
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor





class       __A:

     def __init__(	self					:     Any   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Dict=3   ,					__UpperCamelCase					:     Union[str, Any]=3_2   ,					__UpperCamelCase					:     List[str]=3   ,					__UpperCamelCase					:     List[Any]=1_0   ,					__UpperCamelCase					:     str=[1_0, 2_0, 3_0, 4_0]   ,					__UpperCamelCase					:     int=[1, 1, 2, 1]   ,					__UpperCamelCase					:     List[Any]=True   ,					__UpperCamelCase					:     Any=True   ,					__UpperCamelCase					:     Union[str, Any]="relu"   ,					__UpperCamelCase					:     Dict=3   ,					__UpperCamelCase					:     Dict=None   ,					):
            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					:     Optional[Any]   ):
            lowerCamelCase_							  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

            lowerCamelCase_							  =					None
            if self.use_labels:
                   lowerCamelCase_							  =					ids_tensor([self.batch_size]   ,					self.num_labels   )

            lowerCamelCase_							  =					self.get_config()

            return config, pixel_values, labels

     def    lowercase__      (	self					:     Any   ):
            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   ,					)

     def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Tuple   ):
            lowerCamelCase_							  =					RegNetModel(config=lowerCAmelCase_   )
            model.to(lowerCAmelCase_   )
            model.eval()
            lowerCamelCase_							  =					model(lowerCAmelCase_   )
            # expected last hidden states: B, C, H // 32, W // 32
            self.parent.assertEqual(
                result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)   ,					)

     def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int]   ,					__UpperCamelCase					:     Any   ):
            lowerCamelCase_							  =					self.num_labels
            lowerCamelCase_							  =					RegNetForImageClassification(lowerCAmelCase_   )
            model.to(lowerCAmelCase_   )
            model.eval()
            lowerCamelCase_							  =					model(lowerCAmelCase_   ,					labels=lowerCAmelCase_   )
            self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)   )



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





@require_torch
class       __A(				__lowerCAmelCase     ,		__lowerCAmelCase     ,		unittest.TestCase       ):
     SCREAMING_SNAKE_CASE			    =    (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE			    =    (
         {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE			    =    False
     SCREAMING_SNAKE_CASE			    =    False
     SCREAMING_SNAKE_CASE			    =    False
     SCREAMING_SNAKE_CASE			    =    False

     def    lowercase__      (	self					:     Tuple   ):
            lowerCamelCase_							  =					RegNetModelTester(self   )
            lowerCamelCase_							  =					ConfigTester(self   ,					config_class=lowerCAmelCase_   ,					has_text_modality=lowerCAmelCase_   )

     def    lowercase__      (	self					:     str   ):
            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					:     Dict   ):
            return

     @unittest.skip(reason="""RegNet does not use inputs_embeds"""   )
     def    lowercase__      (	self					:     Union[str, Any]   ):
            pass

     @unittest.skip(reason="""RegNet does not support input and output embeddings"""   )
     def    lowercase__      (	self					:     List[str]   ):
            pass

     def    lowercase__      (	self					:     Optional[int]   ):
            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.forward   )
                   # 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					:     Any   ):
            lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase_   )

     def    lowercase__      (	self					:     Any   ):
            lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                   lowerCamelCase_							  =					model_class(config=lowerCAmelCase_   )
                   for name, module in model.named_modules():
                          if isinstance(lowerCAmelCase_   ,					(nn.BatchNormad, nn.GroupNorm)   ):
                                 self.assertTrue(
                                     torch.all(module.weight == 1   )   ,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''   ,					)
                                 self.assertTrue(
                                     torch.all(module.bias == 0   )   ,					msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''   ,					)

     def    lowercase__      (	self					:     Optional[Any]   ):
            def check_hidden_states_output(__UpperCamelCase					:     Any   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     List[Any]   ):
                   lowerCamelCase_							  =					model_class(lowerCAmelCase_   )
                   model.to(lowerCAmelCase_   )
                   model.eval()

                   with torch.no_grad():
                          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   )

                   # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-2:]   )   ,					[self.model_tester.image_size // 2, self.model_tester.image_size // 2]   ,					)

            lowerCamelCase_  ,   lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs_for_common()
            lowerCamelCase_							  =					["""basic""", """bottleneck"""]
            for model_class in self.all_model_classes:
                   for layer_type in layers_type:
                          lowerCamelCase_							  =					layer_type
                          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					:     Optional[Any]   ):
            lowerCamelCase_							  =					self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_   )



     @slow
     def    lowercase__      (	self					:     Tuple   ):
            for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   lowerCamelCase_							  =					RegNetModel.from_pretrained(lowerCAmelCase_   )
                   self.assertIsNotNone(lowerCAmelCase_   )



def      __lowerCAmelCase						(							)  ->    Optional[int]:
       lowerCamelCase_							  =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
       return image





@require_torch
@require_vision
class       __A(				unittest.TestCase       ):

     @cached_property
     def    lowercase__      (	self					:     Dict   ):
            return (
                AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   )
                if is_vision_available()
                else None
            )



     @slow
     def    lowercase__      (	self					:     Union[str, Any]   ):
            lowerCamelCase_							  =					RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]   ).to(lowerCAmelCase_   )

            lowerCamelCase_							  =					self.default_image_processor
            lowerCamelCase_							  =					prepare_img()
            lowerCamelCase_							  =					image_processor(images=lowerCAmelCase_   ,					return_tensors="""pt"""   ).to(lowerCAmelCase_   )

            # forward pass
            with torch.no_grad():
                   lowerCamelCase_							  =					model(**lowerCAmelCase_   )

            # verify the logits
            lowerCamelCase_							  =					torch.Size((1, 1_0_0_0)   )
            self.assertEqual(outputs.logits.shape   ,					lowerCAmelCase_   )

            lowerCamelCase_							  =					torch.tensor([-0.4180, -1.5051, -3.4836]   ).to(lowerCAmelCase_   )

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







def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float(moles / volume       ) * nfactor       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((moles * 0.0_8_2_1 * temperature) / (volume)       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((moles * 0.0_8_2_1 * temperature) / (pressure)       )       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float			,				UpperCAmelCase__				:      float       )  ->    float:
							return round(float((pressure * volume) / (0.0_8_2_1 * moles)       )       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()





