake_case   :     Optional[int]		     =      (16, 32, 96, 256)
     __snake_case   :     List[str]		     =      jnp.floataa






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						nn.Conv(
               self.block_out_channels[0]						,kernel_size=(3, 3)						,padding=((1, 1), (1, 1))						,dtype=self.dtype						,)

           SCREAMING_SNAKE_CASE		=						[]
           for i in range(len(self.block_out_channels	) - 1	):
                 SCREAMING_SNAKE_CASE		=						self.block_out_channels[i]
                 SCREAMING_SNAKE_CASE		=						self.block_out_channels[i + 1]
                 SCREAMING_SNAKE_CASE		=						nn.Conv(
                     __a						,kernel_size=(3, 3)						,padding=((1, 1), (1, 1))						,dtype=self.dtype						,)
                 blocks.append(__a	)
                 SCREAMING_SNAKE_CASE		=						nn.Conv(
                     __a						,kernel_size=(3, 3)						,strides=(2, 2)						,padding=((1, 1), (1, 1))						,dtype=self.dtype						,)
                 blocks.append(__a	)
           SCREAMING_SNAKE_CASE		=						blocks

           SCREAMING_SNAKE_CASE		=						nn.Conv(
               self.conditioning_embedding_channels						,kernel_size=(3, 3)						,padding=((1, 1), (1, 1))						,kernel_init=nn.initializers.zeros_init()						,bias_init=nn.initializers.zeros_init()						,dtype=self.dtype						,)






     def __call__(							self						:   Any						,lowerCamelCase__						:   int	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.conv_in(__a	)
           SCREAMING_SNAKE_CASE		=						nn.silu(__a	)

           for block in self.blocks:
                 SCREAMING_SNAKE_CASE		=						block(__a	)
                 SCREAMING_SNAKE_CASE		=						nn.silu(__a	)

           SCREAMING_SNAKE_CASE		=						self.conv_out(__a	)

           return embedding

@flax_register_to_config
class 		UpperCamelCase__			(				nn.Module							,				SCREAMING_SNAKE_CASE_							,				SCREAMING_SNAKE_CASE_       ):



     '''simple docstring'''

     __snake_case   :     List[Any]		     =      32
     __snake_case   :     Dict		     =      4
     __snake_case   :     Optional[Any]		     =      (
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "CrossAttnDownBlock2D",
         "DownBlock2D",
     )
     __snake_case   :     List[str]		     =      False
     __snake_case   :     Union[str, Any]		     =      (320, 640, 1280, 1280)
     __snake_case   :     Union[str, Any]		     =      2
     __snake_case   :     Optional[int]		     =      8
     __snake_case   :     str		     =      None
     __snake_case   :     Any		     =      1280
     __snake_case   :     Union[str, Any]		     =      0.0
     __snake_case   :     List[str]		     =      False
     __snake_case   :     List[Any]		     =      jnp.floataa
     __snake_case   :     Optional[Any]		     =      True
     __snake_case   :     Any		     =      0
     __snake_case   :     int		     =      "rgb"
     __snake_case   :     str		     =      (16, 32, 96, 256)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]						,lowerCamelCase__						:   jax.random.KeyArray	)					->		FrozenDict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						(1, self.in_channels, self.sample_size, self.sample_size)
           SCREAMING_SNAKE_CASE		=						jnp.zeros(__a						,dtype=jnp.floataa	)
           SCREAMING_SNAKE_CASE		=						jnp.ones((1,)						,dtype=jnp.intaa	)
           SCREAMING_SNAKE_CASE		=						jnp.zeros((1, 1, self.cross_attention_dim)						,dtype=jnp.floataa	)
           SCREAMING_SNAKE_CASE		=						(1, 3, self.sample_size * 8, self.sample_size * 8)
           SCREAMING_SNAKE_CASE		=						jnp.zeros(__a						,dtype=jnp.floataa	)

           SCREAMING_SNAKE_CASE		=						jax.random.split(__a	)
           SCREAMING_SNAKE_CASE		=						{'params': params_rng, 'dropout': dropout_rng}

           return self.init(__a						,__a						,__a						,__a						,__a	)["params"]






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.block_out_channels
           SCREAMING_SNAKE_CASE		=						block_out_channels[0] * 4

