




"""simple docstring"""


import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
  from transformers import MarkupLMFeatureExtractor


class _lowerCamelCase    (					unittest.TestCase						):






   def __init__(		self       :		str   ,     UpperCamelCase       :		Optional[int]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	parent






   def 	_lowerCAmelCase   (		self       :		str   )			->						int:

         """simple docstring"""
         return {}






def 	lowercase_				(			)						->    Any:
      lowerCAmelCase__      :			Optional[Any]   =	"""<HTML>

    <HEAD>
    <TITLE>sample document</TITLE>
    </HEAD>

    <BODY BGCOLOR=\"FFFFFF\">
    <HR>
    <a href=\"http://google.com\">Goog</a>
    <H1>This is one header</H1>
    <H2>This is a another Header</H2>
    <P>Travel from
        <P>
        <B>SFO to JFK</B>
        <BR>
        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
        <HR>
        <div style=\"color:#0000FF\">
            <h3>Traveler <b> name </b> is
            <p> John Doe </p>
        </div>"""

      lowerCAmelCase__      :			Union[str, Any]   =	"""
    <!DOCTYPE html>
    <html>
    <body>

    <h1>My First Heading</h1>
    <p>My first paragraph.</p>

    </body>
    </html>
    """

      return [html_string_a, html_string_a]


@require_bsa
class _lowerCamelCase    (					a_       ,	unittest.TestCase						):
   _lowerCamelCase    :List[str]     				=     MarkupLMFeatureExtractor if is_bsa_available() else None






   def 	_lowerCAmelCase   (		self       :		Union[str, Any]   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Any   =	MarkupLMFeatureExtractionTester(self   )






   @property
   def 	_lowerCAmelCase   (		self       :		Optional[Any]   )			->						List[str]:

         """simple docstring"""
         return self.feature_extract_tester.prepare_feat_extract_dict()






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						List[str]:

         """simple docstring"""
         # Initialize feature_extractor
         lowerCAmelCase__      :			Dict   =	self.feature_extraction_class()

         # Test not batched input
         lowerCAmelCase__      :			Tuple   =	get_html_strings()[0]
         lowerCAmelCase__      :			Optional[int]   =	feature_extractor(UpperCamelCase   )

         # fmt: off
         lowerCAmelCase__      :			Optional[int]   =	[["""sample document""", """Goog""", """This is one header""", """This is a another Header""", """Travel from""", """SFO to JFK""", """on May 2, 2015 at 2:00 pm. For details go to confirm.com""", """Traveler""", """name""", """is""", """John Doe"""]]
         lowerCAmelCase__      :			Optional[int]   =	[["""/html/head/title""", """/html/body/a""", """/html/body/h1""", """/html/body/h2""", """/html/body/p""", """/html/body/p/p/b[1]""", """/html/body/p/p/b[2]/i""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/b""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/p"""]]
         # fmt: on

         self.assertEqual(encoding.nodes   ,     UpperCamelCase   )
         self.assertEqual(encoding.xpaths   ,     UpperCamelCase   )

         # Test batched
         lowerCAmelCase__      :			Union[str, Any]   =	get_html_strings()
         lowerCAmelCase__      :			Tuple   =	feature_extractor(UpperCamelCase   )

         # fmt: off
         lowerCAmelCase__      :			int   =	expected_nodes + [["""My First Heading""", """My first paragraph."""]]
         lowerCAmelCase__      :			Optional[int]   =	expected_xpaths + [["""/html/body/h1""", """/html/body/p"""]]

         self.assertEqual(len(encoding.nodes   )   ,     2   )
         self.assertEqual(len(encoding.xpaths   )   ,     2   )

         self.assertEqual(encoding.nodes   ,     UpperCamelCase   )
         self.assertEqual(encoding.xpaths   ,     UpperCamelCase   )











