

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


a		=logging.get_logger(__name__)

a		={
    """alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Union[str, Any]						      =      '''mgp-str'''
							def __init__(		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			List[str]=[3_2, 1_2_8]   ,SCREAMING_SNAKE_CASE__		:			Any=4   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=3   ,SCREAMING_SNAKE_CASE__		:			Tuple=2_7   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=3_8   ,SCREAMING_SNAKE_CASE__		:			Tuple=5_0_2_5_7   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=3_0_5_2_2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=7_6_8   ,SCREAMING_SNAKE_CASE__		:			Dict=1_2   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=1_2   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=4.0   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=True   ,SCREAMING_SNAKE_CASE__		:			List[Any]=False   ,SCREAMING_SNAKE_CASE__		:			int=1E-5   ,SCREAMING_SNAKE_CASE__		:			List[str]=0.0   ,SCREAMING_SNAKE_CASE__		:			Union[str, Any]=0.0   ,SCREAMING_SNAKE_CASE__		:			Any=0.0   ,SCREAMING_SNAKE_CASE__		:			int=False   ,SCREAMING_SNAKE_CASE__		:			Optional[Any]=0.02   ,**SCREAMING_SNAKE_CASE__		:			int   ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Tuple					       = image_size
								__lowerCamelCase				:				Optional[Any]					       = patch_size
								__lowerCamelCase				:				Any					       = num_channels
								__lowerCamelCase				:				Tuple					       = max_token_length
								__lowerCamelCase				:				Tuple					       = num_character_labels
								__lowerCamelCase				:				List[str]					       = num_bpe_labels
								__lowerCamelCase				:				int					       = num_wordpiece_labels
								__lowerCamelCase				:				Any					       = hidden_size
								__lowerCamelCase				:				int					       = num_hidden_layers
								__lowerCamelCase				:				int					       = num_attention_heads
								__lowerCamelCase				:				List[str]					       = mlp_ratio
								__lowerCamelCase				:				List[str]					       = distilled
								__lowerCamelCase				:				Optional[Any]					       = layer_norm_eps
								__lowerCamelCase				:				Any					       = drop_rate
								__lowerCamelCase				:				Tuple					       = qkv_bias
								__lowerCamelCase				:				Any					       = attn_drop_rate
								__lowerCamelCase				:				int					       = drop_path_rate
								__lowerCamelCase				:				str					       = output_aa_attentions
								__lowerCamelCase				:				int					       = initializer_range








"""simple docstring"""





from datetime import datetime

import matplotlib.pyplot as plt
import torch


def    _snake_case  (  _snake_case :	int	):
      for param in module.parameters():
            lowerCAmelCase    :		Optional[int]				=      False


def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
      if torch.backends.mps.is_available() and torch.backends.mps.is_built():
            lowerCAmelCase    :		Any				=      '''mps'''
      if device == "mps":
            print(
                '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
                ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
                ''' with generations.'''	)
      return device


def    _snake_case  (  _snake_case :	Dict	):
      lowerCAmelCase    :		Optional[int]				=      plt.imshow(_snake_case	)
      fig.axes.get_xaxis().set_visible(_snake_case	)
      fig.axes.get_yaxis().set_visible(_snake_case	)
      plt.show()






def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datetime.now()
      lowerCAmelCase    :		Union[str, Any]				=      current_time.strftime('''%H:%M:%S'''	)
      return timestamp


