





"""simple docstring"""




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


a		:					Dict	      =     logging.get_logger(__name__)  # pylint: disable=invalid-name






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Optional[Any]   )					->Optional[int]:






	'''simple docstring'''






	if not path:
		return "pipe"

	for ext in PipelineDataFormat.SUPPORTED_FORMATS:
		if path.endswith(_lowercase   ):
			return ext

	raise Exception(
	    F"""Unable to determine file format from file extension {path}. """
	    F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Tuple   )					->int:






	'''simple docstring'''






	a :						Optional[Any]      =							pipeline(
	    task=args.task       ,			model=args.model if args.model else None       ,			config=args.config       ,			tokenizer=args.tokenizer       ,			device=args.device       ,			)
	a :						List[Any]      =							try_infer_format_from_ext(args.input   ) if args.format == "infer" else args.format
	a :						Optional[int]      =							PipelineDataFormat.from_str(
	    format=_lowercase       ,			output_path=args.output       ,			input_path=args.input       ,			column=args.column if args.column else nlp.default_input_names       ,			overwrite=args.overwrite       ,			)
	return RunCommand(_lowercase       ,			_lowercase   )







class 			__UpperCamelCase							(		lowercase_		):






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Any:
								a :						Union[str, Any]      =							nlp
								a :						Any      =							reader






							@staticmethod
							def        __a  (  lowerCAmelCase__ )      ->					Any:
								a :						List[str]      =							parser.add_parser("run" ,   help="Run a pipeline through the CLI" )
								run_parser.add_argument("--task" ,   choices=get_supported_tasks() ,   help="Task to run" )
								run_parser.add_argument("--input" ,   type=lowerCAmelCase__ ,   help="Path to the file to use for inference" )
								run_parser.add_argument("--output" ,   type=lowerCAmelCase__ ,   help="Path to the file that will be used post to write results." )
								run_parser.add_argument("--model" ,   type=lowerCAmelCase__ ,   help="Name or path to the model to instantiate." )
								run_parser.add_argument("--config" ,   type=lowerCAmelCase__ ,   help="Name or path to the model\'s config to instantiate." )
								run_parser.add_argument(
								    "--tokenizer" ,   type=lowerCAmelCase__ ,   help="Name of the tokenizer to use. (default: same as the model name)" )
								run_parser.add_argument(
								    "--column" ,   type=lowerCAmelCase__ ,   help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)" ,   )
								run_parser.add_argument(
								    "--format" ,   type=lowerCAmelCase__ ,   default="infer" ,   choices=PipelineDataFormat.SUPPORTED_FORMATS ,   help="Input format to read from" ,   )
								run_parser.add_argument(
								    "--device" ,   type=lowerCAmelCase__ ,   default=-1 ,   help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)" ,   )
								run_parser.add_argument("--overwrite" ,   action="store_true" ,   help="Allow overwriting the output file." )
								run_parser.set_defaults(func=lowerCAmelCase__ )






							def        __a  (  self )      ->					Tuple:
								a,			a :						str      =							self._nlp, []

								for entry in self._reader:
									a :						Tuple      =							nlp(**lowerCAmelCase__ ) if self._reader.is_multi_columns else nlp(lowerCAmelCase__ )
									if isinstance(lowerCAmelCase__ ,   lowerCAmelCase__ ):
										outputs.append(lowerCAmelCase__ )
									else:
										outputs += output

        # Saving data
								if self._nlp.binary_output:
									a :						int      =							self._reader.save_binary(lowerCAmelCase__ )
									logger.warning(f"""Current pipeline requires output to be in binary format, saving at {binary_path}""" )
								else:
									self._reader.save(lowerCAmelCase__ )






'''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 _SCREAMING_SNAKE_CASE (A    ,					A    ,					A=1E-12			)			->			str:






       """simple docstring"""







       lowercase__      =					jnp.divide(emb_a.T    ,					jnp.clip(jnp.linalg.norm(A    ,					axis=1			)    ,					a_min=A			)			).T
       lowercase__      =					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     __lowerCAmelCase		(nn.Module	):





      '''simple docstring'''






      lowerCAmelCase__   :	CLIPConfig
      lowerCAmelCase__   :	jnp.dtype       =				jnp.floataa



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             lowercase__      =					FlaxCLIPVisionModule(self.config.vision_config					)
             lowercase__      =					nn.Dense(self.config.projection_dim  ,		use_bias=UpperCamelCase  ,		dtype=self.dtype					)

             lowercase__      =					self.param('''concept_embeds'''  ,		jax.nn.initializers.ones  ,		(17, self.config.projection_dim)					)
             lowercase__      =					self.param(
                 '''special_care_embeds'''  ,		jax.nn.initializers.ones  ,		(3, self.config.projection_dim)					)

             lowercase__      =					self.param('''concept_embeds_weights'''  ,		jax.nn.initializers.ones  ,		(17,)					)
             lowercase__      =					self.param('''special_care_embeds_weights'''  ,		jax.nn.initializers.ones  ,		(3,)					)






      def __call__(self       :       Union[str, Any]  ,		UpperCamelCase       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					self.vision_model(UpperCamelCase					)[1]
             lowercase__      =					self.visual_projection(UpperCamelCase					)

             lowercase__      =					jax_cosine_distance(UpperCamelCase  ,		self.special_care_embeds					)
             lowercase__      =					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
             lowercase__      =					0.0

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

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

             return has_nsfw_concepts






class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      lowerCAmelCase__   :	Union[str, Any]       =				CLIPConfig
      lowerCAmelCase__   :	Optional[int]       =				"""clip_input"""
      lowerCAmelCase__   :	Any       =				FlaxStableDiffusionSafetyCheckerModule



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

             '''simple docstring'''







             if input_shape is None:
                    lowercase__      =					(1, 224, 224, 3)
             lowercase__      =					self.module_class(config=UpperCamelCase  ,		dtype=UpperCamelCase  ,		**UpperCamelCase					)
             super().__init__(UpperCamelCase  ,		UpperCamelCase  ,		input_shape=UpperCamelCase  ,		seed=UpperCamelCase  ,		dtype=UpperCamelCase  ,		_do_init=_do_init					)



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       jax.random.KeyArray  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       FrozenDict = None					):

             '''simple docstring'''







             lowercase__      =					jax.random.normal(UpperCamelCase  ,		UpperCamelCase					)

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

             lowercase__      =					self.module.init(UpperCamelCase  ,		UpperCamelCase					)['''params''']

             return random_params






      def __call__(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       dict = None  ,		):

             '''simple docstring'''







             lowercase__      =					jnp.transpose(UpperCamelCase  ,		(0, 2, 3, 1)					)

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




