




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:



 '''simple docstring'''


 def __init__(       self      :						Any		,							A_      :						Tuple		,							A_      :						List[str]=3		,							A_      :						List[str]=32		,							A_      :						Optional[Any]=3		,							A_      :						Optional[Any]=10		,							A_      :						Dict=[10, 20, 30, 40]		,							A_      :						Tuple=[1, 1, 2, 1]		,							A_      :						List[Any]=True		,							A_      :						Any=True		,							A_      :						Optional[Any]="relu"		,							A_      :						List[str]=3		,							A_      :						int=None		,							)     ->			Tuple:

       """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(A_						)

 def        a__				(       self      :						Optional[int]						)     ->			Union[str, Any]:

       """simple docstring"""






       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        a__				(       self      :						Dict						)     ->			Dict:

       """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		,							)

 def        a__				(       self      :						List[Any]		,							A_      :						Optional[Any]		,							A_      :						str		,							A_      :						List[str]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					RegNetModel(config=A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_						)
       # 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 // 32, self.image_size // 32)		,							)

 def        a__				(       self      :						str		,							A_      :						Dict		,							A_      :						List[Any]		,							A_      :						Dict						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					self.num_labels
       lowerCamelCase_	  =					RegNetForImageClassification(A_						)
       model.to(A_						)
       model.eval()
       lowerCamelCase_	  =					model(A_		,							labels=A_						)
       self.parent.assertEqual(result.logits.shape		,							(self.batch_size, self.num_labels)						)





 def        a__				(       self      :						Optional[int]						)     ->			List[Any]:

       """simple docstring"""






       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(		UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


 UpperCamelCase		     =		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
 UpperCamelCase		     =		(
     {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
     if is_torch_available()
     else {}
 )

 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False
 UpperCamelCase		     =		False

 def        a__				(       self      :						List[str]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					RegNetModelTester(self						)
       lowerCamelCase_	  =					ConfigTester(self		,							config_class=A_		,							has_text_modality=A_						)

 def        a__				(       self      :						str						)     ->			Optional[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        a__				(       self      :						Tuple						)     ->			Dict:

       """simple docstring"""






       return

 @unittest.skip(reason='RegNet does not use inputs_embeds'						)
 def        a__				(       self      :						str						)     ->			Any:

       """simple docstring"""






       pass

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

       """simple docstring"""






       pass

 def        a__				(       self      :						Any						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_	  =					model_class(A_						)
             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]		,							A_						)

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

       """simple docstring"""






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

 def        a__				(       self      :						int						)     ->			int:

       """simple docstring"""






       lowerCamelCase_  ,       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             lowerCamelCase_	  =					model_class(config=A_						)
             for name, module in model.named_modules():
                   if isinstance(A_		,							(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        a__				(       self      :						int						)     ->			List[Any]:

       """simple docstring"""






       def check_hidden_states_output(A_      :						Any		,							A_      :						Tuple		,							A_      :						List[str]						):
             lowerCamelCase_	  =					model_class(A_						)
             model.to(A_						)
             model.eval()

             with torch.no_grad():
                   lowerCamelCase_	  =					model(**self._prepare_for_class(A_		,							A_						)						)

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

             lowerCamelCase_	  =					self.model_tester.num_stages
             self.assertEqual(len(A_						)		,							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(A_		,							A_		,							A_						)

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

                   check_hidden_states_output(A_		,							A_		,							A_						)

 def        a__				(       self      :						int						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_						)





 @slow
 def        a__				(       self      :						List[str]						)     ->			Union[str, Any]:

       """simple docstring"""






       for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_	  =					RegNetModel.from_pretrained(A_						)
             self.assertIsNotNone(A_						)

def     _SCREAMING_SNAKE_CASE    (							):


      '''simple docstring'''



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







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



 '''simple docstring'''


 @cached_property
 def        a__				(       self      :						Any						)     ->			Union[str, Any]:

       """simple docstring"""






       return (
           AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
           if is_vision_available()
           else None
       )





 @slow
 def        a__				(       self      :						Any						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(A_						)

       lowerCamelCase_	  =					self.default_image_processor
       lowerCamelCase_	  =					prepare_img()
       lowerCamelCase_	  =					image_processor(images=A_		,							return_tensors='pt'						).to(A_						)

