






import warnings

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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Dict       						=  '''xlnet'''
		_snake_case    :    Optional[int]       						=  ['''mems''']
		_snake_case    :    List[str]       						=  {
		    '''n_token''': '''vocab_size''',  # Backward compatibility
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}



		def __init__(							self				,		_UpperCamelCase=3_2_0_0_0				,		_UpperCamelCase=1_0_2_4				,		_UpperCamelCase=2_4				,		_UpperCamelCase=1_6				,		_UpperCamelCase=4_0_9_6				,		_UpperCamelCase="gelu"				,		_UpperCamelCase=True				,		_UpperCamelCase="bi"				,		_UpperCamelCase=0.02				,		_UpperCamelCase=1E-12				,		_UpperCamelCase=0.1				,		_UpperCamelCase=5_1_2				,		_UpperCamelCase=None				,		_UpperCamelCase=True				,		_UpperCamelCase=False				,		_UpperCamelCase=False				,		_UpperCamelCase=-1				,		_UpperCamelCase=False				,		_UpperCamelCase="last"				,		_UpperCamelCase=True				,		_UpperCamelCase="tanh"				,		_UpperCamelCase=0.1				,		_UpperCamelCase=5				,		_UpperCamelCase=5				,		_UpperCamelCase=5				,		_UpperCamelCase=1				,		_UpperCamelCase=2				,		**_UpperCamelCase				,		)	->							Any:
							UpperCAmelCase_    :      int            =						vocab_size
							UpperCAmelCase_    :      int            =						d_model
							UpperCAmelCase_    :      Any            =						n_layer
							UpperCAmelCase_    :      Tuple            =						n_head
							if d_model % n_head != 0:
												raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0"   )
							if "d_head" in kwargs:
												if kwargs["d_head"] != d_model // n_head:
																	raise ValueError(
																	    f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"   )
							UpperCAmelCase_    :      Dict            =						d_model // n_head
							UpperCAmelCase_    :      int            =						ff_activation
							UpperCAmelCase_    :      Tuple            =						d_inner
							UpperCAmelCase_    :      Any            =						untie_r
							UpperCAmelCase_    :      Optional[int]            =						attn_type

							UpperCAmelCase_    :      Optional[int]            =						initializer_range
							UpperCAmelCase_    :      Union[str, Any]            =						layer_norm_eps

							UpperCAmelCase_    :      int            =						dropout
							UpperCAmelCase_    :      Optional[int]            =						mem_len
							UpperCAmelCase_    :      str            =						reuse_len
							UpperCAmelCase_    :      List[Any]            =						bi_data
							UpperCAmelCase_    :      Tuple            =						clamp_len
							UpperCAmelCase_    :      Dict            =						same_length

							UpperCAmelCase_    :      int            =						summary_type
							UpperCAmelCase_    :      Optional[Any]            =						summary_use_proj
							UpperCAmelCase_    :      List[str]            =						summary_activation
							UpperCAmelCase_    :      Dict            =						summary_last_dropout
							UpperCAmelCase_    :      str            =						start_n_top
							UpperCAmelCase_    :      str            =						end_n_top

							UpperCAmelCase_    :      Any            =						bos_token_id
							UpperCAmelCase_    :      Tuple            =						pad_token_id
							UpperCAmelCase_    :      str            =						eos_token_id

							if "use_cache" in kwargs:
												warnings.warn(
												    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
												    ' instead.'				,		_UpperCamelCase				,		)
												UpperCAmelCase_    :      Any            =						kwargs['use_cache']

							UpperCAmelCase_    :      Union[str, Any]            =						use_mems_eval
							UpperCAmelCase_    :      Union[str, Any]            =						use_mems_train
							super().__init__(pad_token_id=_UpperCamelCase				,		bos_token_id=_UpperCamelCase				,		eos_token_id=_UpperCamelCase				,		**_UpperCamelCase   )



		@property
		def 					__UpperCAmelCase			(							self   )	->							Tuple:
							logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."   )
							return -1



		@max_position_embeddings.setter
		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[int]:
							# Message copied from Transformer-XL documentation
							raise NotImplementedError(
							    f"The model {self.model_type} is one of the few models that has no sequence length limit."   )









'''simple docstring'''
# 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 							A__    (       A__  ):
  A__		 =				(
      '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__		 =				'CIDAS/clipseg-rd64-refined'
  A__		 =				'image_segmenter'
  A__		 =				CLIPSegForImageSegmentation

  A__		 =				['image', 'text']
  A__		 =				['image']





  def __init__(		self      :	Any							,					*_a      :	Dict							,					**_a      :	str )	->    Any:





       '''simple docstring'''






       requires_backends(self							,					['vision'] )
       super().__init__(*_a							,					**_a )





  def 						A  (		self      :	int							,					_a      :	"Image"							,					_a      :	str )	->    Optional[Any]:





       '''simple docstring'''






       return self.pre_processor(text=[label]							,					images=[image]							,					padding=_a							,					return_tensors='pt' )





  def 						A  (		self      :	Dict							,					_a      :	Dict )	->    str:





       '''simple docstring'''






       with torch.no_grad():
            _SCREAMING_SNAKE_CASE							    =self.model(**_a ).logits
       return logits





  def 						A  (		self      :	Any							,					_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =outputs.cpu().detach().numpy()
       _SCREAMING_SNAKE_CASE							    =0
       _SCREAMING_SNAKE_CASE							    =1
       return Image.fromarray((array * 255).astype(np.uinta ) )
