



from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
							import torch


UpperCAmelCase__       =	logging.get_logger(__name__)







class       __lowerCAmelCase   (      _lowerCAmelCase						):
			UpperCamelCase        =					['''pixel_values''']



			def __init__(						self       :  List[str]			,		A       :  int = True			,		A       :  Union[str, Any] = None			,		A       :  Optional[int] = PILImageResampling.BILINEAR			,		A       :  Dict = True			,		A       :  Optional[int] = None			,		A       :  List[Any] = True			,		A       :  int = 1 / 2_55			,		A       :  Dict = True			,		A       :  List[Any] = None			,		A       :  Union[str, Any] = None			,		**A       :  List[Any]			,		)       ->   None:



						"""simple docstring"""






						super().__init__(**SCREAMING_SNAKE_CASE_)
						_UpperCAmelCase			=       size if size is not None else {"""shortest_edge""": 2_56}
						_UpperCAmelCase			=       get_size_dict(SCREAMING_SNAKE_CASE_			,		default_to_square=SCREAMING_SNAKE_CASE_)
						_UpperCAmelCase			=       crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
						_UpperCAmelCase			=       get_size_dict(SCREAMING_SNAKE_CASE_			,		param_name='crop_size')
						_UpperCAmelCase			=       do_resize
						_UpperCAmelCase			=       size
						_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_STANDARD_MEAN
						_UpperCAmelCase			=       image_std if image_std is not None else IMAGENET_STANDARD_STD



			def    _lowerCamelCase	(						self       :  Any			,		A       :  Tuple			,		A       :  Tuple			,		A       :  List[str] = PILImageResampling.BICUBIC			,		A       :  List[str] = None			,		**A       :  Union[str, Any]			,		)       ->   np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase			=       get_size_dict(SCREAMING_SNAKE_CASE_			,		default_to_square=SCREAMING_SNAKE_CASE_)
						if "shortest_edge" not in size:
									raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
						_UpperCAmelCase			=       get_resize_output_image_size(SCREAMING_SNAKE_CASE_			,		size=size['shortest_edge']			,		default_to_square=SCREAMING_SNAKE_CASE_)
						return resize(SCREAMING_SNAKE_CASE_			,		size=SCREAMING_SNAKE_CASE_			,		resample=SCREAMING_SNAKE_CASE_			,		data_format=SCREAMING_SNAKE_CASE_			,		**SCREAMING_SNAKE_CASE_)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  List[Any]			,		A       :  Any			,		A       :  int = None			,		**A       :  Optional[int]			,		)       ->   np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase			=       get_size_dict(SCREAMING_SNAKE_CASE_)
						if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` parameter must contain the keys `height` and `width`. Got {size.keys()}")
						return center_crop(SCREAMING_SNAKE_CASE_			,		size=(size['height'], size['width'])			,		data_format=SCREAMING_SNAKE_CASE_			,		**SCREAMING_SNAKE_CASE_)



			def    _lowerCamelCase	(						self       :  int			,		A       :  Optional[Any]			,		A       :  Optional[Any]			,		A       :  Tuple = None			,		**A       :  Optional[Any])       ->   np.ndarray:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		A       :  Optional[int]			,		A       :  List[str]			,		A       :  List[str]			,		A       :  Optional[int] = None			,		**A       :  str			,		)       ->   np.ndarray:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  int			,		A       :  Optional[int]			,		A       :  Any = None			,		A       :  Optional[int] = None			,		A       :  Optional[int] = None			,		A       :  Optional[Any] = None			,		A       :  List[Any] = None			,		A       :  str = None			,		A       :  List[Any] = None			,		A       :  Optional[Any] = None			,		A       :  List[str] = None			,		A       :  Optional[Any] = None			,		A       :  Optional[Any] = None			,		A       :  List[str] = ChannelDimension.FIRST			,		**A       :  Union[str, Any]			,		)       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase			=       size if size is not None else self.size
						_UpperCAmelCase			=       get_size_dict(SCREAMING_SNAKE_CASE_			,		default_to_square=SCREAMING_SNAKE_CASE_)
						_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			=       crop_size if crop_size is not None else self.crop_size
						_UpperCAmelCase			=       get_size_dict(SCREAMING_SNAKE_CASE_			,		param_name='crop_size')
						_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			=       make_list_of_images(SCREAMING_SNAKE_CASE_)

						if not valid_images(SCREAMING_SNAKE_CASE_):
									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:
									raise ValueError('Size must be specified if do_resize is True.')

