



from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class 				__A					( A_      ):
			UpperCamelCase					:Optional[torch.FloatTensor]     =			None
			UpperCamelCase					:torch.FloatTensor     =			None
			UpperCamelCase					:Optional[Tuple[torch.FloatTensor]]     =			None
			UpperCamelCase					:Optional[Tuple[torch.FloatTensor]]     =			None







class 				__A					( A_      ):





			def __init__(self							,    __magic_name__=1							,    __magic_name__=0							,    __magic_name__=2							,    __magic_name__=512							,    __magic_name__="cls"							,    __magic_name__=False							,    __magic_name__=True							,    **__magic_name__							,    ):
						super().__init__(pad_token_id=__magic_name__							,    bos_token_id=__magic_name__							,    eos_token_id=__magic_name__							,    **__magic_name__				)
						lowerCamelCase__ :      Tuple								=		project_dim
						lowerCamelCase__ :      str								=		pooler_fn
						lowerCamelCase__ :      List[str]								=		learn_encoder
						lowerCamelCase__ :      Union[str, Any]								=		use_attention_mask







class 				__A					( A_      ):
			UpperCamelCase					:Union[str, Any]     =			[r'''pooler''', r'''logit_scale''']
			UpperCamelCase					:Optional[Any]     =			[r'''position_ids''', r'''predictions.decoder.bias''']
			UpperCamelCase					:List[str]     =			'''roberta'''
			UpperCamelCase					:Optional[int]     =			RobertaSeriesConfig





			def __init__(self							,    __magic_name__				):
						super().__init__(__magic_name__				)
						lowerCamelCase__ :      Union[str, Any]								=		XLMRobertaModel(__magic_name__				)
						lowerCamelCase__ :      Optional[int]								=		nn.Linear(config.hidden_size							,    config.project_dim				)
						lowerCamelCase__ :      Any								=		getattr(__magic_name__							,    """has_pre_transformation"""							,    __magic_name__				)
						if self.has_pre_transformation:
									lowerCamelCase__ :      int								=		nn.Linear(config.hidden_size							,    config.project_dim				)
									lowerCamelCase__ :      Any								=		nn.LayerNorm(config.hidden_size							,    eps=config.layer_norm_eps				)
						self.post_init()





			def        _snake_case    (self							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    __magic_name__ = None							,    ):
						lowerCamelCase__ :      Optional[int]								=		return_dict if return_dict is not None else self.config.use_return_dict

						lowerCamelCase__ :      Union[str, Any]								=		self.base_model(
						    input_ids=__magic_name__							,    attention_mask=__magic_name__							,    token_type_ids=__magic_name__							,    position_ids=__magic_name__							,    head_mask=__magic_name__							,    inputs_embeds=__magic_name__							,    encoder_hidden_states=__magic_name__							,    encoder_attention_mask=__magic_name__							,    output_attentions=__magic_name__							,    output_hidden_states=True if self.has_pre_transformation else output_hidden_states							,    return_dict=__magic_name__							,    )

						if self.has_pre_transformation:
									lowerCamelCase__ :      List[str]								=		outputs["""hidden_states"""][-2]
									lowerCamelCase__ :      Optional[Any]								=		self.pre_LN(__magic_name__				)
									lowerCamelCase__ :      Union[str, Any]								=		self.transformation_pre(__magic_name__				)

									return TransformationModelOutput(
									    projection_state=__magic_name__							,    last_hidden_state=outputs.last_hidden_state							,    hidden_states=outputs.hidden_states							,    attentions=outputs.attentions							,    )
						else:
									lowerCamelCase__ :      List[str]								=		self.transformation(outputs.last_hidden_state				)
									return TransformationModelOutput(
									    projection_state=__magic_name__							,    last_hidden_state=outputs.last_hidden_state							,    hidden_states=outputs.hidden_states							,    attentions=outputs.attentions							,    )








import unittest

import numpy as np
import requests

from transformers.testing_utils import require_torch, require_vision
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

				from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
				_lowercase     							=						False

if is_vision_available():
				from PIL import Image

				from transformers import PixaStructImageProcessor
class 				__A					( unittest.TestCase      ):





