




"""simple docstring"""
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
					from PIL import Image

class __lowercase(      _UpperCAmelCase				):




					'''simple docstring'''


					__a						:						List[Any]     		= (
					    'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
					    'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
					    'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
					)
					__a						:						List[str]     		= 'CIDAS/clipseg-rd64-refined'
					__a						:						str     		= 'image_segmenter'
					__a						:						Union[str, Any]     		= CLIPSegForImageSegmentation

					__a						:						List[Any]     		= ['image', 'text']
					__a						:						Optional[Any]     		= ['image']
					def __init__(    self						,	*__a						,	**__a     ):
								requires_backends(self						,	['vision']     )
								super().__init__(*lowerCamelCase_						,	**lowerCamelCase_     )
					def     snake_case_				(    self						,	__a						,	__a     ):
								return self.pre_processor(text=[label]						,	images=[image]						,	padding=lowerCamelCase_						,	return_tensors='pt'     )
					def     snake_case_				(    self						,	__a     ):




								with torch.no_grad():
											__lowerCamelCase					:	List[str]	       =				self.model(**lowerCamelCase_     ).logits
								return logits





					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	List[Any]	       =				outputs.cpu().detach().numpy()
								__lowerCamelCase					:	str	       =				0
								__lowerCamelCase					:	List[Any]	       =				1
								return Image.fromarray((array * 255).astype(np.uinta     )     )









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_				:    Tuple										=     logging.get_logger(__name__)

a_				:    str										=     {'''openai-gpt''': '''https://huggingface.co/openai-gpt/resolve/main/config.json'''}

class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						Union[str, Any]     		= 'openai-gpt'
					__a						:						List[Any]     		= {
					    'max_position_embeddings': 'n_positions',
					    'hidden_size': 'n_embd',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}
					def __init__(    self						,	__a=40478						,	__a=512						,	__a=768						,	__a=12						,	__a=12						,	__a="gelu"						,	__a=0.1						,	__a=0.1						,	__a=0.1						,	__a=1E-5						,	__a=0.02						,	__a="cls_index"						,	__a=True						,	__a=None						,	__a=True						,	__a=0.1						,	**__a						,	):
								__lowerCamelCase					:	List[str]	       =				vocab_size
								__lowerCamelCase					:	Union[str, Any]	       =				n_positions
								__lowerCamelCase					:	Optional[Any]	       =				n_embd
								__lowerCamelCase					:	int	       =				n_layer
								__lowerCamelCase					:	List[str]	       =				n_head
								__lowerCamelCase					:	Dict	       =				afn
								__lowerCamelCase					:	Optional[Any]	       =				resid_pdrop
								__lowerCamelCase					:	int	       =				embd_pdrop
								__lowerCamelCase					:	int	       =				attn_pdrop
								__lowerCamelCase					:	Dict	       =				layer_norm_epsilon
								__lowerCamelCase					:	List[str]	       =				initializer_range
								__lowerCamelCase					:	Any	       =				summary_type
								__lowerCamelCase					:	Tuple	       =				summary_use_proj
								__lowerCamelCase					:	List[Any]	       =				summary_activation
								__lowerCamelCase					:	str	       =				summary_first_dropout
								__lowerCamelCase					:	Tuple	       =				summary_proj_to_labels
								super().__init__(**__a     )