           # If `num_attention_heads` is not defined (which is the case for most models)
           # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
           # The reason for this behavior is to correct for incorrectly named variables that were introduced
           # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
           # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
           # which is why we correct for the naming here.
           SCREAMING_SNAKE_CASE		=						self.num_attention_heads or self.attention_head_dim

           # input
           SCREAMING_SNAKE_CASE		=						nn.Conv(
               block_out_channels[0]						,kernel_size=(3, 3)						,strides=(1, 1)						,padding=((1, 1), (1, 1))						,dtype=self.dtype						,)

           # time
           SCREAMING_SNAKE_CASE		=						FlaxTimesteps(
               block_out_channels[0]						,flip_sin_to_cos=self.flip_sin_to_cos						,freq_shift=self.config.freq_shift	)
           SCREAMING_SNAKE_CASE		=						FlaxTimestepEmbedding(__a						,dtype=self.dtype	)

           SCREAMING_SNAKE_CASE		=						FlaxControlNetConditioningEmbedding(
               conditioning_embedding_channels=block_out_channels[0]						,block_out_channels=self.conditioning_embedding_out_channels						,)

           SCREAMING_SNAKE_CASE		=						self.only_cross_attention
           if isinstance(__a						,__a	):
                 SCREAMING_SNAKE_CASE		=						(only_cross_attention,) * len(self.down_block_types	)

           if isinstance(__a						,__a	):
                 SCREAMING_SNAKE_CASE		=						(num_attention_heads,) * len(self.down_block_types	)

           # down
           SCREAMING_SNAKE_CASE		=						[]
           SCREAMING_SNAKE_CASE		=						[]

           SCREAMING_SNAKE_CASE		=						block_out_channels[0]

           SCREAMING_SNAKE_CASE		=						nn.Conv(
               __a						,kernel_size=(1, 1)						,padding="""VALID"""						,kernel_init=nn.initializers.zeros_init()						,bias_init=nn.initializers.zeros_init()						,dtype=self.dtype						,)
           controlnet_down_blocks.append(__a	)

           for i, down_block_type in enumerate(self.down_block_types	):
                 SCREAMING_SNAKE_CASE		=						output_channel
                 SCREAMING_SNAKE_CASE		=						block_out_channels[i]
                 SCREAMING_SNAKE_CASE		=						i == len(__a	) - 1

                 if down_block_type == "CrossAttnDownBlock2D":
                       SCREAMING_SNAKE_CASE		=						FlaxCrossAttnDownBlockaD(
                           in_channels=__a						,out_channels=__a						,dropout=self.dropout						,num_layers=self.layers_per_block						,num_attention_heads=num_attention_heads[i]						,add_downsample=not is_final_block						,use_linear_projection=self.use_linear_projection						,only_cross_attention=only_cross_attention[i]						,dtype=self.dtype						,)
                 else:
                       SCREAMING_SNAKE_CASE		=						FlaxDownBlockaD(
                           in_channels=__a						,out_channels=__a						,dropout=self.dropout						,num_layers=self.layers_per_block						,add_downsample=not is_final_block						,dtype=self.dtype						,)

                 down_blocks.append(__a	)

                 for _ in range(self.layers_per_block	):
                       SCREAMING_SNAKE_CASE		=						nn.Conv(
                           __a						,kernel_size=(1, 1)						,padding="""VALID"""						,kernel_init=nn.initializers.zeros_init()						,bias_init=nn.initializers.zeros_init()						,dtype=self.dtype						,)
                       controlnet_down_blocks.append(__a	)

                 if not is_final_block:
                       SCREAMING_SNAKE_CASE		=						nn.Conv(
                           __a						,kernel_size=(1, 1)						,padding="""VALID"""						,kernel_init=nn.initializers.zeros_init()						,bias_init=nn.initializers.zeros_init()						,dtype=self.dtype						,)
                       controlnet_down_blocks.append(__a	)

           SCREAMING_SNAKE_CASE		=						down_blocks
           SCREAMING_SNAKE_CASE		=						controlnet_down_blocks