			def __init__(self							,    __magic_name__							,    __magic_name__=7							,    __magic_name__=3							,    __magic_name__=18							,    __magic_name__=30							,    __magic_name__=400							,    __magic_name__=None							,    __magic_name__=True							,    __magic_name__=True							,    __magic_name__=None							,    ):
						lowerCamelCase__ :      Optional[Any]								=		size if size is not None else {"""height""": 20, """width""": 20}
						lowerCamelCase__ :      List[str]								=		parent
						lowerCamelCase__ :      str								=		batch_size
						lowerCamelCase__ :      List[str]								=		num_channels
						lowerCamelCase__ :      Optional[int]								=		image_size
						lowerCamelCase__ :      Union[str, Any]								=		min_resolution
						lowerCamelCase__ :      List[Any]								=		max_resolution
						lowerCamelCase__ :      Optional[int]								=		size
						lowerCamelCase__ :      List[str]								=		do_normalize
						lowerCamelCase__ :      str								=		do_convert_rgb
						lowerCamelCase__ :      str								=		[512, 1024, 2048, 4096]
						lowerCamelCase__ :      Any								=		patch_size if patch_size is not None else {"""height""": 16, """width""": 16}





			def        _snake_case    (self				):
						return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		"""https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
						lowerCamelCase__ :      int								=		Image.open(requests.get(__magic_name__							,    stream=__magic_name__				).raw				).convert("""RGB"""				)
						return raw_image







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					, )
@require_torch
@require_vision
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Union[str, Any]     =			PixaStructImageProcessor if is_vision_available() else None





			def        _snake_case    (self				):
						lowerCamelCase__ :      List[str]								=		PixaStructImageProcessingTester(self				)





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





			def        _snake_case    (self				):
						lowerCamelCase__ :      int								=		self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """do_normalize"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_convert_rgb"""				)				)





			def        _snake_case    (self				):
						lowerCamelCase__ :      Tuple								=		self.image_processor_tester.prepare_dummy_image()

						lowerCamelCase__ :      Tuple								=		self.image_processing_class(**self.image_processor_dict				)
						lowerCamelCase__ :      List[str]								=		2048

						lowerCamelCase__ :      Any								=		image_processor(__magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				)
						self.assertTrue(torch.allclose(inputs.flattened_patches.mean()							,    torch.tensor(0.06_06				)							,    atol=1E-3							,    rtol=1E-3				)				)





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      List[Any]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      Optional[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      Optional[Any]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      List[str]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      Optional[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      List[Any]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						lowerCamelCase__ :      List[str]								=		True

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									with self.assertRaises(__magic_name__				):
												lowerCamelCase__ :      Tuple								=		image_processor(
												    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches

									lowerCamelCase__ :      Optional[Any]								=		"""Hello"""

									lowerCamelCase__ :      Dict								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__							,    header_text=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Tuple								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__							,    header_text=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      Any								=		self.image_processing_class(**self.image_processor_dict				)
						# create random numpy tensors
						lowerCamelCase__ :      Dict								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__							,    numpify=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    np.ndarray				)

						lowerCamelCase__ :      Optional[int]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      List[str]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      List[str]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      Optional[int]								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PyTorch tensors
						lowerCamelCase__ :      Dict								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__							,    torchify=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    torch.Tensor				)

						# Test not batched input
						lowerCamelCase__ :      List[str]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * self.image_processor_tester.num_channels
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Tuple								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )







@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11					, reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''					, )
@require_torch
@require_vision
class 				__A					( A_					, unittest.TestCase      ):
			UpperCamelCase					:Optional[int]     =			PixaStructImageProcessor if is_vision_available() else None





			def        _snake_case    (self				):
						lowerCamelCase__ :      Optional[Any]								=		PixaStructImageProcessingTester(self							,    num_channels=4				)
						lowerCamelCase__ :      Optional[int]								=		3





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





			def        _snake_case    (self				):
						lowerCamelCase__ :      Dict								=		self.image_processing_class(**self.image_processor_dict				)
						self.assertTrue(hasattr(__magic_name__							,    """do_normalize"""				)				)
						self.assertTrue(hasattr(__magic_name__							,    """do_convert_rgb"""				)				)





			def        _snake_case    (self				):
						# Initialize image_processor
						lowerCamelCase__ :      str								=		self.image_processing_class(**self.image_processor_dict				)
						# create random PIL images
						lowerCamelCase__ :      List[Any]								=		prepare_image_inputs(self.image_processor_tester							,    equal_resolution=__magic_name__				)
						for image in image_inputs:
									self.assertIsInstance(__magic_name__							,    Image.Image				)

						# Test not batched input
						lowerCamelCase__ :      List[str]								=		(
						    (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
						    * (self.image_processor_tester.num_channels - 1)
						) + 2

						for max_patch in self.image_processor_tester.max_patches:
									# Test not batched input
									lowerCamelCase__ :      Optional[Any]								=		image_processor(
									    image_inputs[0]							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (1, max_patch, expected_hidden_dim)							,    )

									# Test batched
									lowerCamelCase__ :      str								=		image_processor(
									    __magic_name__							,    return_tensors="""pt"""							,    max_patches=__magic_name__				).flattened_patches
									self.assertEqual(
									    encoded_images.shape							,    (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)							,    )



