





import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
      from PIL import Image
else:

      class 	__snake_case					:






          '''simple docstring'''





          @staticmethod
          def 						A		( *snake_case    :		Optional[Any]      ,    **snake_case    :		Any				):

            """simple docstring"""





            pass

@is_pipeline_test
@require_torch_or_tf
@require_vision
class 	__snake_case					(      unittest.TestCase ):






    '''simple docstring'''
    _lowerCAmelCase  =						MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    _lowerCAmelCase  =						TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING





    def 						A		( self    :		List[str]      ,    snake_case    :		str      ,    snake_case    :		Union[str, Any]      ,    snake_case    :		int				):

      """simple docstring"""





      __lowerCAmelCase								=					ImageClassificationPipeline(model=snake_case      ,    image_processor=snake_case      ,    top_k=2				)
      __lowerCAmelCase								=					[
          Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				),
          '''http://images.cocodataset.org/val2017/000000039769.jpg''',
      ]
      return image_classifier, examples





    def 						A		( self    :		List[Any]      ,    snake_case    :		Optional[Any]      ,    snake_case    :		Optional[int]				):

      """simple docstring"""





      __lowerCAmelCase								=					image_classifier('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)

      self.assertEqual(
          snake_case      ,    [
              {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
              {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
          ]      ,    )

      import datasets

      __lowerCAmelCase								=					datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,    '''image'''      ,    split='''test'''				)

      # Accepts URL + PIL.Image + lists
      __lowerCAmelCase								=					image_classifier(
          [
              Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				),
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              # RGBA
              dataset[0]['''file'''],
              # LA
              dataset[1]['''file'''],
              # L
              dataset[2]['''file'''],
          ]				)
      self.assertEqual(
          snake_case      ,    [
              [
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
              ],
              [
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
              ],
              [
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
              ],
              [
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
              ],
              [
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
                  {'''score''': ANY(snake_case				), '''label''': ANY(snake_case				)},
              ],
          ]      ,    )





    @require_torch
    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      __lowerCAmelCase								=					'''hf-internal-testing/tiny-random-vit'''
      __lowerCAmelCase								=					pipeline('''image-classification'''      ,    model=snake_case				)

      __lowerCAmelCase								=					image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''				)
      self.assertEqual(
          nested_simplify(snake_case      ,    decimals=4				)      ,    [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]      ,    )

      __lowerCAmelCase								=					image_classifier(
          [
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
          ]      ,    top_k=2      ,    )
      self.assertEqual(
          nested_simplify(snake_case      ,    decimals=4				)      ,    [
              [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
              [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
          ]      ,    )





    @require_tf
    def 						A		( self    :		Optional[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					'''hf-internal-testing/tiny-random-vit'''
      __lowerCAmelCase								=					pipeline('''image-classification'''      ,    model=snake_case      ,    framework='''tf'''				)

      __lowerCAmelCase								=					image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''				)
      self.assertEqual(
          nested_simplify(snake_case      ,    decimals=4				)      ,    [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}]      ,    )

      __lowerCAmelCase								=					image_classifier(
          [
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
              '''http://images.cocodataset.org/val2017/000000039769.jpg''',
          ]      ,    top_k=2      ,    )
      self.assertEqual(
          nested_simplify(snake_case      ,    decimals=4				)      ,    [
              [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
              [{'''label''': '''LABEL_1''', '''score''': 0.574}, {'''label''': '''LABEL_0''', '''score''': 0.426}],
          ]      ,    )





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      __lowerCAmelCase								=					pipeline(
          '''image-classification'''      ,    model='''hf-internal-testing/tiny-random-vit'''      ,    tokenizer=snake_case				)

      self.assertIs(image_classifier.tokenizer      ,    snake_case				)





    @slow
    @require_torch
    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      __lowerCAmelCase								=					pipeline('''image-classification'''      ,    model='''deepmind/vision-perceiver-conv'''				)
      __lowerCAmelCase								=					image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''				)
      self.assertEqual(
          nested_simplify(snake_case      ,    decimals=4				)      ,    [
              {'''score''': 0.4385, '''label''': '''tabby, tabby cat'''},
              {'''score''': 0.321, '''label''': '''tiger cat'''},
              {'''score''': 0.0502, '''label''': '''Egyptian cat'''},
              {'''score''': 0.0137, '''label''': '''crib, cot'''},
              {'''score''': 0.007, '''label''': '''radiator'''},
          ]      ,    )

      __lowerCAmelCase								=					pipeline('''image-classification'''      ,    model='''deepmind/vision-perceiver-fourier'''				)
      __lowerCAmelCase								=					image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''				)
      self.assertEqual(
          nested_simplify(snake_case      ,    decimals=4				)      ,    [
              {'''score''': 0.5658, '''label''': '''tabby, tabby cat'''},
              {'''score''': 0.1309, '''label''': '''tiger cat'''},
              {'''score''': 0.0722, '''label''': '''Egyptian cat'''},
              {'''score''': 0.0707, '''label''': '''remote control, remote'''},
              {'''score''': 0.0082, '''label''': '''computer keyboard, keypad'''},
          ]      ,    )

