"""simple docstring"""






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


A	     =					logging.get_logger(__name__)

A	     =					{
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}




class 			__lowercase     (     SCREAMING_SNAKE_CASE_							):







					'''simple docstring'''




					__lowerCAmelCase					=					'''trajectory_transformer'''
					__lowerCAmelCase					=					['''past_key_values''']
					__lowerCAmelCase					=					{
					    '''hidden_size''': '''n_embd''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}







					def __init__(  self		,     _UpperCAmelCase=100		,     _UpperCAmelCase=5		,     _UpperCAmelCase=1		,     _UpperCAmelCase=1		,     _UpperCAmelCase=249		,     _UpperCAmelCase=6		,     _UpperCAmelCase=17		,     _UpperCAmelCase=25		,     _UpperCAmelCase=4		,     _UpperCAmelCase=4		,     _UpperCAmelCase=128		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=0.0_0_0_6		,     _UpperCAmelCase=512		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=1e-1_2		,     _UpperCAmelCase=1		,     _UpperCAmelCase=True		,     _UpperCAmelCase=1		,     _UpperCAmelCase=50256		,     _UpperCAmelCase=50256		,     **_UpperCAmelCase		,     ):
									__a	:  List[Any]         =					vocab_size
									__a	:  Any         =					action_weight
									__a	:  Optional[int]         =					reward_weight
									__a	:  Union[str, Any]         =					value_weight
									__a	:  List[str]         =					max_position_embeddings
									__a	:  Tuple         =					block_size
									__a	:  List[Any]         =					action_dim
									__a	:  List[Any]         =					observation_dim
									__a	:  Union[str, Any]         =					transition_dim
									__a	:  Tuple         =					learning_rate
									__a	:  int         =					n_layer
									__a	:  Any         =					n_head
									__a	:  Tuple         =					n_embd
									__a	:  Optional[Any]         =					embd_pdrop
									__a	:  Union[str, Any]         =					attn_pdrop
									__a	:  Any         =					resid_pdrop
									__a	:  Optional[Any]         =					initializer_range
									__a	:  List[Any]         =					layer_norm_eps
									__a	:  Union[str, Any]         =					kaiming_initializer_range
									__a	:  List[Any]         =					use_cache
									super().__init__(pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     **snake_case__       )






"""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 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					(
					    '''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.'''
					)
					__lowerCAmelCase					=					'''CIDAS/clipseg-rd64-refined'''
					__lowerCAmelCase					=					'''image_segmenter'''
					__lowerCAmelCase					=					CLIPSegForImageSegmentation

					__lowerCAmelCase					=					['''image''', '''text''']
					__lowerCAmelCase					=					['''image''']







					def __init__(  self		,     *_UpperCAmelCase		,     **_UpperCAmelCase       ):
									requires_backends(self		,     ['''vision''']       )
									super().__init__(*_UpperCAmelCase		,     **_UpperCAmelCase       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):
									return self.pre_processor(text=[label]		,     images=[image]		,     padding=_UpperCAmelCase		,     return_tensors='''pt'''       )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									with torch.no_grad():
													__a	:  List[str]         =					self.model(**_UpperCAmelCase       ).logits
									return logits







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase       ):
									__a	:  str         =					outputs.cpu().detach().numpy()
									__a	:  int         =					0
									__a	:  Optional[int]         =					1
									return Image.fromarray((array * 255).astype(np.uinta       )       )