


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_    (       _a	,   _a	,   _a=1e-1_2       ):




		"""simple docstring"""






		lowerCAmelCase__   :			List[str]         =					jnp.divide(emb_a.T	,   jnp.clip(jnp.linalg.norm(__lowerCamelCase	,   axis=1       )	,   a_min=__lowerCamelCase       )       ).T
		lowerCAmelCase__   :			List[Any]         =					jnp.divide(emb_a.T	,   jnp.clip(jnp.linalg.norm(__lowerCamelCase	,   axis=1       )	,   a_min=__lowerCamelCase       )       ).T
		return jnp.matmul(__lowerCamelCase	,   norm_emb_a.T       )





class 					_a							(				nn.Module):
	_a  :  Any          =							42
	_a  :  List[Any]          =							jnp.floataa




	def 							UpperCAmelCase__(					self						:		List[str]		)->       List[str]:
			lowerCAmelCase__   :			int         =					FlaxCLIPVisionModule(self.config.vision_config		)
			lowerCAmelCase__   :			str         =					nn.Dense(self.config.projection_dim		,					use_bias=_SCREAMING_SNAKE_CASE		,					dtype=self.dtype		)

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

			lowerCAmelCase__   :			Tuple         =					self.param('''concept_embeds_weights'''		,					jax.nn.initializers.ones		,					(17,)		)
			lowerCAmelCase__   :			str         =					self.param('''special_care_embeds_weights'''		,					jax.nn.initializers.ones		,					(3,)		)




	def __call__(					self						:		Dict		,					_SCREAMING_SNAKE_CASE						:		Tuple		)->       List[Any]:
			lowerCAmelCase__   :			Optional[Any]         =					self.vision_model(_SCREAMING_SNAKE_CASE		)[1]
			lowerCAmelCase__   :			Optional[int]         =					self.visual_projection(_SCREAMING_SNAKE_CASE		)

			lowerCAmelCase__   :			Tuple         =					jax_cosine_distance(_SCREAMING_SNAKE_CASE		,					self.special_care_embeds		)
			lowerCAmelCase__   :			List[str]         =					jax_cosine_distance(_SCREAMING_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
			lowerCAmelCase__   :			Union[str, Any]         =					0.0

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

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

			return has_nsfw_concepts





class 					_a							(				_lowerCAmelCase):
	_a  :  int          =							CLIPConfig
	_a  :  Tuple          =							'''clip_input'''
	_a  :  Any          =							FlaxStableDiffusionSafetyCheckerModule




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




			if input_shape is None:
					lowerCAmelCase__   :			Tuple         =					(1, 224, 224, 3)
			lowerCAmelCase__   :			Union[str, Any]         =					self.module_class(config=_SCREAMING_SNAKE_CASE		,					dtype=_SCREAMING_SNAKE_CASE		,					**_SCREAMING_SNAKE_CASE		)
			super().__init__(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					input_shape=_SCREAMING_SNAKE_CASE		,					seed=_SCREAMING_SNAKE_CASE		,					dtype=_SCREAMING_SNAKE_CASE		,					_do_init=_do_init		)




	def 							UpperCAmelCase__(					self						:		Optional[int]		,					_SCREAMING_SNAKE_CASE						:		jax.random.KeyArray		,					_SCREAMING_SNAKE_CASE						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		FrozenDict = None		)->       str:
			lowerCAmelCase__   :			Union[str, Any]         =					jax.random.normal(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		)

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

			lowerCAmelCase__   :			Dict         =					self.module.init(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		)["params"]

			return random_params




	def __call__(					self						:		Optional[Any]		,					_SCREAMING_SNAKE_CASE						:		Tuple		,					_SCREAMING_SNAKE_CASE						:		dict = None		,					)->       Optional[int]:
			lowerCAmelCase__   :			int         =					jnp.transpose(_SCREAMING_SNAKE_CASE		,					(0, 2, 3, 1)		)

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











'''simple docstring'''


import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase					:     Optional[int]												=logging.getLogger()
def 							UpperCAmelCase_  (						):
   lowercase_			:Union[str, Any]    			= argparse.ArgumentParser()
   parser.add_argument("-f" )
   lowercase_			:Any    			= parser.parse_args()
   return args.f


class       a_   (				_lowerCAmelCase							):





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:List[Any]    			= logging.StreamHandler(sys.stdout						)
        logger.addHandler(lowercase						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Dict    			= get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0	,      "run_glue_deebert.py"						)
           with patch.object(lowercase	,      "argv"	,      lowercase						):
              lowercase_			:int    			= run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(lowercase	,      0.6_66						)







     @slow
     @require_torch_non_multi_gpu
     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        lowercase_			:List[Any]    			= "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:List[str]    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)

        lowercase_			:Any    			= "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
        self.run_and_check(lowercase						)


