



'''simple docstring'''





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


A_		:     List[Any]        =     logging.get_logger(__name__)

A_		:     str        =     {
    '''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 		_lowercase		(       UpperCAmelCase__	):
   _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			:       int							,   __lowerCAmelCase			:       Tuple=17							,   __lowerCAmelCase			:       List[Any]=4							,   __lowerCAmelCase			:       Tuple=128							,   __lowerCAmelCase			:       Optional[Any]=4096							,   __lowerCAmelCase			:       Any=True							,   __lowerCAmelCase			:       int=1							,   __lowerCAmelCase			:       str=1024							,   __lowerCAmelCase			:       Optional[Any]=3							,   __lowerCAmelCase			:       Tuple=1							,   __lowerCAmelCase			:       Any=None							,   __lowerCAmelCase			:       Optional[Any]="relu"							,   __lowerCAmelCase			:       List[Any]=0.1							,   __lowerCAmelCase			:       Dict=0.1							,   __lowerCAmelCase			:       List[Any]=0.1							,   __lowerCAmelCase			:       int=1E-5							,   __lowerCAmelCase			:       Union[str, Any]=0.0_2							,   __lowerCAmelCase			:       List[Any]=True							,   __lowerCAmelCase			:       Any=True							,   __lowerCAmelCase			:       Optional[int]=5_0256							,   __lowerCAmelCase			:       str=5_0256							,   __lowerCAmelCase			:       Optional[int]=False							,   __lowerCAmelCase			:       Tuple=False							,   **__lowerCAmelCase			:       str							,   )       ->				List[str]:


    """simple docstring"""






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

    a						=		bos_token_id
    a						=		eos_token_id

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






import copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt



class 		_lowercase		:

   def __init__(      self			:       List[str]							)       ->				List[str]:


    """simple docstring"""






    a						=		""
    a						=		""
    a						=		[]
    a						=		0
    a						=		256
    a						=		0
    a						=		0
    a						=		0
    a						=		0

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       Any							)       ->				int:


    """simple docstring"""






    a						=		cva.imread(__lowerCAmelCase							,   0							)
    a						=		copy.deepcopy(self.img							)
    a		,  a		,  a						=		plt.hist(self.img.ravel()							,   256							,   [0, 256]							,   label="x"							)
    a						=		np.sum(__lowerCAmelCase							)
    for i in range(len(__lowerCAmelCase							)							):
     a						=		x[i] / self.k
     self.sk += prk
     a						=		(self.L - 1) * self.sk
     if self.rem != 0:
      a						=		int(last % last							)
     a						=		int(last + 1 if self.rem >= 0.5 else last							)
     self.last_list.append(__lowerCAmelCase							)
     a						=		int(np.ma.count(self.img							) / self.img[1].size							)
     a						=		self.img[1].size
    for i in range(self.number_of_cols							):
     for j in range(self.number_of_rows							):
      a						=		self.img[j][i]
      if num != self.last_list[num]:
       a						=		self.last_list[num]
    cva.imwrite("output_data/output.jpg"							,   self.img							)

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    plt.hist(self.img.ravel()							,   256							,   [0, 256]							)




   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    cva.imshow("Output-Image"							,   self.img							)
    cva.imshow("Input-Image"							,   self.original_image							)
    cva.waitKey(5000							)
    cva.destroyAllWindows()


if __name__ == "__main__":
     A_		:     List[Any]        =     os.path.join(os.path.basename(__file__), '''image_data/input.jpg''')
     A_		:     int        =     ConstantStretch()
     stretcher.stretch(file_path)
     stretcher.plot_histogram()
     stretcher.show_image()