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

       # verify the logits
       lowerCamelCase_	  =					torch.Size((1, 1000)						)
       self.assertEqual(outputs.logits.shape		,							A_						)

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

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









import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf
 from tensorflow.python.eager import context
 from tensorflow.python.framework import ops

 from transformers import GradientAccumulator, create_optimizer







@require_tf
class 						A(		unittest.TestCase     ):



 '''simple docstring'''


 def        a__				(       self      :						Optional[int]		,							A_      :						Tuple		,							A_      :						str		,							A_      :						int						)     ->			Any:

       """simple docstring"""






       self.assertEqual(len(A_						)		,							len(A_						)						)
       for a, b in zip(A_		,							A_						):
             self.assertAlmostEqual(A_		,							A_		,							delta=A_						)

 def        a__				(       self      :						int						)     ->			str:

       """simple docstring"""






       lowerCamelCase_	  =					GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]						)]						)
       accumulator([tf.constant([-2.0, 1.0]						)]						)
       accumulator([tf.constant([-1.0, 2.0]						)]						)
       with self.assertRaises(A_						):
             accumulator([tf.constant([1.0, 1.0]						), tf.constant([2.0, 2.0]						)]						)
       self.assertEqual(accumulator.step		,							3						)
       self.assertEqual(len(accumulator.gradients						)		,							1						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[-2.0, 5.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()		,							[0.0, 0.0]		,							tol=1E-2						)





 def        a__				(       self      :						List[Any]						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					None
       ops.enable_eager_execution_internal()
       lowerCamelCase_	  =					tf.config.list_physical_devices('CPU'						)
       if len(A_						) == 1:
             tf.config.set_logical_device_configuration(
                 physical_devices[0]		,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]						)
       lowerCamelCase_	  =					tf.config.list_logical_devices(device_type='CPU'						)
       lowerCamelCase_	  =					tf.distribute.MirroredStrategy(devices=devices[:2]						)

       with strategy.scope():
             lowerCamelCase_	  =					GradientAccumulator()
             lowerCamelCase_	  =					tf.Variable([4.0, 3.0]						)
             lowerCamelCase_  ,       lowerCamelCase_	  =					create_optimizer(5E-5		,							10		,							5						)
             lowerCamelCase_	  =					tf.Variable([0.0, 0.0]		,							trainable=A_						)

       def accumulate_on_replica(A_      :						Any						):
             accumulator([gradient]						)

       def apply_on_replica():
             optimizer.apply_gradients(list(zip(accumulator.gradients		,							[variable]						)						)						)

       @tf.function
       def accumulate(A_      :						List[Any]		,							A_      :						Tuple						):
             with strategy.scope():
                   lowerCamelCase_	  =					strategy.experimental_local_results(A_						)
                   local_variables[0].assign(A_						)
                   local_variables[1].assign(A_						)
                   strategy.run(A_		,							args=(gradient_placeholder,)						)

       @tf.function
       def apply_grad():
             with strategy.scope():
                   strategy.run(A_						)

       def _check_local_values(A_      :						List[Any]		,							A_      :						str						):
             lowerCamelCase_	  =					strategy.experimental_local_results(accumulator._gradients[0]						)
             self.assertListAlmostEqual(values[0].value()		,							A_		,							tol=1E-2						)
             self.assertListAlmostEqual(values[1].value()		,							A_		,							tol=1E-2						)

       accumulate([1.0, 2.0]		,							[-1.0, 1.0]						)
       accumulate([3.0, -1.0]		,							[-1.0, -1.0]						)
       accumulate([-2.0, 2.0]		,							[3.0, -2.0]						)
       self.assertEqual(accumulator.step		,							3						)
       _check_local_values([2.0, 3.0]		,							[1.0, -2.0]						)
       apply_grad()
       self.assertListAlmostEqual(variable.value()		,							[4.0, 3.0]		,							tol=1E-2						)
       accumulator.reset()
       self.assertEqual(accumulator.step		,							0						)
       _check_local_values([0.0, 0.0]		,							[0.0, 0.0]						)



