


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


UpperCamelCase			:					Union[str, Any]         =							logging.get_logger(__name__)

UpperCamelCase			:					List[str]         =							{
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}

class      __snake_case				(   __a):
					SCREAMING_SNAKE_CASE_		:       int								=			"""gpt2"""
					SCREAMING_SNAKE_CASE_		:       Optional[int]								=			["""past_key_values"""]
					SCREAMING_SNAKE_CASE_		:       Optional[int]								=			{
					    """hidden_size""": """n_embd""",
					    """max_position_embeddings""": """n_positions""",
					    """num_attention_heads""": """n_head""",
					    """num_hidden_layers""": """n_layer""",
					}
					def __init__(						self       :						Dict	,      _lowerCAmelCase       :						Dict=5_0_2_5_7	,      _lowerCAmelCase       :						Tuple=1_0_2_4	,      _lowerCAmelCase       :						Union[str, Any]=7_6_8	,      _lowerCAmelCase       :						int=1_2	,      _lowerCAmelCase       :						List[str]=1_2	,      _lowerCAmelCase       :						Dict=None	,      _lowerCAmelCase       :						Dict="gelu_new"	,      _lowerCAmelCase       :						str=0.1	,      _lowerCAmelCase       :						Any=0.1	,      _lowerCAmelCase       :						str=0.1	,      _lowerCAmelCase       :						Dict=1E-5	,      _lowerCAmelCase       :						Optional[int]=0.02	,      _lowerCAmelCase       :						Dict="cls_index"	,      _lowerCAmelCase       :						Optional[int]=True	,      _lowerCAmelCase       :						Tuple=None	,      _lowerCAmelCase       :						Tuple=True	,      _lowerCAmelCase       :						str=0.1	,      _lowerCAmelCase       :						Union[str, Any]=True	,      _lowerCAmelCase       :						int=True	,      _lowerCAmelCase       :						Optional[Any]=5_0_2_5_6	,      _lowerCAmelCase       :						str=5_0_2_5_6	,      _lowerCAmelCase       :						int=False	,      _lowerCAmelCase       :						Any=False	,      **_lowerCAmelCase       :						str	,      )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      vocab_size
											__magic_name__					=      n_positions
											__magic_name__					=      n_embd
											__magic_name__					=      n_layer
											__magic_name__					=      n_head
											__magic_name__					=      n_inner
											__magic_name__					=      activation_function
											__magic_name__					=      resid_pdrop
											__magic_name__					=      embd_pdrop
											__magic_name__					=      attn_pdrop
											__magic_name__					=      layer_norm_epsilon
											__magic_name__					=      initializer_range
											__magic_name__					=      summary_type
											__magic_name__					=      summary_use_proj
											__magic_name__					=      summary_activation
											__magic_name__					=      summary_first_dropout
											__magic_name__					=      summary_proj_to_labels
											__magic_name__					=      scale_attn_weights
											__magic_name__					=      use_cache
											__magic_name__					=      scale_attn_by_inverse_layer_idx
											__magic_name__					=      reorder_and_upcast_attn

											__magic_name__					=      bos_token_id
											__magic_name__					=      eos_token_id

											super().__init__(bos_token_id=_lowerCAmelCase	,      eos_token_id=_lowerCAmelCase	,      **_lowerCAmelCase  )







class      __snake_case				(   __a):
					def __init__(						self       :						str	,      _lowerCAmelCase       :						PretrainedConfig	,      _lowerCAmelCase       :						str = "default"	,      _lowerCAmelCase       :						List[PatchingSpec] = None	,      _lowerCAmelCase       :						bool = False	,      )							->					Any:



											'''simple docstring'''





											super().__init__(_lowerCAmelCase	,      task=_lowerCAmelCase	,      patching_specs=_lowerCAmelCase	,      use_past=_lowerCAmelCase  )
											if not getattr(self._config	,      'pad_token_id'	,      _lowerCAmelCase  ):
																	# TODO: how to do that better?
																	__magic_name__					=      0
					@property
					def 	a			(						self       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
											if self.use_past:
																	self.fill_with_past_key_values_(_lowerCAmelCase	,      direction='inputs'  )
																	__magic_name__					=      {0: 'batch', 1: 'past_sequence + sequence'}
											else:
																	__magic_name__					=      {0: 'batch', 1: 'sequence'}

											return common_inputs
					@property
					def 	a			(						self       :						List[Any]  )							->					Optional[int]:



											'''simple docstring'''





											return self._config.n_layer
					@property
					def 	a			(						self       :						Optional[Any]  )							->					List[str]:



											'''simple docstring'''





											return self._config.n_head
					def 	a			(						self       :						Tuple	,      _lowerCAmelCase       :						PreTrainedTokenizer	,      _lowerCAmelCase       :						int = -1	,      _lowerCAmelCase       :						int = -1	,      _lowerCAmelCase       :						bool = False	,      _lowerCAmelCase       :						Optional[TensorType] = None	,      )							->					Any:



											'''simple docstring'''





											__magic_name__					=      super(_lowerCAmelCase	,      self  ).generate_dummy_inputs(
											    _lowerCAmelCase	,      batch_size=_lowerCAmelCase	,      seq_length=_lowerCAmelCase	,      is_pair=_lowerCAmelCase	,      framework=_lowerCAmelCase  )

											# We need to order the input in the way they appears in the forward()
											__magic_name__					=      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

																							__magic_name__		,      __magic_name__					=      common_inputs['input_ids'].shape
																							# Not using the same length for past_key_values
																							__magic_name__					=      seqlen + 2
																							__magic_name__					=      (
																							    batch,
																							    self.num_attention_heads,
																							    past_key_values_length,
																							    self._config.hidden_size // self.num_attention_heads,
																							)
																							__magic_name__					=      [
																							    (torch.zeros(_lowerCAmelCase  ), torch.zeros(_lowerCAmelCase  )) for _ in range(self.num_layers  )
																							]

											__magic_name__					=      common_inputs['attention_mask']
											if self.use_past:
																	__magic_name__					=      ordered_inputs['attention_mask'].dtype
																	__magic_name__					=      torch.cat(
																	    [ordered_inputs['attention_mask'], torch.ones(_lowerCAmelCase	,      _lowerCAmelCase	,      dtype=_lowerCAmelCase  )]	,      dim=1  )

											return ordered_inputs







					@property
					def 	a			(						self       :						str  )							->					Union[str, Any]:



											'''simple docstring'''





											return 1_3







'''simple docstring'''







from __future__ import annotations





def 		snake_case_				(_a      :				list[list[int]]       ,  _a      :				list[list[int]]	):
    assert len(_a	) == len(_a	)

    UpperCAmelCase     			=						len(_a	)
    UpperCAmelCase     			=						list(range(_a	)	)
    UpperCAmelCase     			=						[-1] * n  # who the donor has donated to
    UpperCAmelCase     			=						[-1] * n  # who the recipient has received from
    UpperCAmelCase     			=						[0] * n

    while unmatched_donors:
        UpperCAmelCase     			=						unmatched_donors[0]
        UpperCAmelCase     			=						donor_pref[donor]
        UpperCAmelCase     			=						donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        UpperCAmelCase     			=						recipient_pref[recipient]
        UpperCAmelCase     			=						rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(_a	) > rec_preference.index(_a	):
                UpperCAmelCase     			=						donor
                UpperCAmelCase     			=						recipient
                unmatched_donors.append(_a	)
                unmatched_donors.remove(_a	)
        else:
            UpperCAmelCase     			=						donor
            UpperCAmelCase     			=						recipient
            unmatched_donors.remove(_a	)
    return donor_record




