




import math
import sys







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[str]  =     ''''''
     try:
          with open(_lowercase					,		'''rb''' ) as binary_file:
               UpperCAmelCase_     :				Dict  =     binary_file.read()
          for dat in data:
               UpperCAmelCase_     :				Union[str, Any]  =     f'''{dat:08b}'''
               result += curr_byte
          return result
     except OSError:
          print('''File not accessible''' )
          sys.exit()







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Optional[int]  =     {'''0''': '''0''', '''1''': '''1'''}
     UpperCAmelCase_,       UpperCAmelCase_     :				str  =     '''''', ''''''
     UpperCAmelCase_     :				Any  =     len(_lowercase )

     for i in range(len(_lowercase ) ):
          curr_string += data_bits[i]
          if curr_string not in lexicon:
               continue

          UpperCAmelCase_     :				Any  =     lexicon[curr_string]
          result += last_match_id
          UpperCAmelCase_     :				Any  =     last_match_id + '''0'''

          if math.loga(_lowercase ).is_integer():
               UpperCAmelCase_     :				int  =     {}
               for curr_key in list(_lowercase ):
                    UpperCAmelCase_     :				List[Any]  =     lexicon.pop(_lowercase )
               UpperCAmelCase_     :				List[Any]  =     new_lex

          UpperCAmelCase_     :				Any  =     last_match_id + '''1'''
          index += 1
          UpperCAmelCase_     :				List[Any]  =     ''''''
     return result







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				str  =     8
     try:
          with open(_lowercase					,		'''wb''' ) as opened_file:
               UpperCAmelCase_     :				List[str]  =     [
                   to_write[i : i + byte_length]
                   for i in range(0					,		len(_lowercase )					,		_lowercase )
               ]

               if len(result_byte_array[-1] ) % byte_length == 0:
                    result_byte_array.append('''10000000''' )
               else:
                    result_byte_array[-1] += "1" + "0" * (
                        byte_length - len(result_byte_array[-1] ) - 1
                    )

               for elem in result_byte_array[:-1]:
                    opened_file.write(int(_lowercase					,		2 ).to_bytes(1					,		byteorder='''big''' ) )
     except OSError:
          print('''File not accessible''' )
          sys.exit()







def lowerCamelCase__  (     _lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     0
     for letter in data_bits:
          if letter == "1":
               break
          counter += 1

     UpperCAmelCase_     :				Optional[int]  =     data_bits[counter:]
     UpperCAmelCase_     :				Dict  =     data_bits[counter + 1 :]
     return data_bits







def lowerCamelCase__  (     _lowercase					,		_lowercase ):

     '''simple docstring'''


     UpperCAmelCase_     :				List[Any]  =     read_file_binary(_lowercase )
     UpperCAmelCase_     :				Optional[Any]  =     remove_prefix(_lowercase )
     UpperCAmelCase_     :				Tuple  =     decompress_data(_lowercase )
     write_file_binary(_lowercase					,		_lowercase )


if __name__ == "__main__":
     compress(sys.argv[1], sys.argv[2])





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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, TensorType

__a							     =					logging.get_logger(__name__)

__a							     =					{
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''imagegpt'''
    lowerCAmelCase         = ['''past_key_values''']
    lowerCAmelCase         = {
        '''hidden_size''': '''n_embd''',
        '''max_position_embeddings''': '''n_positions''',
        '''num_attention_heads''': '''n_head''',
        '''num_hidden_layers''': '''n_layer''',
    }





    def __init__( self  ,_SCREAMING_SNAKE_CASE=512 + 1  ,_SCREAMING_SNAKE_CASE=32 * 32  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=24  ,_SCREAMING_SNAKE_CASE=8  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE="quick_gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=1e-5  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,_SCREAMING_SNAKE_CASE=False  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[int]:
         UpperCAmelCase_     :				Optional[int]  =     vocab_size
         UpperCAmelCase_     :				Union[str, Any]  =     n_positions
         UpperCAmelCase_     :				Union[str, Any]  =     n_embd
         UpperCAmelCase_     :				Any  =     n_layer
         UpperCAmelCase_     :				Optional[Any]  =     n_head
         UpperCAmelCase_     :				Union[str, Any]  =     n_inner
         UpperCAmelCase_     :				List[Any]  =     activation_function
         UpperCAmelCase_     :				List[str]  =     resid_pdrop
         UpperCAmelCase_     :				str  =     embd_pdrop
         UpperCAmelCase_     :				Optional[Any]  =     attn_pdrop
         UpperCAmelCase_     :				Dict  =     layer_norm_epsilon
         UpperCAmelCase_     :				Union[str, Any]  =     initializer_range
         UpperCAmelCase_     :				Dict  =     scale_attn_weights
         UpperCAmelCase_     :				Any  =     use_cache
         UpperCAmelCase_     :				List[str]  =     scale_attn_by_inverse_layer_idx
         UpperCAmelCase_     :				Tuple  =     reorder_and_upcast_attn
         UpperCAmelCase_     :				int  =     tie_word_embeddings

         super().__init__(tie_word_embeddings=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    @property
    def 				a__ ( self    )					->						Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
             ]    )





    def 				a__ ( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE = 1  ,_SCREAMING_SNAKE_CASE = -1  ,_SCREAMING_SNAKE_CASE = False  ,_SCREAMING_SNAKE_CASE = None  ,_SCREAMING_SNAKE_CASE = 3  ,_SCREAMING_SNAKE_CASE = 32  ,_SCREAMING_SNAKE_CASE = 32  ,)					->						Mapping[str, Any]:
         UpperCAmelCase_     :				Any  =     self._generate_dummy_images(_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[int]  =     dict(preprocessor(images=_SCREAMING_SNAKE_CASE  ,return_tensors=_SCREAMING_SNAKE_CASE    )    )

         return inputs