

import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def 			_lowercase    (      __UpperCamelCase     :		Optional[int]   ,       __UpperCamelCase     :		Tuple ):
      snake_case__					=   UNetaDModel(**__a )
      snake_case__					=   checkpoint['''all_modules.0.W'''].data
      snake_case__					=   checkpoint['''all_modules.0.W'''].data
      snake_case__					=   checkpoint['''all_modules.1.weight'''].data
      snake_case__					=   checkpoint['''all_modules.1.bias'''].data

      snake_case__					=   checkpoint['''all_modules.2.weight'''].data
      snake_case__					=   checkpoint['''all_modules.2.bias'''].data

      snake_case__					=   checkpoint['''all_modules.3.weight'''].data
      snake_case__					=   checkpoint['''all_modules.3.bias'''].data

      snake_case__					=   checkpoint[list(checkpoint.keys() )[-4]].data
      snake_case__					=   checkpoint[list(checkpoint.keys() )[-3]].data
      snake_case__					=   checkpoint[list(checkpoint.keys() )[-2]].data
      snake_case__					=   checkpoint[list(checkpoint.keys() )[-1]].data

      snake_case__					=   4

      def set_attention_weights(__UpperCamelCase     :		str   ,       __UpperCamelCase     :		Union[str, Any]   ,       __UpperCamelCase     :		Optional[Any] ):
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T

            snake_case__					=   old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data

            snake_case__					=   old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data

            snake_case__					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

      def set_resnet_weights(__UpperCamelCase     :		int   ,       __UpperCamelCase     :		Dict   ,       __UpperCamelCase     :		str ):
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data

            snake_case__					=   old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data

            snake_case__					=   old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
            snake_case__					=   old_checkpoint[F'''all_modules.{index}.Dense_0.bias'''].data

            if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                  snake_case__					=   old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
                  snake_case__					=   old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data

      for i, block in enumerate(new_model_architecture.downsample_blocks ):
            snake_case__					=   hasattr(__a   ,       """attentions""" )
            for j in range(len(block.resnets ) ):
                  set_resnet_weights(block.resnets[j]   ,       __a   ,       __a )
                  module_index += 1
                  if has_attentions:
                        set_attention_weights(block.attentions[j]   ,       __a   ,       __a )
                        module_index += 1

            if hasattr(__a   ,       """downsamplers""" ) and block.downsamplers is not None:
                  set_resnet_weights(block.resnet_down   ,       __a   ,       __a )
                  module_index += 1
                  snake_case__					=   checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
                  snake_case__					=   checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
                  module_index += 1

      set_resnet_weights(new_model_architecture.mid_block.resnets[0]   ,       __a   ,       __a )
      module_index += 1
      set_attention_weights(new_model_architecture.mid_block.attentions[0]   ,       __a   ,       __a )
      module_index += 1
      set_resnet_weights(new_model_architecture.mid_block.resnets[1]   ,       __a   ,       __a )
      module_index += 1

      for i, block in enumerate(new_model_architecture.up_blocks ):
            snake_case__					=   hasattr(__a   ,       """attentions""" )
            for j in range(len(block.resnets ) ):
                  set_resnet_weights(block.resnets[j]   ,       __a   ,       __a )
                  module_index += 1
            if has_attentions:
                  set_attention_weights(
                      block.attentions[0]   ,       __a   ,       __a )  # why can there only be a single attention layer for up?
                  module_index += 1

            if hasattr(__a   ,       """resnet_up""" ) and block.resnet_up is not None:
                  snake_case__					=   checkpoint[F'''all_modules.{module_index}.weight'''].data
                  snake_case__					=   checkpoint[F'''all_modules.{module_index}.bias'''].data
                  module_index += 1
                  snake_case__					=   checkpoint[F'''all_modules.{module_index}.weight'''].data
                  snake_case__					=   checkpoint[F'''all_modules.{module_index}.bias'''].data
                  module_index += 1
                  set_resnet_weights(block.resnet_up   ,       __a   ,       __a )
                  module_index += 1

      snake_case__					=   checkpoint[F'''all_modules.{module_index}.weight'''].data
      snake_case__					=   checkpoint[F'''all_modules.{module_index}.bias'''].data
      module_index += 1
      snake_case__					=   checkpoint[F'''all_modules.{module_index}.weight'''].data
      snake_case__					=   checkpoint[F'''all_modules.{module_index}.bias'''].data

      return new_model_architecture.state_dict()


