


"""simple docstring"""



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


__snake_case   			=    logging.get_logger(__name__)

__snake_case   			=    {
    """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				(      __UpperCamelCase	):
   __UpperCAmelCase :       Any							  =  '''longformer'''







   def __init__(	self						,	UpperCamelCase__ = 512						,	UpperCamelCase__ = 2						,	UpperCamelCase__ = 1						,	UpperCamelCase__ = 0						,	UpperCamelCase__ = 2						,	UpperCamelCase__ = 3_0522						,	UpperCamelCase__ = 768						,	UpperCamelCase__ = 12						,	UpperCamelCase__ = 12						,	UpperCamelCase__ = 3072						,	UpperCamelCase__ = "gelu"						,	UpperCamelCase__ = 0.1						,	UpperCamelCase__ = 0.1						,	UpperCamelCase__ = 512						,	UpperCamelCase__ = 2						,	UpperCamelCase__ = 0.02						,	UpperCamelCase__ = 1e-12						,	UpperCamelCase__ = False						,	**UpperCamelCase__						,	)	->		Union[str, Any]:

       '''simple docstring'''




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

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







class _lowerCAmelCase				(      __UpperCamelCase	):







   def __init__(	self						,	UpperCamelCase__						,	UpperCamelCase__ = "default"						,	UpperCamelCase__ = None						)	->		Any:

       '''simple docstring'''




       super().__init__(UpperCamelCase__						,	UpperCamelCase__						,	UpperCamelCase__						)
       snake_case    :      Any      					=    True







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

       '''simple docstring'''




       if self.task == "multiple-choice":
           snake_case    :      int      					=    {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
           snake_case    :      Optional[int]      					=    {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
               ("global_attention_mask", dynamic_axis),
           ]						)







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

       '''simple docstring'''




       snake_case    :      Any      					=    super().outputs
       if self.task == "default":
           snake_case    :      List[Any]      					=    {0: 'batch'}
       return outputs







   @property
   def  lowerCamelCase		(	self						)	->		float:

       '''simple docstring'''




       return 1e-4







   @property
   def  lowerCamelCase		(	self						)	->		int:

       '''simple docstring'''




       return max(super().default_onnx_opset						,	14						)







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

       '''simple docstring'''




       snake_case    :      List[str]      					=    super().generate_dummy_inputs(
           preprocessor=UpperCamelCase__						,	batch_size=UpperCamelCase__						,	seq_length=UpperCamelCase__						,	is_pair=UpperCamelCase__						,	framework=UpperCamelCase__						)
       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
       snake_case    :      Dict      					=    torch.zeros_like(inputs["input_ids"]						)
       # make every second token global
       snake_case    :      Dict      					=    1

       return inputs












import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""M-CLIP"""
 def __init__(      self  :      Optional[Any]			,     UpperCAmelCase  :      Union[str, Any]=1024			,     UpperCAmelCase  :      Tuple=768			,     **UpperCAmelCase  :      Optional[int]	)			->	Dict:
     lowerCamelCase__    :      Optional[int]					 =		transformerDimSize
     lowerCamelCase__    :      Optional[Any]					 =		imageDimSize
     super().__init__(**UpperCAmelCase	)


class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		MCLIPConfig
 def __init__(      self  :      List[Any]			,     UpperCAmelCase  :      Dict			,     *UpperCAmelCase  :      Any			,     **UpperCAmelCase  :      Dict	)			->	Dict:
     super().__init__(UpperCAmelCase			,     *UpperCAmelCase			,     **UpperCAmelCase	)
     lowerCamelCase__    :      Tuple					 =		XLMRobertaModel(UpperCAmelCase	)
     lowerCamelCase__    :      Union[str, Any]					 =		torch.nn.Linear(
         in_features=config.transformerDimensions			,     out_features=config.numDims	)







 def      A_	(      self  :      Optional[int]			,     UpperCAmelCase  :      Optional[Any]			,     UpperCAmelCase  :      Dict	)			->	Tuple:
     lowerCamelCase__    :      Any					 =		self.transformer(input_ids=UpperCAmelCase			,     attention_mask=UpperCAmelCase	)[0]
     lowerCamelCase__    :      int					 =		(embs * attention_mask.unsqueeze(2	)).sum(dim=1	) / attention_mask.sum(dim=1	)[:, None]
     return self.LinearTransformation(UpperCAmelCase	), embs