"""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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=1E-12				)						->    Dict:
      lowerCAmelCase__      :			Optional[int]   =	jnp.divide(emb_a.T    ,		jnp.clip(jnp.linalg.norm(__UpperCAmelCase    ,		axis=1				)    ,		a_min=__UpperCAmelCase				)				).T
      lowerCAmelCase__      :			Any   =	jnp.divide(emb_a.T    ,		jnp.clip(jnp.linalg.norm(__UpperCAmelCase    ,		axis=1				)    ,		a_min=__UpperCAmelCase				)				).T
      return jnp.matmul(__UpperCAmelCase    ,		norm_emb_a.T				)


class _lowerCamelCase    (					nn.Module						):
   _lowerCamelCase    :CLIPConfig
   _lowerCamelCase    :jnp.dtype     				=     jnp.floataa






   def 	_lowerCAmelCase   (		self       :		List[str]   )			->						Tuple:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	FlaxCLIPVisionModule(self.config.vision_config   )
         lowerCAmelCase__      :			Dict   =	nn.Dense(self.config.projection_dim   ,     use_bias=UpperCamelCase   ,     dtype=self.dtype   )

         lowerCAmelCase__      :			Optional[int]   =	self.param("""concept_embeds"""   ,     jax.nn.initializers.ones   ,     (17, self.config.projection_dim)   )
         lowerCAmelCase__      :			Optional[int]   =	self.param(
             """special_care_embeds"""   ,     jax.nn.initializers.ones   ,     (3, self.config.projection_dim)   )

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






   def __call__(		self       :		str   ,     UpperCamelCase       :		List[Any]   )			->						Union[str, Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	self.vision_model(UpperCamelCase   )[1]
         lowerCAmelCase__      :			List[Any]   =	self.visual_projection(UpperCamelCase   )

         lowerCAmelCase__      :			str   =	jax_cosine_distance(UpperCamelCase   ,     self.special_care_embeds   )
         lowerCAmelCase__      :			Optional[int]   =	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
         lowerCAmelCase__      :			Any   =	0.0

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

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

         return has_nsfw_concepts






class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :Optional[Any]     				=     CLIPConfig
   _lowerCamelCase    :Dict     				=     "clip_input"
   _lowerCamelCase    :int     				=     FlaxStableDiffusionSafetyCheckerModule






   def __init__(		self       :		int   ,     UpperCamelCase       :		CLIPConfig   ,     UpperCamelCase       :		Optional[Tuple] = None   ,     UpperCamelCase       :		int = 0   ,     UpperCamelCase       :		jnp.dtype = jnp.floataa   ,     UpperCamelCase       :		bool = True   ,     **UpperCamelCase       :		int   ,     )			->						str:

         """simple docstring"""
         if input_shape is None:
               lowerCAmelCase__      :			str   =	(1, 2_24, 2_24, 3)
         lowerCAmelCase__      :			Union[str, Any]   =	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       :		Dict   ,     UpperCamelCase       :		jax.random.KeyArray   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		FrozenDict = None   )			->						FrozenDict:

         """simple docstring"""
         # init input tensor
         lowerCAmelCase__      :			Union[str, Any]   =	jax.random.normal(UpperCamelCase   ,     UpperCamelCase   )

         lowerCAmelCase__ ,   lowerCAmelCase__      :			Union[str, Any]   =	jax.random.split(UpperCamelCase   )
         lowerCAmelCase__      :			List[Any]   =	{"""params""": params_rng, """dropout""": dropout_rng}

         lowerCAmelCase__      :			str   =	self.module.init(UpperCamelCase   ,     UpperCamelCase   )["""params"""]

         return random_params






   def __call__(		self       :		Dict   ,     UpperCamelCase       :		Tuple   ,     UpperCamelCase       :		dict = None   ,     )			->						List[Any]:

         """simple docstring"""
         lowerCAmelCase__      :			Union[str, Any]   =	jnp.transpose(UpperCamelCase   ,     (0, 2, 3, 1)   )

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





