



"""simple docstring"""





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowerCAmelCase							:       Optional[int]          =       logging.get_logger(__name__)

__lowerCAmelCase							:       Tuple          =       {
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}



class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''gptj'''
		_lowerCamelCase      	=							{
		    '''max_position_embeddings''': '''n_positions''',
		    '''hidden_size''': '''n_embd''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}





		def __init__(       self				,     _lowercase=5_0_4_0_0				,     _lowercase=2_0_4_8				,     _lowercase=4_0_9_6				,     _lowercase=2_8				,     _lowercase=1_6				,     _lowercase=6_4				,     _lowercase=None				,     _lowercase="gelu_new"				,     _lowercase=0.0				,     _lowercase=0.0				,     _lowercase=0.0				,     _lowercase=1E-5				,     _lowercase=0.02				,     _lowercase=True				,     _lowercase=5_0_2_5_6				,     _lowercase=5_0_2_5_6				,     _lowercase=False				,     **_lowercase				,     )			->    Any:







								'''simple docstring'''

								snake_case_ :			Dict            =      vocab_size
								snake_case_ :			str            =      n_positions
								snake_case_ :			List[str]            =      n_embd
								snake_case_ :			List[str]            =      n_layer
								snake_case_ :			str            =      n_head
								snake_case_ :			List[Any]            =      n_inner
								snake_case_ :			Union[str, Any]            =      rotary_dim
								snake_case_ :			str            =      activation_function
								snake_case_ :			Tuple            =      resid_pdrop
								snake_case_ :			str            =      embd_pdrop
								snake_case_ :			Optional[int]            =      attn_pdrop
								snake_case_ :			Tuple            =      layer_norm_epsilon
								snake_case_ :			Union[str, Any]            =      initializer_range
								snake_case_ :			Dict            =      use_cache

								snake_case_ :			Any            =      bos_token_id
								snake_case_ :			Dict            =      eos_token_id

								super().__init__(
								    bos_token_id=_lowercase				,     eos_token_id=_lowercase				,     tie_word_embeddings=_lowercase				,     **_lowercase )




class  _lowerCAmelCase     (					SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		def __init__(       self				,     _lowercase				,     _lowercase = "default"				,     _lowercase = None				,     _lowercase = False				,     )			->    Optional[int]:







								'''simple docstring'''

								super().__init__(_lowercase				,     task=_lowercase				,     patching_specs=_lowercase				,     use_past=_lowercase )
								if not getattr(self._config				,     """pad_token_id"""				,     _lowercase ):
														# TODO: how to do that better?
														snake_case_ :			Dict            =      0





		@property
		def  UpperCAmelCase__				(       self )			->    Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								snake_case_ :			Dict            =      OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}} )
								if self.use_past:
														self.fill_with_past_key_values_(_lowercase				,     direction="""inputs""" )
														snake_case_ :			int            =      {0: """batch""", 1: """past_sequence + sequence"""}
								else:
														snake_case_ :			Optional[int]            =      {0: """batch""", 1: """sequence"""}

								return common_inputs





		@property
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								return self._config.n_layer





		@property
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								return self._config.n_head





		def  UpperCAmelCase__				(       self				,     _lowercase				,     _lowercase = -1				,     _lowercase = -1				,     _lowercase = False				,     _lowercase = None				,     )			->    Mapping[str, Any]:







								'''simple docstring'''

								snake_case_ :			List[str]            =      super(_lowercase				,     self ).generate_dummy_inputs(
								    _lowercase				,     batch_size=_lowercase				,     seq_length=_lowercase				,     is_pair=_lowercase				,     framework=_lowercase )

								# We need to order the input in the way they appears in the forward()
								snake_case_ :			Optional[Any]            =      OrderedDict({"""input_ids""": common_inputs["""input_ids"""]} )

								# Need to add the past_keys
								if self.use_past:
														if not is_torch_available():
																				raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
														else:
																				import torch

																				snake_case_      ,		snake_case_ :			Dict            =      common_inputs["""input_ids"""].shape
																				# Not using the same length for past_key_values
																				snake_case_ :			List[Any]            =      seqlen + 2
																				snake_case_ :			Dict            =      (
																				    batch,
																				    self.num_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // self.num_attention_heads,
																				)
																				snake_case_ :			str            =      [
																				    (torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(self.num_layers )
																				]

								snake_case_ :			Dict            =      common_inputs["""attention_mask"""]
								if self.use_past:
														snake_case_ :			List[Any]            =      ordered_inputs["""attention_mask"""].dtype
														snake_case_ :			Union[str, Any]            =      torch.cat(
														    [ordered_inputs["""attention_mask"""], torch.ones(_lowercase				,     _lowercase				,     dtype=_lowercase )]				,     dim=1 )

								return ordered_inputs





		@property
		def  UpperCAmelCase__				(       self )			->    int:







								'''simple docstring'''

								return 1_3




from PIL import Image







def 						lowerCamelCase__      ( __A    :Image		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case       	=							0
       __snake_case       	=							image.load()
       for i in range(__A		):
              for j in range(__A		):
                     __snake_case       	=							pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A		):
              for i in range(__A		):
                     __snake_case       	=							2_5_5 if pixels[i, j] > mean else 0
       return image


if __name__ == "__main__":
    UpperCamelCase__		 =     mean_threshold(Image.open('''path_to_image''').convert('''L'''))
    image.save('''output_image_path''')

