



"""simple docstring"""







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


A    =			logging.get_logger(__name__)


A    =			{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}






class      UpperCAmelCase__		(      UpperCamelCase     ):
  lowerCAmelCase_       :	List[str] 							=     """codegen"""
  lowerCAmelCase_       :	Optional[int] 							=     {
      """max_position_embeddings""": """n_positions""",
      """hidden_size""": """n_embd""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }



  def __init__( self  :    int      ,							snake_case  :    Dict=50_400      ,							snake_case  :    Dict=2_048      ,							snake_case  :    str=2_048      ,							snake_case  :    int=4_096      ,							snake_case  :    Union[str, Any]=28      ,							snake_case  :    Optional[Any]=16      ,							snake_case  :    str=64      ,							snake_case  :    Tuple=None      ,							snake_case  :    Any="gelu_new"      ,							snake_case  :    Optional[Any]=0.0      ,							snake_case  :    Optional[Any]=0.0      ,							snake_case  :    Any=0.0      ,							snake_case  :    Any=1E-5      ,							snake_case  :    Dict=0.02      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Optional[Any]=50_256      ,							snake_case  :    List[Any]=50_256      ,							snake_case  :    Dict=False      ,							**snake_case  :    int      ,							)			->						List[Any]:






         '''simple docstring'''

         A         =					vocab_size
         A         =					n_ctx
         A         =					n_positions
         A         =					n_embd
         A         =					n_layer
         A         =					n_head
         A         =					n_inner
         A         =					rotary_dim
         A         =					activation_function
         A         =					resid_pdrop
         A         =					embd_pdrop
         A         =					attn_pdrop
         A         =					layer_norm_epsilon
         A         =					initializer_range
         A         =					use_cache

         A         =					bos_token_id
         A         =					eos_token_id

         super().__init__(
             bos_token_id=snake_case      ,							eos_token_id=snake_case      ,							tie_word_embeddings=snake_case      ,							**snake_case				)






class      UpperCAmelCase__		(      UpperCamelCase     ):



  def __init__( self  :    Tuple      ,							snake_case  :    PretrainedConfig      ,							snake_case  :    str = "default"      ,							snake_case  :    List[PatchingSpec] = None      ,							snake_case  :    bool = False      ,							)			->						Tuple:






         '''simple docstring'''

         super().__init__(snake_case      ,							task=snake_case      ,							patching_specs=snake_case      ,							use_past=snake_case				)
         if not getattr(self._config      ,							'pad_token_id'      ,							snake_case				):
                # TODO: how to do that better?
                A         =					0



  @property
  def 					A_		( self  :    List[Any]				)			->						Mapping[str, Mapping[int, str]]:






         '''simple docstring'''

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

         return common_inputs



  @property
  def 					A_		( self  :    List[Any]				)			->						int:






         '''simple docstring'''

         return self._config.n_layer



  @property
  def 					A_		( self  :    int				)			->						int:






         '''simple docstring'''

         return self._config.n_head



  def 					A_		( self  :    Optional[int]      ,							snake_case  :    PreTrainedTokenizer      ,							snake_case  :    int = -1      ,							snake_case  :    int = -1      ,							snake_case  :    bool = False      ,							snake_case  :    Optional[TensorType] = None      ,							)			->						Mapping[str, Any]:






         '''simple docstring'''

         A         =					super(snake_case      ,							self				).generate_dummy_inputs(
             snake_case      ,							batch_size=snake_case      ,							seq_length=snake_case      ,							is_pair=snake_case      ,							framework=snake_case				)

         # We need to order the input in the way they appears in the forward()
         A         =					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         =					common_inputs['input_ids'].shape
                       # Not using the same length for past_key_values
                       A         =					seqlen + 2
                       A         =					(
                           batch,
                           self.num_attention_heads,
                           past_key_values_length,
                           self._config.hidden_size // self.num_attention_heads,
                       )
                       A         =					[
                           (torch.zeros(snake_case				), torch.zeros(snake_case				)) for _ in range(self.num_layers				)
                       ]

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

         return ordered_inputs



  @property
  def 					A_		( self  :    Any				)			->						int:






         '''simple docstring'''

         return 13








"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


A    =			{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'PLBartForCausalLM',
							    'PLBartForConditionalGeneration',
							    'PLBartForSequenceClassification',
							    'PLBartModel',
							    'PLBartPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_plbart import PLBartTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_plbart import (
														    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PLBartForCausalLM,
														    PLBartForConditionalGeneration,
														    PLBartForSequenceClassification,
														    PLBartModel,
														    PLBartPreTrainedModel,
														)


else:
							import sys

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



