






"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCAmelCase_   : List[str]   			= {
    """configuration_ctrl""": ["""CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CTRLConfig"""],
    """tokenization_ctrl""": ["""CTRLTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : Union[str, Any]   			= [
      """CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CTRLForSequenceClassification""",
      """CTRLLMHeadModel""",
      """CTRLModel""",
      """CTRLPreTrainedModel""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_   : List[Any]   			= [
      """TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFCTRLForSequenceClassification""",
      """TFCTRLLMHeadModel""",
      """TFCTRLModel""",
      """TFCTRLPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
  from .tokenization_ctrl import CTRLTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_ctrl import (
        CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        CTRLForSequenceClassification,
        CTRLLMHeadModel,
        CTRLModel,
        CTRLPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_ctrl import (
        TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCTRLForSequenceClassification,
        TFCTRLLMHeadModel,
        TFCTRLModel,
        TFCTRLPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_   : List[str]   			= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




'''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


lowercase			:				List[str]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    '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				(							__snake_case						):
     __magic_name__						=		'''gpt_neo'''
     __magic_name__						=		['''past_key_values''']
     __magic_name__						=		{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





     def __init__(							self						,			SCREAMING_SNAKE_CASE=50257						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=2048						,			SCREAMING_SNAKE_CASE=24						,			SCREAMING_SNAKE_CASE=[[["global", "local"], 12]]						,			SCREAMING_SNAKE_CASE=16						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE="gelu_new"						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=1e-5						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=50256						,			SCREAMING_SNAKE_CASE=50256						,			**SCREAMING_SNAKE_CASE						,			) ->				int:






         """simple docstring"""






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

         A       :   Optional[int]					=						bos_token_id
         A       :   List[Any]					=						eos_token_id

         A       :   int					=						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							(							SCREAMING_SNAKE_CASE	) ->				int:






         """simple docstring"""






         A       :   List[str]					=						[]
         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__						):




    '''simple docstring'''



    import torch

    A       :   Tuple					=						input.size()
    A       :   Union[str, Any]					=						len(snake_case__						)
    A       :   List[str]					=						shape[dimension]

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

    A       :   str					=						[slice(snake_case__						)] * rank
    A       :   List[Any]					=						indices
    A       :   Union[str, Any]					=						input[s]

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

    return sliced.permute(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    import torch

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







class  A				(							__snake_case						):





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






         """simple docstring"""






         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[Any]					=						{0: '''batch''', 1: '''past_sequence + sequence'''}
         else:
             A       :   Dict					=						{0: '''batch''', 1: '''sequence'''}

         return common_inputs





     @property
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         return self._config.num_heads





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






         """simple docstring"""






         A       :   List[str]					=						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       :   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

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

         A       :   str					=						common_inputs['''attention_mask''']
         if self.use_past:
             A       :   Optional[int]					=						ordered_inputs['''attention_mask'''].dtype
             A       :   List[str]					=						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	) ->				int:






         """simple docstring"""






         return 13