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

       parser.add_argument(
           '''--checkpoint_path''',
           default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
           type=str,
           required=False,
           help='''Path to the checkpoint to convert.''',
       )

       parser.add_argument(
           '''--config_file''',
           default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
           type=str,
           required=False,
           help='''The config json file corresponding to the architecture.''',
       )

       parser.add_argument(
           '''--dump_path''',
           default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
           type=str,
           required=False,
           help='''Path to the output model.''',
       )

       lowerCAmelCase										=    parser.parse_args()

       lowerCAmelCase										=    torch.load(args.checkpoint_path, map_location='''cpu''')

       with open(args.config_file) as f:
              lowerCAmelCase										=    json.loads(f.read())

       lowerCAmelCase										=    convert_ncsnpp_checkpoint(
           checkpoint,
           config,
       )

       if "sde" in config:
              del config["sde"]

       lowerCAmelCase										=    UNetaDModel(**config)
       model.load_state_dict(converted_checkpoint)

       try:
              lowerCAmelCase										=    ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

              lowerCAmelCase										=    ScoreSdeVePipeline(unet=model, scheduler=scheduler)
              pipe.save_pretrained(args.dump_path)
       except:  # noqa: E722
              model.save_pretrained(args.dump_path)









import inspect
import unittest

from transformers import ResNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
       import jax
       import jax.numpy as jnp

       from transformers.models.resnet.modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel

if is_vision_available():
       from PIL import Image

       from transformers import AutoImageProcessor
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




       def __init__(						self       :						Optional[Any]    ,		lowerCAmelCase__       :						List[str]    ,		lowerCAmelCase__       :						Optional[Any]=3    ,		lowerCAmelCase__       :						int=32    ,		lowerCAmelCase__       :						Tuple=3    ,		lowerCAmelCase__       :						Optional[Any]=10    ,		lowerCAmelCase__       :						int=[10, 20, 30, 40]    ,		lowerCAmelCase__       :						Any=[1, 1, 2, 1]    ,		lowerCAmelCase__       :						Dict=True    ,		lowerCAmelCase__       :						Tuple=True    ,		lowerCAmelCase__       :						Dict="relu"    ,		lowerCAmelCase__       :						str=3    ,		lowerCAmelCase__       :						Optional[Any]=None    ,		)			->    Union[str, Any]:
             snake_case__					=   parent
             snake_case__					=   batch_size
             snake_case__					=   image_size
             snake_case__					=   num_channels
             snake_case__					=   embeddings_size
             snake_case__					=   hidden_sizes
             snake_case__					=   depths
             snake_case__					=   is_training
             snake_case__					=   use_labels
             snake_case__					=   hidden_act
             snake_case__					=   num_labels
             snake_case__					=   scope
             snake_case__					=   len(lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Dict							)			->    Union[str, Any]:
             snake_case__					=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             snake_case__					=   self.get_config()

             return config, pixel_values

       def UpperCAmelCase_						(						self       :						List[str]							)			->    List[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 UpperCAmelCase_						(						self       :						str    ,		lowerCAmelCase__       :						List[str]    ,		lowerCAmelCase__       :						Optional[Any]							)			->    int:
             snake_case__					=   FlaxResNetModel(config=lowerCAmelCase__							)
             snake_case__					=   model(lowerCAmelCase__							)

             # Output shape (b, c, h, w)
             self.parent.assertEqual(
                 result.last_hidden_state.shape    ,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)    ,		)

       def UpperCAmelCase_						(						self       :						Optional[int]    ,		lowerCAmelCase__       :						Tuple    ,		lowerCAmelCase__       :						Optional[int]							)			->    Optional[int]:
             snake_case__					=   self.num_labels
             snake_case__					=   FlaxResNetForImageClassification(config=lowerCAmelCase__							)
             snake_case__					=   model(lowerCAmelCase__							)
             self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)							)

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



@require_flax
class     SCREAMING_SNAKE_CASE__     (						__a ,	unittest.TestCase		):





       '''simple docstring'''




       UpperCamelCase__ :     Any				    =       (FlaxResNetModel, FlaxResNetForImageClassification) if is_flax_available() else ()

