





"""simple docstring"""






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 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase					=					'gpt_neo'
					__lowerCAmelCase					=					['past_key_values']
					__lowerCAmelCase					=					{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}







					def __init__(  self		,     _UpperCAmelCase=50257		,     _UpperCAmelCase=2048		,     _UpperCAmelCase=2048		,     _UpperCAmelCase=24		,     _UpperCAmelCase=[[["global", "local"], 12]]		,     _UpperCAmelCase=16		,     _UpperCAmelCase=None		,     _UpperCAmelCase=256		,     _UpperCAmelCase="gelu_new"		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=0.0		,     _UpperCAmelCase=0.1		,     _UpperCAmelCase=1e-5		,     _UpperCAmelCase=0.0_2		,     _UpperCAmelCase=True		,     _UpperCAmelCase=50256		,     _UpperCAmelCase=50256		,     **_UpperCAmelCase		,     ):
									__a	:  List[str]         =					vocab_size
									__a	:  Union[str, Any]         =					max_position_embeddings
									__a	:  str         =					hidden_size
									__a	:  Any         =					num_layers
									__a	:  Optional[int]         =					num_heads
									__a	:  Optional[Any]         =					intermediate_size
									__a	:  Any         =					window_size
									__a	:  str         =					activation_function
									__a	:  List[str]         =					resid_dropout
									__a	:  Union[str, Any]         =					embed_dropout
									__a	:  Any         =					attention_dropout
									__a	:  List[Any]         =					classifier_dropout
									__a	:  int         =					layer_norm_epsilon
									__a	:  List[Any]         =					initializer_range
									__a	:  List[str]         =					use_cache

									__a	:  Tuple         =					bos_token_id
									__a	:  Optional[int]         =					eos_token_id

									__a	:  List[str]         =					attention_types
									__a	:  int         =					self.expand_attention_types_params(_SCREAMING_SNAKE_CASE       )

									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=_SCREAMING_SNAKE_CASE		,     eos_token_id=_SCREAMING_SNAKE_CASE		,     **_SCREAMING_SNAKE_CASE       )







					@staticmethod
					def 	_lowerCamelCase						(  _UpperCAmelCase       ):
									__a	:  Union[str, Any]         =					[]
									for item in attention_types:
													for _ in range(item[1]       ):
																	attentions.extend(item[0]       )
									return attentions






def       __A (     a_  :int				,    a_  :int				,    a_  :Any				,    a_  :Dict)						->						str:
				import torch

				__a	:  Tuple         =					input.size()
				__a	:  Union[str, Any]         =					len(_UpperCamelCase)
				__a	:  Dict         =					shape[dimension]

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

				__a	:  int         =					[slice(_UpperCamelCase)] * rank
				__a	:  str         =					indices
				__a	:  Any         =					input[s]

				__a	:  List[Any]         =					list(range(0				,    rank + 1))
				perm.append(perm.pop(dimension + 1))

				return sliced.permute(_UpperCamelCase)

def       __A (     a_  :int				,    a_  :List[str])						->						str:
				import torch

				__a	:  List[str]         =					torch.arange(1				,    _UpperCamelCase)
				__a	:  List[Any]         =					torch.remainder(_UpperCamelCase				,    _UpperCamelCase)
				__a	:  List[str]         =					remainders == 0
				__a	:  str         =					candidates[divisor_indices]
				__a	:  List[Any]         =					torch.max(_UpperCamelCase)
				return largest_divisor, torch.div(_UpperCamelCase				,    _UpperCamelCase				,    rounding_mode='''floor''')






class 			__lowercase     (     _UpperCamelCase							):







					'''simple docstring'''




					@property
					def 	_lowerCamelCase						(  self       ):
									__a	:  Tuple         =					OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}       )
									if self.use_past:
													self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE		,     direction='''inputs'''       )
													__a	:  Optional[int]         =					{0: '''batch''', 1: '''past_sequence + sequence'''}
									else:
													__a	:  str         =					{0: '''batch''', 1: '''sequence'''}

									return common_inputs







					@property
					def 	_lowerCamelCase						(  self       ):
									return self._config.num_heads







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase = -1		,     _UpperCAmelCase = -1		,     _UpperCAmelCase = False		,     _UpperCAmelCase = None		,     ):
									__a	:  Union[str, Any]         =					super(_SCREAMING_SNAKE_CASE		,     self       ).generate_dummy_inputs(
									    _SCREAMING_SNAKE_CASE		,     batch_size=_SCREAMING_SNAKE_CASE		,     seq_length=_SCREAMING_SNAKE_CASE		,     is_pair=_SCREAMING_SNAKE_CASE		,     framework=_SCREAMING_SNAKE_CASE       )

									# We need to order the input in the way they appears in the forward()
									__a	:  str         =					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

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

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

									return ordered_inputs







					@property
					def 	_lowerCamelCase						(  self       ):
									return 13






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A	     =					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					['''MvpTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A	     =					[
		    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MvpForCausalLM''',
		    '''MvpForConditionalGeneration''',
		    '''MvpForQuestionAnswering''',
		    '''MvpForSequenceClassification''',
		    '''MvpModel''',
		    '''MvpPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
		from .tokenization_mvp import MvpTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_mvp_fast import MvpTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mvp import (
				    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MvpForCausalLM,
				    MvpForConditionalGeneration,
				    MvpForQuestionAnswering,
				    MvpForSequenceClassification,
				    MvpModel,
				    MvpPreTrainedModel,
				)

else:
		import sys

		A	     =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)