





"""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     __A      (			a_						:Dict   ,       a_						:Dict   ,       a_						:Dict=1e-12)		->		Union[str, Any]:
				__a :						Any			       =  jnp.divide(emb_a.T   ,       jnp.clip(jnp.linalg.norm(a_   ,       axis=1)   ,       a_min=a_)).T
				__a :						str			       =  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 				__lowercase			(			nn.Module							):







					'''simple docstring'''




					__lowerCAmelCase  =  42
					__lowerCAmelCase  =  jnp.floataa







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  FlaxCLIPVisionModule(self.config.vision_config     )
									__a :						int			       =  nn.Dense(self.config.projection_dim	,			use_bias=_UpperCAmelCase	,			dtype=self.dtype     )

									__a :						Any			       =  self.param('''concept_embeds'''	,			jax.nn.initializers.ones	,			(17, self.config.projection_dim)     )
									__a :						Dict			       =  self.param(
									    '''special_care_embeds'''	,			jax.nn.initializers.ones	,			(3, self.config.projection_dim)     )

									__a :						Tuple			       =  self.param('''concept_embeds_weights'''	,			jax.nn.initializers.ones	,			(17,)     )
									__a :						Union[str, Any]			       =  self.param('''special_care_embeds_weights'''	,			jax.nn.initializers.ones	,			(3,)     )







					def __call__(						self	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  self.vision_model(_UpperCAmelCase     )[1]
									__a :						Dict			       =  self.visual_projection(_UpperCAmelCase     )

									__a :						List[str]			       =  jax_cosine_distance(_UpperCAmelCase	,			self.special_care_embeds     )
									__a :						Any			       =  jax_cosine_distance(_UpperCAmelCase	,			self.concept_embeds     )

									# increase this value to create a stronger `nfsw` filter
									# at the cost of increasing the possibility of filtering benign image inputs
									__a :						List[str]			       =  0.0

									__a :						str			       =  special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
									__a :						Tuple			       =  jnp.round(_UpperCAmelCase	,			3     )
									__a :						Tuple			       =  jnp.any(special_scores > 0	,			axis=1	,			keepdims=_UpperCAmelCase     )
									# Use a lower threshold if an image has any special care concept
									__a :						Optional[Any]			       =  is_special_care * 0.0_1

									__a :						Union[str, Any]			       =  cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
									__a :						Dict			       =  jnp.round(_UpperCAmelCase	,			3     )
									__a :						str			       =  jnp.any(concept_scores > 0	,			axis=1     )

									return has_nsfw_concepts






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  CLIPConfig
					__lowerCAmelCase  =  '''clip_input'''
					__lowerCAmelCase  =  FlaxStableDiffusionSafetyCheckerModule







					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			_UpperCAmelCase = 0	,			_UpperCAmelCase = jnp.floataa	,			_UpperCAmelCase = True	,			**_UpperCAmelCase	,			):
									if input_shape is None:
													__a :						str			       =  (1, 224, 224, 3)
									__a :						Optional[int]			       =  self.module_class(config=_UpperCAmelCase	,			dtype=_UpperCAmelCase	,			**_UpperCAmelCase     )
									super().__init__(_UpperCAmelCase	,			_UpperCAmelCase	,			input_shape=_UpperCAmelCase	,			seed=_UpperCAmelCase	,			dtype=_UpperCAmelCase	,			_do_init=_do_init     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase = None     ):
									# init input tensor
									__a :						Any			       =  jax.random.normal(_UpperCAmelCase	,			_UpperCAmelCase     )

									__a       ,						__a :						List[str]			       =  jax.random.split(_UpperCAmelCase     )
									__a :						Tuple			       =  {'''params''': params_rng, '''dropout''': dropout_rng}

									__a :						str			       =  self.module.init(_UpperCAmelCase	,			_UpperCAmelCase     )['''params''']

									return random_params







					def __call__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase = None	,			):
									__a :						List[str]			       =  jnp.transpose(_UpperCAmelCase	,			(0, 2, 3, 1)     )

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






"""simple docstring"""






