





import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor



class    _a     :






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     str							,	a				:     str=13							,	a				:     str=30							,	a				:     Tuple=2							,	a				:     List[Any]=3							,	a				:     List[Any]=True							,	a				:     str=True							,	a				:     Optional[int]=32							,	a				:     Optional[int]=5							,	a				:     List[str]=4							,	a				:     Optional[int]=37							,	a				:     Tuple="gelu"							,	a				:     Optional[int]=0.1							,	a				:     str=0.1							,	a				:     Optional[int]=10							,	a				:     Any=0.02							,	a				:     Union[str, Any]=None							,	a				:     Any=2							,	)	->int:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					parent
													SCREAMING_SNAKE_CASE__     :				List[str]   =					batch_size
													SCREAMING_SNAKE_CASE__     :				int   =					image_size
													SCREAMING_SNAKE_CASE__     :				List[str]   =					patch_size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					num_channels
													SCREAMING_SNAKE_CASE__     :				Dict   =					is_training
													SCREAMING_SNAKE_CASE__     :				Dict   =					use_labels
													SCREAMING_SNAKE_CASE__     :				Any   =					hidden_size
													SCREAMING_SNAKE_CASE__     :				Any   =					num_hidden_layers
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					num_attention_heads
													SCREAMING_SNAKE_CASE__     :				int   =					intermediate_size
													SCREAMING_SNAKE_CASE__     :				Dict   =					hidden_act
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					hidden_dropout_prob
													SCREAMING_SNAKE_CASE__     :				int   =					attention_probs_dropout_prob
													SCREAMING_SNAKE_CASE__     :				Tuple   =					type_sequence_label_size
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					initializer_range
													SCREAMING_SNAKE_CASE__     :				Tuple   =					scope
													SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					encoder_stride

													# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
													SCREAMING_SNAKE_CASE__     :				Dict   =					(image_size // patch_size) ** 2
													SCREAMING_SNAKE_CASE__     :				Dict   =					num_patches + 1

							def      A_  (      self				:     Dict    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Any   =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

													SCREAMING_SNAKE_CASE__     :				str   =					None
													if self.use_labels:
																			SCREAMING_SNAKE_CASE__     :				Any   =					ids_tensor([self.batch_size]							,	self.type_sequence_label_size    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.get_config()

													return config, pixel_values, labels

							def      A_  (      self				:     List[Any]    )	->Tuple:
													return ViTConfig(
													    image_size=self.image_size							,	patch_size=self.patch_size							,	num_channels=self.num_channels							,	hidden_size=self.hidden_size							,	num_hidden_layers=self.num_hidden_layers							,	num_attention_heads=self.num_attention_heads							,	intermediate_size=self.intermediate_size							,	hidden_act=self.hidden_act							,	hidden_dropout_prob=self.hidden_dropout_prob							,	attention_probs_dropout_prob=self.attention_probs_dropout_prob							,	is_decoder=a							,	initializer_range=self.initializer_range							,	encoder_stride=self.encoder_stride							,	)

							def      A_  (      self				:     Tuple							,	a				:     Dict							,	a				:     Optional[int]							,	a				:     str    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__     :				Any   =					ViTModel(config=a    )
													model.to(a    )
													model.eval()
													SCREAMING_SNAKE_CASE__     :				int   =					model(a    )
													self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)    )

							def      A_  (      self				:     Optional[int]							,	a				:     Optional[Any]							,	a				:     Optional[int]							,	a				:     Optional[Any]    )	->Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					ViTForMaskedImageModeling(config=a    )
													model.to(a    )
													model.eval()
													SCREAMING_SNAKE_CASE__     :				Any   =					model(a    )
													self.parent.assertEqual(
													    result.reconstruction.shape							,	(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

													# test greyscale images
													SCREAMING_SNAKE_CASE__     :				int   =					1
													SCREAMING_SNAKE_CASE__     :				str   =					ViTForMaskedImageModeling(a    )
													model.to(a    )
													model.eval()

													SCREAMING_SNAKE_CASE__     :				int   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model(a    )
													self.parent.assertEqual(result.reconstruction.shape							,	(self.batch_size, 1, self.image_size, self.image_size)    )

							def      A_  (      self				:     Optional[int]							,	a				:     Optional[int]							,	a				:     Optional[int]							,	a				:     Any    )	->Any:
													SCREAMING_SNAKE_CASE__     :				List[str]   =					self.type_sequence_label_size
													SCREAMING_SNAKE_CASE__     :				Any   =					ViTForImageClassification(a    )
													model.to(a    )
													model.eval()
													SCREAMING_SNAKE_CASE__     :				Dict   =					model(a							,	labels=a    )
													self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size)    )

