




import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Dict							,							_lowerCAmelCase    :						Any							,							_lowerCAmelCase    :						List[Any]=sys.maxsize		):
       SCREAMING_SNAKE_CASE_      		=     'bilinear'
       SCREAMING_SNAKE_CASE_      		=     max_size
       SCREAMING_SNAKE_CASE_      		=     short_edge_length




 def __call__(				self    :						int							,							_lowerCAmelCase    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     []
       for img in imgs:
             SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     img.shape[:2]
             # later: provide list and randomly choose index for resize
             SCREAMING_SNAKE_CASE_      		=     np.random.randint(self.short_edge_length[0]							,							self.short_edge_length[1] + 1		)
             if size == 0:
                   return img
             SCREAMING_SNAKE_CASE_      		=     size * 1.0 / min(_lowerCAmelCase							,							_lowerCAmelCase		)
             if h < w:
                   SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     size, scale * w
             else:
                   SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     scale * h, size
             if max(_lowerCAmelCase							,							_lowerCAmelCase		) > self.max_size:
                   SCREAMING_SNAKE_CASE_      		=     self.max_size * 1.0 / max(_lowerCAmelCase							,							_lowerCAmelCase		)
                   SCREAMING_SNAKE_CASE_      		=     newh * scale
                   SCREAMING_SNAKE_CASE_      		=     neww * scale
             SCREAMING_SNAKE_CASE_      		=     int(neww + 0.5		)
             SCREAMING_SNAKE_CASE_      		=     int(newh + 0.5		)

             if img.dtype == np.uinta:
                   SCREAMING_SNAKE_CASE_      		=     Image.fromarray(_lowerCAmelCase		)
                   SCREAMING_SNAKE_CASE_      		=     pil_image.resize((neww, newh)							,							PILImageResampling.BILINEAR		)
                   SCREAMING_SNAKE_CASE_      		=     np.asarray(_lowerCAmelCase		)
             else:
                   SCREAMING_SNAKE_CASE_      		=     img.permute(2							,							0							,							1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
                   SCREAMING_SNAKE_CASE_      		=     nn.functional.interpolate(
                       _lowerCAmelCase							,							(newh, neww)							,							mode=self.interp_method							,							align_corners=_lowerCAmelCase		).squeeze(0		)
             img_augs.append(_lowerCAmelCase		)

       return img_augs





class 			lowerCamelCase_				:


 '''simple docstring'''







 def __init__(				self    :						Optional[int]							,							_lowerCAmelCase    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]							,							cfg.INPUT.MAX_SIZE_TEST		)
       SCREAMING_SNAKE_CASE_      		=     cfg.INPUT.FORMAT
       SCREAMING_SNAKE_CASE_      		=     cfg.SIZE_DIVISIBILITY
       SCREAMING_SNAKE_CASE_      		=     cfg.PAD_VALUE
       SCREAMING_SNAKE_CASE_      		=     cfg.INPUT.MAX_SIZE_TEST
       SCREAMING_SNAKE_CASE_      		=     cfg.MODEL.DEVICE
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)							,							1							,							1		)
       SCREAMING_SNAKE_CASE_      		=     torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		)							,							1							,							1		)
       SCREAMING_SNAKE_CASE_      		=     lambda _lowerCAmelCase		: (x - self.pixel_mean) / self.pixel_std



 def  lowerCAmelCase_    (				self    :						Tuple							,							_lowerCAmelCase    :						List[Any]		):
       SCREAMING_SNAKE_CASE_      		=     tuple(max(_lowerCAmelCase		) for s in zip(*[img.shape for img in images]		)		)
       SCREAMING_SNAKE_CASE_      		=     [im.shape[-2:] for im in images]
       SCREAMING_SNAKE_CASE_      		=     [
           nn.functional.pad(
               _lowerCAmelCase							,							[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]]							,							value=self.pad_value							,							)
           for size, im in zip(_lowerCAmelCase							,							_lowerCAmelCase		)
       ]

       return torch.stack(_lowerCAmelCase		), torch.tensor(_lowerCAmelCase		)




 def __call__(				self    :						List[str]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						List[str]=False		):
       with torch.no_grad():
             if not isinstance(_lowerCAmelCase							,							_lowerCAmelCase		):
                   SCREAMING_SNAKE_CASE_      		=     [images]
             if single_image:
                   assert len(_lowerCAmelCase		) == 1
             for i in range(len(_lowerCAmelCase		)		):
                   if isinstance(images[i]							,							torch.Tensor		):
                         images.insert(_lowerCAmelCase							,							images.pop(_lowerCAmelCase		).to(self.device		).float()		)
                   elif not isinstance(images[i]							,							torch.Tensor		):
                         images.insert(
                             _lowerCAmelCase							,							torch.as_tensor(img_tensorize(images.pop(_lowerCAmelCase		)							,							input_format=self.input_format		)		)
                             .to(self.device		)
                             .float()							,							)
            # resize smallest edge
             SCREAMING_SNAKE_CASE_      		=     torch.tensor([im.shape[:2] for im in images]		)
             SCREAMING_SNAKE_CASE_      		=     self.aug(_lowerCAmelCase		)
             # transpose images and convert to torch tensors
             # images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
             # now normalize before pad to avoid useless arithmetic
             SCREAMING_SNAKE_CASE_      		=     [self.normalizer(_lowerCAmelCase		) for x in images]
             # now pad them to do the following operations
             SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     self.pad(_lowerCAmelCase		)
             # Normalize

