






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


__UpperCAmelCase								=						logging.get_logger(__name__)

__UpperCAmelCase								=						{
    '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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



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



		def __init__(							self				,		_UpperCamelCase=1_0_0				,		_UpperCamelCase=5				,		_UpperCamelCase=1				,		_UpperCamelCase=1				,		_UpperCamelCase=2_4_9				,		_UpperCamelCase=6				,		_UpperCamelCase=1_7				,		_UpperCamelCase=2_5				,		_UpperCamelCase=4				,		_UpperCamelCase=4				,		_UpperCamelCase=1_2_8				,		_UpperCamelCase=0.1				,		_UpperCamelCase=0.1				,		_UpperCamelCase=0.1				,		_UpperCamelCase=0.00_06				,		_UpperCamelCase=5_1_2				,		_UpperCamelCase=0.02				,		_UpperCamelCase=1E-12				,		_UpperCamelCase=1				,		_UpperCamelCase=True				,		_UpperCamelCase=1				,		_UpperCamelCase=5_0_2_5_6				,		_UpperCamelCase=5_0_2_5_6				,		**_UpperCamelCase				,		)	->							int:
							UpperCAmelCase_    :      Dict            =						vocab_size
							UpperCAmelCase_    :      Dict            =						action_weight
							UpperCAmelCase_    :      str            =						reward_weight
							UpperCAmelCase_    :      Any            =						value_weight
							UpperCAmelCase_    :      Dict            =						max_position_embeddings
							UpperCAmelCase_    :      str            =						block_size
							UpperCAmelCase_    :      Any            =						action_dim
							UpperCAmelCase_    :      int            =						observation_dim
							UpperCAmelCase_    :      Dict            =						transition_dim
							UpperCAmelCase_    :      Tuple            =						learning_rate
							UpperCAmelCase_    :      Dict            =						n_layer
							UpperCAmelCase_    :      Dict            =						n_head
							UpperCAmelCase_    :      Union[str, Any]            =						n_embd
							UpperCAmelCase_    :      Optional[int]            =						embd_pdrop
							UpperCAmelCase_    :      Optional[Any]            =						attn_pdrop
							UpperCAmelCase_    :      Any            =						resid_pdrop
							UpperCAmelCase_    :      int            =						initializer_range
							UpperCAmelCase_    :      Optional[int]            =						layer_norm_eps
							UpperCAmelCase_    :      Optional[int]            =						kaiming_initializer_range
							UpperCAmelCase_    :      Optional[int]            =						use_cache
							super().__init__(pad_token_id=_UpperCamelCase				,		bos_token_id=_UpperCamelCase				,		eos_token_id=_UpperCamelCase				,		**_UpperCamelCase   )














"""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.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
  from PIL import Image






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Dict						= """Salesforce/blip-image-captioning-base"""
    SCREAMING_SNAKE_CASE						:     int						= (
        """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
        """image to caption, and returns a text that contains the description in English."""
    )
    SCREAMING_SNAKE_CASE						:     Optional[int]						= """image_captioner"""
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= AutoModelForVisionaSeq

    SCREAMING_SNAKE_CASE						:     int						= ["""image"""]
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= ["""text"""]






    def __init__(       self     :							Optional[Any]       ,						*__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						**__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      requires_backends(self       ,						['vision']					)
      super().__init__(*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							"Image"					)     ->		Dict:
      return self.pre_processor(images=__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		str:
      return self.model.generate(**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		Optional[Any]:
      return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE       ,						skip_special_tokens=__SCREAMING_SNAKE_CASE					)[0].strip()




