# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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   snake_case_       (		__A       ):
   __A	:		Tuple         =				(
       "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	:		str         =				"CIDAS/clipseg-rd64-refined"
   __A	:		int         =				"image_segmenter"
   __A	:		Union[str, Any]         =				CLIPSegForImageSegmentation

   __A	:		str         =				["image", "text"]
   __A	:		Union[str, Any]         =				["image"]






   def __init__(      self				:     Optional[Any]	,						*lowercase_				:     Optional[int]	,						**lowercase_				:     List[Any]  )					->						Tuple:
       requires_backends(self	,						["vision"]  )
       super().__init__(*lowercase_	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     List[str]	,						lowercase_				:     "Image"	,						lowercase_				:     str  )					->						Any:
       return self.pre_processor(text=[label]	,						images=[image]	,						padding=lowercase_	,						return_tensors="pt"  )






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     Any  )					->						List[str]:
       with torch.no_grad():
           lowercase__ :   List[str]    					=				self.model(**lowercase_  ).logits
       return logits







   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     Any  )					->						Any:
       lowercase__ :   List[str]    					=				outputs.cpu().detach().numpy()
       lowercase__ :   int    					=				0
       lowercase__ :   Optional[int]    					=				1
       return Image.fromarray((array * 2_55).astype(np.uinta  )  )








"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__    :  int							    =	logging.get_logger(__name__)

lowercase__    :  List[Any]							    =	{
    '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			List[Any]							=		"""gpt_neox"""

 def __init__(     self		:       List[str]  ,     lowercase_		:       str=50432  ,     lowercase_		:       List[Any]=6144  ,     lowercase_		:       List[Any]=44  ,     lowercase_		:       Union[str, Any]=64  ,     lowercase_		:       List[str]=24576  ,     lowercase_		:       List[Any]="gelu"  ,     lowercase_		:       str=0.25  ,     lowercase_		:       Optional[int]=10000  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       Optional[int]=0.0  ,     lowercase_		:       int=0.1  ,     lowercase_		:       Tuple=2048  ,     lowercase_		:       Union[str, Any]=0.02  ,     lowercase_		:       List[str]=1E-5  ,     lowercase_		:       str=True  ,     lowercase_		:       str=0  ,     lowercase_		:       Union[str, Any]=2  ,     lowercase_		:       List[str]=False  ,     lowercase_		:       Optional[int]=True  ,     lowercase_		:       List[Any]=None  ,     **lowercase_		:       Optional[int]  ,     ):
     super().__init__(bos_token_id=lowercase_  ,     eos_token_id=lowercase_  ,     **lowercase_				)
     snake_case_ :     List[str]				=	vocab_size
     snake_case_ :     Optional[Any]				=	max_position_embeddings
     snake_case_ :     str				=	hidden_size
     snake_case_ :     Dict				=	num_hidden_layers
     snake_case_ :     Dict				=	num_attention_heads
     snake_case_ :     List[Any]				=	intermediate_size
     snake_case_ :     List[Any]				=	hidden_act
     snake_case_ :     str				=	rotary_pct
     snake_case_ :     Dict				=	rotary_emb_base
     snake_case_ :     Optional[int]				=	attention_dropout
     snake_case_ :     Tuple				=	hidden_dropout
     snake_case_ :     Tuple				=	classifier_dropout
     snake_case_ :     List[str]				=	initializer_range
     snake_case_ :     Union[str, Any]				=	layer_norm_eps
     snake_case_ :     Any				=	use_cache
     snake_case_ :     Optional[int]				=	tie_word_embeddings
     snake_case_ :     Any				=	use_parallel_residual
     snake_case_ :     Union[str, Any]				=	rope_scaling
     self._rope_scaling_validation()

     if self.hidden_size % self.num_attention_heads != 0:
         raise ValueError(
             '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''				)






 def 						_snake_case					(     self		:       Optional[int]				):
     if self.rope_scaling is None:
         return

     if not isinstance(self.rope_scaling  ,     lowercase_				) or len(self.rope_scaling				) != 2:
         raise ValueError(
             '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
             f"got {self.rope_scaling}"				)
     snake_case_ :     Any				=	self.rope_scaling.get('''type'''  ,     lowercase_				)
     snake_case_ :     Union[str, Any]				=	self.rope_scaling.get('''factor'''  ,     lowercase_				)
     if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
         raise ValueError(
             f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"				)
     if rope_scaling_factor is None or not isinstance(lowercase_  ,     lowercase_				) or rope_scaling_factor <= 1.0:
         raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"				)