													# test greyscale images
													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					1
													SCREAMING_SNAKE_CASE__     :				int   =					ViTForImageClassification(a    )
													model.to(a    )
													model.eval()

													SCREAMING_SNAKE_CASE__     :				List[str]   =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model(a    )
													self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.type_sequence_label_size)    )






							def      A_  (      self				:     Any    )	->List[str]:
													SCREAMING_SNAKE_CASE__     :				Any   =					self.prepare_config_and_inputs()
													(
													    (
													    SCREAMING_SNAKE_CASE__
													),							(
													    SCREAMING_SNAKE_CASE__
													),							(
													    SCREAMING_SNAKE_CASE__
													),							
													)     :				Any   =					config_and_inputs
													SCREAMING_SNAKE_CASE__     :				Any   =					{"pixel_values": pixel_values}
													return config, inputs_dict







@require_torch
class    _a     (				lowercase__					,							lowercase__					,							unittest.TestCase							):






							"""simple docstring"""

							snake_case_							 =						(
							    (
							        ViTModel,
							        ViTForImageClassification,
							        ViTForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							snake_case_							 =						(
							    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
							    if is_torch_available()
							    else {}
							)
							snake_case_							 =						True

							snake_case_							 =						False
							snake_case_							 =						False
							snake_case_							 =						False

							def      A_  (      self				:     Tuple    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				str   =					ViTModelTester(self    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					ConfigTester(self							,	config_class=a							,	has_text_modality=a							,	hidden_size=37    )

							def      A_  (      self				:     Tuple    )	->List[str]:
													self.config_tester.run_common_tests()

							@unittest.skip(reason="ViT does not use inputs_embeds"    )
							def      A_  (      self				:     Union[str, Any]    )	->List[Any]:
													pass

							def      A_  (      self				:     List[str]    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					model_class(a    )
																			self.assertIsInstance(model.get_input_embeddings()							,	(nn.Module)    )
																			SCREAMING_SNAKE_CASE__     :				Any   =					model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(a							,	nn.Linear    )    )

							def      A_  (      self				:     Dict    )	->Optional[Any]:
													SCREAMING_SNAKE_CASE__,							SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			SCREAMING_SNAKE_CASE__     :				str   =					model_class(a    )
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					inspect.signature(model.forward    )
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			SCREAMING_SNAKE_CASE__     :				str   =					[*signature.parameters.keys()]

																			SCREAMING_SNAKE_CASE__     :				Optional[int]   =					["pixel_values"]
																			self.assertListEqual(arg_names[:1]							,	a    )

							def      A_  (      self				:     Union[str, Any]    )	->Optional[int]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*a    )

							def      A_  (      self				:     Tuple    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Dict   =					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_masked_image_modeling(*a    )

							def      A_  (      self				:     int    )	->str:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_image_classification(*a    )






							@slow
							def      A_  (      self				:     Tuple    )	->List[Any]:
													for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			SCREAMING_SNAKE_CASE__     :				int   =					ViTModel.from_pretrained(a    )
																			self.assertIsNotNone(a    )







def       UpperCAmelCase							(     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Optional[int]   =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
						return image



@require_torch
@require_vision
class    _a     (				unittest.TestCase							):






							"""simple docstring"""

							@cached_property
							def      A_  (      self				:     Optional[Any]    )	->Tuple:
													return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224"    ) if is_vision_available() else None

							@slow
							def      A_  (      self				:     str    )	->List[Any]:
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					ViTForImageClassification.from_pretrained("google/vit-base-patch16-224"    ).to(a    )

													SCREAMING_SNAKE_CASE__     :				Optional[int]   =					self.default_image_processor
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					prepare_img()
													SCREAMING_SNAKE_CASE__     :				int   =					image_processor(images=a							,	return_tensors="pt"    ).to(a    )

													# forward pass
													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				List[Any]   =					model(**a    )

													# verify the logits
													SCREAMING_SNAKE_CASE__     :				Any   =					torch.Size((1, 10_00)    )
													self.assertEqual(outputs.logits.shape							,	a    )