           # mid
           SCREAMING_SNAKE_CASE		=						block_out_channels[-1]
           SCREAMING_SNAKE_CASE		=						FlaxUNetMidBlockaDCrossAttn(
               in_channels=__a						,dropout=self.dropout						,num_attention_heads=num_attention_heads[-1]						,use_linear_projection=self.use_linear_projection						,dtype=self.dtype						,)

           SCREAMING_SNAKE_CASE		=						nn.Conv(
               __a						,kernel_size=(1, 1)						,padding="""VALID"""						,kernel_init=nn.initializers.zeros_init()						,bias_init=nn.initializers.zeros_init()						,dtype=self.dtype						,)






     def __call__(							self						:   str						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Any						,lowerCamelCase__						:   Optional[Any]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   float = 1.0						,lowerCamelCase__						:   bool = True						,lowerCamelCase__						:   bool = False						,)					->		Union[FlaxControlNetOutput, Tuple]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.controlnet_conditioning_channel_order
           if channel_order == "bgr":
                 SCREAMING_SNAKE_CASE		=						jnp.flip(__a						,axis=1	)

           # 1. time
           if not isinstance(__a						,jnp.ndarray	):
                 SCREAMING_SNAKE_CASE		=						jnp.array([timesteps]						,dtype=jnp.intaa	)
           elif isinstance(__a						,jnp.ndarray	) and len(timesteps.shape	) == 0:
                 SCREAMING_SNAKE_CASE		=						timesteps.astype(dtype=jnp.floataa	)
                 SCREAMING_SNAKE_CASE		=						jnp.expand_dims(__a						,0	)

           SCREAMING_SNAKE_CASE		=						self.time_proj(__a	)
           SCREAMING_SNAKE_CASE		=						self.time_embedding(__a	)

           # 2. pre-process
           SCREAMING_SNAKE_CASE		=						jnp.transpose(__a						,(0, 2, 3, 1)	)
           SCREAMING_SNAKE_CASE		=						self.conv_in(__a	)

           SCREAMING_SNAKE_CASE		=						jnp.transpose(__a						,(0, 2, 3, 1)	)
           SCREAMING_SNAKE_CASE		=						self.controlnet_cond_embedding(__a	)
           sample += controlnet_cond

           # 3. down
           SCREAMING_SNAKE_CASE		=						(sample,)
           for down_block in self.down_blocks:
                 if isinstance(__a						,__a	):
                       SCREAMING_SNAKE_CASE		=						down_block(__a						,__a						,__a						,deterministic=not train	)
                 else:
                       SCREAMING_SNAKE_CASE		=						down_block(__a						,__a						,deterministic=not train	)
                 down_block_res_samples += res_samples

           # 4. mid
           SCREAMING_SNAKE_CASE		=						self.mid_block(__a						,__a						,__a						,deterministic=not train	)

           # 5. contronet blocks
           SCREAMING_SNAKE_CASE		=						()
           for down_block_res_sample, controlnet_block in zip(__a						,self.controlnet_down_blocks	):
                 SCREAMING_SNAKE_CASE		=						controlnet_block(__a	)
                 controlnet_down_block_res_samples += (down_block_res_sample,)

           SCREAMING_SNAKE_CASE		=						controlnet_down_block_res_samples

           SCREAMING_SNAKE_CASE		=						self.controlnet_mid_block(__a	)

           # 6. scaling
           SCREAMING_SNAKE_CASE		=						[sample * conditioning_scale for sample in down_block_res_samples]
           mid_block_res_sample *= conditioning_scale

           if not return_dict:
                 return (down_block_res_samples, mid_block_res_sample)

           return FlaxControlNetOutput(
               down_block_res_samples=__a						,mid_block_res_sample=__a	)









import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


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

      from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     def __init__(							self						:   Optional[int]						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   Optional[Any]=3						,lowerCamelCase__						:   List[str]=32						,lowerCamelCase__						:   List[Any]=3						,lowerCamelCase__						:   str=10						,lowerCamelCase__						:   Any=[10, 20, 30, 40]						,lowerCamelCase__						:   Optional[Any]=[1, 1, 2, 1]						,lowerCamelCase__						:   Union[str, Any]=True						,lowerCamelCase__						:   int=True						,lowerCamelCase__						:   Tuple="relu"						,lowerCamelCase__						:   Dict=3						,lowerCamelCase__						:   Optional[int]=None						,)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						parent
           SCREAMING_SNAKE_CASE		=						batch_size
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						embeddings_size
           SCREAMING_SNAKE_CASE		=						hidden_sizes
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						is_training
           SCREAMING_SNAKE_CASE		=						use_labels
           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						num_labels
           SCREAMING_SNAKE_CASE		=						scope
           SCREAMING_SNAKE_CASE		=						len(lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		Tuple:




