





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


UpperCAmelCase						:   str			  =     logging.get_logger(__name__)

UpperCAmelCase						:   List[Any]			  =     {
    "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 						_A(					_a   ):


				"""simple docstring"""


				UpperCamelCase    :  List[str]					    =	"""trajectory_transformer"""
				UpperCamelCase    :  Optional[Any]					    =	["""past_key_values"""]
				UpperCamelCase    :  Any					    =	{
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}



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








from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE       :      List[str]												= logging.get_logger(__name__)






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a   ):
       def __init__(					self,      *lowerCamelCase,      **lowerCamelCase)       ->   int:




           """simple docstring"""

           super().__init__(*lowerCamelCase,      **lowerCamelCase)
           requires_backends(self,      'vision')
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == 'tf'
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)
       def 			UpperCamelCase     (					self,      lowerCamelCase=None)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		{}
           if top_k is not None:
               _lowercase       :							List[str]      		=		top_k
           return {}, {}, postprocess_params
       def __call__(					self,      lowerCamelCase,      **lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           return super().__call__(lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		load_image(lowerCamelCase)
           _lowercase       :							List[str]      		=		self.image_processor(images=lowerCamelCase,      return_tensors=self.framework)
           return model_inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.model(**lowerCamelCase)
           return model_outputs




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=5)       ->   Dict:




           """simple docstring"""

           if top_k > self.model.config.num_labels:
               _lowercase       :							List[Any]      		=		self.model.config.num_labels

           if self.framework == "pt":
               _lowercase       :							int      		=		model_outputs.logits.softmax(-1)[0]
               _lowercase		,				_lowercase       :							Union[str, Any]      		=		probs.topk(lowerCamelCase)
           elif self.framework == "tf":
               _lowercase       :							int      		=		stable_softmax(model_outputs.logits,      axis=-1)[0]
               _lowercase       :							List[Any]      		=		tf.math.top_k(lowerCamelCase,      k=lowerCamelCase)
               _lowercase		,				_lowercase       :							Any      		=		topk.values.numpy(), topk.indices.numpy()
           else:
               raise ValueError(F'''Unsupported framework: {self.framework}''')

           _lowercase       :							str      		=		scores.tolist()
           _lowercase       :							str      		=		ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase,      lowerCamelCase)]


