
'''simple docstring'''
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DetrImageProcessor






class    UpperCAmelCase_				(	unittest.TestCase					):





 def __init__(    self ,       lowercase_ ,       lowercase_=7 ,       lowercase_=3 ,       lowercase_=30 ,       lowercase_=4_00 ,       lowercase_=True ,       lowercase_=None ,       lowercase_=True ,       lowercase_=1 / 2_55 ,       lowercase_=True ,       lowercase_=[0.5, 0.5, 0.5] ,       lowercase_=[0.5, 0.5, 0.5] ,       lowercase_=True ,       ):
     # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
     snake_case_ :    Union[str, Any]     			=						size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
     snake_case_ :    Optional[Any]     			=						parent
     snake_case_ :    List[Any]     			=						batch_size
     snake_case_ :    str     			=						num_channels
     snake_case_ :    Optional[int]     			=						min_resolution
     snake_case_ :    List[str]     			=						max_resolution
     snake_case_ :    str     			=						do_resize
     snake_case_ :    Tuple     			=						size
     snake_case_ :    Tuple     			=						do_rescale
     snake_case_ :    List[str]     			=						rescale_factor
     snake_case_ :    Optional[int]     			=						do_normalize
     snake_case_ :    List[Any]     			=						image_mean
     snake_case_ :    List[Any]     			=						image_std
     snake_case_ :    Optional[Any]     			=						do_pad





 def        snake_case__		(    self):
     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_rescale": self.do_rescale,
         "rescale_factor": self.rescale_factor,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_pad": self.do_pad,
     }





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=False):
     if not batched:
         snake_case_ :    Tuple     			=						image_inputs[0]
         if isinstance(lowercase_ ,       Image.Image):
             snake_case_			, snake_case_ :    Any     			=						image.size
         else:
             snake_case_			, snake_case_ :    str     			=						image.shape[1], image.shape[2]
         if w < h:
             snake_case_ :    Optional[int]     			=						int(self.size["shortest_edge"] * h / w)
             snake_case_ :    Optional[Any]     			=						self.size["shortest_edge"]
         elif w > h:
             snake_case_ :    Union[str, Any]     			=						self.size["shortest_edge"]
             snake_case_ :    Union[str, Any]     			=						int(self.size["shortest_edge"] * w / h)
         else:
             snake_case_ :    Tuple     			=						self.size["shortest_edge"]
             snake_case_ :    str     			=						self.size["shortest_edge"]

     else:
         snake_case_ :    str     			=						[]
         for image in image_inputs:
             snake_case_			, snake_case_ :    Optional[Any]     			=						self.get_expected_values([image])
             expected_values.append((expected_height, expected_width))
         snake_case_ :    Optional[int]     			=						max(lowercase_ ,       key=lambda lowercase_: item[0])[0]
         snake_case_ :    List[str]     			=						max(lowercase_ ,       key=lambda lowercase_: item[1])[1]

     return expected_height, expected_width






@require_torch
@require_vision
class    UpperCAmelCase_				(	snake_case__    ,						unittest.TestCase					):
 UpperCAmelCase_		    =						DetrImageProcessor if is_vision_available() else None





 def        snake_case__		(    self):
     snake_case_ :    Optional[int]     			=						DetrImageProcessingTester(self)





 @property
 def        snake_case__		(    self):
     return self.image_processor_tester.prepare_image_processor_dict()





 def        snake_case__		(    self):
     snake_case_ :    Any     			=						self.image_processing_class(**self.image_processor_dict)
     self.assertTrue(hasattr(lowercase_ ,       "image_mean"))
     self.assertTrue(hasattr(lowercase_ ,       "image_std"))
     self.assertTrue(hasattr(lowercase_ ,       "do_normalize"))
     self.assertTrue(hasattr(lowercase_ ,       "do_rescale"))
     self.assertTrue(hasattr(lowercase_ ,       "rescale_factor"))
     self.assertTrue(hasattr(lowercase_ ,       "do_resize"))
     self.assertTrue(hasattr(lowercase_ ,       "size"))
     self.assertTrue(hasattr(lowercase_ ,       "do_pad"))





 def        snake_case__		(    self):
     snake_case_ :    List[Any]     			=						self.image_processing_class.from_dict(self.image_processor_dict)
     self.assertEqual(image_processor.size ,       {"shortest_edge": 18, "longest_edge": 13_33})
     self.assertEqual(image_processor.do_pad ,       lowercase_)

     snake_case_ :    int     			=						self.image_processing_class.from_dict(
         self.image_processor_dict ,       size=42 ,       max_size=84 ,       pad_and_return_pixel_mask=lowercase_)
     self.assertEqual(image_processor.size ,       {"shortest_edge": 42, "longest_edge": 84})
     self.assertEqual(image_processor.do_pad ,       lowercase_)





 def        snake_case__		(    self):
     pass





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    List[Any]     			=						self.image_processing_class(**self.image_processor_dict)
     # create random PIL images
     snake_case_ :    Dict     			=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=lowercase_)
     for image in image_inputs:
         self.assertIsInstance(lowercase_ ,       Image.Image)