						if do_center_crop and crop_size is None:
									raise ValueError('Crop size 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(SCREAMING_SNAKE_CASE_) for image in images]

						if do_resize:
									_UpperCAmelCase			=       [self.resize(image=SCREAMING_SNAKE_CASE_			,		size=SCREAMING_SNAKE_CASE_			,		resample=SCREAMING_SNAKE_CASE_) for image in images]

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

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

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

						_UpperCAmelCase			=       [to_channel_dimension_format(SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_) for image in images]

						_UpperCAmelCase			=       {"""pixel_values""": images}
						return BatchFeature(data=SCREAMING_SNAKE_CASE_			,		tensor_type=SCREAMING_SNAKE_CASE_)



			def    _lowerCamelCase	(						self       :  Any			,		A       :  List[Any]			,		A       :  List[Any] = None)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(SCREAMING_SNAKE_CASE_) != len(SCREAMING_SNAKE_CASE_):
												raise ValueError(
												    'Make sure that you pass in as many target sizes as the batch dimension of the logits')

									if is_torch_tensor(SCREAMING_SNAKE_CASE_):
												_UpperCAmelCase			=       target_sizes.numpy()

									_UpperCAmelCase			=       []

									for idx in range(len(SCREAMING_SNAKE_CASE_)):
												_UpperCAmelCase			=       torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0)			,		size=target_sizes[idx]			,		mode='bilinear'			,		align_corners=SCREAMING_SNAKE_CASE_)
												_UpperCAmelCase			=       resized_logits[0].argmax(dim=0)
												semantic_segmentation.append(SCREAMING_SNAKE_CASE_)
						else:
									_UpperCAmelCase			=       logits.argmax(dim=1)
									_UpperCAmelCase			=       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

						return semantic_segmentation





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


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "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   (      A						):
			UpperCamelCase        =					'''decision_transformer'''
			UpperCamelCase        =					['''past_key_values''']
			UpperCamelCase        =					{
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self       :  Any			,		A       :  Optional[int]=17			,		A       :  List[str]=4			,		A       :  int=1_28			,		A       :  Union[str, Any]=40_96			,		A       :  Any=True			,		A       :  Any=1			,		A       :  List[Any]=10_24			,		A       :  List[Any]=3			,		A       :  Tuple=1			,		A       :  Any=None			,		A       :  Optional[int]="relu"			,		A       :  Union[str, Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=1E-5			,		A       :  List[Any]=0.0_2			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  str=5_02_56			,		A       :  Union[str, Any]=5_02_56			,		A       :  List[Any]=False			,		A       :  Optional[int]=False			,		**A       :  int			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       state_dim
						_UpperCAmelCase			=       act_dim
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       max_ep_len
						_UpperCAmelCase			=       action_tanh
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       n_positions
						_UpperCAmelCase			=       n_layer
						_UpperCAmelCase			=       n_head
						_UpperCAmelCase			=       n_inner
						_UpperCAmelCase			=       activation_function
						_UpperCAmelCase			=       resid_pdrop
						_UpperCAmelCase			=       embd_pdrop
						_UpperCAmelCase			=       attn_pdrop
						_UpperCAmelCase			=       layer_norm_epsilon
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scale_attn_weights
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       scale_attn_by_inverse_layer_idx
						_UpperCAmelCase			=       reorder_and_upcast_attn

						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       eos_token_id

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