
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFResNetForImageClassification, TFResNetModel
     from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor




class 					_SCREAMING_SNAKE_CASE  :
      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		,	):
          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 A_			(			self ):
          snake_case__						=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

          snake_case__						=				self.get_config()

          return config, pixel_values, labels
      def A_			(			self ):
          return ResNetConfig(
              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		,	lowerCamelCase ):
          snake_case__						=				TFResNetModel(config=_lowerCAmelCase )
          snake_case__						=				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 // 32, self.image_size // 32)		,	)
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
          snake_case__						=				self.num_labels
          snake_case__						=				TFResNetForImageClassification(_lowerCAmelCase )
          snake_case__						=				model(_lowerCAmelCase		,	labels=_lowerCAmelCase )
          self.parent.assertEqual(result.logits.shape		,	(self.batch_size, self.num_labels) )



      def A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          snake_case__						,					snake_case__						,					snake_case__						=				config_and_inputs
          snake_case__						=				{"pixel_values": pixel_values}
          return config, inputs_dict





@require_tf
class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		List[Any]    	=     (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      _A						:		Optional[Any]    	=     (
          {'feature-extraction': TFResNetModel, 'image-classification': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      _A						:		Optional[int]    	=     False
      _A						:		List[str]    	=     False
      _A						:		Union[str, Any]    	=     False
      _A						:		Tuple    	=     False
      _A						:		Dict    	=     False
      def A_			(			self ):
          snake_case__						=				TFResNetModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=_lowerCAmelCase		,	has_text_modality=_lowerCAmelCase )
      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
      @unittest.skip(reason="ResNet does not use inputs_embeds" )
      def A_			(			self ):
          pass
      @unittest.skip(reason="ResNet does not support input and output embeddings" )
      def A_			(			self ):
          pass
      def A_			(			self ):
          snake_case__						,					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 A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_lowerCAmelCase )
      def A_			(			self ):
          def check_hidden_states_output(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase ):
              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 )

              # ResNet'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 // 4, self.model_tester.image_size // 4]		,	)

          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()
          snake_case__						=				["basic", "bottleneck"]
          for model_class in self.all_model_classes:
              for layer_type in layers_type:
                  snake_case__						=				layer_type
                  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 A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )



      @slow
      def A_			(			self ):
          for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__						=				TFResNetModel.from_pretrained(_lowerCAmelCase )
              self.assertIsNotNone(_lowerCAmelCase )





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





@require_tf
@require_vision
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      @cached_property
      def A_			(			self ):
          return (
              AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
              if is_vision_available()
              else None
          )



      @slow
      def A_			(			self ):
          snake_case__						=				TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

          snake_case__						=				self.default_image_processor
          snake_case__						=				prepare_img()
          snake_case__						=				image_processor(images=_lowerCAmelCase		,	return_tensors="tf" )

          # forward pass
          snake_case__						=				model(**_lowerCAmelCase )

          # verify the logits
          snake_case__						=				tf.TensorShape((1, 10_00) )
          self.assertEqual(outputs.logits.shape		,	_lowerCAmelCase )

          snake_case__						=				tf.constant([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7] )

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





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    if (voltage, current, resistance).count(0							) != 1:
        raise ValueError("One and only one argument must be 0"							)
    if resistance < 0:
        raise ValueError("Resistance cannot be negative"							)
    if voltage == 0:
        return {"voltage": float(current * resistance							)}
    elif current == 0:
        return {"current": voltage / resistance}
    elif resistance == 0:
        return {"resistance": voltage / current}
    else:
        raise ValueError("Exactly one argument must be 0"							)


if __name__ == "__main__":
     import doctest

     doctest.testmod()



