




"""simple docstring"""






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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """nllb-moe"""
			lowerCamelCase__   =    ["""past_key_values"""]
			lowerCamelCase__   =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
			def __init__(						self						,   lowercase=128112						,   lowercase=1024						,   lowercase=12						,   lowercase=4096						,   lowercase=16						,   lowercase=12						,   lowercase=4096						,   lowercase=16						,   lowercase=0.05						,   lowercase=0.05						,   lowercase=True						,   lowercase=True						,   lowercase="relu"						,   lowercase=1024						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.0						,   lowercase=0.02						,   lowercase=2						,   lowercase=True						,   lowercase=False						,   lowercase="float32"						,   lowercase=False						,   lowercase=128						,   lowercase=64						,   lowercase=4						,   lowercase=4						,   lowercase=0.0_01						,   lowercase=0.0_01						,   lowercase="all"						,   lowercase=False						,   lowercase=False						,   lowercase=1.0						,   lowercase=0.2						,   lowercase=1						,   lowercase=0						,   lowercase=2						,   lowercase=False						,   **lowercase						,   ):
							_lowerCamelCase  :    Union[str, Any]					=					vocab_size
							_lowerCamelCase  :    Optional[int]					=					max_position_embeddings
							_lowerCamelCase  :    Tuple					=					d_model
							_lowerCamelCase  :    str					=					encoder_ffn_dim
							_lowerCamelCase  :    Union[str, Any]					=					encoder_layers
							_lowerCamelCase  :    List[Any]					=					encoder_attention_heads
							_lowerCamelCase  :    Dict					=					decoder_ffn_dim
							_lowerCamelCase  :    List[str]					=					decoder_layers
							_lowerCamelCase  :    Any					=					decoder_attention_heads
							_lowerCamelCase  :    Optional[Any]					=					dropout
							_lowerCamelCase  :    Union[str, Any]					=					attention_dropout
							_lowerCamelCase  :    List[str]					=					activation_dropout
							_lowerCamelCase  :    int					=					activation_function
							_lowerCamelCase  :    Dict					=					init_std
							_lowerCamelCase  :    List[str]					=					encoder_layerdrop
							_lowerCamelCase  :    Optional[int]					=					decoder_layerdrop
							_lowerCamelCase  :    Any					=					use_cache
							_lowerCamelCase  :    Union[str, Any]					=					encoder_layers
							_lowerCamelCase  :    str					=					scale_embedding  # scale factor will be sqrt(d_model) if True
							_lowerCamelCase  :    Any					=					router_z_loss_coef
							_lowerCamelCase  :    Union[str, Any]					=					router_aux_loss_coef
							_lowerCamelCase  :    Optional[int]					=					decoder_sparse_step
							_lowerCamelCase  :    str					=					encoder_sparse_step
							_lowerCamelCase  :    str					=					num_experts
							_lowerCamelCase  :    Union[str, Any]					=					expert_capacity
							_lowerCamelCase  :    int					=					router_bias
							if router_dtype not in ["float32", "float16", "bfloat16"]:
											raise ValueError(F'''`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}'''						)
							_lowerCamelCase  :    Any					=					router_dtype

							_lowerCamelCase  :    Optional[Any]					=					router_ignore_padding_tokens
							_lowerCamelCase  :    Union[str, Any]					=					batch_prioritized_routing
							_lowerCamelCase  :    Optional[Any]					=					second_expert_policy
							_lowerCamelCase  :    int					=					normalize_router_prob_before_dropping
							_lowerCamelCase  :    Any					=					moe_eval_capacity_token_fraction
							_lowerCamelCase  :    Dict					=					moe_token_dropout
							_lowerCamelCase  :    Union[str, Any]					=					output_router_logits
							super().__init__(
							    pad_token_id=lowercase						,   bos_token_id=lowercase						,   eos_token_id=lowercase						,   is_encoder_decoder=lowercase						,   decoder_start_token_id=lowercase						,   **lowercase						,   )





"""simple docstring"""






import copy
import os

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






class lowerCAmelCase__			:






			'''simple docstring'''



			def __init__(						self						):
							_lowerCamelCase  :    Tuple					=					''
							_lowerCamelCase  :    Any					=					''
							_lowerCamelCase  :    List[Any]					=					[]
							_lowerCamelCase  :    Tuple					=					0
							_lowerCamelCase  :    Dict					=					256
							_lowerCamelCase  :    Optional[Any]					=					0
							_lowerCamelCase  :    List[Any]					=					0
							_lowerCamelCase  :    int					=					0
							_lowerCamelCase  :    Dict					=					0
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Any					=					cva.imread(lowercase						,   0						)
							_lowerCamelCase  :    Optional[int]					=					copy.deepcopy(self.img						)
							_lowerCamelCase,				_lowerCamelCase,				_lowerCamelCase  :    List[str]					=					plt.hist(self.img.ravel()						,   256						,   [0, 256]						,   label='x'						)
							_lowerCamelCase  :    Optional[int]					=					np.sum(lowercase						)
							for i in range(len(lowercase						)						):
											_lowerCamelCase  :    List[Any]					=					x[i] / self.k
											self.sk += prk
											_lowerCamelCase  :    int					=					(self.L - 1) * self.sk
											if self.rem != 0:
															_lowerCamelCase  :    Union[str, Any]					=					int(last % last						)
											_lowerCamelCase  :    int					=					int(last + 1 if self.rem >= 0.5 else last						)
											self.last_list.append(lowercase						)
											_lowerCamelCase  :    List[str]					=					int(np.ma.count(self.img						) / self.img[1].size						)
											_lowerCamelCase  :    int					=					self.img[1].size
							for i in range(self.number_of_cols						):
											for j in range(self.number_of_rows						):
															_lowerCamelCase  :    Optional[Any]					=					self.img[j][i]
															if num != self.last_list[num]:
																			_lowerCamelCase  :    Any					=					self.last_list[num]
							cva.imwrite('output_data/output.jpg'						,   self.img						)
			def       A_					(						self						):
							plt.hist(self.img.ravel()						,   256						,   [0, 256]						)






			def       A_					(						self						):
							cva.imshow('Output-Image'						,   self.img						)
							cva.imshow('Input-Image'						,   self.original_image						)
							cva.waitKey(5000						)
							cva.destroyAllWindows()


if __name__ == "__main__":
			lowercase__  		=		os.path.join(os.path.basename(__file__), """image_data/input.jpg""")
			lowercase__  		=		ConstantStretch()
			stretcher.stretch(file_path)
			stretcher.plot_histogram()
			stretcher.show_image()