

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        lowerCAmelCase__  :



       '''simple docstring'''







       def __init__(							self ,   lowercase__ ,   lowercase__=3 ,   lowercase__=3_2 ,   lowercase__=3 ,   lowercase__=1_0 ,   lowercase__=[1_0, 2_0, 3_0, 4_0] ,   lowercase__=[1, 1, 2, 1] ,   lowercase__=True ,   lowercase__=True ,   lowercase__="relu" ,   lowercase__=3 ,   lowercase__=None ,   ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

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

             __A    =self.get_config()

             return config, pixel_values, labels

       def     __UpperCamelCase     (							self     ):







             '''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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =TFResNetModel(config=lowercase__     )
             __A    =model(lowercase__     )
             # 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     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



             __A    =self.num_labels
             __A    =TFResNetForImageClassification(lowercase__     )
             __A    =model(lowercase__ ,   labels=lowercase__     )
             self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels)     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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





@require_tf
class        lowerCAmelCase__  (					__magic_name__				,							__magic_name__				,							unittest.TestCase			):



       '''simple docstring'''







       lowercase_						=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
       lowercase_						=    (
           {"""feature-extraction""": TFResNetModel, """image-classification""": TFResNetForImageClassification}
           if is_tf_available()
           else {}
       )

       lowercase_						=    False
       lowercase_						=    False
       lowercase_						=    False
       lowercase_						=    False
       lowercase_						=    False

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =TFResNetModelTester(self     )
             __A    =ConfigTester(self ,   config_class=lowercase__ ,   has_text_modality=lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return

       @unittest.skip(reason='''ResNet does not use inputs_embeds'''     )
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       @unittest.skip(reason='''ResNet does not support input and output embeddings'''     )
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             pass

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ,		__A    =self.model_tester.prepare_config_and_inputs_for_common()

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

                   __A    =['''pixel_values''']
                   self.assertListEqual(arg_names[:1] ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             def check_hidden_states_output(lowercase__ ,   lowercase__ ,   lowercase__     ):
                   __A    =model_class(lowercase__     )
                   __A    =model(**self._prepare_for_class(lowercase__ ,   lowercase__     )     )

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

                   __A    =self.model_tester.num_stages
                   self.assertEqual(len(lowercase__     ) ,   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] ,   )

             __A    ,		__A    =self.model_tester.prepare_config_and_inputs_for_common()
             __A    =['''basic''', '''bottleneck''']
             for model_class in self.all_model_classes:
                   for layer_type in layers_type:
                         __A    =layer_type
                         __A    =True
                         check_hidden_states_output(lowercase__ ,   lowercase__ ,   lowercase__     )

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

                         check_hidden_states_output(lowercase__ ,   lowercase__ ,   lowercase__     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*lowercase__     )


       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __A    =TFResNetModel.from_pretrained(lowercase__     )
                   self.assertIsNotNone(lowercase__     )





def      A__					(			)					->Optional[int]:
      __A    =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
      return image





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



       '''simple docstring'''







       @cached_property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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


       @slow
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )

             __A    =self.default_image_processor
             __A    =prepare_img()
             __A    =image_processor(images=lowercase__ ,   return_tensors='''tf'''     )

             # forward pass
             __A    =model(**lowercase__     )

             # verify the logits
             __A    =tf.TensorShape((1, 1_0_0_0)     )
             self.assertEqual(outputs.logits.shape ,   lowercase__     )

             __A    =tf.constant([-11.1069, -9.7877, -8.3777]     )

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





from typing import TYPE_CHECKING

from ...utils import _LazyModule


_lowerCamelCase      :					Tuple					    =      {'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
   from .tokenization_byta import ByTaTokenizer
else:
   import sys

   _lowerCamelCase      :					Optional[int]					    =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


