"""simple docstring"""


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    a__    (   unittest.TestCase	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase=3,				_UpperCAmelCase=32,				_UpperCAmelCase=3,				_UpperCAmelCase=10,				_UpperCAmelCase=[10, 20, 30, 40],				_UpperCAmelCase=[1, 1, 2, 1],				_UpperCAmelCase=True,				_UpperCAmelCase=True,				_UpperCAmelCase="relu",				_UpperCAmelCase=3,				_UpperCAmelCase=None,				):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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

        lowercase__				 =							self.get_config()

        return config, pixel_values


   def 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							FlaxRegNetModel(config=_UpperCAmelCase  )
        lowercase__				 =							model(_UpperCAmelCase  )

        # 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 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



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


   def 	snake_case__	(     self  ):
        '''simple docstring'''



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






@require_flax
class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

   snake_case_							=      False
   snake_case_							=      False
   snake_case_							=      False


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							FlaxRegNetModelTester(self  )
        lowercase__				 =							ConfigTester(self,				config_class=_UpperCAmelCase,				has_text_modality=_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''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 	snake_case__	(     self  ):
        '''simple docstring'''



        return


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase  )


   @unittest.skip(reason="RegNet does not use inputs_embeds"  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   @unittest.skip(reason="RegNet does not support input and output embeddings"  )
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

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

             lowercase__				 =							["pixel_values"]
             self.assertListEqual(arg_names[:1],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        def check_hidden_states_output(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
             lowercase__				 =							model_class(_UpperCAmelCase  )

             lowercase__				 =							model(**self._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase  )  )

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

             lowercase__				 =							self.model_tester.num_stages
             self.assertEqual(len(_UpperCAmelCase  ),				expected_num_stages + 1  )

        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase__				 =							True
             check_hidden_states_output(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )

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

             check_hidden_states_output(_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__  ):
                  lowercase__				 =							self._prepare_for_class(_UpperCAmelCase,				_UpperCAmelCase  )
                  lowercase__				 =							model_class(_UpperCAmelCase  )

                  @jax.jit
                  def model_jitted(_UpperCAmelCase,				**_UpperCAmelCase  ):
                       return model(pixel_values=_UpperCAmelCase,				**_UpperCAmelCase  )

                  with self.subTest("JIT Enabled"  ):
                       lowercase__				 =							model_jitted(**_UpperCAmelCase  ).to_tuple()

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

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







def 					__a   (		):






     '''simple docstring'''



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






@require_flax
class    a__    (   unittest.TestCase	):


   @cached_property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   @slow
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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

        lowercase__				 =							self.default_image_processor
        lowercase__				 =							prepare_img()
        lowercase__				 =							image_processor(images=_UpperCAmelCase,				return_tensors="np"  )

        lowercase__				 =							model(**_UpperCAmelCase  )

        # verify the logits
        lowercase__				 =							(1, 1000)
        self.assertEqual(outputs.logits.shape,				_UpperCAmelCase  )

        lowercase__				 =							jnp.array([-0.4_180, -1.5_051, -3.4_836]  )

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





"""simple docstring"""


import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()






class    a__    (   _a					,					unittest.TestCase	):
   snake_case_							=      PriorTransformer
   snake_case_							=      "hidden_states"


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							floats_tensor((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							floats_tensor((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   def 	snake_case__	(     self,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							4
        lowercase__				 =							8
        lowercase__				 =							7

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return (4, 8)


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							{
            "num_attention_heads": 2,
            "attention_head_dim": 4,
            "num_layers": 2,
            "embedding_dim": 8,
            "num_embeddings": 7,
            "additional_embeddings": 4,
        }
        lowercase__				 =							self.dummy_input
        return init_dict, inputs_dict


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							PriorTransformer.from_pretrained(
            "hf-internal-testing/prior-dummy",				output_loading_info=_UpperCAmelCase  )
        self.assertIsNotNone(_UpperCAmelCase  )
        self.assertEqual(len(loading_info["missing_keys"]  ),				0  )

        model.to(_UpperCAmelCase  )
        lowercase__				 =							model(**self.dummy_input  )[0]

        assert hidden_states is not None, "Make sure output is not None"


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__ ,						lowercase__				 =							self.prepare_init_args_and_inputs_for_common()

        lowercase__				 =							self.model_class(**_UpperCAmelCase  )
        lowercase__				 =							inspect.signature(model.forward  )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase__				 =							[*signature.parameters.keys()]

        lowercase__				 =							["hidden_states", "timestep"]
        self.assertListEqual(arg_names[:2],				_UpperCAmelCase  )


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"  )
        lowercase__				 =							model.to(_UpperCAmelCase  )

        if hasattr(_UpperCAmelCase,				"set_default_attn_processor"  ):
             model.set_default_attn_processor()

        lowercase__				 =							self.get_dummy_seed_input()

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        lowercase__				 =							output[0, :5].flatten().cpu()
        print(_UpperCAmelCase  )

        # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
        # the expected output slices are not the same for CPU and GPU.
        lowercase__				 =							torch.tensor([-1.3_436, -0.2_870, 0.7_538, 0.4_368, -0.0_239]  )
        self.assertTrue(torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				rtol=1E-2  )  )






@slow
class    a__    (   unittest.TestCase	):


   def 	snake_case__	(     self,				_UpperCAmelCase=1,				_UpperCAmelCase=768,				_UpperCAmelCase=77,				_UpperCAmelCase=0  ):
        '''simple docstring'''



        torch.manual_seed(_UpperCAmelCase  )
        lowercase__				 =							batch_size
        lowercase__				 =							embedding_dim
        lowercase__				 =							num_embeddings

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )

        lowercase__				 =							torch.randn((batch_size, embedding_dim)  ).to(_UpperCAmelCase  )
        lowercase__				 =							torch.randn((batch_size, num_embeddings, embedding_dim)  ).to(_UpperCAmelCase  )

        return {
            "hidden_states": hidden_states,
            "timestep": 2,
            "proj_embedding": proj_embedding,
            "encoder_hidden_states": encoder_hidden_states,
        }


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


   @parameterized.expand(
       [
           # fmt: off
           [13, [-0.5_861,  0.1_283, -0.0_931,  0.0_882,  0.4_476,  0.1_329, -0.0_498,  0.0_640]],
           [37, [-0.4_913,  0.0_110, -0.0_483,  0.0_541,  0.4_954, -0.0_170,  0.0_354,  0.1_651]],
           # fmt: on
       ]  )
   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior",				subfolder="prior"  )
        model.to(_UpperCAmelCase  )
        lowercase__				 =							self.get_dummy_seed_input(seed=_UpperCAmelCase  )

        with torch.no_grad():
             lowercase__				 =							model(**_UpperCAmelCase  )[0]

        assert list(sample.shape  ) == [1, 768]

        lowercase__				 =							sample[0, :8].flatten().cpu()
        print(_UpperCAmelCase  )
        lowercase__				 =							torch.tensor(_UpperCAmelCase  )

        assert torch_all_close(_UpperCAmelCase,				_UpperCAmelCase,				atol=1E-3  )




