'''simple docstring'''




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__lowercase          =  False
__lowercase          =  True
__lowercase          =  False


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

						parser.add_argument(
						    '''--repo_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the architecture.''',
						)

						parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')

						__lowercase          =  parser.parse_args()

						__lowercase          =  {
						    '''image_size''': '''sample_size''',
						    '''num_res_blocks''': '''layers_per_block''',
						    '''block_channels''': '''block_out_channels''',
						    '''down_blocks''': '''down_block_types''',
						    '''up_blocks''': '''up_block_types''',
						    '''downscale_freq_shift''': '''freq_shift''',
						    '''resnet_num_groups''': '''norm_num_groups''',
						    '''resnet_act_fn''': '''act_fn''',
						    '''resnet_eps''': '''norm_eps''',
						    '''num_head_channels''': '''attention_head_dim''',
						}

						__lowercase          =  {
						    '''time_steps''': '''time_proj''',
						    '''mid''': '''mid_block''',
						    '''downsample_blocks''': '''down_blocks''',
						    '''upsample_blocks''': '''up_blocks''',
						}

						__lowercase          =  '''''' if has_file(args.repo_path, '''config.json''') else '''unet'''

						with open(os.path.join(args.repo_path, subfolder, '''config.json'''), '''r''', encoding='''utf-8''') as reader:
												__lowercase          =  reader.read()
												__lowercase          =  json.loads(text)

						if do_only_config:
												for key in config_parameters_to_change.keys():
																		config.pop(key, None)

						if has_file(args.repo_path, '''config.json'''):
												__lowercase          =  UNetaDModel(**config)
						else:
												__lowercase          =  UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
												__lowercase          =  class_name(**config)

						if do_only_config:
												model.save_config(os.path.join(args.repo_path, subfolder))

						__lowercase          =  dict(model.config)

						if do_only_renaming:
												for key, value in config_parameters_to_change.items():
																		if key in config:
																								__lowercase          =  config[key]
																								del config[key]

												__lowercase          =  [k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
												__lowercase          =  [k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

						if do_only_weights:
												__lowercase          =  torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

												__lowercase          =  {}
												for param_key, param_value in state_dict.items():
																		if param_key.endswith('''.op.bias''') or param_key.endswith('''.op.weight'''):
																								continue
																		__lowercase          =  False
																		for key, new_key in key_parameters_to_change.items():
																								if not has_changed and param_key.split('''.''')[0] == key:
																														__lowercase          =  param_value
																														__lowercase          =  True
																		if not has_changed:
																								__lowercase          =  param_value

												model.load_state_dict(new_state_dict)
												model.save_pretrained(os.path.join(args.repo_path, subfolder))





'''simple docstring'''




from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule






def 				snake_case__							(			_A:					Union[str, Any]      ,   _A:					Tuple      ,   _A:					Any=1e-12 )    ->	str:

		'''simple docstring'''


		lowerCAmelCase   					=  jnp.divide(emb_a.T      ,   jnp.clip(jnp.linalg.norm(_A      ,   axis=1 )      ,   a_min=_A ) ).T
		lowerCAmelCase   					=  jnp.divide(emb_a.T      ,   jnp.clip(jnp.linalg.norm(_A      ,   axis=1 )      ,   a_min=_A ) ).T
		return jnp.matmul(_A      ,   norm_emb_a.T )






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     CLIPConfig
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			def 							a_       (   self):




					"""simple docstring"""


					lowerCAmelCase   					=  FlaxCLIPVisionModule(self.config.vision_config)
					lowerCAmelCase   					=  nn.Dense(self.config.projection_dim       ,    use_bias=__lowerCAmelCase       ,    dtype=self.dtype)

					lowerCAmelCase   					=  self.param("""concept_embeds"""       ,    jax.nn.initializers.ones       ,    (17, self.config.projection_dim))
					lowerCAmelCase   					=  self.param(
					    """special_care_embeds"""       ,    jax.nn.initializers.ones       ,    (3, self.config.projection_dim))

					lowerCAmelCase   					=  self.param("""concept_embeds_weights"""       ,    jax.nn.initializers.ones       ,    (17,))
					lowerCAmelCase   					=  self.param("""special_care_embeds_weights"""       ,    jax.nn.initializers.ones       ,    (3,))







			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  self.vision_model(__lowerCAmelCase)[1]
					lowerCAmelCase   					=  self.visual_projection(__lowerCAmelCase)

					lowerCAmelCase   					=  jax_cosine_distance(__lowerCAmelCase       ,    self.special_care_embeds)
					lowerCAmelCase   					=  jax_cosine_distance(__lowerCAmelCase       ,    self.concept_embeds)

					# increase this value to create a stronger `nfsw` filter
					# at the cost of increasing the possibility of filtering benign image inputs
					lowerCAmelCase   					=  0.0

					lowerCAmelCase   					=  special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
					lowerCAmelCase   					=  jnp.round(__lowerCAmelCase       ,    3)
					lowerCAmelCase   					=  jnp.any(special_scores > 0       ,    axis=1       ,    keepdims=__lowerCAmelCase)
					# Use a lower threshold if an image has any special care concept
					lowerCAmelCase   					=  is_special_care * 0.01

					lowerCAmelCase   					=  cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
					lowerCAmelCase   					=  jnp.round(__lowerCAmelCase       ,    3)
					lowerCAmelCase   					=  jnp.any(concept_scores > 0       ,    axis=1)

					return has_nsfw_concepts







class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       CLIPConfig
			UpperCAmelCase_			:     Any      =       '''clip_input'''
			UpperCAmelCase_			:     List[str]      =       FlaxStableDiffusionSafetyCheckerModule



			def __init__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    __lowerCAmelCase = 0       ,    __lowerCAmelCase = jnp.floataa       ,    __lowerCAmelCase = True       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					if input_shape is None:
							lowerCAmelCase   					=  (1, 224, 224, 3)
					lowerCAmelCase   					=  self.module_class(config=__lowerCAmelCase       ,    dtype=__lowerCAmelCase       ,    **__lowerCAmelCase)
					super().__init__(__lowerCAmelCase       ,    __lowerCAmelCase       ,    input_shape=__lowerCAmelCase       ,    seed=__lowerCAmelCase       ,    dtype=__lowerCAmelCase       ,    _do_init=_do_init)



			def 							a_       (   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase   					=  jax.random.normal(__lowerCAmelCase       ,    __lowerCAmelCase)

					lowerCAmelCase					,     lowerCAmelCase   					=  jax.random.split(__lowerCAmelCase)
					lowerCAmelCase   					=  {"""params""": params_rng, """dropout""": dropout_rng}

					lowerCAmelCase   					=  self.module.init(__lowerCAmelCase       ,    __lowerCAmelCase)["""params"""]

					return random_params







			def __call__(   self       ,    __lowerCAmelCase       ,    __lowerCAmelCase = None       ,    ):




					"""simple docstring"""


					lowerCAmelCase   					=  jnp.transpose(__lowerCAmelCase       ,    (0, 2, 3, 1))

					return self.module.apply(
					    {"""params""": params or self.params}       ,    jnp.array(__lowerCAmelCase       ,    dtype=jnp.floataa)       ,    rngs={}       ,    )




