





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel



def 							lowerCAmelCase_				(    A_  ,A_):
    UpperCamelCase__:					List[str]												=      UNetaDModel(**A_)
    UpperCamelCase__:					Dict												=      checkpoint["all_modules.0.W"].data
    UpperCamelCase__:					Tuple												=      checkpoint["all_modules.0.W"].data
    UpperCamelCase__:					Any												=      checkpoint["all_modules.1.weight"].data
    UpperCamelCase__:					Dict												=      checkpoint["all_modules.1.bias"].data

    UpperCamelCase__:					Dict												=      checkpoint["all_modules.2.weight"].data
    UpperCamelCase__:					Optional[int]												=      checkpoint["all_modules.2.bias"].data

    UpperCamelCase__:					List[str]												=      checkpoint["all_modules.3.weight"].data
    UpperCamelCase__:					Optional[int]												=      checkpoint["all_modules.3.bias"].data

    UpperCamelCase__:					str												=      checkpoint[list(checkpoint.keys())[-4]].data
    UpperCamelCase__:					List[str]												=      checkpoint[list(checkpoint.keys())[-3]].data
    UpperCamelCase__:					str												=      checkpoint[list(checkpoint.keys())[-2]].data
    UpperCamelCase__:					int												=      checkpoint[list(checkpoint.keys())[-1]].data

    UpperCamelCase__:					List[str]												=      4

    def set_attention_weights(A_  ,A_  ,A_):
        UpperCamelCase__:					Any												=      old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
        UpperCamelCase__:					Optional[int]												=      old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
        UpperCamelCase__:					Optional[Any]												=      old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

        UpperCamelCase__:					Optional[int]												=      old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
        UpperCamelCase__:					Any												=      old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
        UpperCamelCase__:					Any												=      old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

        UpperCamelCase__:					List[str]												=      old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
        UpperCamelCase__:					List[Any]												=      old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

        UpperCamelCase__:					Union[str, Any]												=      old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
        UpperCamelCase__:					Dict												=      old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

    def set_resnet_weights(A_  ,A_  ,A_):
        UpperCamelCase__:					str												=      old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
        UpperCamelCase__:					List[Any]												=      old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
        UpperCamelCase__:					Union[str, Any]												=      old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
        UpperCamelCase__:					Dict												=      old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

        UpperCamelCase__:					int												=      old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
        UpperCamelCase__:					Any												=      old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
        UpperCamelCase__:					int												=      old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
        UpperCamelCase__:					Optional[int]												=      old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data

        UpperCamelCase__:					str												=      old_checkpoint[F"all_modules.{index}.Dense_0.weight"].data
        UpperCamelCase__:					Dict												=      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:
            UpperCamelCase__:					Union[str, Any]												=      old_checkpoint[F"all_modules.{index}.Conv_2.weight"].data
            UpperCamelCase__:					List[str]												=      old_checkpoint[F"all_modules.{index}.Conv_2.bias"].data

    for i, block in enumerate(new_model_architecture.downsample_blocks):
        UpperCamelCase__:					Optional[Any]												=      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
            UpperCamelCase__:					Tuple												=      checkpoint[F"all_modules.{module_index}.Conv_0.weight"].data
            UpperCamelCase__:					Dict												=      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):
        UpperCamelCase__:					Tuple												=      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:
            UpperCamelCase__:					Optional[int]												=      checkpoint[F"all_modules.{module_index}.weight"].data
            UpperCamelCase__:					Tuple												=      checkpoint[F"all_modules.{module_index}.bias"].data
            module_index += 1
            UpperCamelCase__:					Any												=      checkpoint[F"all_modules.{module_index}.weight"].data
            UpperCamelCase__:					Optional[Any]												=      checkpoint[F"all_modules.{module_index}.bias"].data
            module_index += 1
            set_resnet_weights(block.resnet_up  ,A_  ,A_)
            module_index += 1

    UpperCamelCase__:					Optional[int]												=      checkpoint[F"all_modules.{module_index}.weight"].data
    UpperCamelCase__:					int												=      checkpoint[F"all_modules.{module_index}.bias"].data
    module_index += 1
    UpperCamelCase__:					List[str]												=      checkpoint[F"all_modules.{module_index}.weight"].data
    UpperCamelCase__:					List[str]												=      checkpoint[F"all_modules.{module_index}.bias"].data

    return new_model_architecture.state_dict()


if __name__ == "__main__":
   A__:  Union[str, Any]       					=					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.''',
   )

   A__:  List[str]       					=					parser.parse_args()

   A__:  List[str]       					=					torch.load(args.checkpoint_path, map_location='''cpu''')

   with open(args.config_file) as f:
      A__:  Union[str, Any]       					=					json.loads(f.read())

   A__:  Optional[int]       					=					convert_ncsnpp_checkpoint(
       checkpoint,
       config,
   )

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

   A__:  Dict       					=					UNetaDModel(**config)
   model.load_state_dict(converted_checkpoint)

