'''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					(		UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   List[str]=1E-1_2       ):


	"""simple docstring"""
	__UpperCAmelCase							=			jnp.divide(emb_a.T    ,					jnp.clip(jnp.linalg.norm(UpperCamelCase__    ,					axis=1       )    ,					a_min=UpperCamelCase__       )       ).T
	__UpperCAmelCase							=			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 						A   (   nn.Module   ):
							a_        =      42
							a_        =      jnp.floataa





							def       snake_case__						(      self				:							int	)							->	str:
								__UpperCAmelCase							=			FlaxCLIPVisionModule(self.config.vision_config	)
								__UpperCAmelCase							=			nn.Dense(self.config.projection_dim     ,		use_bias=__a     ,		dtype=self.dtype	)

								__UpperCAmelCase							=			self.param('''concept_embeds'''     ,		jax.nn.initializers.ones     ,		(1_7, self.config.projection_dim)	)
								__UpperCAmelCase							=			self.param(
								    '''special_care_embeds'''     ,		jax.nn.initializers.ones     ,		(3, self.config.projection_dim)	)

								__UpperCAmelCase							=			self.param('''concept_embeds_weights'''     ,		jax.nn.initializers.ones     ,		(1_7,)	)
								__UpperCAmelCase							=			self.param('''special_care_embeds_weights'''     ,		jax.nn.initializers.ones     ,		(3,)	)






							def __call__(      self				:							Tuple     ,		__a				:							Any	)							->	str:
								__UpperCAmelCase							=			self.vision_model(__a	)[1]
								__UpperCAmelCase							=			self.visual_projection(__a	)

								__UpperCAmelCase							=			jax_cosine_distance(__a     ,		self.special_care_embeds	)
								__UpperCAmelCase							=			jax_cosine_distance(__a     ,		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							=			0.0

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

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

								return has_nsfw_concepts






class 						A   (   UpperCAmelCase   ):
							a_        =      CLIPConfig
							a_        =      '''clip_input'''
							a_        =      FlaxStableDiffusionSafetyCheckerModule





							def __init__(      self				:							Optional[int]     ,		__a				:							CLIPConfig     ,		__a				:							Optional[Tuple] = None     ,		__a				:							int = 0     ,		__a				:							jnp.dtype = jnp.floataa     ,		__a				:							bool = True     ,		**__a				:							Optional[Any]     ,		)							->	Optional[Any]:
								if input_shape is None:
									__UpperCAmelCase							=			(1, 2_2_4, 2_2_4, 3)
								__UpperCAmelCase							=			self.module_class(config=__a     ,		dtype=__a     ,		**__a	)
								super().__init__(__a     ,		__a     ,		input_shape=__a     ,		seed=__a     ,		dtype=__a     ,		_do_init=_do_init	)





							def       snake_case__						(      self				:							List[Any]     ,		__a				:							jax.random.KeyArray     ,		__a				:							Tuple     ,		__a				:							FrozenDict = None	)							->	FrozenDict:
								# init input tensor
								__UpperCAmelCase							=			jax.random.normal(__a     ,		__a	)

								__UpperCAmelCase				,			__UpperCAmelCase							=			jax.random.split(__a	)
								__UpperCAmelCase							=			{'''params''': params_rng, '''dropout''': dropout_rng}

								__UpperCAmelCase							=			self.module.init(__a     ,		__a	)['''params''']

								return random_params






							def __call__(      self				:							int     ,		__a				:							List[str]     ,		__a				:							dict = None     ,		)							->	Dict:
								__UpperCAmelCase							=			jnp.transpose(__a     ,		(0, 2, 3, 1)	)

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

'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)






class 						A   :





							def __init__(      self				:							List[Any]     ,		__a				:							Any     ,		)							->	Dict:
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			1_3
								__UpperCAmelCase							=			7
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			False
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			9_9
								__UpperCAmelCase							=			3_2
								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			4
								__UpperCAmelCase							=			3_7
								__UpperCAmelCase							=			'''gelu'''
								__UpperCAmelCase							=			0.1
								__UpperCAmelCase							=			0.1
								__UpperCAmelCase							=			5_1_2
								__UpperCAmelCase							=			1_6
								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			0.0_2
								__UpperCAmelCase							=			3
								__UpperCAmelCase							=			4
								__UpperCAmelCase							=			None





							def       snake_case__						(      self				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)

								__UpperCAmelCase							=			None
								if self.use_input_mask:
									__UpperCAmelCase							=			random_attention_mask([self.batch_size, self.seq_length]	)

								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								if self.use_labels:
									__UpperCAmelCase							=			ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)
									__UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels	)
									__UpperCAmelCase							=			ids_tensor([self.batch_size]     ,		self.num_choices	)

								__UpperCAmelCase							=			DistilBertConfig(
								    vocab_size=self.vocab_size     ,		dim=self.hidden_size     ,		n_layers=self.num_hidden_layers     ,		n_heads=self.num_attention_heads     ,		hidden_dim=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		dropout=self.hidden_dropout_prob     ,		attention_dropout=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		initializer_range=self.initializer_range     ,		)

