"""simple docstring"""

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


UpperCAmelCase       =  logging.get_logger(__name__)







class      UpperCAmelCase_       (						_UpperCAmelCase):
							snake_case__        =      ['''pixel_values''']






							def __init__(		self :      Tuple     ,						__UpperCamelCase :      Union[str, Any] = True     ,						__UpperCamelCase :      Optional[Any] = None     ,						__UpperCamelCase :      Dict = 0.9     ,						__UpperCamelCase :      int = PILImageResampling.BICUBIC     ,						__UpperCamelCase :      List[Any] = True     ,						__UpperCamelCase :      Union[str, Any] = None     ,						__UpperCamelCase :      str = 1 / 255     ,						__UpperCamelCase :      str = True     ,						__UpperCamelCase :      Tuple = True     ,						__UpperCamelCase :      List[str] = None     ,						__UpperCamelCase :      Tuple = None     ,						**__UpperCamelCase :      Optional[int]     ,						)       ->       None:
												super().__init__(**A_  )
												_UpperCamelCase      						=	size if size is not None else {'''shortest_edge''': 224}
												_UpperCamelCase      						=	get_size_dict(A_     ,						default_to_square=A_  )
												_UpperCamelCase      						=	crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
												_UpperCamelCase      						=	get_size_dict(A_     ,						param_name='''crop_size'''  )

												_UpperCamelCase      						=	do_resize
												_UpperCamelCase      						=	size
												_UpperCamelCase      						=	crop_pct
												_UpperCamelCase      						=	resample
												_UpperCamelCase      						=	do_center_crop
												_UpperCamelCase      						=	crop_size
												_UpperCamelCase      						=	do_rescale
												_UpperCamelCase      						=	rescale_factor
												_UpperCamelCase      						=	do_normalize
												_UpperCamelCase      						=	image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
												_UpperCamelCase      						=	image_std if image_std is not None else IMAGENET_DEFAULT_STD






							def       _UpperCamelCase			(		self :      List[str]     ,						__UpperCamelCase :      List[Any]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      List[Any] = None     ,						__UpperCamelCase :      List[Any] = PILImageResampling.BICUBIC     ,						__UpperCamelCase :      int = None     ,						**__UpperCamelCase :      Union[str, Any]     ,						)       ->       np.ndarray:
												_UpperCamelCase      						=	get_size_dict(A_     ,						default_to_square=A_  )
												if "shortest_edge" not in size and ("height" not in size or "width" not in size):
																	raise ValueError(F'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''  )
												if crop_pct is not None:
																	if "shortest_edge" in size:
																						_UpperCamelCase      						=	int(size['''shortest_edge'''] / crop_pct  )
																	elif "height" in size and "width" in size:
																						if size["height"] == size["width"]:
																											_UpperCamelCase      						=	int(size['''height'''] / crop_pct  )
																						else:
																											_UpperCamelCase      						=	(int(size['''height'''] / crop_pct  ), int(size['''width'''] / crop_pct  ))
																	else:
																						raise ValueError('''Invalid size for resize: {}'''.format(A_  )  )

																	_UpperCamelCase      						=	get_resize_output_image_size(A_     ,						size=A_     ,						default_to_square=A_  )
												else:
																	if "shortest_edge" in size:
																						_UpperCamelCase      						=	get_resize_output_image_size(A_     ,						size=size['''shortest_edge''']     ,						default_to_square=A_  )
																	elif "height" in size and "width" in size:
																						_UpperCamelCase      						=	(size['''height'''], size['''width'''])
																	else:
																						raise ValueError('''Invalid size for resize: {}'''.format(A_  )  )

												return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_  )






							def       _UpperCamelCase			(		self :      Union[str, Any]     ,						__UpperCamelCase :      Dict     ,						__UpperCamelCase :      Optional[Any]     ,						__UpperCamelCase :      Any = None     ,						**__UpperCamelCase :      Tuple     ,						)       ->       np.ndarray:
												_UpperCamelCase      						=	get_size_dict(A_  )
												if "height" not in size or "width" not in size:
																	raise ValueError(F'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''  )
												return center_crop(A_     ,						size=(size['''height'''], size['''width'''])     ,						data_format=A_     ,						**A_  )