     # Test not batched input
     snake_case_ :    List[Any]     			=						image_processing(image_inputs[0] ,       return_tensors="pt").pixel_values

     snake_case_			, snake_case_ :    Dict     			=						self.image_processor_tester.get_expected_values(lowercase_)

     self.assertEqual(
         encoded_images.shape ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,       )

     # Test batched
     snake_case_			, snake_case_ :    List[Any]     			=						self.image_processor_tester.get_expected_values(lowercase_ ,       batched=lowercase_)

     snake_case_ :    Dict     			=						image_processing(lowercase_ ,       return_tensors="pt").pixel_values
     self.assertEqual(
         encoded_images.shape ,       (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,       )





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    Optional[Any]     			=						self.image_processing_class(**self.image_processor_dict)
     # create random numpy tensors
     snake_case_ :    int     			=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=lowercase_ ,       numpify=lowercase_)
     for image in image_inputs:
         self.assertIsInstance(lowercase_ ,       np.ndarray)

     # Test not batched input
     snake_case_ :    Dict     			=						image_processing(image_inputs[0] ,       return_tensors="pt").pixel_values

     snake_case_			, snake_case_ :    Any     			=						self.image_processor_tester.get_expected_values(lowercase_)

     self.assertEqual(
         encoded_images.shape ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,       )

     # Test batched
     snake_case_ :    Tuple     			=						image_processing(lowercase_ ,       return_tensors="pt").pixel_values

     snake_case_			, snake_case_ :    int     			=						self.image_processor_tester.get_expected_values(lowercase_ ,       batched=lowercase_)

     self.assertEqual(
         encoded_images.shape ,       (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,       )





 def        snake_case__		(    self):
     # Initialize image_processing
     snake_case_ :    str     			=						self.image_processing_class(**self.image_processor_dict)
     # create random PyTorch tensors
     snake_case_ :    Tuple     			=						prepare_image_inputs(self.image_processor_tester ,       equal_resolution=lowercase_ ,       torchify=lowercase_)
     for image in image_inputs:
         self.assertIsInstance(lowercase_ ,       torch.Tensor)

     # Test not batched input
     snake_case_ :    int     			=						image_processing(image_inputs[0] ,       return_tensors="pt").pixel_values

     snake_case_			, snake_case_ :    Optional[Any]     			=						self.image_processor_tester.get_expected_values(lowercase_)

     self.assertEqual(
         encoded_images.shape ,       (1, self.image_processor_tester.num_channels, expected_height, expected_width) ,       )

     # Test batched
     snake_case_ :    List[Any]     			=						image_processing(lowercase_ ,       return_tensors="pt").pixel_values

     snake_case_			, snake_case_ :    Tuple     			=						self.image_processor_tester.get_expected_values(lowercase_ ,       batched=lowercase_)

     self.assertEqual(
         encoded_images.shape ,       (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             expected_height,
             expected_width,
         ) ,       )





 @slow
 def        snake_case__		(    self):
     # prepare image and target
     snake_case_ :    Dict     			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" ,       "r") as f:
         snake_case_ :    List[str]     			=						json.loads(f.read())

     snake_case_ :    Optional[Any]     			=						{"image_id": 3_97_69, "annotations": target}

     # encode them
     snake_case_ :    str     			=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
     snake_case_ :    int     			=						image_processing(images=lowercase_ ,       annotations=lowercase_ ,       return_tensors="pt")

     # verify pixel values
     snake_case_ :    List[str]     			=						torch.Size([1, 3, 8_00, 10_66])
     self.assertEqual(encoding["pixel_values"].shape ,       lowercase_)

     snake_case_ :    Optional[int]     			=						torch.tensor([0.2_796, 0.3_138, 0.3_481])
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,       lowercase_ ,       atol=1E-4))

     # verify area
     snake_case_ :    List[Any]     			=						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438])
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,       lowercase_))
     # verify boxes
     snake_case_ :    Any     			=						torch.Size([6, 4])
     self.assertEqual(encoding["labels"][0]["boxes"].shape ,       lowercase_)
     snake_case_ :    Optional[Any]     			=						torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215])
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,       lowercase_ ,       atol=1E-3))
     # verify image_id
     snake_case_ :    List[str]     			=						torch.tensor([3_97_69])
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,       lowercase_))
     # verify is_crowd
     snake_case_ :    Optional[int]     			=						torch.tensor([0, 0, 0, 0, 0, 0])
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,       lowercase_))
     # verify class_labels
     snake_case_ :    Tuple     			=						torch.tensor([75, 75, 63, 65, 17, 17])
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,       lowercase_))
     # verify orig_size
     snake_case_ :    int     			=						torch.tensor([4_80, 6_40])
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,       lowercase_))
     # verify size
     snake_case_ :    Any     			=						torch.tensor([8_00, 10_66])
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,       lowercase_))