             if self.size_divisibility > 0:
                   raise NotImplementedError()
             # pad
             SCREAMING_SNAKE_CASE_      		=     torch.true_divide(_lowerCAmelCase							,							_lowerCAmelCase		)
             if single_image:
                   return images[0], sizes[0], scales_yx[0]
             else:
                   return images, sizes, scales_yx


def    UpperCAmelCase_				( __UpperCAmelCase		:      List[Any]     ,     __UpperCAmelCase		:      Any				)   ->					Union[str, Any]:
      boxes[:, 0::2] *= scale_yx[:, 1]
      boxes[:, 1::2] *= scale_yx[:, 0]
      return boxes





def    UpperCAmelCase_				( __UpperCAmelCase		:      Dict     ,     __UpperCAmelCase		:      Tuple[int, int]				)   ->					Any:
      assert torch.isfinite(__UpperCAmelCase				).all(), "Box tensor contains infinite or NaN!"
      SCREAMING_SNAKE_CASE_		,					SCREAMING_SNAKE_CASE_      		=     box_size
      tensor[:, 0].clamp_(min=0     ,     max=__UpperCAmelCase				)
      tensor[:, 1].clamp_(min=0     ,     max=__UpperCAmelCase				)
      tensor[:, 2].clamp_(min=0     ,     max=__UpperCAmelCase				)
      tensor[:, 3].clamp_(min=0     ,     max=__UpperCAmelCase				)





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


lowerCamelCase__					:  Optional[int]      							=				logging.get_logger(__name__)

lowerCamelCase__					:  Dict      							=				{
    '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_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       "longformer"



 def __init__(				self    :						Union[str, Any]							,							_lowerCAmelCase    :						Union[List[int], int] = 512							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 1							,							_lowerCAmelCase    :						int = 0							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						int = 30_522							,							_lowerCAmelCase    :						int = 768							,							_lowerCAmelCase    :						int = 12							,							_lowerCAmelCase    :						int = 12							,							_lowerCAmelCase    :						int = 3_072							,							_lowerCAmelCase    :						str = "gelu"							,							_lowerCAmelCase    :						float = 0.1							,							_lowerCAmelCase    :						float = 0.1							,							_lowerCAmelCase    :						int = 512							,							_lowerCAmelCase    :						int = 2							,							_lowerCAmelCase    :						float = 0.02							,							_lowerCAmelCase    :						float = 1E-12							,							_lowerCAmelCase    :						bool = False							,							**_lowerCAmelCase    :						Union[str, Any]							,							):
       super().__init__(pad_token_id=_lowerCAmelCase							,							**_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     attention_window
       SCREAMING_SNAKE_CASE_      		=     sep_token_id
       SCREAMING_SNAKE_CASE_      		=     bos_token_id
       SCREAMING_SNAKE_CASE_      		=     eos_token_id
       SCREAMING_SNAKE_CASE_      		=     vocab_size
       SCREAMING_SNAKE_CASE_      		=     hidden_size
       SCREAMING_SNAKE_CASE_      		=     num_hidden_layers
       SCREAMING_SNAKE_CASE_      		=     num_attention_heads
       SCREAMING_SNAKE_CASE_      		=     hidden_act
       SCREAMING_SNAKE_CASE_      		=     intermediate_size
       SCREAMING_SNAKE_CASE_      		=     hidden_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE_      		=     max_position_embeddings
       SCREAMING_SNAKE_CASE_      		=     type_vocab_size
       SCREAMING_SNAKE_CASE_      		=     initializer_range
       SCREAMING_SNAKE_CASE_      		=     layer_norm_eps
       SCREAMING_SNAKE_CASE_      		=     onnx_export





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 def __init__(				self    :						Optional[Any]							,							_lowerCAmelCase    :						"PretrainedConfig"							,							_lowerCAmelCase    :						str = "default"							,							_lowerCAmelCase    :						"List[PatchingSpec]" = None		):
       super().__init__(_lowerCAmelCase							,							_lowerCAmelCase							,							_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     True



 @property
 def  lowerCAmelCase_    (				self    :						Any		):
       if self.task == "multiple-choice":
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('global_attention_mask', dynamic_axis),
           ]		)



 @property
 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     super().outputs
       if self.task == "default":
             SCREAMING_SNAKE_CASE_      		=     {0: 'batch'}
       return outputs



 @property
 def  lowerCAmelCase_    (				self    :						str		):
       return 1E-4



 @property
 def  lowerCAmelCase_    (				self    :						Optional[Any]		):
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset							,							14		)




 def  lowerCAmelCase_    (				self    :						str							,							_lowerCAmelCase    :						"PreTrainedTokenizerBase"							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						int = -1							,							_lowerCAmelCase    :						bool = False							,							_lowerCAmelCase    :						Optional[TensorType] = None							,							):
       SCREAMING_SNAKE_CASE_      		=     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
       SCREAMING_SNAKE_CASE_      		=     torch.zeros_like(inputs['input_ids']		)
       # make every second token global
       SCREAMING_SNAKE_CASE_      		=     1

       return inputs