							def       _UpperCamelCase			(		self :      int     ,						__UpperCamelCase :      List[str]     ,						__UpperCamelCase :      Union[str, Any]     ,						__UpperCamelCase :      str = None     ,						**__UpperCamelCase :      Dict     ,						)       ->       Union[str, Any]:
												return rescale(A_     ,						scale=A_     ,						data_format=A_     ,						**A_  )






							def       _UpperCamelCase			(		self :      int     ,						__UpperCamelCase :      Union[str, Any]     ,						__UpperCamelCase :      Any     ,						__UpperCamelCase :      Tuple     ,						__UpperCamelCase :      Tuple = None     ,						**__UpperCamelCase :      str     ,						)       ->       np.ndarray:
												return normalize(A_     ,						mean=A_     ,						std=A_     ,						data_format=A_     ,						**A_  )






							def       _UpperCamelCase			(		self :      Optional[int]     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      str = None     ,						__UpperCamelCase :      Optional[int] = None     ,						__UpperCamelCase :      Union[str, Any] = None     ,						__UpperCamelCase :      str = None     ,						__UpperCamelCase :      List[Any] = None     ,						__UpperCamelCase :      Union[str, Any] = None     ,						__UpperCamelCase :      Optional[Any] = None     ,						__UpperCamelCase :      str = None     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      Union[str, Any] = None     ,						__UpperCamelCase :      int = None     ,						__UpperCamelCase :      Any = None     ,						__UpperCamelCase :      Optional[Any] = ChannelDimension.FIRST     ,						**__UpperCamelCase :      int     ,						)       ->       PIL.Image.Image:
												_UpperCamelCase      						=	do_resize if do_resize is not None else self.do_resize
												_UpperCamelCase      						=	crop_pct if crop_pct is not None else self.crop_pct
												_UpperCamelCase      						=	resample if resample is not None else self.resample
												_UpperCamelCase      						=	do_center_crop if do_center_crop is not None else self.do_center_crop
												_UpperCamelCase      						=	do_rescale if do_rescale is not None else self.do_rescale
												_UpperCamelCase      						=	rescale_factor if rescale_factor is not None else self.rescale_factor
												_UpperCamelCase      						=	do_normalize if do_normalize is not None else self.do_normalize
												_UpperCamelCase      						=	image_mean if image_mean is not None else self.image_mean
												_UpperCamelCase      						=	image_std if image_std is not None else self.image_std

												_UpperCamelCase      						=	size if size is not None else self.size
												_UpperCamelCase      						=	get_size_dict(A_     ,						default_to_square=A_  )
												_UpperCamelCase      						=	crop_size if crop_size is not None else self.crop_size
												_UpperCamelCase      						=	get_size_dict(A_     ,						param_name='''crop_size'''  )

												_UpperCamelCase      						=	make_list_of_images(A_  )

												if not valid_images(A_  ):
																	raise ValueError(
																	    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																	    '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

												if do_resize and size is None or resample is None:
																	raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

												if do_center_crop and crop_pct is None:
																	raise ValueError('''Crop_pct must be specified if do_center_crop is True.'''  )

												if do_rescale and rescale_factor is None:
																	raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

												if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

												# All transformations expect numpy arrays.
												_UpperCamelCase      						=	[to_numpy_array(A_  ) for image in images]

												if do_resize:
																	_UpperCamelCase      						=	[self.resize(image=A_     ,						size=A_     ,						crop_pct=A_     ,						resample=A_  ) for image in images]

												if do_center_crop:
																	_UpperCamelCase      						=	[self.center_crop(image=A_     ,						size=A_  ) for image in images]

												if do_rescale:
																	_UpperCamelCase      						=	[self.rescale(image=A_     ,						scale=A_  ) for image in images]

												if do_normalize:
																	_UpperCamelCase      						=	[self.normalize(image=A_     ,						mean=A_     ,						std=A_  ) for image in images]

												_UpperCamelCase      						=	[to_channel_dimension_format(A_     ,						A_  ) for image in images]

												_UpperCamelCase      						=	{'''pixel_values''': images}
												return BatchFeature(data=A_     ,						tensor_type=A_  )



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   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """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_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


