



import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
     from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor

class 							a__( _a     ):







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: Any					       =					self.config_class(**self.inputs_dict     )
      self.parent.assertTrue(hasattr(_a						,      'tf_padding'     )     )
      self.parent.assertTrue(hasattr(_a						,      'depth_multiplier'     )     )

class 							a__:







    def __init__(				self			:  Any						,      __snake_case			:  List[str]						,      __snake_case			:  Optional[Any]=13						,      __snake_case			:  Dict=3						,      __snake_case			:  str=32						,      __snake_case			:  List[Any]=0.25						,      __snake_case			:  List[Any]=8						,      __snake_case			:  Optional[Any]=True						,      __snake_case			:  Optional[int]=10_24						,      __snake_case			:  int=32						,      __snake_case			:  Optional[Any]="relu6"						,      __snake_case			:  Tuple=0.1						,      __snake_case			:  Any=0.02						,      __snake_case			:  Any=True						,      __snake_case			:  Union[str, Any]=True						,      __snake_case			:  List[str]=10						,      __snake_case			:  Dict=None						,      ):
      a						: Optional[int]					       =					parent
      a						: List[str]					       =					batch_size
      a						: List[Any]					       =					num_channels
      a						: List[Any]					       =					image_size
      a						: Dict					       =					depth_multiplier
      a						: Optional[int]					       =					min_depth
      a						: str					       =					tf_padding
      a						: str					       =					int(last_hidden_size * depth_multiplier     )
      a						: Any					       =					output_stride
      a						: str					       =					hidden_act
      a						: Optional[int]					       =					classifier_dropout_prob
      a						: Any					       =					use_labels
      a						: Any					       =					is_training
      a						: str					       =					num_labels
      a						: Dict					       =					initializer_range
      a						: Optional[int]					       =					scope







    def       lowercase_	(				self			:  Optional[int]     ):
      a						: int					       =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

      a						: int					       =					None
      a						: Tuple					       =					None
      if self.use_labels:
        a						: Any					       =					ids_tensor([self.batch_size]						,      self.num_labels     )
        a						: Dict					       =					ids_tensor([self.batch_size, self.image_size, self.image_size]						,      self.num_labels     )

      a						: str					       =					self.get_config()

      return config, pixel_values, labels, pixel_labels







    def       lowercase_	(				self			:  Union[str, Any]     ):
      return MobileNetVaConfig(
          num_channels=self.num_channels						,      image_size=self.image_size						,      depth_multiplier=self.depth_multiplier						,      min_depth=self.min_depth						,      tf_padding=self.tf_padding						,      hidden_act=self.hidden_act						,      classifier_dropout_prob=self.classifier_dropout_prob						,      initializer_range=self.initializer_range						,      )







    def       lowercase_	(				self			:  Union[str, Any]						,      __snake_case			:  List[str]						,      __snake_case			:  Dict						,      __snake_case			:  int						,      __snake_case			:  Optional[Any]     ):
      a						: Optional[Any]					       =					MobileNetVaModel(config=_a     )
      model.to(_a     )
      model.eval()
      a						: Union[str, Any]					       =					model(_a     )
      self.parent.assertEqual(
          result.last_hidden_state.shape						,      (
              self.batch_size,
              self.last_hidden_size,
              self.image_size // self.output_stride,
              self.image_size // self.output_stride,
          )						,      )







    def       lowercase_	(				self			:  str						,      __snake_case			:  Optional[Any]						,      __snake_case			:  int						,      __snake_case			:  str						,      __snake_case			:  Optional[int]     ):
      a						: Tuple					       =					self.num_labels
      a						: str					       =					MobileNetVaForImageClassification(_a     )
      model.to(_a     )
      model.eval()
      a						: Optional[int]					       =					model(_a						,      labels=_a     )
      self.parent.assertEqual(result.logits.shape						,      (self.batch_size, self.num_labels)     )







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Optional[int]					       =					self.prepare_config_and_inputs()
      a						: Dict					       =					config_and_inputs
      a						: Tuple					       =					{"pixel_values": pixel_values}
      return config, inputs_dict

@require_torch
class 							a__( _a		,			_a		,			unittest.TestCase     ):
    lowercase__			=   (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
    lowercase__			=   (
        {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False
    lowercase__			=   False







    def       lowercase_	(				self			:  List[Any]     ):
      a						: Optional[int]					       =					MobileNetVaModelTester(self     )
      a						: str					       =					MobileNetVaConfigTester(self						,      config_class=_a						,      has_text_modality=_a     )







    def       lowercase_	(				self			:  Dict     ):
      self.config_tester.run_common_tests()







