

"""simple docstring"""


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      __a							:






     '''simple docstring'''

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	parent
           SCREAMING_SNAKE_CASE__   :      Dict      		=	batch_size
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	image_size
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_channels
           SCREAMING_SNAKE_CASE__   :      Dict      		=	embeddings_size
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	hidden_sizes
           SCREAMING_SNAKE_CASE__   :      Any      		=	depths
           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	is_training
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	use_labels
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	hidden_act
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	num_labels
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	scope
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	len(_a  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

           SCREAMING_SNAKE_CASE__   :      str      		=	None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	ids_tensor([self.batch_size]	,    self.num_labels  )

           SCREAMING_SNAKE_CASE__   :      str      		=	self.get_config()

           return config, pixel_values, labels

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


           """simple docstring"""


           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	,    _a	,    _a	,    _a  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	TFResNetModel(config=_a  )
           SCREAMING_SNAKE_CASE__   :      int      		=	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	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.num_labels
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	TFResNetForImageClassification(_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model(_a	,    labels=_a  )
           self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)  )





     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      Dict      		=	{"""pixel_values""": pixel_values}
           return config, inputs_dict





@require_tf
class      __a							(UpperCamelCase_   ,	UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:List[str]       = (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = (
         {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     _SCREAMING_SNAKE_CASE						:Tuple       = False
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = False
     _SCREAMING_SNAKE_CASE						:Tuple       = False
     _SCREAMING_SNAKE_CASE						:Optional[Any]       = False
     _SCREAMING_SNAKE_CASE						:Dict       = False

     def 					_a			(						self  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	TFResNetModelTester(self  )
           SCREAMING_SNAKE_CASE__   :      Dict      		=	ConfigTester(self	,    config_class=_a	,    has_text_modality=_a  )

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


           """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  )  ->      Optional[Any]:


           """simple docstring"""


           return

     @unittest.skip(reason="""ResNet does not use inputs_embeds"""  )
     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           pass

     @unittest.skip(reason="""ResNet does not support input and output embeddings"""  )
     def 					_a			(						self  )  ->      Union[str, Any]:


           """simple docstring"""


           pass

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                 SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	model_class(_a  )
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	inspect.signature(model.call  )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 SCREAMING_SNAKE_CASE__   :      int      		=	[*signature.parameters.keys()]

                 SCREAMING_SNAKE_CASE__   :      Any      		=	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]	,    _a  )

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_a  )

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           def check_hidden_states_output(_a	,    _a	,    _a  ):
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	model_class(_a  )
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	model(**self._prepare_for_class(_a	,    _a  )  )

                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.model_tester.num_stages
                 self.assertEqual(len(_a  )	,    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]	,    )

           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.model_tester.prepare_config_and_inputs_for_common()
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	["""basic""", """bottleneck"""]
           for model_class in self.all_model_classes:
                 for layer_type in layers_type:
                       SCREAMING_SNAKE_CASE__   :      Dict      		=	layer_type
                       SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	True
                       check_hidden_states_output(_a	,    _a	,    _a  )

                       # check that output_hidden_states also work using config
                       del inputs_dict["output_hidden_states"]
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	True

                       check_hidden_states_output(_a	,    _a	,    _a  )

     def 					_a			(						self  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      str      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*_a  )





     @slow
     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	TFResNetModel.from_pretrained(_a  )
                 self.assertIsNotNone(_a  )





def  _lowercase					(    )       ->				List[Any]:

      '''simple docstring'''





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



@require_tf
@require_vision
class      __a							(unittest.TestCase):






     '''simple docstring'''

     @cached_property
     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


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





     @slow
     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	self.default_image_processor
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	prepare_img()
           SCREAMING_SNAKE_CASE__   :      str      		=	image_processor(images=_a	,    return_tensors="""tf"""  )

           # forward pass
           SCREAMING_SNAKE_CASE__   :      str      		=	model(**_a  )

           # verify the logits
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	tf.TensorShape((1, 1_000)  )
           self.assertEqual(outputs.logits.shape	,    _a  )

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tf.constant([-11.1_069, -9.7_877, -8.3_777]  )

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







"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
  import numpy as np
  import tensorflow as tf

  from transformers import TFCamembertModel

@require_tf
@require_sentencepiece
@require_tokenizers
class      __a							(unittest.TestCase):






     '''simple docstring'''

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	tf.convert_to_tensor(
               [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]	,    dtype=tf.intaa	,    )  # J'aime le camembert !"

           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(_a  )["""last_hidden_state"""]
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	tf.TensorShape((1, 10, 768)  )
           self.assertEqual(output.shape	,    _a  )
           # compare the actual values for a slice.
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	tf.convert_to_tensor(
               [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]	,    dtype=tf.floataa	,    )
           # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
           # camembert.eval()
           # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

           self.assertTrue(np.allclose(output[:, :3, :3].numpy()	,    expected_slice.numpy()	,    atol=1E-4  )  )




