





import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
      import os

      from flax.core.frozen_dict import unfreeze
      from flax.traverse_util import flatten_dict

      from transformers import FlaxBertModel

      SCREAMING_SNAKE_CASE_		     = """0.12"""  # assumed parallelism: 8

@require_flax
@is_staging_test
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     @classmethod
     def        SCREAMING_SNAKE_CASE__						(							cls		:					Optional[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TOKEN
           HfFolder.save_token(lowerCamelCase__ )






     @classmethod
     def        SCREAMING_SNAKE_CASE__						(							cls		:					int )  ->	str:




           '''simple docstring'''


           try:
                 delete_repo(token=cls._token		,repo_id="""test-model-flax""" )
           except HTTPError:
                 pass

           try:
                 delete_repo(token=cls._token		,repo_id="""valid_org/test-model-flax-org""" )
           except HTTPError:
                 pass






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		BertConfig(
               vocab_size=99		,hidden_size=32		,num_hidden_layers=5		,num_attention_heads=4		,intermediate_size=37 )
           SCREAMING_SNAKE_CASE	=		FlaxBertModel(lowerCamelCase__ )
           model.push_to_hub("""test-model-flax"""		,use_auth_token=self._token )

           SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )

           SCREAMING_SNAKE_CASE	=		flatten_dict(unfreeze(model.params ) )
           SCREAMING_SNAKE_CASE	=		flatten_dict(unfreeze(new_model.params ) )

           for key in base_params.keys():
                 SCREAMING_SNAKE_CASE	=		(base_params[key] - new_params[key]).sum().item()
                 self.assertLessEqual(lowerCamelCase__		,1e-3		,msg=F"""{key} not identical""" )

           # Reset repo
           delete_repo(token=self._token		,repo_id="""test-model-flax""" )

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                 model.save_pretrained(lowerCamelCase__		,repo_id="""test-model-flax"""		,push_to_hub=lowerCamelCase__		,use_auth_token=self._token )

           SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""" )

           SCREAMING_SNAKE_CASE	=		flatten_dict(unfreeze(model.params ) )
           SCREAMING_SNAKE_CASE	=		flatten_dict(unfreeze(new_model.params ) )

           for key in base_params.keys():
                 SCREAMING_SNAKE_CASE	=		(base_params[key] - new_params[key]).sum().item()
                 self.assertLessEqual(lowerCamelCase__		,1e-3		,msg=F"""{key} not identical""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		BertConfig(
               vocab_size=99		,hidden_size=32		,num_hidden_layers=5		,num_attention_heads=4		,intermediate_size=37 )
           SCREAMING_SNAKE_CASE	=		FlaxBertModel(lowerCamelCase__ )
           model.push_to_hub("""valid_org/test-model-flax-org"""		,use_auth_token=self._token )

           SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org""" )

           SCREAMING_SNAKE_CASE	=		flatten_dict(unfreeze(model.params ) )
           SCREAMING_SNAKE_CASE	=		flatten_dict(unfreeze(new_model.params ) )

           for key in base_params.keys():
                 SCREAMING_SNAKE_CASE	=		(base_params[key] - new_params[key]).sum().item()
                 self.assertLessEqual(lowerCamelCase__		,1e-3		,msg=F"""{key} not identical""" )

           # Reset repo
           delete_repo(token=self._token		,repo_id="""valid_org/test-model-flax-org""" )

           # Push to hub via save_pretrained
           with tempfile.TemporaryDirectory() as tmp_dir:
                 model.save_pretrained(
                     lowerCamelCase__		,repo_id="""valid_org/test-model-flax-org"""		,push_to_hub=lowerCamelCase__		,use_auth_token=self._token )

           SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained("""valid_org/test-model-flax-org""" )

           SCREAMING_SNAKE_CASE	=		flatten_dict(unfreeze(model.params ) )
           SCREAMING_SNAKE_CASE	=		flatten_dict(unfreeze(new_model.params ) )

           for key in base_params.keys():
                 SCREAMING_SNAKE_CASE	=		(base_params[key] - new_params[key]).sum().item()
                 self.assertLessEqual(lowerCamelCase__		,1e-3		,msg=F"""{key} not identical""" )



def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		True
      SCREAMING_SNAKE_CASE	=		flatten_dict(modela.params			)
      SCREAMING_SNAKE_CASE	=		flatten_dict(modela.params			)
      for key in flat_params_a.keys():
            if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]			)			) > 1E-4:
                  SCREAMING_SNAKE_CASE	=		False

      return models_are_equal



@require_flax
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
           SCREAMING_SNAKE_CASE	=		FlaxBertModel(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		"""bert"""
           with tempfile.TemporaryDirectory() as tmp_dir:
                 model.save_pretrained(os.path.join(lowerCamelCase__		,lowerCamelCase__ ) )

                 with self.assertRaises(lowerCamelCase__ ):
                       SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(lowerCamelCase__ )

