


from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
      )
      from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor





class   SCREAMING_SNAKE_CASE_			:




       '''simple docstring'''



       def __init__(						self				:				Optional[int]		,				__a				:				Optional[Any]		,				__a				:				Dict=13		,				__a				:				Tuple=30		,				__a				:				Tuple=2		,				__a				:				Optional[int]=3		,				__a				:				Optional[int]=True		,				__a				:				List[str]=True		,				__a				:				Optional[int]=32		,				__a				:				Optional[int]=2		,				__a				:				Any=4		,				__a				:				str=37		,				__a				:				List[str]="gelu"		,				__a				:				Dict=0.1		,				__a				:				Optional[int]=0.1		,				__a				:				Dict=10		,				__a				:				Union[str, Any]=0.02		,				__a				:				Optional[Any]=3		,				__a				:				Optional[Any]=None		,				__a				:				List[Any]=2		,				)					->List[str]:
          lowerCamelCase_						:   Tuple		= parent
          lowerCamelCase_						:   Optional[Any]		= batch_size
          lowerCamelCase_						:   str		= image_size
          lowerCamelCase_						:   List[Any]		= patch_size
          lowerCamelCase_						:   Optional[int]		= num_channels
          lowerCamelCase_						:   Dict		= is_training
          lowerCamelCase_						:   Optional[int]		= use_labels
          lowerCamelCase_						:   Union[str, Any]		= hidden_size
          lowerCamelCase_						:   Union[str, Any]		= num_hidden_layers
          lowerCamelCase_						:   List[Any]		= num_attention_heads
          lowerCamelCase_						:   Tuple		= intermediate_size
          lowerCamelCase_						:   Any		= hidden_act
          lowerCamelCase_						:   Optional[int]		= hidden_dropout_prob
          lowerCamelCase_						:   Dict		= attention_probs_dropout_prob
          lowerCamelCase_						:   List[Any]		= type_sequence_label_size
          lowerCamelCase_						:   Union[str, Any]		= initializer_range
          lowerCamelCase_						:   List[str]		= scope
          lowerCamelCase_						:   int		= encoder_stride

          # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
          lowerCamelCase_						:   Dict		= (image_size // patch_size) ** 2
          lowerCamelCase_						:   List[Any]		= num_patches + 2



       def 				_lowerCAmelCase							(						self				:				Tuple							)					->Optional[Any]:
          lowerCamelCase_						:   Union[str, Any]		= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          lowerCamelCase_						:   Any		= None
          if self.use_labels:
             lowerCamelCase_						:   List[Any]		= ids_tensor([self.batch_size]		,				self.type_sequence_label_size							)

          lowerCamelCase_						:   str		= self.get_config()

          return config, pixel_values, labels



       def 				_lowerCAmelCase							(						self				:				str							)					->Optional[Any]:
          return DeiTConfig(
              image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				is_decoder=__a		,				initializer_range=self.initializer_range		,				encoder_stride=self.encoder_stride		,				)



       def 				_lowerCAmelCase							(						self				:				Optional[int]		,				__a				:				List[str]		,				__a				:				Optional[int]		,				__a				:				Union[str, Any]							)					->Any:
          lowerCamelCase_						:   List[Any]		= TFDeiTModel(config=__a							)
          lowerCamelCase_						:   str		= model(__a							)
          self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)



       def 				_lowerCAmelCase							(						self				:				int		,				__a				:				Dict		,				__a				:				Tuple		,				__a				:				str							)					->List[str]:
          lowerCamelCase_						:   str		= TFDeiTForMaskedImageModeling(config=__a							)
          lowerCamelCase_						:   List[Any]		= model(__a							)
          self.parent.assertEqual(
              result.reconstruction.shape		,				(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

          # test greyscale images
          lowerCamelCase_						:   str		= 1
          lowerCamelCase_						:   List[Any]		= TFDeiTForMaskedImageModeling(__a							)

          lowerCamelCase_						:   Optional[Any]		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          lowerCamelCase_						:   List[str]		= model(__a							)
          self.parent.assertEqual(result.reconstruction.shape		,				(self.batch_size, 1, self.image_size, self.image_size)							)



       def 				_lowerCAmelCase							(						self				:				Tuple		,				__a				:				List[Any]		,				__a				:				Tuple		,				__a				:				Union[str, Any]							)					->str:
          lowerCamelCase_						:   List[str]		= self.type_sequence_label_size
          lowerCamelCase_						:   str		= TFDeiTForImageClassification(__a							)
          lowerCamelCase_						:   int		= model(__a		,				labels=__a							)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)							)

