





import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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

if is_vision_available():
					from PIL import Image

					from transformers import DonutImageProcessor

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=7	,			__lowerCAmelCase=3	,			__lowerCAmelCase=18	,			__lowerCAmelCase=30	,			__lowerCAmelCase=400	,			__lowerCAmelCase=True	,			__lowerCAmelCase=None	,			__lowerCAmelCase=True	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=[0.5, 0.5, 0.5]	,			__lowerCAmelCase=[0.5, 0.5, 0.5]	,			):
														UpperCamelCase__					  =      parent
														UpperCamelCase__					  =      batch_size
														UpperCamelCase__					  =      num_channels
														UpperCamelCase__					  =      image_size
														UpperCamelCase__					  =      min_resolution
														UpperCamelCase__					  =      max_resolution
														UpperCamelCase__					  =      do_resize
														UpperCamelCase__					  =      size if size is not None else {"""height""": 18, """width""": 20}
														UpperCamelCase__					  =      do_thumbnail
														UpperCamelCase__					  =      do_align_axis
														UpperCamelCase__					  =      do_pad
														UpperCamelCase__					  =      do_normalize
														UpperCamelCase__					  =      image_mean
														UpperCamelCase__					  =      image_std







							def 					_lowerCamelCase		(    self	):
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_thumbnail": self.do_thumbnail,
														    "do_align_long_axis": self.do_align_axis,
														    "do_pad": self.do_pad,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														}






@require_torch
@require_vision
class 							__SCREAMING_SNAKE_CASE       (  _a ,		unittest.TestCase							):
							snake_case  :   Dict      =							DonutImageProcessor if is_vision_available() else None






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      DonutImageProcessingTester(self	)






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






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.image_processing_class(**self.image_processor_dict	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""do_resize"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""size"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""do_thumbnail"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""do_align_long_axis"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""do_pad"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""do_normalize"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""image_mean"""	)	)
														self.assertTrue(hasattr(__lowerCAmelCase	,			"""image_std"""	)	)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      self.image_processing_class.from_dict(self.image_processor_dict	)
														self.assertEqual(image_processor.size	,			{"""height""": 18, """width""": 20}	)

														UpperCamelCase__					  =      self.image_processing_class.from_dict(self.image_processor_dict	,			size=42	)
														self.assertEqual(image_processor.size	,			{"""height""": 42, """width""": 42}	)

														# Previous config had dimensions in (width, height) order
														UpperCamelCase__					  =      self.image_processing_class.from_dict(self.image_processor_dict	,			size=(42, 84)	)
														self.assertEqual(image_processor.size	,			{"""height""": 84, """width""": 42}	)






							def 					_lowerCamelCase		(    self	):
														pass






							@is_flaky()
							def 					_lowerCamelCase		(    self	):
														# Initialize image_processing
														UpperCamelCase__					  =      self.image_processing_class(**self.image_processor_dict	)
														# create random PIL images
														UpperCamelCase__					  =      prepare_image_inputs(self.image_processor_tester	,			equal_resolution=__lowerCAmelCase	)
														for image in image_inputs:
																					self.assertIsInstance(__lowerCAmelCase	,			Image.Image	)

														# Test not batched input
														UpperCamelCase__					  =      image_processing(image_inputs[0]	,			return_tensors="""pt"""	).pixel_values
														self.assertEqual(
														    encoded_images.shape	,			(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )	,			)

														# Test batched
														UpperCamelCase__					  =      image_processing(__lowerCAmelCase	,			return_tensors="""pt"""	).pixel_values
														self.assertEqual(
														    encoded_images.shape	,			(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )	,			)






							@is_flaky()
							def 					_lowerCamelCase		(    self	):
														# Initialize image_processing
														UpperCamelCase__					  =      self.image_processing_class(**self.image_processor_dict	)
														# create random numpy tensors
														UpperCamelCase__					  =      prepare_image_inputs(self.image_processor_tester	,			equal_resolution=__lowerCAmelCase	,			numpify=__lowerCAmelCase	)
														for image in image_inputs:
																					self.assertIsInstance(__lowerCAmelCase	,			np.ndarray	)