      __lowerCAmelCase								=					pipeline('''image-classification'''      ,    model='''deepmind/vision-perceiver-learned'''				)
      __lowerCAmelCase								=					image_classifier('''http://images.cocodataset.org/val2017/000000039769.jpg'''				)
      self.assertEqual(
          nested_simplify(snake_case      ,    decimals=4				)      ,    [
              {'''score''': 0.3022, '''label''': '''tabby, tabby cat'''},
              {'''score''': 0.2362, '''label''': '''Egyptian cat'''},
              {'''score''': 0.1856, '''label''': '''tiger cat'''},
              {'''score''': 0.0324, '''label''': '''remote control, remote'''},
              {'''score''': 0.0096, '''label''': '''quilt, comforter, comfort, puff'''},
          ]      ,    )







import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__lowercase				    =      {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






  __lowerCAmelCase								=					F'''pretrained_models/{model_name}'''
  if not os.path.isfile(__lowercase):
    os.makedirs('''pretrained_models'''  ,				exist_ok=__lowercase)
    __lowerCAmelCase								=					F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
    download_url(__lowercase  ,				'''pretrained_models''')
  __lowerCAmelCase								=					torch.load(__lowercase  ,				map_location=lambda __lowercase  ,				__lowercase: storage)
  return model


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __lowerCAmelCase								=					download_model(pretrained_models[args.image_size])

  __lowerCAmelCase								=					state_dict['''x_embedder.proj.weight''']
  __lowerCAmelCase								=					state_dict['''x_embedder.proj.bias''']
  state_dict.pop('''x_embedder.proj.weight''')
  state_dict.pop('''x_embedder.proj.bias''')

  for depth in range(28):
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.0.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''t_embedder.mlp.2.bias'''
    ]
    __lowerCAmelCase								=					state_dict[
        '''y_embedder.embedding_table.weight'''
    ]

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.weight'''
    ]
    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.adaLN_modulation.1.bias'''
    ]

    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.weight''']  ,				3  ,				dim=0)
    __lowerCAmelCase						,    __lowerCAmelCase						,    __lowerCAmelCase								=					torch.chunk(state_dict[F'''blocks.{depth}.attn.qkv.bias''']  ,				3  ,				dim=0)

    __lowerCAmelCase								=					q
    __lowerCAmelCase								=					q_bias
    __lowerCAmelCase								=					k
    __lowerCAmelCase								=					k_bias
    __lowerCAmelCase								=					v
    __lowerCAmelCase								=					v_bias

    __lowerCAmelCase								=					state_dict[
        F'''blocks.{depth}.attn.proj.weight'''
    ]
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.attn.proj.bias''']

    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
    __lowerCAmelCase								=					state_dict[F'''blocks.{depth}.mlp.fc2.bias''']

    state_dict.pop(F'''blocks.{depth}.attn.qkv.weight''')
    state_dict.pop(F'''blocks.{depth}.attn.qkv.bias''')
    state_dict.pop(F'''blocks.{depth}.attn.proj.weight''')
    state_dict.pop(F'''blocks.{depth}.attn.proj.bias''')
    state_dict.pop(F'''blocks.{depth}.mlp.fc1.weight''')
    state_dict.pop(F'''blocks.{depth}.mlp.fc1.bias''')
    state_dict.pop(F'''blocks.{depth}.mlp.fc2.weight''')
    state_dict.pop(F'''blocks.{depth}.mlp.fc2.bias''')
    state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.weight''')
    state_dict.pop(F'''blocks.{depth}.adaLN_modulation.1.bias''')

  state_dict.pop('''t_embedder.mlp.0.weight''')
  state_dict.pop('''t_embedder.mlp.0.bias''')
  state_dict.pop('''t_embedder.mlp.2.weight''')
  state_dict.pop('''t_embedder.mlp.2.bias''')
  state_dict.pop('''y_embedder.embedding_table.weight''')

  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.adaLN_modulation.1.bias''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.weight''']
  __lowerCAmelCase								=					state_dict['''final_layer.linear.bias''']

  state_dict.pop('''final_layer.linear.weight''')
  state_dict.pop('''final_layer.linear.bias''')
  state_dict.pop('''final_layer.adaLN_modulation.1.weight''')
  state_dict.pop('''final_layer.adaLN_modulation.1.bias''')

  # DiT XL/2
  __lowerCAmelCase								=					TransformeraDModel(
      sample_size=args.image_size // 8  ,				num_layers=28  ,				attention_head_dim=72  ,				in_channels=4  ,				out_channels=8  ,				patch_size=2  ,				attention_bias=__lowercase  ,				num_attention_heads=16  ,				activation_fn='''gelu-approximate'''  ,				num_embeds_ada_norm=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

  if args.save:
    pipeline.save_pretrained(args.checkpoint_path)


if __name__ == "__main__":
      __lowercase				    =      argparse.ArgumentParser()

      parser.add_argument(
          """--image_size""",
          default=256,
          type=int,
          required=False,
          help="""Image size of pretrained model, either 256 or 512.""",
      )
      parser.add_argument(
          """--vae_model""",
          default="""stabilityai/sd-vae-ft-ema""",
          type=str,
          required=False,
          help="""Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.""",
      )
      parser.add_argument(
          """--save""", default=True, type=bool, required=False, help="""Whether to save the converted pipeline or not."""
      )
      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output pipeline."""
      )

      __lowercase				    =      parser.parse_args()
      main(args)
