"""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  snake_case__      (       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    ,		):
        __a       =    parent
        __a       =    batch_size
        __a       =    image_size
        __a       =    num_channels
        __a       =    embeddings_size
        __a       =    hidden_sizes
        __a       =    depths
        __a       =    is_training
        __a       =    use_labels
        __a       =    hidden_act
        __a       =    num_labels
        __a       =    scope
        __a       =    len(a__						)


 def 						a__   ( self						):
        __a       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

        __a       =    self.get_config()

        return config, pixel_values


 def 						a__   ( self						):
        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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    FlaxRegNetModel(config=a__						)
        __a       =    model(a__						)

        # 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 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    self.num_labels
        __a       =    FlaxRegNetForImageClassification(config=a__						)
        __a       =    model(a__						)
        self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)						)






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






@require_flax
class  snake_case__      (       lowerCamelCase__,						unittest.TestCase       ):
 _snake_case       :					Optional[int]							  =						(FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

 _snake_case       :					Optional[Any]							  =						False
 _snake_case       :					Any							  =						False
 _snake_case       :					List[str]							  =						False


 def 						a__   ( self						):
        __a       =    FlaxRegNetModelTester(self						)
        __a       =    ConfigTester(self    ,		config_class=a__    ,		has_text_modality=a__						)


 def 						a__   ( self						):
        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 						a__   ( self						):
        return


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*a__						)


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*a__						)


 @unittest.skip(reason="RegNet does not use inputs_embeds"						)
 def 						a__   ( self						):
        pass


 @unittest.skip(reason="RegNet does not support input and output embeddings"						)
 def 						a__   ( self						):
        pass


 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs_for_common()

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

               __a       =    ['''pixel_values''']
               self.assertListEqual(arg_names[:1]    ,		a__						)


 def 						a__   ( self						):





        def check_hidden_states_output(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
               __a       =    model_class(a__						)

               __a       =    model(**self._prepare_for_class(a__    ,		a__						)						)

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

               __a       =    self.model_tester.num_stages
               self.assertEqual(len(a__						)    ,		expected_num_stages + 1						)

        __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               __a       =    True
               check_hidden_states_output(a__    ,		a__    ,		a__						)

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

               check_hidden_states_output(a__    ,		a__    ,		a__						)






 def 						a__   ( self						):
        __a       =    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__						):
                      __a       =    self._prepare_for_class(a__    ,		a__						)
                      __a       =    model_class(a__						)

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

                      with self.subTest("JIT Enabled"						):
                             __a       =    model_jitted(**a__						).to_tuple()

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

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



def 	_lowerCamelCase(	):
       __a       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
       return image






@require_flax
class  snake_case__      (       unittest.TestCase       ):


 @cached_property
 def 						a__   ( self						):
        return AutoImageProcessor.from_pretrained("facebook/regnet-y-040"						) if is_vision_available() else None






 @slow
 def 						a__   ( self						):
        __a       =    FlaxRegNetForImageClassification.from_pretrained("facebook/regnet-y-040"						)

        __a       =    self.default_image_processor
        __a       =    prepare_img()
        __a       =    image_processor(images=a__    ,		return_tensors="np"						)

        __a       =    model(**a__						)

        # verify the logits
        __a       =    (1, 1000)
        self.assertEqual(outputs.logits.shape    ,		a__						)

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

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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase      							=   {
    'configuration_clipseg': [
        'CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPSegConfig',
        'CLIPSegTextConfig',
        'CLIPSegVisionConfig',
    ],
    'processing_clipseg': ['CLIPSegProcessor'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _lowerCamelCase      							=   [
           'CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST',
           'CLIPSegModel',
           'CLIPSegPreTrainedModel',
           'CLIPSegTextModel',
           'CLIPSegVisionModel',
           'CLIPSegForImageSegmentation',
       ]

if TYPE_CHECKING:
       from .configuration_clipseg import (
           CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP,
           CLIPSegConfig,
           CLIPSegTextConfig,
           CLIPSegVisionConfig,
       )
       from .processing_clipseg import CLIPSegProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_clipseg import (
                  CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CLIPSegForImageSegmentation,
                  CLIPSegModel,
                  CLIPSegPreTrainedModel,
                  CLIPSegTextModel,
                  CLIPSegVisionModel,
              )

else:
       import sys

       _lowerCamelCase      							=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