													SCREAMING_SNAKE_CASE__     :				str   =					torch.tensor([-0.2744, 0.8215, -0.0836]    ).to(a    )

													self.assertTrue(torch.allclose(outputs.logits[0, :3]							,	a							,	atol=1E-4    )    )

							@slow
							def      A_  (      self				:     Tuple    )	->int:
													# ViT models have an `interpolate_pos_encoding` argument in their forward method,
													# allowing to interpolate the pre-trained position embeddings in order to use
													# the model on higher resolutions. The DINO model by Facebook AI leverages this
													# to visualize self-attention on higher resolution images.
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ViTModel.from_pretrained("facebook/dino-vits8"    ).to(a    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					ViTImageProcessor.from_pretrained("facebook/dino-vits8"							,	size=4_80    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					prepare_img()
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					image_processor(images=a							,	return_tensors="pt"    )
													SCREAMING_SNAKE_CASE__     :				List[str]   =					inputs.pixel_values.to(a    )

													# forward pass
													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					model(a							,	interpolate_pos_encoding=a    )

													# verify the logits
													SCREAMING_SNAKE_CASE__     :				List[str]   =					torch.Size((1, 36_01, 3_84)    )
													self.assertEqual(outputs.last_hidden_state.shape							,	a    )

													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					torch.tensor(
													    [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]    ).to(a    )

													self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]							,	a							,	atol=1E-4    )    )






							@slow
							@require_accelerate
							@require_torch_gpu
							def      A_  (      self				:     int    )	->Any:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					ViTModel.from_pretrained("facebook/dino-vits8"							,	torch_dtype=torch.floataa							,	device_map="auto"    )
													SCREAMING_SNAKE_CASE__     :				List[Any]   =					self.default_image_processor

													SCREAMING_SNAKE_CASE__     :				str   =					prepare_img()
													SCREAMING_SNAKE_CASE__     :				str   =					image_processor(images=a							,	return_tensors="pt"    )
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					inputs.pixel_values.to(a    )

													# forward pass to make sure inference works in fp16
													with torch.no_grad():
																			SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					model(a    )






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


__lowercase       :List[Any]       =  logging.get_logger(__name__)

__lowercase       :Optional[int]       =  {
    "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    _a     (				lowercase__							):






							"""simple docstring"""

							snake_case_							 =						"longformer"

							def __init__(      self				:     List[str]							,	a				:     Union[List[int], int] = 5_12							,	a				:     int = 2							,	a				:     int = 1							,	a				:     int = 0							,	a				:     int = 2							,	a				:     int = 3_05_22							,	a				:     int = 7_68							,	a				:     int = 12							,	a				:     int = 12							,	a				:     int = 30_72							,	a				:     str = "gelu"							,	a				:     float = 0.1							,	a				:     float = 0.1							,	a				:     int = 5_12							,	a				:     int = 2							,	a				:     float = 0.02							,	a				:     float = 1E-12							,	a				:     bool = False							,	**a				:     Dict							,	)	->Tuple:
													super().__init__(pad_token_id=a							,	**a    )

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







class    _a     (				lowercase__							):






							"""simple docstring"""

							def __init__(      self				:     int							,	a				:     "PretrainedConfig"							,	a				:     str = "default"							,	a				:     "List[PatchingSpec]" = None    )	->str:
													super().__init__(a							,	a							,	a    )
													SCREAMING_SNAKE_CASE__     :				Any   =					True

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

							@property
							def      A_  (      self				:     Optional[Any]    )	->Mapping[str, Mapping[int, str]]:
													SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					super().outputs
													if self.task == "default":
																			SCREAMING_SNAKE_CASE__     :				List[str]   =					{0: "batch"}
													return outputs

							@property
							def      A_  (      self				:     str    )	->float:
													return 1E-4

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






							def      A_  (      self				:     str							,	a				:     "PreTrainedTokenizerBase"							,	a				:     int = -1							,	a				:     int = -1							,	a				:     bool = False							,	a				:     Optional[TensorType] = None							,	)	->Mapping[str, Any]:
													SCREAMING_SNAKE_CASE__     :				Tuple   =					super().generate_dummy_inputs(
													    preprocessor=a							,	batch_size=a							,	seq_length=a							,	is_pair=a							,	framework=a    )
													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__     :				Any   =					torch.zeros_like(inputs["input_ids"]    )
													# make every second token global
													SCREAMING_SNAKE_CASE__     :				str   =					1

													return inputs