

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 					__UpperCAmelCase     (       unittest.TestCase						):



   def        __magic_name__    (							self							:     Tuple						):
     super().tearDown()
     gc.collect()



   def        __magic_name__    (							self							:     List[str]						):
     UpperCAmelCase       :							Dict  = FlaxControlNetModel.from_pretrained(
         '''lllyasviel/sd-controlnet-canny''',		from_pt=_snake_case,		dtype=jnp.bfloataa						)
     UpperCAmelCase       :							Optional[int]  = FlaxStableDiffusionControlNetPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5''',		controlnet=_snake_case,		from_pt=_snake_case,		dtype=jnp.bfloataa						)
     UpperCAmelCase       :							Union[str, Any]  = controlnet_params

     UpperCAmelCase       :							Optional[Any]  = '''bird'''
     UpperCAmelCase       :							str  = jax.device_count()
     UpperCAmelCase       :							Union[str, Any]  = pipe.prepare_text_inputs([prompts] * num_samples						)

     UpperCAmelCase       :							Optional[int]  = load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''						)
     UpperCAmelCase       :							Optional[int]  = pipe.prepare_image_inputs([canny_image] * num_samples						)

     UpperCAmelCase       :							str  = jax.random.PRNGKey(0						)
     UpperCAmelCase       :							Dict  = jax.random.split(_snake_case,		jax.device_count()						)

     UpperCAmelCase       :							Any  = replicate(_snake_case						)
     UpperCAmelCase       :							List[str]  = shard(_snake_case						)
     UpperCAmelCase       :							Dict  = shard(_snake_case						)

     UpperCAmelCase       :							Optional[Any]  = pipe(
         prompt_ids=_snake_case,		image=_snake_case,		params=_snake_case,		prng_seed=_snake_case,		num_inference_steps=5_0,		jit=_snake_case,		).images
     assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

     UpperCAmelCase       :							Any  = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
     UpperCAmelCase       :							Optional[int]  = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

     UpperCAmelCase       :							str  = jnp.asarray(jax.device_get(image_slice.flatten()						)						)
     UpperCAmelCase       :							Any  = 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        __magic_name__    (							self							:     List[Any]						):
     UpperCAmelCase       :							int  = FlaxControlNetModel.from_pretrained(
         '''lllyasviel/sd-controlnet-openpose''',		from_pt=_snake_case,		dtype=jnp.bfloataa						)
     UpperCAmelCase       :							int  = FlaxStableDiffusionControlNetPipeline.from_pretrained(
         '''runwayml/stable-diffusion-v1-5''',		controlnet=_snake_case,		from_pt=_snake_case,		dtype=jnp.bfloataa						)
     UpperCAmelCase       :							Optional[Any]  = controlnet_params

     UpperCAmelCase       :							Optional[Any]  = '''Chef in the kitchen'''
     UpperCAmelCase       :							Union[str, Any]  = jax.device_count()
     UpperCAmelCase       :							List[str]  = pipe.prepare_text_inputs([prompts] * num_samples						)

     UpperCAmelCase       :							Union[str, Any]  = load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png'''						)
     UpperCAmelCase       :							Tuple  = pipe.prepare_image_inputs([pose_image] * num_samples						)

     UpperCAmelCase       :							Dict  = jax.random.PRNGKey(0						)
     UpperCAmelCase       :							Optional[int]  = jax.random.split(_snake_case,		jax.device_count()						)

     UpperCAmelCase       :							Any  = replicate(_snake_case						)
     UpperCAmelCase       :							Optional[int]  = shard(_snake_case						)
     UpperCAmelCase       :							List[str]  = shard(_snake_case						)

     UpperCAmelCase       :							str  = pipe(
         prompt_ids=_snake_case,		image=_snake_case,		params=_snake_case,		prng_seed=_snake_case,		num_inference_steps=5_0,		jit=_snake_case,		).images
     assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

     UpperCAmelCase       :							Optional[Any]  = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
     UpperCAmelCase       :							Dict  = images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

     UpperCAmelCase       :							Union[str, Any]  = jnp.asarray(jax.device_get(image_slice.flatten()						)						)
     UpperCAmelCase       :							Tuple  = 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










"""simple docstring"""







import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM



def      __UpperCAmelCase      (    __lowerCamelCase	)							->      Optional[int]:
		if "model" in orig_key:
				lowercase__   :    Tuple				=			orig_key.replace('''model.'''						,     ''''''	)
		if "norm1" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm1'''						,     '''attention.output.LayerNorm'''	)
		if "norm2" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm2'''						,     '''output.LayerNorm'''	)
		if "norm" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''norm'''						,     '''LayerNorm'''	)
		if "transformer" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.split('''.'''	)[0].split('''_'''	)[-1]
				lowercase__   :    List[str]				=			orig_key.replace(f"""transformer_{layer_num}"""						,     f"""encoder.layer.{layer_num}"""	)
		if "mha.attn" in orig_key:
				lowercase__   :    Union[str, Any]				=			orig_key.replace('''mha.attn'''						,     '''attention.self'''	)
		if "mha" in orig_key:
				lowercase__   :    str				=			orig_key.replace('''mha'''						,     '''attention'''	)
		if "W_q" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_q'''						,     '''self.query'''	)
		if "W_k" in orig_key:
				lowercase__   :    List[Any]				=			orig_key.replace('''W_k'''						,     '''self.key'''	)
		if "W_v" in orig_key:
				lowercase__   :    Any				=			orig_key.replace('''W_v'''						,     '''self.value'''	)
		if "ff1" in orig_key:
				lowercase__   :    Optional[int]				=			orig_key.replace('''ff1'''						,     '''intermediate.dense'''	)
		if "ff2" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''ff2'''						,     '''output.dense'''	)
		if "ff" in orig_key:
				lowercase__   :    List[str]				=			orig_key.replace('''ff'''						,     '''output.dense'''	)
		if "mlm_class" in orig_key:
				lowercase__   :    int				=			orig_key.replace('''mlm.mlm_class'''						,     '''cls.predictions.decoder'''	)
		if "mlm" in orig_key:
				lowercase__   :    Optional[Any]				=			orig_key.replace('''mlm'''						,     '''cls.predictions.transform'''	)
		if "cls" not in orig_key:
				lowercase__   :    Optional[Any]				=			'''yoso.''' + orig_key

		return orig_key



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[int]:
		for key in orig_state_dict.copy().keys():
				lowercase__   :    Optional[Any]				=			orig_state_dict.pop(__lowerCamelCase	)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowercase__   :    Tuple				=			val

		lowercase__   :    Union[str, Any]				=			orig_state_dict['''cls.predictions.decoder.bias''']
		lowercase__   :    List[str]				=			torch.arange(__lowerCamelCase	).expand((1, -1)	) + 2

		return orig_state_dict



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase	)							->      Optional[Any]:
		lowercase__   :    Tuple				=			torch.load(__lowerCamelCase						,     map_location='''cpu'''	)['''model_state_dict''']
		lowercase__   :    List[Any]				=			YosoConfig.from_json_file(__lowerCamelCase	)
		lowercase__   :    List[Any]				=			YosoForMaskedLM(__lowerCamelCase	)

		lowercase__   :    Optional[Any]				=			convert_checkpoint_helper(config.max_position_embeddings						,     __lowerCamelCase	)

		print(model.load_state_dict(__lowerCamelCase	)	)
		model.eval()
		model.save_pretrained(__lowerCamelCase	)

		print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""	)


if __name__ == "__main__":
							lowerCAmelCase_   						=   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The json file for YOSO model config.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							lowerCAmelCase_   						=   parser.parse_args()
							convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