           '''simple docstring'''


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

           SCREAMING_SNAKE_CASE		=						self.get_config()

           return config, pixel_values






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Optional[int]:




           '''simple docstring'''


           return RegNetConfig(
               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						,image_size=self.image_size						,)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModel(config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)

           # Output shape (b, c, h, w)
           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						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]						,lowerCamelCase__						:   Optional[Any]	)					->		Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.num_labels
           SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification(config=lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						model(lowerCamelCase__	)
           self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.num_labels)	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						config_and_inputs
           SCREAMING_SNAKE_CASE		=						{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_flax
class 		UpperCamelCase__			(				lowerCAmelCase_							,				unittest.TestCase       ):



     '''simple docstring'''

     __snake_case   :     Union[str, Any]		     =      (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

     __snake_case   :     Tuple		     =      False
     __snake_case   :     int		     =      False
     __snake_case   :     Tuple		     =      False






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		None:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetModelTester(self	)
           SCREAMING_SNAKE_CASE		=						ConfigTester(self						,config_class=lowerCamelCase__						,has_text_modality=lowerCamelCase__	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   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						:   int	)					->		List[str]:




           '''simple docstring'''


           return






     def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__	)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__	)






     @unittest.skip(reason="""RegNet does not use inputs_embeds"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           pass






     @unittest.skip(reason="""RegNet does not support input and output embeddings"""	)
     def     SCREAMING_SNAKE_CASE__  (							self						:   str	)					->		Any:




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE		=						["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]						,lowerCamelCase__	)






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




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Optional[int]	):
                 SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

                 SCREAMING_SNAKE_CASE		=						model(**self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)	)

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

                 SCREAMING_SNAKE_CASE		=						self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__	)						,expected_num_stages + 1	)

           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE		=						True
                 check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)

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

                 check_hidden_states_output(lowerCamelCase__						,lowerCamelCase__						,lowerCamelCase__	)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 with self.subTest(model_class.__name__	):
                       SCREAMING_SNAKE_CASE		=						self._prepare_for_class(lowerCamelCase__						,lowerCamelCase__	)
                       SCREAMING_SNAKE_CASE		=						model_class(lowerCamelCase__	)

                       @jax.jit
                       def model_jitted(lowerCamelCase__						:   Dict						,**lowerCamelCase__						:   Optional[Any]	):
                             return model(pixel_values=lowerCamelCase__						,**lowerCamelCase__	)

                       with self.subTest("""JIT Enabled"""	):
                             SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

                       with self.subTest("""JIT Disabled"""	):
                             with jax.disable_jit():
                                   SCREAMING_SNAKE_CASE		=						model_jitted(**lowerCamelCase__	).to_tuple()

                       self.assertEqual(len(lowerCamelCase__	)						,len(lowerCamelCase__	)	)
                       for jitted_output, output in zip(lowerCamelCase__						,lowerCamelCase__	):
                             self.assertEqual(jitted_output.shape						,output.shape	)



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


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
      return image



@require_flax
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

     @cached_property
     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		str:




           '''simple docstring'''


           return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040"""	) if is_vision_available() else None






     @slow
     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040"""	)

           SCREAMING_SNAKE_CASE		=						self.default_image_processor
           SCREAMING_SNAKE_CASE		=						prepare_img()
           SCREAMING_SNAKE_CASE		=						image_processor(images=lowerCamelCase__						,return_tensors="""np"""	)

           SCREAMING_SNAKE_CASE		=						model(**lowerCamelCase__	)

           # verify the logits
           SCREAMING_SNAKE_CASE		=						(1, 1000)
           self.assertEqual(outputs.logits.shape						,lowerCamelCase__	)

           SCREAMING_SNAKE_CASE		=						jnp.array([-0.4180, -1.5051, -3.4836]	)

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