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

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








import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor



def 							lowerCAmelCase_				(    ):
    UpperCamelCase__:					Optional[Any]												=      set()
    # Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
    for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
        UpperCamelCase__:					Tuple												=      getattr(transformers.models.auto  ,A_  ,A_)
        if module is None:
            continue
        # all mappings in a single auto modeling file
        UpperCamelCase__:					Union[str, Any]												=      [
            x
            for x in dir(A_)
            if x.endswith("_MAPPING_NAMES")
            and (x.startswith("MODEL_") or x.startswith("TF_MODEL_") or x.startswith("FLAX_MODEL_"))
        ]
        for name in mapping_names:
            UpperCamelCase__:					Union[str, Any]												=      getattr(A_  ,A_)
            if mapping is not None:
                for v in mapping.values():
                    if isinstance(A_  ,(list, tuple)):
                        model_names.update(A_)
                    elif isinstance(A_  ,A_):
                        model_names.add(A_)

    return sorted(A_)



def 							lowerCAmelCase_				(    ):
    # All model names defined in auto mappings
    UpperCamelCase__:					Any												=      set(get_all_model_names())

    with open("tests/utils/tiny_model_summary.json") as fp:
        UpperCamelCase__:					Union[str, Any]												=      json.load(A_)
    UpperCamelCase__:					Union[str, Any]												=      set()
    for model_base_name in tiny_model_info:
        tiny_models_names.update(tiny_model_info[model_base_name]["model_classes"])

    # Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
    UpperCamelCase__:					Any												=      model_names.difference(A_)
    for model_name in copy.copy(A_):
        if not model_name.startswith("TF") and F"TF{model_name}" in not_on_hub:
            tiny_models_names.remove(A_)
        elif model_name.startswith("TF") and model_name[2:] in not_on_hub:
            tiny_models_names.remove(A_)

    return sorted(A_)



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      COMPOSITE_MODELS.values()

    # All tiny model base names on Hub
    UpperCamelCase__:					Any												=      get_all_model_names()
    UpperCamelCase__:					Tuple												=      hf_api.list_models(
        filter=ModelFilter(
            author="hf-internal-testing"  ,))
    UpperCamelCase__:					Optional[Any]												=      set()
    for x in models:
        UpperCamelCase__:					List[str]												=      x.modelId
        UpperCamelCase__							,				UpperCamelCase__:					Any												=      model.split("/")
        if not model.startswith("tiny-random-"):
            continue
        UpperCamelCase__:					Tuple												=      model.replace("tiny-random-"  ,"")
        if not model[0].isupper():
            continue
        if model not in model_names and model not in special_models:
            continue
        _models.add(A_)

    UpperCamelCase__:					List[Any]												=      sorted(_models)
    # All tiny model names on Hub
    UpperCamelCase__:					List[str]												=      {}
    for model in models:
        UpperCamelCase__:					str												=      F"hf-internal-testing/tiny-random-{model}"
        UpperCamelCase__:					Union[str, Any]												=      model.split("-")[0]
        try:
            UpperCamelCase__:					Any												=      hf_api.repo_info(A_)
            UpperCamelCase__:					int												=      {
                "tokenizer_classes": set(),
                "processor_classes": set(),
                "model_classes": set(),
                "sha": repo_info.sha,
            }
        except Exception:
            continue
        try:
            time.sleep(1)
            UpperCamelCase__:					Any												=      AutoTokenizer.from_pretrained(A_)
            content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					str												=      AutoTokenizer.from_pretrained(A_  ,use_fast=A_)
            content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					List[str]												=      AutoImageProcessor.from_pretrained(A_)
            content["processor_classes"].add(img_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					Union[str, Any]												=      AutoFeatureExtractor.from_pretrained(A_)
            if not isinstance(A_  ,A_):
                content["processor_classes"].add(feat_p.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					List[str]												=      getattr(A_  ,A_)
            UpperCamelCase__:					Union[str, Any]												=      model_class.from_pretrained(A_)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass
        try:
            time.sleep(1)
            UpperCamelCase__:					str												=      getattr(A_  ,F"TF{model}")
            UpperCamelCase__:					Optional[Any]												=      model_class.from_pretrained(A_)
            content["model_classes"].add(m.__class__.__name__)
        except Exception:
            pass

        UpperCamelCase__:					Optional[int]												=      sorted(content["tokenizer_classes"])
        UpperCamelCase__:					List[str]												=      sorted(content["processor_classes"])
        UpperCamelCase__:					Optional[int]												=      sorted(content["model_classes"])

        UpperCamelCase__:					int												=      content
        with open(os.path.join(A_  ,"hub_tiny_model_summary.json")  ,"w") as fp:
            json.dump(A_  ,A_  ,ensure_ascii=A_  ,indent=4)


if __name__ == "__main__":
   A__:  Optional[Any]       					=					argparse.ArgumentParser()
   parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
   A__:  Dict       					=					parser.parse_args()

   # This has to be `spawn` to avoid hanging forever!
   multiprocessing.set_start_method('''spawn''')

   A__:  List[Any]       					=					'''tiny_models'''
   A__:  Dict       					=					True
   A__:  Dict       					=					None
   A__:  Optional[Any]       					=					get_tiny_model_names_from_repo()
   A__:  int       					=					True
   A__:  int       					=					True
   A__:  int       					=					'''hf-internal-testing'''

   create_tiny_models(
       output_path,
       all,
       model_types,
       models_to_skip,
       no_check,
       upload,
       organization,
       token=os.environ.get('''TOKEN''', None),
       num_workers=args.num_workers,
   )

