


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


SCREAMING_SNAKE_CASE       :      Optional[Any]												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Optional[Any]												= {
    "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json",
    "allenai/longformer-large-4096": "https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json",
    "allenai/longformer-large-4096-finetuned-triviaqa": (
        "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"
    ),
    "allenai/longformer-base-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
    "allenai/longformer-large-4096-extra.pos.embd.only": (
        "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"
    ),
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	Dict     =			"""longformer"""
       def __init__(					self,      lowerCamelCase = 5_12,      lowerCamelCase = 2,      lowerCamelCase = 1,      lowerCamelCase = 0,      lowerCamelCase = 2,      lowerCamelCase = 3_05_22,      lowerCamelCase = 7_68,      lowerCamelCase = 12,      lowerCamelCase = 12,      lowerCamelCase = 30_72,      lowerCamelCase = "gelu",      lowerCamelCase = 0.1,      lowerCamelCase = 0.1,      lowerCamelCase = 5_12,      lowerCamelCase = 2,      lowerCamelCase = 0.0_2,      lowerCamelCase = 1E-12,      lowerCamelCase = False,      **lowerCamelCase,      )       ->   List[str]:




           """simple docstring"""

           super().__init__(pad_token_id=lowerCamelCase,      **lowerCamelCase)

           _lowercase       :							Union[str, Any]      		=		attention_window
           _lowercase       :							Union[str, Any]      		=		sep_token_id
           _lowercase       :							int      		=		bos_token_id
           _lowercase       :							Tuple      		=		eos_token_id
           _lowercase       :							List[Any]      		=		vocab_size
           _lowercase       :							Tuple      		=		hidden_size
           _lowercase       :							Optional[int]      		=		num_hidden_layers
           _lowercase       :							Optional[Any]      		=		num_attention_heads
           _lowercase       :							Optional[Any]      		=		hidden_act
           _lowercase       :							Union[str, Any]      		=		intermediate_size
           _lowercase       :							int      		=		hidden_dropout_prob
           _lowercase       :							Dict      		=		attention_probs_dropout_prob
           _lowercase       :							Optional[Any]      		=		max_position_embeddings
           _lowercase       :							int      		=		type_vocab_size
           _lowercase       :							str      		=		initializer_range
           _lowercase       :							List[Any]      		=		layer_norm_eps
           _lowercase       :							Dict      		=		onnx_export






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase = "default",      lowerCamelCase = None)       ->   Dict:




           """simple docstring"""

           super().__init__(lowerCamelCase,      lowerCamelCase,      lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		True
       @property
       def 			UpperCamelCase     (					self)       ->   Mapping[str, Mapping[int, str]]:




           """simple docstring"""

           if self.task == "multiple-choice":
               _lowercase       :							str      		=		{0: 'batch', 1: 'choice', 2: 'sequence'}
           else:
               _lowercase       :							Union[str, Any]      		=		{0: 'batch', 1: 'sequence'}
           return OrderedDict(
               [
                   ('input_ids', dynamic_axis),
                   ('attention_mask', dynamic_axis),
                   ('global_attention_mask', dynamic_axis),
               ])
       @property
       def 			UpperCamelCase     (					self)       ->   Mapping[str, Mapping[int, str]]:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		super().outputs
           if self.task == "default":
               _lowercase       :							List[Any]      		=		{0: 'batch'}
           return outputs
       @property
       def 			UpperCamelCase     (					self)       ->   float:




           """simple docstring"""

           return 1E-4
       @property
       def 			UpperCamelCase     (					self)       ->   int:




           """simple docstring"""

           return max(super().default_onnx_opset,      14)




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




           """simple docstring"""

           _lowercase       :							str      		=		super().generate_dummy_inputs(
               preprocessor=lowerCamelCase,      batch_size=lowerCamelCase,      seq_length=lowerCamelCase,      is_pair=lowerCamelCase,      framework=lowerCamelCase)
           import torch

           # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
           # makes the export fail randomly
           _lowercase       :							Any      		=		torch.zeros_like(inputs['input_ids'])
           # make every second token global
           _lowercase       :							List[str]      		=		1

           return inputs






from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE       :      str												= {
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Tuple												= ["LlamaTokenizer"]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      Optional[Any]												= ["LlamaTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE       :      int												= [
     "LlamaForCausalLM",
     "LlamaModel",
     "LlamaPreTrainedModel",
     "LlamaForSequenceClassification",
 ]


if TYPE_CHECKING:
 from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama import LlamaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_llama_fast import LlamaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
 import sys

 SCREAMING_SNAKE_CASE       :      Optional[int]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


