


import argparse
import os

import torch

from transformers import FlavaImageCodebook, FlavaImageCodebookConfig



def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_       )->       Any:
				lowerCAmelCase_       :					Dict			 = s.rsplit(_lowercase       ,      _lowercase       )
				return new.join(_lowercase       )



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       List[Any]:
				return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items()       )



def 	lowerCAmelCase       (						lowerCAmelCase_       )->       List[Any]:
				lowerCAmelCase_       :					Tuple			 = {}

				lowerCAmelCase_       :					Union[str, Any]			 = ["""group_1""", """group_2""", """group_3""", """group_4"""]
				for key, value in state_dict.items():
								for group_key in group_keys:
												if group_key in key:
																lowerCAmelCase_       :					int			 = key.replace(f"""{group_key}."""       ,      f"""{group_key}.group."""       )

								if "res_path" in key:
												lowerCAmelCase_       :					Tuple			 = key.replace('''res_path.'''       ,      '''res_path.path.'''       )

								if key.endswith('''.w'''       ):
												lowerCAmelCase_       :					List[str]			 = rreplace(_lowercase       ,      '''.w'''       ,      '''.weight'''       ,      1       )
								if key.endswith('''.b'''       ):
												lowerCAmelCase_       :					Dict			 = rreplace(_lowercase       ,      '''.b'''       ,      '''.bias'''       ,      1       )

								lowerCAmelCase_       :					Union[str, Any]			 = value.float()

				return upgrade



@torch.no_grad()
def 	lowerCAmelCase       (						lowerCAmelCase_       ,      lowerCAmelCase_       ,      lowerCAmelCase_=None       ,      lowerCAmelCase_=True       )->       Union[str, Any]:
				from dall_e import Encoder

				lowerCAmelCase_       :					Tuple			 = Encoder()
				if os.path.exists(_lowercase       ):
								lowerCAmelCase_       :					Any			 = torch.load(_lowercase       )
				else:
								lowerCAmelCase_       :					str			 = torch.hub.load_state_dict_from_url(_lowercase       )

				if isinstance(_lowercase       ,      _lowercase       ):
								lowerCAmelCase_       :					str			 = ckpt.state_dict()
				encoder.load_state_dict(_lowercase       )

				if config_path is not None:
								lowerCAmelCase_       :					Tuple			 = FlavaImageCodebookConfig.from_pretrained(_lowercase       )
				else:
								lowerCAmelCase_       :					int			 = FlavaImageCodebookConfig()

				lowerCAmelCase_       :					List[Any]			 = FlavaImageCodebook(_lowercase       ).eval()
				lowerCAmelCase_       :					Union[str, Any]			 = encoder.state_dict()

				lowerCAmelCase_       :					List[Any]			 = upgrade_state_dict(_lowercase       )
				hf_model.load_state_dict(_lowercase       )
				lowerCAmelCase_       :					str			 = hf_model.state_dict()
				lowerCAmelCase_       :					Tuple			 = count_parameters(_lowercase       )
				lowerCAmelCase_       :					Optional[Any]			 = count_parameters(_lowercase       )

				assert torch.allclose(_lowercase       ,      _lowercase       ,      atol=1e-3       )

				if save_checkpoint:
								hf_model.save_pretrained(_lowercase       )
				else:
								return hf_state_dict


if __name__ == "__main__":
						_UpperCAmelCase	:     List[str]   =argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						_UpperCAmelCase	:     Optional[Any]   =parser.parse_args()

						convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




"""simple docstring"""
import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard
@slow
@require_flax
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Any:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_,       snake_case_ :List[str]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-canny"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :Union[str, Any]						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :Union[str, Any]						=			controlnet_params

											snake_case_ :Union[str, Any]						=			"""bird"""
											snake_case_ :List[Any]						=			jax.device_count()
											snake_case_ :List[Any]						=			pipe.prepare_text_inputs([prompts] * num_samples						)

											snake_case_ :List[str]						=			load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""						)
											snake_case_ :List[str]						=			pipe.prepare_image_inputs([canny_image] * num_samples						)

											snake_case_ :Any						=			jax.random.PRNGKey(0						)
											snake_case_ :List[str]						=			jax.random.split(snake_case						,     jax.device_count()						)

											snake_case_ :List[Any]						=			replicate(snake_case						)
											snake_case_ :List[str]						=			shard(snake_case						)
											snake_case_ :str						=			shard(snake_case						)

											snake_case_ :Dict						=			pipe(
											    prompt_ids=snake_case						,     image=snake_case						,     params=snake_case						,     prng_seed=snake_case						,     num_inference_steps=50						,     jit=snake_case						,     ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_ :str						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											snake_case_ :Union[str, Any]						=			images[0, 253:256, 253:256, -1]

											snake_case_ :str						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Dict						=			jnp.array(
											    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]						)
											print(f"""output_slice: {output_slice}"""						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1E-2






					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											snake_case_,       snake_case_ :List[Any]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-openpose"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :int						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :str						=			controlnet_params

											snake_case_ :Optional[int]						=			"""Chef in the kitchen"""
											snake_case_ :Union[str, Any]						=			jax.device_count()
											snake_case_ :Any						=			pipe.prepare_text_inputs([prompts] * num_samples						)

											snake_case_ :str						=			load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""						)
											snake_case_ :Optional[Any]						=			pipe.prepare_image_inputs([pose_image] * num_samples						)

											snake_case_ :str						=			jax.random.PRNGKey(0						)
											snake_case_ :str						=			jax.random.split(snake_case						,     jax.device_count()						)

											snake_case_ :Tuple						=			replicate(snake_case						)
											snake_case_ :str						=			shard(snake_case						)
											snake_case_ :int						=			shard(snake_case						)

											snake_case_ :List[str]						=			pipe(
											    prompt_ids=snake_case						,     image=snake_case						,     params=snake_case						,     prng_seed=snake_case						,     num_inference_steps=50						,     jit=snake_case						,     ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_ :str						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											snake_case_ :int						=			images[0, 253:256, 253:256, -1]

											snake_case_ :Dict						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Optional[int]						=			jnp.array(
											    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]						)
											print(f"""output_slice: {output_slice}"""						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1E-2
