




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  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: List[Any]    =					"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					: Tuple ,       snake_case					: Dict=50400 ,       snake_case					: Union[str, Any]=2048 ,       snake_case					: List[str]=2048 ,       snake_case					: List[Any]=4096 ,       snake_case					: Union[str, Any]=28 ,       snake_case					: Any=16 ,       snake_case					: Optional[int]=64 ,       snake_case					: str=None ,       snake_case					: Union[str, Any]="gelu_new" ,       snake_case					: Union[str, Any]=0.0 ,       snake_case					: Any=0.0 ,       snake_case					: Tuple=0.0 ,       snake_case					: Tuple=1E-5 ,       snake_case					: Dict=0.02 ,       snake_case					: Dict=True ,       snake_case					: Dict=50256 ,       snake_case					: Union[str, Any]=50256 ,       snake_case					: str=False ,       **snake_case					: str ,       ):
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						n_ctx
          __UpperCamelCase  		=						n_positions
          __UpperCamelCase  		=						n_embd
          __UpperCamelCase  		=						n_layer
          __UpperCamelCase  		=						n_head
          __UpperCamelCase  		=						n_inner
          __UpperCamelCase  		=						rotary_dim
          __UpperCamelCase  		=						activation_function
          __UpperCamelCase  		=						resid_pdrop
          __UpperCamelCase  		=						embd_pdrop
          __UpperCamelCase  		=						attn_pdrop
          __UpperCamelCase  		=						layer_norm_epsilon
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id

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







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: PretrainedConfig ,       snake_case					: str = "default" ,       snake_case					: List[PatchingSpec] = None ,       snake_case					: bool = False ,       ):
          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?
              __UpperCamelCase  		=						0







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

          return common_inputs







      @property
      def       snake_case	(     self					: Union[str, Any]				):
          return self._config.n_layer







      @property
      def       snake_case	(     self					: Optional[Any]				):
          return self._config.n_head







      def       snake_case	(     self					: str ,       snake_case					: PreTrainedTokenizer ,       snake_case					: int = -1 ,       snake_case					: int = -1 ,       snake_case					: bool = False ,       snake_case					: Optional[TensorType] = None ,       ):
          __UpperCamelCase  		=						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()
          __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		,      __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(snake_case				), torch.zeros(snake_case				)) 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(snake_case ,       snake_case ,       dtype=snake_case				)] ,       dim=1				)

          return ordered_inputs







      @property
      def       snake_case	(     self					: str				):
          return 13












from typing import TYPE_CHECKING

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


a_   		=     {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     ["LongformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongformerForMaskedLM",
     "LongformerForMultipleChoice",
     "LongformerForQuestionAnswering",
     "LongformerForSequenceClassification",
     "LongformerForTokenClassification",
     "LongformerModel",
     "LongformerPreTrainedModel",
     "LongformerSelfAttention",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLongformerForMaskedLM",
     "TFLongformerForMultipleChoice",
     "TFLongformerForQuestionAnswering",
     "TFLongformerForSequenceClassification",
     "TFLongformerForTokenClassification",
     "TFLongformerModel",
     "TFLongformerPreTrainedModel",
     "TFLongformerSelfAttention",
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

 a_   		=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