       UpperCamelCase__ :     int				    =       False
       UpperCamelCase__ :     int				    =       False
       UpperCamelCase__ :     int				    =       False

       def UpperCAmelCase_						(						self       :						Dict							)			->    None:
             snake_case__					=   FlaxResNetModelTester(self							)
             snake_case__					=   ConfigTester(self    ,		config_class=lowerCAmelCase__    ,		has_text_modality=lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						int							)			->    Union[str, Any]:
             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       :						Tuple							)			->    int:
             return

       def UpperCAmelCase_						(						self       :						Any							)			->    Dict:
             snake_case__					=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Union[str, Any]							)			->    str:
             snake_case__					=   self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__							)

       @unittest.skip(reason="""ResNet does not use inputs_embeds"""							)
       def UpperCAmelCase_						(						self       :						Union[str, Any]							)			->    Optional[Any]:
             pass

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

       def UpperCAmelCase_						(						self       :						str							)			->    Union[str, Any]:
             snake_case__	,			snake_case__					=   self.model_tester.prepare_config_and_inputs_for_common()

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

                   snake_case__					=   ["""pixel_values"""]
                   self.assertListEqual(arg_names[:1]    ,		lowerCAmelCase__							)

       def UpperCAmelCase_						(						self       :						Union[str, Any]							)			->    Union[str, Any]:
             def check_hidden_states_output(lowerCAmelCase__       :						Dict    ,		lowerCAmelCase__       :						Tuple    ,		lowerCAmelCase__       :						str							):
                   snake_case__					=   model_class(lowerCAmelCase__							)

                   snake_case__					=   model(**self._prepare_for_class(lowerCAmelCase__    ,		lowerCAmelCase__							)							)

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

                   snake_case__					=   self.model_tester.num_stages
                   self.assertEqual(len(lowerCAmelCase__							)    ,		expected_num_stages + 1							)

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

       def UpperCAmelCase_						(						self       :						List[Any]							)			->    Any:
             snake_case__	,			snake_case__					=   self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__							):
                         snake_case__					=   self._prepare_for_class(lowerCAmelCase__    ,		lowerCAmelCase__							)
                         snake_case__					=   model_class(lowerCAmelCase__							)

                         @jax.jit
                         def model_jitted(lowerCAmelCase__       :						Optional[Any]    ,		**lowerCAmelCase__       :						List[Any]							):
                               return model(pixel_values=lowerCAmelCase__    ,		**lowerCAmelCase__							)

                         with self.subTest("""JIT Enabled"""							):
                               snake_case__					=   model_jitted(**lowerCAmelCase__							).to_tuple()

                         with self.subTest("""JIT Disabled"""							):
                               with jax.disable_jit():
                                     snake_case__					=   model_jitted(**lowerCAmelCase__							).to_tuple()

                         self.assertEqual(len(lowerCAmelCase__							)    ,		len(lowerCAmelCase__							)							)
                         for jitted_output, output in zip(lowerCAmelCase__    ,		lowerCAmelCase__							):
                               self.assertEqual(jitted_output.shape    ,		output.shape							)





def 			_lowercase    (      ):
      snake_case__					=   Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
      return image
@require_flax
class     SCREAMING_SNAKE_CASE__     (						unittest.TestCase		):





       '''simple docstring'''




       @cached_property
       def UpperCAmelCase_						(						self       :						Tuple							)			->    int:
             return AutoImageProcessor.from_pretrained("""microsoft/resnet-50"""							) if is_vision_available() else None

       @slow
       def UpperCAmelCase_						(						self       :						str							)			->    str:
             snake_case__					=   FlaxResNetForImageClassification.from_pretrained("""microsoft/resnet-50"""							)

             snake_case__					=   self.default_image_processor
             snake_case__					=   prepare_img()
             snake_case__					=   image_processor(images=lowerCAmelCase__    ,		return_tensors="""np"""							)

             snake_case__					=   model(**lowerCAmelCase__							)

             # verify the logits
             snake_case__					=   (1, 1000)
             self.assertEqual(outputs.logits.shape    ,		lowerCAmelCase__							)

             snake_case__					=   jnp.array([-11.1_069, -9.7_877, -8.3_777]							)

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






