





'''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 				lowerCamelCase_					(     SCREAMING_SNAKE_CASE__					:	int,	SCREAMING_SNAKE_CASE__					:	List[str],	SCREAMING_SNAKE_CASE__					:	Tuple=1E-12      )       ->		Optional[int]:
						UpperCAmelCase_	:					Dict     =     jnp.divide(emb_a.T,	jnp.clip(jnp.linalg.norm(a__,	axis=1      ),	a_min=a__      )      ).T
						UpperCAmelCase_	:					Dict     =     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   ):
							__a    :      Any  	=				42
							__a    :      Dict  	=				jnp.floataa

							def 			UpperCAmelCase__		(							self							:   List[str]      )    ->				List[Any]:







													"""simple docstring"""
													UpperCAmelCase_	:					Tuple     =     FlaxCLIPVisionModule(self.config.vision_config      )
													UpperCAmelCase_	:					int     =     nn.Dense(self.config.projection_dim				,   use_bias=_snake_case				,   dtype=self.dtype      )

													UpperCAmelCase_	:					List[Any]     =     self.param('''concept_embeds'''				,   jax.nn.initializers.ones				,   (17, self.config.projection_dim)      )
													UpperCAmelCase_	:					Optional[Any]     =     self.param(
													    '''special_care_embeds'''				,   jax.nn.initializers.ones				,   (3, self.config.projection_dim)      )

													UpperCAmelCase_	:					List[Any]     =     self.param('''concept_embeds_weights'''				,   jax.nn.initializers.ones				,   (17,)      )
													UpperCAmelCase_	:					Any     =     self.param('''special_care_embeds_weights'''				,   jax.nn.initializers.ones				,   (3,)      )



							def __call__(							self							:   int				,   __magic_name__							:   List[str]      )    ->				List[str]:







													"""simple docstring"""
													UpperCAmelCase_	:					Any     =     self.vision_model(_snake_case      )[1]
													UpperCAmelCase_	:					List[str]     =     self.visual_projection(_snake_case      )

													UpperCAmelCase_	:					Tuple     =     jax_cosine_distance(_snake_case				,   self.special_care_embeds      )
													UpperCAmelCase_	:					List[Any]     =     jax_cosine_distance(_snake_case				,   self.concept_embeds      )

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

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

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

													return has_nsfw_concepts





class     __a					(lowerCamelCase   ):
							__a    :      int  	=				CLIPConfig
							__a    :      Optional[Any]  	=				"clip_input"
							__a    :      Optional[Any]  	=				FlaxStableDiffusionSafetyCheckerModule

							def __init__(							self							:   Dict				,   __magic_name__							:   Tuple				,   __magic_name__							:   str = None				,   __magic_name__							:   str = 0				,   __magic_name__							:   Union[str, Any] = jnp.floataa				,   __magic_name__							:   Optional[int] = True				,   **__magic_name__							:   Tuple				,   )    ->				Union[str, Any]:







													"""simple docstring"""




													if input_shape is None:
																			UpperCAmelCase_	:					str     =     (1, 2_24, 2_24, 3)
													UpperCAmelCase_	:					Optional[Any]     =     self.module_class(config=_snake_case				,   dtype=_snake_case				,   **_snake_case      )
													super().__init__(_snake_case				,   _snake_case				,   input_shape=_snake_case				,   seed=_snake_case				,   dtype=_snake_case				,   _do_init=_do_init      )

							def 			UpperCAmelCase__		(							self							:   Any				,   __magic_name__							:   Tuple				,   __magic_name__							:   int				,   __magic_name__							:   List[Any] = None      )    ->				FrozenDict:







													"""simple docstring"""
													UpperCAmelCase_	:					Tuple     =     jax.random.normal(_snake_case				,   _snake_case      )

													UpperCAmelCase_  ,				UpperCAmelCase_	:					str     =     jax.random.split(_snake_case      )
													UpperCAmelCase_	:					str     =     {'''params''': params_rng, '''dropout''': dropout_rng}

													UpperCAmelCase_	:					int     =     self.module.init(_snake_case				,   _snake_case      )['''params''']

													return random_params



							def __call__(							self							:   Any				,   __magic_name__							:   Optional[int]				,   __magic_name__							:   str = None				,   )    ->				List[Any]:







													"""simple docstring"""
													UpperCAmelCase_	:					Dict     =     jnp.transpose(_snake_case				,   (0, 2, 3, 1)      )

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







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save


def     __lowerCAmelCase			(			a__						,    a__						,    a__=1024						,    a__=1024						,    a__=False						,    **a__  )       ->  Optional[Any]:
				__a										=    AutoTokenizer.from_pretrained(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''train'''						,    **a__  )
				__a										=    tok.pad_token_id

				def get_lens(a__  ):
								__a										=    tqdm(
								    DataLoader(a__						,    batch_size=512						,    num_workers=8						,    shuffle=a__						,    collate_fn=ds.collate_fn  )						,    desc=str(ds.len_file  )						,    )
								__a										=    []
								for batch in dl:
												__a										=    batch['''input_ids'''].ne(a__  ).sum(1  ).tolist()
												__a										=    batch['''labels'''].ne(a__  ).sum(1  ).tolist()
												if consider_target:
																for src, tgt in zip(a__						,    a__  ):
																				max_lens.append(max(a__						,    a__  )  )
												else:
																max_lens.extend(a__  )
								return max_lens

				__a										=    get_lens(a__  )
				__a										=    SeqaSeqDataset(a__						,    a__						,    a__						,    a__						,    type_path='''val'''						,    **a__  )
				__a										=    get_lens(a__  )
				pickle_save(a__						,    train_ds.len_file  )
				pickle_save(a__						,    val_ds.len_file  )


if __name__ == "__main__":
					fire.Fire(save_len_file)