 @slow
 def        snake_case__		(    self):
     # prepare image, target and masks_path
     snake_case_ :    int     			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" ,       "r") as f:
         snake_case_ :    Optional[Any]     			=						json.loads(f.read())

     snake_case_ :    List[Any]     			=						{"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

     snake_case_ :    Union[str, Any]     			=						pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")

     # encode them
     snake_case_ :    Any     			=						DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
     snake_case_ :    Optional[Any]     			=						image_processing(images=lowercase_ ,       annotations=lowercase_ ,       masks_path=lowercase_ ,       return_tensors="pt")

     # verify pixel values
     snake_case_ :    Any     			=						torch.Size([1, 3, 8_00, 10_66])
     self.assertEqual(encoding["pixel_values"].shape ,       lowercase_)

     snake_case_ :    Optional[int]     			=						torch.tensor([0.2_796, 0.3_138, 0.3_481])
     self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] ,       lowercase_ ,       atol=1E-4))

     # verify area
     snake_case_ :    Optional[Any]     			=						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147])
     self.assertTrue(torch.allclose(encoding["labels"][0]["area"] ,       lowercase_))
     # verify boxes
     snake_case_ :    Optional[int]     			=						torch.Size([6, 4])
     self.assertEqual(encoding["labels"][0]["boxes"].shape ,       lowercase_)
     snake_case_ :    Tuple     			=						torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625])
     self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] ,       lowercase_ ,       atol=1E-3))
     # verify image_id
     snake_case_ :    List[str]     			=						torch.tensor([3_97_69])
     self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] ,       lowercase_))
     # verify is_crowd
     snake_case_ :    Union[str, Any]     			=						torch.tensor([0, 0, 0, 0, 0, 0])
     self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] ,       lowercase_))
     # verify class_labels
     snake_case_ :    Tuple     			=						torch.tensor([17, 17, 63, 75, 75, 93])
     self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] ,       lowercase_))
     # verify masks
     snake_case_ :    Any     			=						82_28_73
     self.assertEqual(encoding["labels"][0]["masks"].sum().item() ,       lowercase_)
     # verify orig_size
     snake_case_ :    int     			=						torch.tensor([4_80, 6_40])
     self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] ,       lowercase_))
     # verify size
     snake_case_ :    Optional[int]     			=						torch.tensor([8_00, 10_66])
     self.assertTrue(torch.allclose(encoding["labels"][0]["size"] ,       lowercase_))






'''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


a_											=						logging.get_logger(__name__)

a_											=						{
    "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    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						"""longformer"""





 def __init__(    self ,       lowercase_ = 5_12 ,       lowercase_ = 2 ,       lowercase_ = 1 ,       lowercase_ = 0 ,       lowercase_ = 2 ,       lowercase_ = 3_05_22 ,       lowercase_ = 7_68 ,       lowercase_ = 12 ,       lowercase_ = 12 ,       lowercase_ = 30_72 ,       lowercase_ = "gelu" ,       lowercase_ = 0.1 ,       lowercase_ = 0.1 ,       lowercase_ = 5_12 ,       lowercase_ = 2 ,       lowercase_ = 0.02 ,       lowercase_ = 1E-12 ,       lowercase_ = False ,       **lowercase_ ,       ):
     super().__init__(pad_token_id=lowercase_ ,       **lowercase_)

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






class    UpperCAmelCase_				(	snake_case__					):





 def __init__(    self ,       lowercase_ ,       lowercase_ = "default" ,       lowercase_ = None):
     super().__init__(lowercase_ ,       lowercase_ ,       lowercase_)
     snake_case_ :    Dict     			=						True





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





 @property
 def        snake_case__		(    self):
     snake_case_ :    Union[str, Any]     			=						super().outputs
     if self.task == "default":
         snake_case_ :    str     			=						{0: "batch"}
     return outputs





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





 @property
 def        snake_case__		(    self):
     # needs to be >= 14 to support tril operator
     return max(super().default_onnx_opset ,       14)





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = -1 ,       lowercase_ = -1 ,       lowercase_ = False ,       lowercase_ = None ,       ):
     snake_case_ :    Optional[Any]     			=						super().generate_dummy_inputs(
         preprocessor=lowercase_ ,       batch_size=lowercase_ ,       seq_length=lowercase_ ,       is_pair=lowercase_ ,       framework=lowercase_)
     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_ :    Any     			=						torch.zeros_like(inputs["input_ids"])
     # make every second token global
     snake_case_ :    Tuple     			=						1

     return inputs




