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#
# 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 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			(
       '''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.'''
   )
   _UpperCAmelCase     	=			'''CIDAS/clipseg-rd64-refined'''
   _UpperCAmelCase     	=			'''image_segmenter'''
   _UpperCAmelCase     	=			CLIPSegForImageSegmentation

   _UpperCAmelCase     	=			['''image''', '''text''']
   _UpperCAmelCase     	=			['''image''']

   def __init__(      self			:       Optional[int]							,   *__lowerCAmelCase			:       Optional[int]							,   **__lowerCAmelCase			:       Tuple							)       ->				Dict:


    """simple docstring"""






    requires_backends(self							,   ["vision"]							)
    super().__init__(*__A							,   **__A							)

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       "Image"							,   __lowerCAmelCase			:       str							)       ->				List[Any]:


    """simple docstring"""






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

   def 		A							(      self			:       int							,   __lowerCAmelCase			:       Optional[int]							)       ->				str:


    """simple docstring"""






    with torch.no_grad():
     a						=		self.model(**__A							).logits
    return logits




   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       Dict							)       ->				Union[str, Any]:


    """simple docstring"""






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






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


A_		:     str        =     logging.get_logger(__name__)

A_		:     List[Any]        =     {
    '''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
    '''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
    '''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
    '''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''rwkv'''
   _UpperCAmelCase     	=			{'''max_position_embeddings''': '''context_length'''}

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]=5_0277							,   __lowerCAmelCase			:       str=1024							,   __lowerCAmelCase			:       Union[str, Any]=4096							,   __lowerCAmelCase			:       Optional[int]=32							,   __lowerCAmelCase			:       Optional[int]=None							,   __lowerCAmelCase			:       Optional[Any]=None							,   __lowerCAmelCase			:       List[Any]=1E-5							,   __lowerCAmelCase			:       Union[str, Any]=0							,   __lowerCAmelCase			:       Optional[Any]=0							,   __lowerCAmelCase			:       Dict=6							,   __lowerCAmelCase			:       int=False							,   __lowerCAmelCase			:       Tuple=True							,   **__lowerCAmelCase			:       List[str]							,   )       ->				List[Any]:


    """simple docstring"""






    a						=		vocab_size
    a						=		context_length
    a						=		hidden_size
    a						=		num_hidden_layers
    a						=		attention_hidden_size if attention_hidden_size is not None else hidden_size
    a						=		intermediate_size if intermediate_size is not None else 4 * hidden_size
    a						=		layer_norm_epsilon
    a						=		rescale_every
    a						=		use_cache

    a						=		bos_token_id
    a						=		eos_token_id

    super().__init__(
        tie_word_embeddings=__lowerCAmelCase							,   bos_token_id=__lowerCAmelCase							,   eos_token_id=__lowerCAmelCase							,   **__lowerCAmelCase							)