    @unittest.skip(reason='MobileNetV1 does not use inputs_embeds'     )
    def       lowercase_	(				self			:  Dict     ):
      pass







    @unittest.skip(reason='MobileNetV1 does not support input and output embeddings'     )
    def       lowercase_	(				self			:  List[Any]     ):
      pass







    @unittest.skip(reason='MobileNetV1 does not output attentions'     )
    def       lowercase_	(				self			:  Optional[Any]     ):
      pass







    def       lowercase_	(				self			:  List[str]     ):
      a						: Union[str, Any]					       =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						: Optional[Any]					       =					model_class(_a     )
        a						: Tuple					       =					inspect.signature(model.forward     )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        a						: Any					       =					[*signature.parameters.keys()]

        a						: str					       =					["pixel_values"]
        self.assertListEqual(arg_names[:1]						,      _a     )







    def       lowercase_	(				self			:  Tuple     ):
      a						: Optional[Any]					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_a     )







    def       lowercase_	(				self			:  Optional[Any]     ):
      def check_hidden_states_output(__snake_case			:  List[str]						,      __snake_case			:  Optional[Any]						,      __snake_case			:  Any     ):
        a						: List[Any]					       =					model_class(_a     )
        model.to(_a     )
        model.eval()

        with torch.no_grad():
          a						: str					       =					model(**self._prepare_for_class(_a						,      _a     )     )

        a						: Dict					       =					outputs.hidden_states

        a						: List[Any]					       =					26
        self.assertEqual(len(_a     )						,      _a     )

      a						: Dict					       =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        a						: Optional[Any]					       =					True
        check_hidden_states_output(_a						,      _a						,      _a     )

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

        check_hidden_states_output(_a						,      _a						,      _a     )







    def       lowercase_	(				self			:  Any     ):
      a						: int					       =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_a     )







    @slow
    def       lowercase_	(				self			:  Optional[int]     ):
      for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        a						: Optional[int]					       =					MobileNetVaModel.from_pretrained(_a     )
        self.assertIsNotNone(_a     )
def 				lowerCamelCase__	(						):
  a						: Optional[Any]					       =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
  return image

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







    @cached_property
    def       lowercase_	(				self			:  int     ):
      return (
          MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v1_1.0_224'     ) if is_vision_available() else None
      )







    @slow
    def       lowercase_	(				self			:  int     ):
      a						: Any					       =					MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v1_1.0_224'     ).to(_a     )

      a						: Optional[Any]					       =					self.default_image_processor
      a						: List[str]					       =					prepare_img()
      a						: Optional[int]					       =					image_processor(images=_a						,      return_tensors='pt'     ).to(_a     )

      # forward pass
      with torch.no_grad():
        a						: Optional[int]					       =					model(**_a     )

      # verify the logits
      a						: Tuple					       =					torch.Size((1, 10_01)     )
      self.assertEqual(outputs.logits.shape						,      _a     )

      a						: int					       =					torch.tensor([-4.1739, -1.1233, 3.1205]     ).to(_a     )

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




'''simple docstring'''






import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
     lowerCAmelCase: Any							=						argparse.ArgumentParser()

     parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

     parser.add_argument(
         '--txt2img_unclip',
         default='kakaobrain/karlo-v1-alpha',
         type=str,
         required=False,
         help='The pretrained txt2img unclip.',
     )

     lowerCAmelCase: Optional[int]							=						parser.parse_args()

     lowerCAmelCase: List[Any]							=						UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

     lowerCAmelCase: Optional[Any]							=						CLIPImageProcessor()
     lowerCAmelCase: Tuple							=						CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

     lowerCAmelCase: List[str]							=						UnCLIPImageVariationPipeline(
         decoder=txtaimg.decoder,
         text_encoder=txtaimg.text_encoder,
         tokenizer=txtaimg.tokenizer,
         text_proj=txtaimg.text_proj,
         feature_extractor=feature_extractor,
         image_encoder=image_encoder,
         super_res_first=txtaimg.super_res_first,
         super_res_last=txtaimg.super_res_last,
         decoder_scheduler=txtaimg.decoder_scheduler,
         super_res_scheduler=txtaimg.super_res_scheduler,
     )

     imgaimg.save_pretrained(args.dump_path)