




'''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      lowerCAmelCase (			unittest.TestCase):


       def __init__( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=32     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=10     ,	__SCREAMING_SNAKE_CASE=[10, 20, 30, 40]     ,	__SCREAMING_SNAKE_CASE=[1, 1, 2, 1]     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE=True     ,	__SCREAMING_SNAKE_CASE="relu"     ,	__SCREAMING_SNAKE_CASE=3     ,	__SCREAMING_SNAKE_CASE=None     ,	)   ->	List[str]:



            '''simple docstring'''




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


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



            '''simple docstring'''




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

            __snake_case            =	self.get_config()

            return config, pixel_values


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




            __snake_case            =	FlaxRegNetModel(config=__lowerCamelCase	)
            __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    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	)   ->	Tuple:



            '''simple docstring'''




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




       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




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





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

       __lowercase	:   Optional[int]										=      False
       __lowercase	:   str										=      False
       __lowercase	:   Tuple										=      False


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	FlaxRegNetModelTester(self	)
            __snake_case            =	ConfigTester(self     ,	config_class=__lowerCamelCase     ,	has_text_modality=__lowerCamelCase	)


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''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    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            return


       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*__lowerCamelCase	)


       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




            __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    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            pass


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



            '''simple docstring'''




            pass


       def    lowerCAmelCase   ( self	)   ->	List[str]:



            '''simple docstring'''




            __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()

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

                 __snake_case            =	['''pixel_values''']
                 self.assertListEqual(arg_names[:1]     ,	__lowerCamelCase	)


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            def check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE	):
                 __snake_case            =	model_class(__lowerCamelCase	)

                 __snake_case            =	model(**self._prepare_for_class(__lowerCamelCase     ,	__lowerCamelCase	)	)

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

                 __snake_case            =	self.model_tester.num_stages
                 self.assertEqual(len(__lowerCamelCase	)     ,	expected_num_stages + 1	)

            __snake_case            =	self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 __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"]
                 __snake_case            =	True

                 check_hidden_states_output(__lowerCamelCase     ,	__lowerCamelCase     ,	__lowerCamelCase	)




       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __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__	):
                      __snake_case            =	self._prepare_for_class(__lowerCamelCase     ,	__lowerCamelCase	)
                      __snake_case            =	model_class(__lowerCamelCase	)

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

                      with self.subTest('''JIT Enabled'''	):
                           __snake_case            =	model_jitted(**__lowerCamelCase	).to_tuple()

                      with self.subTest('''JIT Disabled'''	):
                           with jax.disable_jit():
                                __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       _UpperCamelCase			()-> Dict:




     '''simple docstring'''





     __snake_case            =	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
     return image







@require_flax
class      lowerCAmelCase (			unittest.TestCase):


       @cached_property
       def    lowerCAmelCase   ( self	)   ->	Dict:



            '''simple docstring'''




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




       @slow
       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




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

            __snake_case            =	self.default_image_processor
            __snake_case            =	prepare_img()
            __snake_case            =	image_processor(images=__lowerCamelCase     ,	return_tensors='''np'''	)

            __snake_case            =	model(**__lowerCamelCase	)

            # verify the logits
            __snake_case            =	(1, 1000)
            self.assertEqual(outputs.logits.shape     ,	__lowerCamelCase	)

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

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










'''simple docstring'''





from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class 				UpperCAmelCase			(			nn.Module						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	int = 1_6			,       __lowerCamelCase							:	int = 8_8			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 3_2			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	bool = False			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	Optional[int] = None			,       __lowerCamelCase							:	str = "geglu"			,       __lowerCamelCase							:	Optional[int] = None			,       ):
    super().__init__()
    UpperCAmelCase__	:Optional[int]      =      nn.ModuleList(
        [
            TransformeraDModel(
                num_attention_heads=__lowerCamelCase			,       attention_head_dim=__lowerCamelCase			,       in_channels=__lowerCamelCase			,       num_layers=__lowerCamelCase			,       dropout=__lowerCamelCase			,       norm_num_groups=__lowerCamelCase			,       cross_attention_dim=__lowerCamelCase			,       attention_bias=__lowerCamelCase			,       sample_size=__lowerCamelCase			,       num_vector_embeds=__lowerCamelCase			,       activation_fn=__lowerCamelCase			,       num_embeds_ada_norm=__lowerCamelCase			,       )
            for _ in range(2 )
        ] )

    # Variables that can be set by a pipeline:

    # The ratio of transformer1 to transformer2's output states to be combined during inference
    UpperCAmelCase__	:Tuple      =      0.5

    # The shape of `encoder_hidden_states` is expected to be
    # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
    UpperCAmelCase__	:int      =      [7_7, 2_5_7]

    # Which transformer to use to encode which condition.
    # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
    UpperCAmelCase__	:Any      =      [1, 0]





 def        __SCREAMING_SNAKE_CASE					(      self							:	str			,       __lowerCamelCase							:	Optional[int]			,       __lowerCamelCase							:	Any			,       __lowerCamelCase							:	int=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	Dict=None			,       __lowerCamelCase							:	bool = True			,       ):
    UpperCAmelCase__	:Optional[int]      =      hidden_states

    UpperCAmelCase__	:Dict      =      []
    UpperCAmelCase__	:Tuple      =      0
    # attention_mask is not used yet
    for i in range(2 ):
       # for each of the two transformers, pass the corresponding condition tokens
       UpperCAmelCase__	:List[Any]      =      encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       UpperCAmelCase__	:Dict      =      self.transformer_index_for_condition[i]
       UpperCAmelCase__	:Tuple      =      self.transformers[transformer_index](
           __lowerCamelCase			,       encoder_hidden_states=__lowerCamelCase			,       timestep=__lowerCamelCase			,       cross_attention_kwargs=__lowerCamelCase			,       return_dict=__lowerCamelCase			,       )[0]
       encoded_states.append(encoded_state - input_states )
       tokens_start += self.condition_lengths[i]

    UpperCAmelCase__	:Optional[int]      =      encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
    UpperCAmelCase__	:Optional[Any]      =      output_states + input_states

    if not return_dict:
       return (output_states,)

    return TransformeraDModelOutput(sample=__lowerCamelCase )



