


'''simple docstring'''






import argparse
import json

from tqdm import tqdm


def    _snake_case  (	)		->       Optional[Any]:
							lowerCAmelCase__			  =      argparse.ArgumentParser()

							# Required parameters
							parser.add_argument(
							    '''--src_path'''					,       type=A					,       default='''biencoder-nq-dev.json'''					,       help='''Path to raw DPR training data'''					,       )
							parser.add_argument(
							    '''--evaluation_set'''					,       type=A					,       help='''where to store parsed evaluation_set file'''					,       )
							parser.add_argument(
							    '''--gold_data_path'''					,       type=A					,       help='''where to store parsed gold_data_path file'''					,       )
							lowerCAmelCase__			  =      parser.parse_args()

							with open(args.src_path					,       '''r'''			) as src_file, open(args.evaluation_set					,       '''w'''			) as eval_file, open(
							    args.gold_data_path					,       '''w'''			) as gold_file:
														lowerCAmelCase__			  =      json.load(A			)
														for dpr_record in tqdm(A			):
																					lowerCAmelCase__			  =      dpr_record['''question''']
																					lowerCAmelCase__			  =      [context['''title'''] for context in dpr_record['''positive_ctxs''']]
																					eval_file.write(question + '''\n'''			)
																					gold_file.write('''\t'''.join(A			) + '''\n'''			)


if __name__ == "__main__":
		main()

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       _lowerCAmelCase   (      __a			):
				_lowercase     						='''imagegpt'''
				_lowercase     						=['''past_key_values''']
				_lowercase     						={
				    '''hidden_size''': '''n_embd''',
				    '''max_position_embeddings''': '''n_positions''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}




				def __init__(   self							,							_UpperCamelCase=512 + 1							,							_UpperCamelCase=32 * 32							,							_UpperCamelCase=512							,							_UpperCamelCase=24							,							_UpperCamelCase=8							,							_UpperCamelCase=None							,							_UpperCamelCase="quick_gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=1e-5							,							_UpperCamelCase=0.02							,							_UpperCamelCase=True							,							_UpperCamelCase=True							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							_UpperCamelCase=False							,							**_UpperCamelCase							,							)							-> Tuple:
							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      n_positions
							lowerCAmelCase_         =      n_embd
							lowerCAmelCase_         =      n_layer
							lowerCAmelCase_         =      n_head
							lowerCAmelCase_         =      n_inner
							lowerCAmelCase_         =      activation_function
							lowerCAmelCase_         =      resid_pdrop
							lowerCAmelCase_         =      embd_pdrop
							lowerCAmelCase_         =      attn_pdrop
							lowerCAmelCase_         =      layer_norm_epsilon
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      scale_attn_weights
							lowerCAmelCase_         =      use_cache
							lowerCAmelCase_         =      scale_attn_by_inverse_layer_idx
							lowerCAmelCase_         =      reorder_and_upcast_attn
							lowerCAmelCase_         =      tie_word_embeddings

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





class       _lowerCAmelCase   (      __a			):




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




				def 	__a		(   self							,							_UpperCamelCase							,							_UpperCamelCase = 1							,							_UpperCamelCase = -1							,							_UpperCamelCase = False							,							_UpperCamelCase = None							,							_UpperCamelCase = 3							,							_UpperCamelCase = 32							,							_UpperCamelCase = 32							,							)							-> Mapping[str, Any]:
							lowerCAmelCase_         =      self._generate_dummy_images(_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase							,							_UpperCamelCase	)
							lowerCAmelCase_         =      dict(preprocessor(images=_UpperCamelCase							,							return_tensors=_UpperCamelCase	)	)

							return inputs



