




'''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							,)      ->				str:
          snake_case			:      Dict        =			parent
          snake_case			:      Union[str, Any]        =			batch_size
          snake_case			:      Union[str, Any]        =			image_size
          snake_case			:      Tuple        =			num_channels
          snake_case			:      List[Any]        =			embeddings_size
          snake_case			:      Optional[Any]        =			hidden_sizes
          snake_case			:      int        =			depths
          snake_case			:      str        =			is_training
          snake_case			:      List[Any]        =			use_labels
          snake_case			:      Optional[Any]        =			hidden_act
          snake_case			:      Tuple        =			num_labels
          snake_case			:      Optional[Any]        =			scope
          snake_case			:      Tuple        =			len(__a							)







     def 			snake_case_      (				self							)      ->				int:
          snake_case			:      str        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          snake_case			:      int        =			None
          if self.use_labels:
               snake_case			:      Optional[int]        =			ids_tensor([self.batch_size]							,self.num_labels							)

          snake_case			:      List[Any]        =			self.get_config()

          return config, pixel_values, labels







     def 			snake_case_      (				self							)      ->				Any:
          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 			snake_case_      (				self							,__a							,__a							,__a							)      ->				Optional[Any]:
          snake_case			:      Dict        =			TFResNetModel(config=__a							)
          snake_case			:      str        =			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 			snake_case_      (				self							,__a							,__a							,__a							)      ->				List[Any]:
          snake_case			:      Any        =			self.num_labels
          snake_case			:      Dict        =			TFResNetForImageClassification(__a							)
          snake_case			:      Union[str, Any]        =			model(__a							,labels=__a							)
          self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels)							)







     def 			snake_case_      (				self							)      ->				str:
          snake_case			:      Any        =			self.prepare_config_and_inputs()
          snake_case ,		snake_case ,		snake_case			:      Optional[int]        =			config_and_inputs
          snake_case			:      Optional[Any]        =			{"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_tf
class 					_a (a__,		a__,		unittest.TestCase	):





     '''simple docstring'''



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

     lowerCAmelCase_  :     Optional[Any]          =					False
     lowerCAmelCase_  :     Any          =					False
     lowerCAmelCase_  :     int          =					False
     lowerCAmelCase_  :     List[str]          =					False
     lowerCAmelCase_  :     str          =					False







     def 			snake_case_      (				self							)      ->				Dict:
          snake_case			:      Any        =			TFResNetModelTester(self							)
          snake_case			:      Dict        =			ConfigTester(self							,config_class=__a							,has_text_modality=__a							)







     def 			snake_case_      (				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 			snake_case_      (				self							)      ->				List[Any]:
          return







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







     @unittest.skip(reason="""ResNet does not support input and output embeddings"""							)
     def 			snake_case_      (				self							)      ->				int:
          pass







     def 			snake_case_      (				self							)      ->				Any:
          snake_case ,		snake_case			:      int        =			self.model_tester.prepare_config_and_inputs_for_common()

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

               snake_case			:      List[Any]        =			["""pixel_values"""]
               self.assertListEqual(arg_names[:1]							,__a							)







     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      Any        =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__a							)







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          def check_hidden_states_output(__a							,__a							,__a							):
               snake_case			:      List[str]        =			model_class(__a							)
               snake_case			:      int        =			model(**self._prepare_for_class(__a							,__a							)							)

               snake_case			:      int        =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               snake_case			:      str        =			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]							,)

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

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    snake_case			:      str        =			True

                    check_hidden_states_output(__a							,__a							,__a							)







     def 			snake_case_      (				self							)      ->				Union[str, Any]:
          snake_case			:      Optional[int]        =			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__a							)







     @slow
     def 			snake_case_      (				self							)      ->				Optional[Any]:
          for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               snake_case			:      List[str]        =			TFResNetModel.from_pretrained(__a							)
               self.assertIsNotNone(__a							)



def        lowerCamelCase__				(      ):
     snake_case			:      str        =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
     return image







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





     '''simple docstring'''



     @cached_property
     def 			snake_case_      (				self							)      ->				List[str]:
          return (
              AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
              if is_vision_available()
              else None
          )







     @slow
     def 			snake_case_      (				self							)      ->				List[str]:
          snake_case			:      int        =			TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)

          snake_case			:      Any        =			self.default_image_processor
          snake_case			:      str        =			prepare_img()
          snake_case			:      int        =			image_processor(images=__a							,return_tensors="""tf"""							)

          # forward pass
          snake_case			:      int        =			model(**__a							)

          # verify the logits
          snake_case			:      str        =			tf.TensorShape((1, 1_000)							)
          self.assertEqual(outputs.logits.shape							,__a							)

          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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :   Union[str, Any]      =       {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase      :   Union[str, Any]      =       [
          """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """InstructBlipQFormerModel""",
          """InstructBlipPreTrainedModel""",
          """InstructBlipForConditionalGeneration""",
          """InstructBlipVisionModel""",
      ]

if TYPE_CHECKING:
      from .configuration_instructblip import (
          INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
          InstructBlipConfig,
          InstructBlipQFormerConfig,
          InstructBlipVisionConfig,
      )
      from .processing_instructblip import InstructBlipProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_instructblip import (
                INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                InstructBlipForConditionalGeneration,
                InstructBlipPreTrainedModel,
                InstructBlipQFormerModel,
                InstructBlipVisionModel,
            )

else:
      import sys

      lowercase      :   List[Any]      =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