          # test greyscale images
          lowerCamelCase_						:   Optional[Any]		= 1
          lowerCamelCase_						:   str		= TFDeiTForImageClassification(__a							)

          lowerCamelCase_						:   Optional[Any]		= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          lowerCamelCase_						:   Any		= model(__a		,				labels=__a							)
          self.parent.assertEqual(result.logits.shape		,				(self.batch_size, self.type_sequence_label_size)							)




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







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




       '''simple docstring'''



       _a   							=							(
           (
               TFDeiTModel,
               TFDeiTForImageClassification,
               TFDeiTForImageClassificationWithTeacher,
               TFDeiTForMaskedImageModeling,
           )
           if is_tf_available()
           else ()
       )
       _a   							=							(
           {
               "feature-extraction": TFDeiTModel,
               "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
           }
           if is_tf_available()
           else {}
       )

       _a   							=							False
       _a   							=							False
       _a   							=							False
       _a   							=							False



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->List[Any]:
          lowerCamelCase_						:   List[str]		= TFDeiTModelTester(self							)
          lowerCamelCase_						:   Tuple		= ConfigTester(self		,				config_class=__a		,				has_text_modality=__a		,				hidden_size=37							)



       def 				_lowerCAmelCase							(						self				:				int							)					->Any:
          self.config_tester.run_common_tests()



       @unittest.skip(reason="""DeiT does not use inputs_embeds"""							)
       def 				_lowerCAmelCase							(						self				:				str							)					->List[str]:
          pass



       def 				_lowerCAmelCase							(						self				:				int							)					->Union[str, Any]:
          lowerCamelCase_,	lowerCamelCase_						:   str		= self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             lowerCamelCase_						:   Optional[int]		= model_class(__a							)
             self.assertIsInstance(model.get_input_embeddings()		,				(tf.keras.layers.Layer)							)
             lowerCamelCase_						:   Optional[int]		= model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__a		,				tf.keras.layers.Dense							)							)



       def 				_lowerCAmelCase							(						self				:				Dict							)					->List[str]:
          lowerCamelCase_,	lowerCamelCase_						:   List[Any]		= self.model_tester.prepare_config_and_inputs_for_common()

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

             lowerCamelCase_						:   int		= ["""pixel_values"""]
             self.assertListEqual(arg_names[:1]		,				__a							)



       def 				_lowerCAmelCase							(						self				:				str							)					->int:
          lowerCamelCase_						:   Tuple		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__a							)



       def 				_lowerCAmelCase							(						self				:				Optional[Any]							)					->Optional[int]:
          lowerCamelCase_						:   Union[str, Any]		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*__a							)



       def 				_lowerCAmelCase							(						self				:				Union[str, Any]							)					->int:
          lowerCamelCase_						:   Tuple		= self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__a							)



       def 				_lowerCAmelCase							(						self				:				Tuple		,				__a				:				Union[str, Any]		,				__a				:				Optional[Any]		,				__a				:				Optional[int]=False							)					->int:
          lowerCamelCase_						:   Any		= super()._prepare_for_class(__a		,				__a		,				return_labels=__a							)

          if return_labels:
             if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call							).parameters:
                del inputs_dict["labels"]

          return inputs_dict




       @slow
       def 				_lowerCAmelCase							(						self				:				Any							)					->Any:
          for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowerCamelCase_						:   int		= TFDeiTModel.from_pretrained(__a							)
             self.assertIsNotNone(__a							)







def    __lowerCamelCase   (      )     ->    int:
   lowerCamelCase_						:   Optional[Any]		= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
   return image





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




       '''simple docstring'''



       @cached_property
       def 				_lowerCAmelCase							(						self				:				List[Any]							)					->Union[str, Any]:
          return (
              DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							)
              if is_vision_available()
              else None
          )




       @slow
       def 				_lowerCAmelCase							(						self				:				Any							)					->List[Any]:
          lowerCamelCase_						:   Optional[int]		= TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""							)

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

          # forward pass
          lowerCamelCase_						:   Tuple		= model(**__a							)

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

          lowerCamelCase_						:   Tuple		= tf.constant([-1.0_266, 0.1_912, -1.2_861]							)

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









import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
      snake_case__				:	Optional[int]					       =					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.',
      )

      snake_case__				:	Tuple					       =					parser.parse_args()

      snake_case__				:	Dict					       =					UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

      snake_case__				:	Dict					       =					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)





