
"""simple docstring"""


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


A_    							=					logging.get_logger(__name__)

A_    							=					{
    """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  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      Optional[int]          =    'decision_transformer'
				a__:      str          =    ['past_key_values']
				a__:      Dict          =    {
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}




				def __init__(				self							,						UpperCAmelCase=17							,						UpperCAmelCase=4							,						UpperCAmelCase=128							,						UpperCAmelCase=4096							,						UpperCAmelCase=True							,						UpperCAmelCase=1							,						UpperCAmelCase=1024							,						UpperCAmelCase=3							,						UpperCAmelCase=1							,						UpperCAmelCase=None							,						UpperCAmelCase="relu"							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=0.1							,						UpperCAmelCase=1e-5							,						UpperCAmelCase=0.0_2							,						UpperCAmelCase=True							,						UpperCAmelCase=True							,						UpperCAmelCase=5_0256							,						UpperCAmelCase=5_0256							,						UpperCAmelCase=False							,						UpperCAmelCase=False							,						**UpperCAmelCase							,						):
							lowerCamelCase_					=  state_dim
							lowerCamelCase_					=  act_dim
							lowerCamelCase_					=  hidden_size
							lowerCamelCase_					=  max_ep_len
							lowerCamelCase_					=  action_tanh
							lowerCamelCase_					=  vocab_size
							lowerCamelCase_					=  n_positions
							lowerCamelCase_					=  n_layer
							lowerCamelCase_					=  n_head
							lowerCamelCase_					=  n_inner
							lowerCamelCase_					=  activation_function
							lowerCamelCase_					=  resid_pdrop
							lowerCamelCase_					=  embd_pdrop
							lowerCamelCase_					=  attn_pdrop
							lowerCamelCase_					=  layer_norm_epsilon
							lowerCamelCase_					=  initializer_range
							lowerCamelCase_					=  scale_attn_weights
							lowerCamelCase_					=  use_cache
							lowerCamelCase_					=  scale_attn_by_inverse_layer_idx
							lowerCamelCase_					=  reorder_and_upcast_attn

							lowerCamelCase_					=  bos_token_id
							lowerCamelCase_					=  eos_token_id

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





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_STANDARD_MEAN,
    IMAGENET_STANDARD_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


lowerCamelCase__						=				logging.get_logger(__name__)







class 							lowerCAmelCase__   ( __lowercase						):
						UpperCamelCase_				:			Dict						=		["pixel_values"]

						def __init__(      self							,				a = True							,				a = None							,				a = None							,				a = PILImageResampling.BILINEAR							,				a = True							,				a = 1 / 2_55							,				a = True							,				a = None							,				a = None							,				**a							,				)       ->   None:


								'''simple docstring'''

								super().__init__(**a					)
								_UpperCamelCase         =						size if size is not None else {"""shortest_edge""": 3_84}
								_UpperCamelCase         =						get_size_dict(a							,				default_to_square=a					)

								_UpperCamelCase         =						do_resize
								_UpperCamelCase         =						size
								# Default value set here for backwards compatibility where the value in config is None
								_UpperCamelCase         =						crop_pct if crop_pct is not None else 2_24 / 2_56
								_UpperCamelCase         =						resample
								_UpperCamelCase         =						do_rescale
								_UpperCamelCase         =						rescale_factor
								_UpperCamelCase         =						do_normalize
								_UpperCamelCase         =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								_UpperCamelCase         =						image_std if image_std is not None else IMAGENET_STANDARD_STD

						def 			A_					(      self							,				a							,				a							,				a							,				a = PILImageResampling.BICUBIC							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								_UpperCamelCase         =						get_size_dict(a							,				default_to_square=a					)
								if "shortest_edge" not in size:
										raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'					)
								_UpperCamelCase         =						size["""shortest_edge"""]

								if shortest_edge < 3_84:
										# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
										_UpperCamelCase         =						int(shortest_edge / crop_pct					)
										_UpperCamelCase         =						get_resize_output_image_size(a							,				size=a							,				default_to_square=a					)
										_UpperCamelCase         =						resize(image=a							,				size=a							,				resample=a							,				data_format=a							,				**a					)
										# then crop to (shortest_edge, shortest_edge)
										return center_crop(image=a							,				size=(shortest_edge, shortest_edge)							,				data_format=a							,				**a					)
								else:
										# warping (no cropping) when evaluated at 384 or larger
										return resize(
										    a							,				size=(shortest_edge, shortest_edge)							,				resample=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a = None							,				**a							,				)       ->   Optional[int]:


								'''simple docstring'''

								return rescale(a							,				scale=a							,				data_format=a							,				**a					)

						def 			A_					(      self							,				a							,				a							,				a							,				a = None							,				**a							,				)       ->   np.ndarray:


								'''simple docstring'''

								return normalize(a							,				mean=a							,				std=a							,				data_format=a							,				**a					)







						def 			A_					(      self							,				a							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = None							,				a = ChannelDimension.FIRST							,				**a							,				)       ->   PIL.Image.Image:


								'''simple docstring'''

								_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_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         =						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_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
										raise ValueError("""crop_pct must be specified if size < 384."""					)

								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_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					)



