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

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


_a														=		logging.get_logger(__name__)

_a														=		{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class    _UpperCAmelCase(   SCREAMING_SNAKE_CASE__     ):
		lowercase__      =				'gpt_neo'
		lowercase__      =				['past_key_values']
		lowercase__      =				{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



		def __init__( self    ,		__a=5_02_57    ,		__a=20_48    ,		__a=20_48    ,		__a=24    ,		__a=[[["global", "local"], 12]]    ,		__a=16    ,		__a=None    ,		__a=2_56    ,		__a="gelu_new"    ,		__a=0.0    ,		__a=0.0    ,		__a=0.0    ,		__a=0.1    ,		__a=1e-5    ,		__a=0.02    ,		__a=True    ,		__a=5_02_56    ,		__a=5_02_56    ,		**__a    ,		)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_layers
							_UpperCamelCase										=  num_heads
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  window_size
							_UpperCamelCase										=  activation_function
							_UpperCamelCase										=  resid_dropout
							_UpperCamelCase										=  embed_dropout
							_UpperCamelCase										=  attention_dropout
							_UpperCamelCase										=  classifier_dropout
							_UpperCamelCase										=  layer_norm_epsilon
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  use_cache

							_UpperCamelCase										=  bos_token_id
							_UpperCamelCase										=  eos_token_id

							_UpperCamelCase										=  attention_types
							_UpperCamelCase										=  self.expand_attention_types_params(__a)

							if len(self.attention_layers) != self.num_layers:
												raise ValueError(
												    '''Configuration for convolutional module is incorrect. '''
												    '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
												    F'''but is `len(config.attention_layers) = {len(self.attention_layers)}`, '''
												    F'''`config.num_layers = {self.num_layers}`. '''
												    '''`config.attention_layers` is prepared using `config.attention_types`. '''
												    '''Please verify the value of `config.attention_types` argument.''')

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




		@staticmethod
		def 						UpperCAmelCase							( __a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  []
							for item in attention_types:
												for _ in range(item[1]):
																	attentions.extend(item[0])
							return attentions





def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case,					__snake_case     )							->		int:


					"""simple docstring"""





					import torch

					_UpperCamelCase										=  input.size()
					_UpperCamelCase										=  len(_UpperCAmelCase     )
					_UpperCamelCase										=  shape[dimension]

					_UpperCamelCase										=  torch.arange(0,					_UpperCAmelCase,					_UpperCAmelCase     )
					_UpperCamelCase										=  torch.div(sizedim - size,					_UpperCAmelCase,					rounding_mode='''floor'''     ) + 1
					_UpperCamelCase										=  torch.arange(_UpperCAmelCase     ) + low_indices[:min_length][:, None]

					_UpperCamelCase										=  [slice(_UpperCAmelCase     )] * rank
					_UpperCamelCase										=  indices
					_UpperCamelCase										=  input[s]

					_UpperCamelCase										=  list(range(0,					rank + 1     )     )
					perm.append(perm.pop(dimension + 1     )     )

					return sliced.permute(_UpperCAmelCase     )



def 				lowerCamelCase__		(      __snake_case,					__snake_case     )							->		Any:


					"""simple docstring"""





					import torch

					_UpperCamelCase										=  torch.arange(1,					_UpperCAmelCase     )
					_UpperCamelCase										=  torch.remainder(_UpperCAmelCase,					_UpperCAmelCase     )
					_UpperCamelCase										=  remainders == 0
					_UpperCamelCase										=  candidates[divisor_indices]
					_UpperCamelCase										=  torch.max(_UpperCAmelCase     )
					return largest_divisor, torch.div(_UpperCAmelCase,					_UpperCAmelCase,					rounding_mode='''floor'''     )






class    _UpperCAmelCase(   SCREAMING_SNAKE_CASE__     ):



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






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

							return common_inputs



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self._config.num_heads



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






							'''simple docstring'''
							_UpperCamelCase										=  super(__a    ,		self).generate_dummy_inputs(
							    __a    ,		batch_size=__a    ,		seq_length=__a    ,		is_pair=__a    ,		framework=__a)

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

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

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

							return ordered_inputs




		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return 13








"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


_a														=		logging.get_logger(__name__)

_a														=		{
    """bert-base-uncased""": """https://huggingface.co/bert-base-uncased/resolve/main/config.json""",
    """bert-large-uncased""": """https://huggingface.co/bert-large-uncased/resolve/main/config.json""",
    """bert-base-cased""": """https://huggingface.co/bert-base-cased/resolve/main/config.json""",
    """bert-large-cased""": """https://huggingface.co/bert-large-cased/resolve/main/config.json""",
    """bert-base-multilingual-uncased""": """https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json""",
    """bert-base-multilingual-cased""": """https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json""",
    """bert-base-chinese""": """https://huggingface.co/bert-base-chinese/resolve/main/config.json""",
    """bert-base-german-cased""": """https://huggingface.co/bert-base-german-cased/resolve/main/config.json""",
    """bert-large-uncased-whole-word-masking""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json"""
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        """https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json"""
    ),
    """bert-base-cased-finetuned-mrpc""": """https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json""",
    """bert-base-german-dbmdz-cased""": """https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json""",
    """bert-base-german-dbmdz-uncased""": """https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese""": """https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json""",
    """cl-tohoku/bert-base-japanese-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json"""
    ),
    """cl-tohoku/bert-base-japanese-char-whole-word-masking""": (
        """https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-cased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json"""
    ),
    """TurkuNLP/bert-base-finnish-uncased-v1""": (
        """https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json"""
    ),
    """wietsedv/bert-base-dutch-cased""": """https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json""",
    # See all BERT models at https://huggingface.co/models?filter=bert
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'bert'



		def __init__( self    ,		__a=3_05_22    ,		__a=7_68    ,		__a=12    ,		__a=12    ,		__a=30_72    ,		__a="gelu"    ,		__a=0.1    ,		__a=0.1    ,		__a=5_12    ,		__a=2    ,		__a=0.02    ,		__a=1e-12    ,		__a=0    ,		__a="absolute"    ,		__a=True    ,		__a=None    ,		**__a    ,		)	->	Optional[Any]:






							'''simple docstring'''
							super().__init__(pad_token_id=__a    ,		**__a)

							_UpperCamelCase										=  vocab_size
							_UpperCamelCase										=  hidden_size
							_UpperCamelCase										=  num_hidden_layers
							_UpperCamelCase										=  num_attention_heads
							_UpperCamelCase										=  hidden_act
							_UpperCamelCase										=  intermediate_size
							_UpperCamelCase										=  hidden_dropout_prob
							_UpperCamelCase										=  attention_probs_dropout_prob
							_UpperCamelCase										=  max_position_embeddings
							_UpperCamelCase										=  type_vocab_size
							_UpperCamelCase										=  initializer_range
							_UpperCamelCase										=  layer_norm_eps
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  use_cache
							_UpperCamelCase										=  classifier_dropout






class    _UpperCAmelCase(   lowerCamelCase     ):



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






							'''simple docstring'''
							if self.task == "multiple-choice":
												_UpperCamelCase										=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												_UpperCamelCase										=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							        ('''token_type_ids''', dynamic_axis),
							    ])






