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


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

__lowerCamelCase					:	Union[str, Any]       =  {
    '''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 		a__						(					A__					):
					A	       =							'gpt_neo'
					A	       =							['past_key_values']
					A	       =							{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}

					def __init__(    self      :       Tuple,_A      :       str=5_0257,_A      :       Dict=2048,_A      :       Any=2048,_A      :       Optional[int]=24,_A      :       List[str]=[[["global", "local"], 12]],_A      :       List[str]=16,_A      :       Union[str, Any]=None,_A      :       Optional[Any]=256,_A      :       Any="gelu_new",_A      :       int=0.0,_A      :       Optional[Any]=0.0,_A      :       Any=0.0,_A      :       str=0.1,_A      :       Any=1E-5,_A      :       int=0.02,_A      :       List[Any]=True,_A      :       Union[str, Any]=5_0256,_A      :       List[str]=5_0256,**_A      :       Tuple,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict				      =    vocab_size
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    max_position_embeddings
							SCREAMING_SNAKE_CASE_  :		Dict				      =    hidden_size
							SCREAMING_SNAKE_CASE_  :		str				      =    num_layers
							SCREAMING_SNAKE_CASE_  :		str				      =    num_heads
							SCREAMING_SNAKE_CASE_  :		Dict				      =    intermediate_size
							SCREAMING_SNAKE_CASE_  :		str				      =    window_size
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    activation_function
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    resid_dropout
							SCREAMING_SNAKE_CASE_  :		int				      =    embed_dropout
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    attention_dropout
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    classifier_dropout
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    layer_norm_epsilon
							SCREAMING_SNAKE_CASE_  :		List[Any]				      =    initializer_range
							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    use_cache

							SCREAMING_SNAKE_CASE_  :		Tuple				      =    bos_token_id
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    eos_token_id

							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    attention_types
							SCREAMING_SNAKE_CASE_  :		str				      =    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      :       Union[str, Any]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    []
							for item in attention_types:
									for _ in range(item[1]    ):
											attentions.extend(item[0]    )
							return attentions







def       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		import torch

		SCREAMING_SNAKE_CASE_  :		str				      =    input.size()
		SCREAMING_SNAKE_CASE_  :		Any				      =    len(lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    shape[dimension]

		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    torch.arange(0   , lowerCAmelCase   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    torch.div(sizedim - size   , lowerCAmelCase   , rounding_mode="floor"	) + 1
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    torch.arange(lowerCAmelCase	) + low_indices[:min_length][:, None]

		SCREAMING_SNAKE_CASE_  :		List[str]				      =    [slice(lowerCAmelCase	)] * rank
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    indices
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    input[s]

		SCREAMING_SNAKE_CASE_  :		Any				      =    list(range(0   , rank + 1	)	)
		perm.append(perm.pop(dimension + 1	)	)

		return sliced.permute(lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		import torch

		SCREAMING_SNAKE_CASE_  :		str				      =    torch.arange(1   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    torch.remainder(lowerCAmelCase   , lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    remainders == 0
		SCREAMING_SNAKE_CASE_  :		str				      =    candidates[divisor_indices]
		SCREAMING_SNAKE_CASE_  :		str				      =    torch.max(lowerCAmelCase	)
		return largest_divisor, torch.div(lowerCAmelCase   , lowerCAmelCase   , rounding_mode="floor"	)







class 		a__						(					A__					):

					@property
					def 				__UpperCamelCase   (    self      :       Optional[int]    ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
							if self.use_past:
									self.fill_with_past_key_values_(_A,direction="inputs"    )
									SCREAMING_SNAKE_CASE_  :		int				      =    {0: "batch", 1: "past_sequence + sequence"}
							else:
									SCREAMING_SNAKE_CASE_  :		Dict				      =    {0: "batch", 1: "sequence"}

							return common_inputs

					@property
					def 				__UpperCamelCase   (    self      :       List[Any]    ):







							"""simple docstring"""






							return self._config.num_heads

					def 				__UpperCamelCase   (    self      :       Optional[int],_A      :       PreTrainedTokenizer,_A      :       int = -1,_A      :       int = -1,_A      :       bool = False,_A      :       Optional[TensorType] = None,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    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()
							SCREAMING_SNAKE_CASE_  :		Any				      =    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

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

							SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    common_inputs["attention_mask"]
							if self.use_past:
									SCREAMING_SNAKE_CASE_  :		Tuple				      =    ordered_inputs["attention_mask"].dtype
									SCREAMING_SNAKE_CASE_  :		str				      =    torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(_A,_A,dtype=_A    )],dim=1    )

							return ordered_inputs






					@property
					def 				__UpperCamelCase   (    self      :       Optional[Any]    ):







							"""simple docstring"""






							return 13



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase :       Union[str, Any]			   =     {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase :       Union[str, Any]			   =     [
      'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
      'SEWForCTC',
      'SEWForSequenceClassification',
      'SEWModel',
      'SEWPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_sew import (
        SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
        SEWForCTC,
        SEWForSequenceClassification,
        SEWModel,
        SEWPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase :       int			   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