														# Test not batched input
														UpperCamelCase__					  =      image_processing(image_inputs[0]	,			return_tensors="""pt"""	).pixel_values
														self.assertEqual(
														    encoded_images.shape	,			(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )	,			)

														# Test batched
														UpperCamelCase__					  =      image_processing(__lowerCAmelCase	,			return_tensors="""pt"""	).pixel_values
														self.assertEqual(
														    encoded_images.shape	,			(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )	,			)







							@is_flaky()
							def 					_lowerCamelCase		(    self	):
														# Initialize image_processing
														UpperCamelCase__					  =      self.image_processing_class(**self.image_processor_dict	)
														# create random PyTorch tensors
														UpperCamelCase__					  =      prepare_image_inputs(self.image_processor_tester	,			equal_resolution=__lowerCAmelCase	,			torchify=__lowerCAmelCase	)
														for image in image_inputs:
																					self.assertIsInstance(__lowerCAmelCase	,			torch.Tensor	)

														# Test not batched input
														UpperCamelCase__					  =      image_processing(image_inputs[0]	,			return_tensors="""pt"""	).pixel_values
														self.assertEqual(
														    encoded_images.shape	,			(
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )	,			)

														# Test batched
														UpperCamelCase__					  =      image_processing(__lowerCAmelCase	,			return_tensors="""pt"""	).pixel_values
														self.assertEqual(
														    encoded_images.shape	,			(
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.size["""height"""],
														        self.image_processor_tester.size["""width"""],
														    )	,			)











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 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[torch.FloatTensor]      =							None
							snake_case  :   torch.FloatTensor      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None






class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			__lowerCAmelCase=1	,			__lowerCAmelCase=0	,			__lowerCAmelCase=2	,			__lowerCAmelCase=512	,			__lowerCAmelCase="cls"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			**__lowerCAmelCase	,			):
														super().__init__(pad_token_id=__lowerCAmelCase	,			bos_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      project_dim
														UpperCamelCase__					  =      pooler_fn
														UpperCamelCase__					  =      learn_encoder
														UpperCamelCase__					  =      use_attention_mask






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   int      =							[r"""pooler""", r"""logit_scale"""]
							snake_case  :   Tuple      =							[r"""position_ids""", r"""predictions.decoder.bias"""]
							snake_case  :   str      =							"""roberta"""
							snake_case  :   Dict      =							RobertaSeriesConfig






							def __init__(    self	,			__lowerCAmelCase	):
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      XLMRobertaModel(__lowerCAmelCase	)
														UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""has_pre_transformation"""	,			__lowerCAmelCase	)
														if self.has_pre_transformation:
																					UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
																					UpperCamelCase__					  =      nn.LayerNorm(config.hidden_size	,			eps=config.layer_norm_eps	)
														self.post_init()







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict

														UpperCamelCase__					  =      self.base_model(
														    input_ids=__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			token_type_ids=__lowerCAmelCase	,			position_ids=__lowerCAmelCase	,			head_mask=__lowerCAmelCase	,			inputs_embeds=__lowerCAmelCase	,			encoder_hidden_states=__lowerCAmelCase	,			encoder_attention_mask=__lowerCAmelCase	,			output_attentions=__lowerCAmelCase	,			output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,			return_dict=__lowerCAmelCase	,			)

														if self.has_pre_transformation:
																					UpperCamelCase__					  =      outputs["""hidden_states"""][-2]
																					UpperCamelCase__					  =      self.pre_LN(__lowerCAmelCase	)
																					UpperCamelCase__					  =      self.transformation_pre(__lowerCAmelCase	)

																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)
														else:
																					UpperCamelCase__					  =      self.transformation(outputs.last_hidden_state	)
																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)