                 SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(lowerCamelCase__		,subfolder=lowerCamelCase__ )

           self.assertTrue(check_models_equal(lowerCamelCase__		,lowerCamelCase__ ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		BertConfig.from_pretrained("""hf-internal-testing/tiny-bert-flax-only""" )
           SCREAMING_SNAKE_CASE	=		FlaxBertModel(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		"""bert"""
           with tempfile.TemporaryDirectory() as tmp_dir:
                 model.save_pretrained(os.path.join(lowerCamelCase__		,lowerCamelCase__ )		,max_shard_size="""10KB""" )

                 with self.assertRaises(lowerCamelCase__ ):
                       SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(lowerCamelCase__ )

                 SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(lowerCamelCase__		,subfolder=lowerCamelCase__ )

           self.assertTrue(check_models_equal(lowerCamelCase__		,lowerCamelCase__ ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""bert"""
           SCREAMING_SNAKE_CASE	=		"""hf-internal-testing/tiny-random-bert-subfolder"""

           with self.assertRaises(lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(lowerCamelCase__		,subfolder=lowerCamelCase__ )

           self.assertIsNotNone(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""bert"""
           SCREAMING_SNAKE_CASE	=		"""hf-internal-testing/tiny-random-bert-sharded-subfolder"""
           with self.assertRaises(lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		FlaxBertModel.from_pretrained(lowerCamelCase__		,subfolder=lowerCamelCase__ )

           self.assertIsNotNone(lowerCamelCase__ )









import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

      parser.add_argument(
          """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
      )
      # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
      parser.add_argument(
          """--original_config_file""",
          default=None,
          type=str,
          help="""The YAML config file corresponding to the original architecture.""",
      )
      parser.add_argument(
          """--num_in_channels""",
          default=None,
          type=int,
          help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
      )
      parser.add_argument(
          """--scheduler_type""",
          default="""pndm""",
          type=str,
          help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
      )
      parser.add_argument(
          """--pipeline_type""",
          default=None,
          type=str,
          help=(
              """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
              """. If `None` pipeline will be automatically inferred."""
          ),
      )
      parser.add_argument(
          """--image_size""",
          default=None,
          type=int,
          help=(
              """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
              """ Base. Use 768 for Stable Diffusion v2."""
          ),
      )
      parser.add_argument(
          """--prediction_type""",
          default=None,
          type=str,
          help=(
              """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
              """ Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."""
          ),
      )
      parser.add_argument(
          """--extract_ema""",
          action="""store_true""",
          help=(
              """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
              """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
              """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
          ),
      )
      parser.add_argument(
          """--upcast_attention""",
          action="""store_true""",
          help=(
              """Whether the attention computation should always be upcasted. This is necessary when running stable"""
              """ diffusion 2.1."""
          ),
      )
      parser.add_argument(
          """--from_safetensors""",
          action="""store_true""",
          help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
      )
      parser.add_argument(
          """--to_safetensors""",
          action="""store_true""",
          help="""Whether to store pipeline in safetensors format or not.""",
      )
      parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
      parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
      parser.add_argument(
          """--stable_unclip""",
          type=str,
          default=None,
          required=False,
          help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
      )
      parser.add_argument(
          """--stable_unclip_prior""",
          type=str,
          default=None,
          required=False,
          help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
      )
      parser.add_argument(
          """--clip_stats_path""",
          type=str,
          help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
          required=False,
      )
      parser.add_argument(
          """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
      )
      parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
      parser.add_argument(
          """--vae_path""",
          type=str,
          default=None,
          required=False,
          help="""Set to a path, hub id to an already converted vae to not convert it again.""",
      )
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      SCREAMING_SNAKE_CASE_		     = download_from_original_stable_diffusion_ckpt(
          checkpoint_path=args.checkpoint_path,
          original_config_file=args.original_config_file,
          image_size=args.image_size,
          prediction_type=args.prediction_type,
          model_type=args.pipeline_type,
          extract_ema=args.extract_ema,
          scheduler_type=args.scheduler_type,
          num_in_channels=args.num_in_channels,
          upcast_attention=args.upcast_attention,
          from_safetensors=args.from_safetensors,
          device=args.device,
          stable_unclip=args.stable_unclip,
          stable_unclip_prior=args.stable_unclip_prior,
          clip_stats_path=args.clip_stats_path,
          controlnet=args.controlnet,
          vae_path=args.vae_path,
      )

      if args.half:
            pipe.to(torch_dtype=torch.floataa)

      if args.controlnet:
            # only save the controlnet model
            pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
      else:
            pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


