

# 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 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  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						:		Union[str, Any]     ,       *__lowerCamelCase						:		List[str]     ,       **__lowerCamelCase						:		Optional[int] )		->							Optional[int]:
       requires_backends(self     ,       ['''vision'''] )
       super().__init__(*__lowerCamelCase     ,       **__lowerCamelCase )






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






  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Any )		->							Optional[Any]:
       with torch.no_grad():
            SCREAMING_SNAKE_CASE__   =   self.model(**__lowerCamelCase ).logits
       return logits







  def 				lowercase_		(							self						:		List[Any]     ,       __lowerCamelCase						:		Optional[int] )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   outputs.cpu().detach().numpy()
       SCREAMING_SNAKE_CASE__   =   0
       SCREAMING_SNAKE_CASE__   =   1
       return Image.fromarray((array * 255).astype(np.uinta ) )





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


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "decision_transformer"
  a									= ["past_key_values"]
  a									= {
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		Any=17     ,       __lowerCamelCase						:		Any=4     ,       __lowerCamelCase						:		List[Any]=128     ,       __lowerCamelCase						:		Union[str, Any]=4096     ,       __lowerCamelCase						:		Optional[Any]=True     ,       __lowerCamelCase						:		Any=1     ,       __lowerCamelCase						:		List[Any]=1024     ,       __lowerCamelCase						:		Any=3     ,       __lowerCamelCase						:		Dict=1     ,       __lowerCamelCase						:		List[Any]=None     ,       __lowerCamelCase						:		str="relu"     ,       __lowerCamelCase						:		Dict=0.1     ,       __lowerCamelCase						:		int=0.1     ,       __lowerCamelCase						:		List[str]=0.1     ,       __lowerCamelCase						:		str=1e-5     ,       __lowerCamelCase						:		Optional[Any]=0.02     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		Optional[int]=5_0256     ,       __lowerCamelCase						:		Tuple=5_0256     ,       __lowerCamelCase						:		int=False     ,       __lowerCamelCase						:		Optional[int]=False     ,       **__lowerCamelCase						:		Tuple     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   state_dim
       SCREAMING_SNAKE_CASE__   =   act_dim
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   max_ep_len
       SCREAMING_SNAKE_CASE__   =   action_tanh
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   n_positions
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   n_inner
       SCREAMING_SNAKE_CASE__   =   activation_function
       SCREAMING_SNAKE_CASE__   =   resid_pdrop
       SCREAMING_SNAKE_CASE__   =   embd_pdrop
       SCREAMING_SNAKE_CASE__   =   attn_pdrop
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   scale_attn_weights
       SCREAMING_SNAKE_CASE__   =   use_cache
       SCREAMING_SNAKE_CASE__   =   scale_attn_by_inverse_layer_idx
       SCREAMING_SNAKE_CASE__   =   reorder_and_upcast_attn

       SCREAMING_SNAKE_CASE__   =   bos_token_id
       SCREAMING_SNAKE_CASE__   =   eos_token_id

       super().__init__(bos_token_id=__lowerCamelCase     ,       eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )


