

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 				UpperCAmelCase_		:
							"""simple docstring"""







							def __init__(			self       ,							_a       ,							_a=1_3       ,							_a=3_0       ,							_a=2       ,							_a=3       ,							_a=True       ,							_a=True       ,							_a=3_2       ,							_a=5       ,							_a=4       ,							_a=3_7       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_0       ,							_a=0.02       ,							_a=None       ,							_a=2       ,							)    ->    List[Any]:
												_a	:    Union[str, Any]         =		parent
												_a	:    Optional[Any]         =		batch_size
												_a	:    List[str]         =		image_size
												_a	:    Dict         =		patch_size
												_a	:    int         =		num_channels
												_a	:    Tuple         =		is_training
												_a	:    Dict         =		use_labels
												_a	:    List[str]         =		hidden_size
												_a	:    Dict         =		num_hidden_layers
												_a	:    Dict         =		num_attention_heads
												_a	:    Optional[Any]         =		intermediate_size
												_a	:    Union[str, Any]         =		hidden_act
												_a	:    Tuple         =		hidden_dropout_prob
												_a	:    List[Any]         =		attention_probs_dropout_prob
												_a	:    Optional[int]         =		type_sequence_label_size
												_a	:    Optional[int]         =		initializer_range
												_a	:    Tuple         =		scope
												_a	:    int         =		encoder_stride

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


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Union[str, Any]         =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

												_a	:    Optional[Any]         =		None
												if self.use_labels:
																	_a	:    List[Any]         =		ids_tensor([self.batch_size]       ,							self.type_sequence_label_size       )

												_a	:    str         =		self.get_config()

												return config, pixel_values, labels


							def 				__lowercase				(			self       )    ->    str:
												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 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    str:
												_a	:    Tuple         =		ViTModel(config=_a       )
												model.to(_a       )
												model.eval()
												_a	:    str         =		model(_a       )
												self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)       )


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

												# test greyscale images
												_a	:    Optional[Any]         =		1
												_a	:    Tuple         =		ViTForMaskedImageModeling(_a       )
												model.to(_a       )
												model.eval()

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


							def 				__lowercase				(			self       ,							_a       ,							_a       ,							_a       )    ->    List[Any]:
												_a	:    Tuple         =		self.type_sequence_label_size
												_a	:    Dict         =		ViTForImageClassification(_a       )
												model.to(_a       )
												model.eval()
												_a	:    List[str]         =		model(_a       ,							labels=_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)       )

												# test greyscale images
												_a	:    str         =		1
												_a	:    Tuple         =		ViTForImageClassification(_a       )
												model.to(_a       )
												model.eval()

												_a	:    Dict         =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
												_a	:    Union[str, Any]         =		model(_a       )
												self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.type_sequence_label_size)       )




							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    List[Any]         =		self.prepare_config_and_inputs()
												(
												    (
												    _a
												)      ,					(
												    _a
												)      ,					(
												    _a
												)      ,					
												)	:    Optional[Any]         =		config_and_inputs
												_a	:    Optional[Any]         =		{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 				UpperCAmelCase_		(			__lowercase				,       __lowercase				,       unittest.TestCase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Union[str, Any]	    =    (
							    (
							        ViTModel,
							        ViTForImageClassification,
							        ViTForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__       :						str	    =    (
							    {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
							    if is_torch_available()
							    else {}
							)
							UpperCAmelCase__       :						Dict	    =    True

							UpperCAmelCase__       :						Union[str, Any]	    =    False
							UpperCAmelCase__       :						Optional[int]	    =    False
							UpperCAmelCase__       :						Dict	    =    False


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a	:    List[str]         =		ViTModelTester(self       )
												_a	:    List[Any]         =		ConfigTester(self       ,							config_class=_a       ,							has_text_modality=_a       ,							hidden_size=3_7       )


							def 				__lowercase				(			self       )    ->    Dict:
												self.config_tester.run_common_tests()


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


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a      ,					_a	:    List[Any]         =		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a	:    Optional[Any]         =		model_class(_a       )
																	self.assertIsInstance(model.get_input_embeddings()       ,							(nn.Module)       )
																	_a	:    Tuple         =		model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(_a       ,							nn.Linear       )       )


							def 				__lowercase				(			self       )    ->    Optional[Any]:
												_a      ,					_a	:    Any         =		self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a	:    str         =		['''pixel_values''']
																	self.assertListEqual(arg_names[:1]       ,							_a       )


							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    str         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a       )


							def 				__lowercase				(			self       )    ->    Dict:
												_a	:    List[str]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_image_modeling(*_a       )


