
'''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_       :    int     ,		A_       :    List[str]     ,		A_       :    Union[str, Any]=1E-12   ):
  """simple docstring"""







  a_      :	Optional[int]      =							jnp.divide(emb_a.T     ,		jnp.clip(jnp.linalg.norm(A_     ,		axis=1   )     ,		a_min=A_   )   ).T
  a_      :	Optional[Any]      =							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  _UpperCAmelCase ( nn.Module    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					jnp.floataa


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Optional[int]      =							FlaxCLIPVisionModule(self.config.vision_config )
       a_      :	Tuple      =							nn.Dense(self.config.projection_dim							,		use_bias=lowerCAmelCase_							,		dtype=self.dtype )

       a_      :	List[str]      =							self.param("""concept_embeds"""							,		jax.nn.initializers.ones							,		(17, self.config.projection_dim) )
       a_      :	Union[str, Any]      =							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_      :	List[Any]      =							self.param("""special_care_embeds_weights"""							,		jax.nn.initializers.ones							,		(3,) )






     def __call__(      self							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	List[str]      =							self.vision_model(lowerCAmelCase_ )[1]
       a_      :	Tuple      =							self.visual_projection(lowerCAmelCase_ )

       a_      :	Union[str, Any]      =							jax_cosine_distance(lowerCAmelCase_							,		self.special_care_embeds )
       a_      :	List[str]      =							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
       a_      :	int      =							0.0

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

       a_      :	int      =							cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
       a_      :	str      =							jnp.round(lowerCAmelCase_							,		3 )
       a_      :	Tuple      =							jnp.any(concept_scores > 0							,		axis=1 )

       return has_nsfw_concepts







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					CLIPConfig
     a_    	=					"clip_input"
     a_    	=					FlaxStableDiffusionSafetyCheckerModule


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

       '''simple docstring'''

       if input_shape is None:
         a_      :	Tuple      =							(1, 2_24, 2_24, 3)
       a_      :	int      =							self.module_class(config=lowerCAmelCase_							,		dtype=lowerCAmelCase_							,		**lowerCAmelCase_ )
       super().__init__(lowerCAmelCase_							,		lowerCAmelCase_							,		input_shape=lowerCAmelCase_							,		seed=lowerCAmelCase_							,		dtype=lowerCAmelCase_							,		_do_init=_do_init )


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       a_      :	str      =							jax.random.normal(lowerCAmelCase_							,		lowerCAmelCase_ )

       a_      , a_      :	Tuple      =							jax.random.split(lowerCAmelCase_ )
       a_      :	Union[str, Any]      =							{"""params""": params_rng, """dropout""": dropout_rng}

       a_      :	Optional[int]      =							self.module.init(lowerCAmelCase_							,		lowerCAmelCase_ )["""params"""]

       return random_params






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

       '''simple docstring'''

       a_      :	Optional[Any]      =							jnp.transpose(lowerCAmelCase_							,		(0, 2, 3, 1) )

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




'''simple docstring'''

import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							"""ylacombe/bark-small"""
       a_      :	Dict      =							tempfile.mkdtemp()
       a_      :	Union[str, Any]      =							"""en_speaker_1"""
       a_      :	Dict      =							"""This is a test string"""
       a_      :	Optional[int]      =							"""speaker_embeddings_path.json"""
       a_      :	int      =							"""speaker_embeddings"""


     def 							_lowerCAmelCase  (      self							,		**lowerCAmelCase_ ):

       '''simple docstring'''

       return AutoTokenizer.from_pretrained(self.checkpoint							,		**lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       shutil.rmtree(self.tmpdirname )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Union[str, Any]      =							self.get_tokenizer()

       a_      :	Optional[Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       processor.save_pretrained(self.tmpdirname )
       a_      :	List[str]      =							BarkProcessor.from_pretrained(self.tmpdirname )

       self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer.get_vocab() )


     @slow
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Any      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)
       processor.save_pretrained(
           self.tmpdirname							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		speaker_embeddings_directory=self.speaker_embeddings_directory							,		)

       a_      :	List[str]      =							self.get_tokenizer(bos_token="""(BOS)"""							,		eos_token="""(EOS)""" )

       a_      :	Any      =							BarkProcessor.from_pretrained(
           self.tmpdirname							,		self.speaker_embeddings_dict_path							,		bos_token="""(BOS)"""							,		eos_token="""(EOS)"""							,		)

       self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab() )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint							,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path							,		)

       a_      :	Dict      =							35
       a_      :	List[Any]      =							2
       a_      :	Optional[int]      =							8

       a_      :	int      =							{
           """semantic_prompt""": np.ones(lowerCAmelCase_ ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len) ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len) ),
       }

       # test providing already loaded voice_preset
       a_      :	Optional[Any]      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )

       a_      :	Tuple      =							inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from npz file
       a_      :	Any      =							os.path.join(self.tmpdirname							,		"""file.npz""" )
       np.savez(lowerCAmelCase_							,		**lowerCAmelCase_ )
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=lowerCAmelCase_ )
       a_      :	List[Any]      =							inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()							,		processed_voice_preset.get(lowerCAmelCase_							,		np.array([] ) ).tolist() )

       # test loading voice preset from the hub
       a_      :	Any      =							processor(text=self.input_string							,		voice_preset=self.voice_preset )






     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Tuple      =							self.get_tokenizer()

       a_      :	Union[str, Any]      =							BarkProcessor(tokenizer=lowerCAmelCase_ )

       a_      :	Optional[int]      =							processor(text=self.input_string )

       a_      :	Optional[int]      =							tokenizer(
           self.input_string							,		padding="""max_length"""							,		max_length=2_56							,		add_special_tokens=lowerCAmelCase_							,		return_attention_mask=lowerCAmelCase_							,		return_token_type_ids=lowerCAmelCase_							,		)

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]							,		encoded_processor[key].squeeze().tolist() )