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							List[str]     ,		__a				:							int     ,		__a				:							Union[str, Any]     ,		__a				:							Union[str, Any]     ,		__a				:							List[Any]     ,		__a				:							int	)							->	Any:
								__UpperCAmelCase							=			TFDistilBertModel(config=__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}

								__UpperCAmelCase							=			model(__a	)

								__UpperCAmelCase							=			[input_ids, input_mask]

								__UpperCAmelCase							=			model(__a	)

								self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)	)





							def       snake_case__						(      self				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							int     ,		__a				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							Union[str, Any]     ,		__a				:							List[Any]	)							->	int:
								__UpperCAmelCase							=			TFDistilBertForMaskedLM(config=__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)	)





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							Any     ,		__a				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							int     ,		__a				:							Optional[Any]     ,		__a				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			TFDistilBertForQuestionAnswering(config=__a	)
								__UpperCAmelCase							=			{
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.start_logits.shape     ,		(self.batch_size, self.seq_length)	)
								self.parent.assertEqual(result.end_logits.shape     ,		(self.batch_size, self.seq_length)	)





							def       snake_case__						(      self				:							Any     ,		__a				:							Optional[Any]     ,		__a				:							List[str]     ,		__a				:							Dict     ,		__a				:							Dict     ,		__a				:							int     ,		__a				:							List[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.num_labels
								__UpperCAmelCase							=			TFDistilBertForSequenceClassification(__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)	)





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							int     ,		__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							List[str]     ,		__a				:							Dict	)							->	str:
								__UpperCAmelCase							=			self.num_choices
								__UpperCAmelCase							=			TFDistilBertForMultipleChoice(__a	)
								__UpperCAmelCase							=			tf.tile(tf.expand_dims(__a     ,		1	)     ,		(1, self.num_choices, 1)	)
								__UpperCAmelCase							=			tf.tile(tf.expand_dims(__a     ,		1	)     ,		(1, self.num_choices, 1)	)
								__UpperCAmelCase							=			{
								    '''input_ids''': multiple_choice_inputs_ids,
								    '''attention_mask''': multiple_choice_input_mask,
								}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_choices)	)





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]     ,		__a				:							int     ,		__a				:							Tuple     ,		__a				:							int     ,		__a				:							Optional[int]     ,		__a				:							Optional[int]	)							->	int:
								__UpperCAmelCase							=			self.num_labels
								__UpperCAmelCase							=			TFDistilBertForTokenClassification(__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels)	)






							def       snake_case__						(      self				:							str	)							->	Any:
								__UpperCAmelCase							=			self.prepare_config_and_inputs()
								((__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase))							=			config_and_inputs
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict






@require_tf
class 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      (
							    (
							        TFDistilBertModel,
							        TFDistilBertForMaskedLM,
							        TFDistilBertForQuestionAnswering,
							        TFDistilBertForSequenceClassification,
							        TFDistilBertForTokenClassification,
							        TFDistilBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else None
							)
							a_        =      (
							    {
							        '''feature-extraction''': TFDistilBertModel,
							        '''fill-mask''': TFDistilBertForMaskedLM,
							        '''question-answering''': TFDistilBertForQuestionAnswering,
							        '''text-classification''': TFDistilBertForSequenceClassification,
							        '''token-classification''': TFDistilBertForTokenClassification,
							        '''zero-shot''': TFDistilBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							a_        =      False
							a_        =      False





							def       snake_case__						(      self				:							Any	)							->	Any:
								__UpperCAmelCase							=			TFDistilBertModelTester(self	)
								__UpperCAmelCase							=			ConfigTester(self     ,		config_class=__a     ,		dim=3_7	)





							def       snake_case__						(      self				:							List[Any]	)							->	Optional[int]:
								self.config_tester.run_common_tests()





							def       snake_case__						(      self				:							Any	)							->	str:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_model(*__a	)





							def       snake_case__						(      self				:							Tuple	)							->	Dict:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_masked_lm(*__a	)





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Any:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_question_answering(*__a	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_sequence_classification(*__a	)





							def       snake_case__						(      self				:							Any	)							->	int:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_multiple_choice(*__a	)





							def       snake_case__						(      self				:							List[str]	)							->	List[Any]:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_token_classification(*__a	)






							@slow
							def       snake_case__						(      self				:							Dict	)							->	Tuple:
								for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
									__UpperCAmelCase							=			TFDistilBertModel.from_pretrained(__a	)
									self.assertIsNotNone(__a	)






@require_tf
class 						A   (   unittest.TestCase   ):





							@slow
							def       snake_case__						(      self				:							int	)							->	Dict:
								__UpperCAmelCase							=			TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''	)
								__UpperCAmelCase							=			tf.constant([[0, 1, 2, 3, 4, 5]]	)
								__UpperCAmelCase							=			model(__a	)[0]

								__UpperCAmelCase							=			[1, 6, 7_6_8]
								self.assertEqual(output.shape     ,		__a	)

								__UpperCAmelCase							=			tf.constant(
								    [
								        [
								            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
								            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
								            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
								        ]
								    ]	)
								tf.debugging.assert_near(output[:, :3, :3]     ,		__a     ,		atol=1e-4	)