							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Optional[int]         =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a       )




							@slow
							def 				__lowercase				(			self       )    ->    Union[str, Any]:
												for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a	:    Any         =		ViTModel.from_pretrained(_a       )
																	self.assertIsNotNone(_a       )



def    __UpperCAmelCase (	)			->     Any:

					"""simple docstring"""

					_a	:    Union[str, Any]         =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
					return image







@require_torch
@require_vision
class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							@cached_property
							def 				__lowercase				(			self       )    ->    List[str]:
												return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''       ) if is_vision_available() else None


							@slow
							def 				__lowercase				(			self       )    ->    List[Any]:
												_a	:    List[Any]         =		ViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''       ).to(_a       )

												_a	:    int         =		self.default_image_processor
												_a	:    Any         =		prepare_img()
												_a	:    str         =		image_processor(images=_a       ,							return_tensors='''pt'''       ).to(_a       )

												# forward pass
												with torch.no_grad():
																	_a	:    Dict         =		model(**_a       )

												# verify the logits
												_a	:    Optional[Any]         =		torch.Size((1, 1_0_0_0)       )
												self.assertEqual(outputs.logits.shape       ,							_a       )

												_a	:    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 				__lowercase				(			self       )    ->    Any:
												# 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.
												_a	:    Dict         =		ViTModel.from_pretrained('''facebook/dino-vits8'''       ).to(_a       )

												_a	:    List[Any]         =		ViTImageProcessor.from_pretrained('''facebook/dino-vits8'''       ,							size=4_8_0       )
												_a	:    Dict         =		prepare_img()
												_a	:    str         =		image_processor(images=_a       ,							return_tensors='''pt'''       )
												_a	:    str         =		inputs.pixel_values.to(_a       )

												# forward pass
												with torch.no_grad():
																	_a	:    List[Any]         =		model(_a       ,							interpolate_pos_encoding=_a       )

												# verify the logits
												_a	:    Tuple         =		torch.Size((1, 3_6_0_1, 3_8_4)       )
												self.assertEqual(outputs.last_hidden_state.shape       ,							_a       )

												_a	:    Union[str, 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 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Tuple         =		ViTModel.from_pretrained('''facebook/dino-vits8'''       ,							torch_dtype=torch.floataa       ,							device_map='''auto'''       )
												_a	:    Any         =		self.default_image_processor

												_a	:    Any         =		prepare_img()
												_a	:    Union[str, Any]         =		image_processor(images=_a       ,							return_tensors='''pt'''       )
												_a	:    Optional[Any]         =		inputs.pixel_values.to(_a       )

												# forward pass to make sure inference works in fp16
												with torch.no_grad():
																	_a	:    int         =		model(_a       )






from collections import OrderedDict
from typing import Mapping

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


a__				=		logging.get_logger(__name__)

a__				=		{
    '''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
    '''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
    '''junnyu/roformer_chinese_char_small''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
    ),
    '''junnyu/roformer_chinese_char_base''': (
        '''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_discriminator''': (
        '''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
    ),
    '''junnyu/roformer_small_generator''': (
        '''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Optional[Any]	    =    "roformer"


							def __init__(			self       ,							_a=5_0_0_0_0       ,							_a=None       ,							_a=7_6_8       ,							_a=1_2       ,							_a=1_2       ,							_a=3_0_7_2       ,							_a="gelu"       ,							_a=0.1       ,							_a=0.1       ,							_a=1_5_3_6       ,							_a=2       ,							_a=0.02       ,							_a=1e-1_2       ,							_a=0       ,							_a=False       ,							_a=True       ,							**_a       ,							)    ->    List[str]:
												super().__init__(pad_token_id=_a       ,							**_a       )

												_a	:    Tuple         =		vocab_size
												_a	:    List[Any]         =		hidden_size if embedding_size is None else embedding_size
												_a	:    Any         =		hidden_size
												_a	:    Any         =		num_hidden_layers
												_a	:    List[Any]         =		num_attention_heads
												_a	:    str         =		hidden_act
												_a	:    Any         =		intermediate_size
												_a	:    Dict         =		hidden_dropout_prob
												_a	:    Optional[Any]         =		attention_probs_dropout_prob
												_a	:    str         =		max_position_embeddings
												_a	:    Dict         =		type_vocab_size
												_a	:    List[Any]         =		initializer_range
												_a	:    Dict         =		layer_norm_eps
												_a	:    Dict         =		rotary_value
												_a	:    Dict         =		use_cache



class 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







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



