




'''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   __UpperCamelCase       ( unittest.TestCase				):






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



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







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

              lowerCamelCase_							=self.get_config()

              return config, pixel_values






       def      lowercase__     (							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      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=FlaxRegNetModel(config=lowerCAmelCase  )
              lowerCamelCase_							=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      lowercase__     (							self,		lowerCAmelCase,		lowerCAmelCase  ):



              """simple docstring"""







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






       def      lowercase__     (							self  ):



              """simple docstring"""







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





@require_flax
class   __UpperCamelCase       ( lowerCamelCase__			,					unittest.TestCase				):
       lowercase	:    List[Any]      						=(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

       lowercase	:    List[str]      						=False
       lowercase	:    List[str]      						=False
       lowercase	:    Optional[int]      						=False






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=FlaxRegNetModelTester(self  )
              lowerCamelCase_							=ConfigTester(self,		config_class=lowerCAmelCase,		has_text_modality=lowerCAmelCase  )






       def      lowercase__     (							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      lowercase__     (							self  ):



              """simple docstring"""







              return






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=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      lowercase__     (							self  ):



              """simple docstring"""







              pass






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



              """simple docstring"""







              pass






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

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

                     lowerCamelCase_							=['''pixel_values''']
                     self.assertListEqual(arg_names[:1],		lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              def check_hidden_states_output(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  ):
                     lowerCamelCase_							=model_class(lowerCAmelCase  )

                     lowerCamelCase_							=model(**self._prepare_for_class(lowerCAmelCase,		lowerCAmelCase  )  )

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

                     lowerCamelCase_							=self.model_tester.num_stages
                     self.assertEqual(len(lowerCAmelCase  ),		expected_num_stages + 1  )

              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     lowerCamelCase_							=True
                     check_hidden_states_output(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  )

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

                     check_hidden_states_output(lowerCAmelCase,		lowerCAmelCase,		lowerCAmelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_,      lowerCamelCase_							=self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     with self.subTest(model_class.__name__  ):
                            lowerCamelCase_							=self._prepare_for_class(lowerCAmelCase,		lowerCAmelCase  )
                            lowerCamelCase_							=model_class(lowerCAmelCase  )

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

                            with self.subTest('''JIT Enabled'''  ):
                                   lowerCamelCase_							=model_jitted(**lowerCAmelCase  ).to_tuple()

                            with self.subTest('''JIT Disabled'''  ):
                                   with jax.disable_jit():
                                          lowerCamelCase_							=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      a_							(      )							->						int:



       """simple docstring"""
       lowerCamelCase_							=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   )
       return image


@require_flax
class   __UpperCamelCase       ( unittest.TestCase				):






       @cached_property
       def      lowercase__     (							self  ):



              """simple docstring"""







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






       @slow
       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=FlaxRegNetForImageClassification.from_pretrained('''facebook/regnet-y-040'''  )

              lowerCamelCase_							=self.default_image_processor
              lowerCamelCase_							=prepare_img()
              lowerCamelCase_							=image_processor(images=lowerCAmelCase,		return_tensors='''np'''  )

              lowerCamelCase_							=model(**lowerCAmelCase  )

              # verify the logits
              lowerCamelCase_							=(1, 1_000)
              self.assertEqual(outputs.logits.shape,		lowerCAmelCase  )

              lowerCamelCase_							=jnp.array([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]  )

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








import math

import flax.linen as nn
import jax.numpy as jnp







def        SCREAMING_SNAKE_CASE_						(			__A :			jnp.ndarray  ,    __A :			int  ,    __A :			float = 1  ,    __A :			float = 1  ,    __A :			float = 1.0e4  ,    __A :			bool = False  ,    __A :			float = 1.0  ,    )	->		jnp.ndarray:

       """simple docstring"""

       assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
       assert embedding_dim % 2 == 0, F"""Embedding dimension {embedding_dim} should be even"""
       a_					:		int							=  float(embedding_dim // 2				)
       a_					:		str							=  math.log(max_timescale / min_timescale				) / (num_timescales - freq_shift)
       a_					:		Optional[int]							=  min_timescale * jnp.exp(jnp.arange(__A  ,    dtype=jnp.floataa				) * -log_timescale_increment				)
       a_					:		Optional[int]							=  jnp.expand_dims(__A  ,    1				) * jnp.expand_dims(__A  ,    0				)

       # scale embeddings
       a_					:		str							=  scale * emb

       if flip_sin_to_cos:
              a_					:		str							=  jnp.concatenate([jnp.cos(__A				), jnp.sin(__A				)]  ,    axis=1				)
       else:
              a_					:		Any							=  jnp.concatenate([jnp.sin(__A				), jnp.cos(__A				)]  ,    axis=1				)
       a_					:		Optional[int]							=  jnp.reshape(__A  ,    [jnp.shape(__A				)[0], embedding_dim]				)
       return signal
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     jnp.dtype        =						jnp.floataa
      @nn.compact
      def __call__(       self				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			List[str]:
             a_					:		Optional[Any]							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_1'     )(SCREAMING_SNAKE_CASE__     )
             a_					:		Tuple							=  nn.silu(SCREAMING_SNAKE_CASE__     )
             a_					:		str							=  nn.Dense(self.time_embed_dim     ,							dtype=self.dtype     ,							name='linear_2'     )(SCREAMING_SNAKE_CASE__     )
             return temb
class  SCREAMING_SNAKE_CASE__  (				nn.Module			):
      snake_case__				:     int        =						32
      snake_case__				:     bool        =						False
      snake_case__				:     float        =						1
      @nn.compact
      def __call__(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			Tuple:
             return get_sinusoidal_embeddings(
                 SCREAMING_SNAKE_CASE__     ,							embedding_dim=self.dim     ,							flip_sin_to_cos=self.flip_sin_to_cos     ,							freq_shift=self.freq_shift     )