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


A 			=  logging.get_logger(__name__)
logging.set_verbosity_info()

def     __A      (			a_						:str   ,       a_						:str)		->		List[Any]:
				if "xprophetnet" in prophetnet_checkpoint_path:
								__a :						List[str]			       =  XLMProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						int			       =  XLMProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)
				else:
								__a :						Optional[int]			       =  ProphetNetForConditionalGenerationOld.from_pretrained(a_)
								__a       ,						__a :						Optional[Any]			       =  ProphetNetForConditionalGeneration.from_pretrained(
								    a_   ,       output_loading_info=a_)

				__a :						Tuple			       =  ['''key_proj''', '''value_proj''', '''query_proj''']

				__a :						List[str]			       =  {
				    '''self_attn''': '''ngram_self_attn''',
				    '''cross_attn''': '''encoder_attn''',
				    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
				    '''feed_forward_layer_norm''': '''final_layer_norm''',
				    '''feed_forward''': '''''',
				    '''intermediate''': '''fc1''',
				    '''output''': '''fc2''',
				    '''key_proj''': '''k_proj''',
				    '''query_proj''': '''q_proj''',
				    '''value_proj''': '''v_proj''',
				    '''word_embeddings''': '''embed_tokens''',
				    '''embeddings_layer_norm''': '''emb_layer_norm''',
				    '''relative_pos_embeddings''': '''relative_linear''',
				    '''ngram_embeddings''': '''ngram_input_embed''',
				    '''position_embeddings''': '''embed_positions''',
				}

				for key in loading_info["missing_keys"]:
								__a :						List[str]			       =  key.split('''.''')

								if attributes[0] == "lm_head":
												__a :						Dict			       =  prophet
												__a :						Optional[Any]			       =  prophet_old
								else:
												__a :						Union[str, Any]			       =  prophet.prophetnet
												__a :						int			       =  prophet_old.model

								__a :						Optional[int]			       =  False
								for attribute in attributes:
												if attribute in mapping:
																__a :						str			       =  mapping[attribute]
																if not hasattr(a_   ,       a_) and len(a_) > 0:
																				__a :						List[str]			       =  attribute
												elif hasattr(a_   ,       a_):
																__a :						int			       =  attribute

												if attribute == "weight":
																assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																__a :						Optional[int]			       =  old_model.weight
																logger.info(F"""{attribute} is initialized.""")
																__a :						List[str]			       =  True
																break
												elif attribute == "bias":
																assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																__a :						Dict			       =  old_model.bias
																logger.info(F"""{attribute} is initialized""")
																__a :						Tuple			       =  True
																break
												elif attribute in special_keys and hasattr(a_   ,       '''in_proj_weight'''):
																__a :						List[Any]			       =  old_model.in_proj_weight.shape[0] // 3
																__a :						Tuple			       =  getattr(a_   ,       a_)
																param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																if attribute == "query_proj":
																				__a :						Any			       =  nn.Parameter(old_model.in_proj_weight[:embed_dim, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[:embed_dim])

																elif attribute == "key_proj":
																				__a :						Union[str, Any]			       =  nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :])
																				__a :						int			       =  nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim])
																elif attribute == "value_proj":
																				__a :						Dict			       =  nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :])
																				__a :						List[Any]			       =  nn.Parameter(old_model.in_proj_bias[2 * embed_dim :])
																__a :						int			       =  True
																break
												elif attribute == "position_embeddings":
																assert (
																    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																), "Hidden size has to match"
																assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
																__a :						Optional[Any]			       =  nn.Parameter(old_model.embed_positions.weight[:5_12, :])
																__a :						List[Any]			       =  True
																break

												if attribute.isdigit():
																__a :						Optional[Any]			       =  model[int(a_)]
																__a :						str			       =  old_model[int(a_)]
												else:
																__a :						Optional[int]			       =  getattr(a_   ,       a_)

																if old_attribute == "":
																				__a :						str			       =  old_model
																else:
																				if not hasattr(a_   ,       a_):
																								raise ValueError(F"""{old_model} does not have {old_attribute}""")
																				__a :						Union[str, Any]			       =  getattr(a_   ,       a_)

								if not is_key_init:
												raise ValueError(F"""{key} was not correctly initialized!""")

				print(F"""Saving model to {pytorch_dump_folder_path}""")
				prophet.save_pretrained(a_)


if __name__ == "__main__":
		A 			=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		A 			=  parser.parse_args()
		convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)