

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
					import torch

					from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin
class 		SCREAMING_SNAKE_CASE__       :







						"""simple docstring"""




						def __init__(					self			,			__UpperCamelCase			,			__UpperCamelCase=None			,			__UpperCamelCase=None			,			__UpperCamelCase=None			,			__UpperCamelCase="resnet50"			,			__UpperCamelCase=3			,			__UpperCamelCase=32			,			__UpperCamelCase=3			,			__UpperCamelCase=True			,			__UpperCamelCase=True			,			)				->						Optional[int]:




									'''simple docstring'''


									__UpperCamelCase     :     List[Any]										=   parent
									__UpperCamelCase     :     Optional[Any]										=   out_indices if out_indices is not None else [4]
									__UpperCamelCase     :     Tuple										=   stage_names
									__UpperCamelCase     :     Optional[int]										=   out_features
									__UpperCamelCase     :     Dict										=   backbone
									__UpperCamelCase     :     Any										=   batch_size
									__UpperCamelCase     :     Tuple										=   image_size
									__UpperCamelCase     :     List[str]										=   num_channels
									__UpperCamelCase     :     Dict										=   use_pretrained_backbone
									__UpperCamelCase     :     Dict										=   is_training

						def __lowerCamelCase       (					self     )				->						Optional[int]:




									'''simple docstring'''


									__UpperCamelCase     :     int										=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )
									__UpperCamelCase     :     Optional[int]										=   self.get_config()

									return config, pixel_values

						def __lowerCamelCase       (					self     )				->						Union[str, Any]:




									'''simple docstring'''


									return TimmBackboneConfig(
									    image_size=self.image_size			,			num_channels=self.num_channels			,			out_features=self.out_features			,			out_indices=self.out_indices			,			stage_names=self.stage_names			,			use_pretrained_backbone=self.use_pretrained_backbone			,			backbone=self.backbone			,			)

						def __lowerCamelCase       (					self			,			__UpperCamelCase			,			__UpperCamelCase     )				->						Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase     :     Optional[Any]										=   TimmBackbone(config=__UpperCAmelCase     )
									model.to(__UpperCAmelCase     )
									model.eval()
									with torch.no_grad():
												__UpperCamelCase     :     int										=   model(__UpperCAmelCase     )
									self.parent.assertEqual(
									    result.feature_map[-1].shape			,			(self.batch_size, model.channels[-1], 14, 14)			,			)




						def __lowerCamelCase       (					self     )				->						str:




									'''simple docstring'''


									__UpperCamelCase     :     str										=   self.prepare_config_and_inputs()
									__UpperCamelCase     :     Tuple										=   config_and_inputs
									__UpperCamelCase     :     List[str]										=   {"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
@require_timm
class 		SCREAMING_SNAKE_CASE__       (		lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__    ,						unittest.TestCase ):







						"""simple docstring"""




						lowercase	:					int           =				(TimmBackbone,) if is_torch_available() else ()
						lowercase	:					Dict           =				{"feature-extraction": TimmBackbone} if is_torch_available() else {}
						lowercase	:					Union[str, Any]           =				False
						lowercase	:					Optional[int]           =				False
						lowercase	:					Any           =				False
						lowercase	:					Dict           =				False

						def __lowerCamelCase       (					self     )				->						Any:




									'''simple docstring'''


									__UpperCamelCase     :     Any										=   TimmBackboneModelTester(self     )
									__UpperCamelCase     :     str										=   ConfigTester(self			,			config_class=__UpperCAmelCase			,			has_text_modality=__UpperCAmelCase     )

						def __lowerCamelCase       (					self     )				->						str:




									'''simple docstring'''


									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()

						def __lowerCamelCase       (					self     )				->						Optional[int]:




									'''simple docstring'''


									__UpperCamelCase     :     Optional[Any]										=   "resnet18"
									__UpperCamelCase     :     List[str]										=   "microsoft/resnet-18"

									__UpperCamelCase     :     Optional[int]										=   AutoBackbone.from_pretrained(__UpperCAmelCase			,			use_timm_backbone=__UpperCAmelCase     )
									__UpperCamelCase     :     int										=   AutoBackbone.from_pretrained(__UpperCAmelCase     )

									self.assertEqual(len(timm_model.out_features     )			,			len(transformers_model.out_features     )     )
									self.assertEqual(len(timm_model.stage_names     )			,			len(transformers_model.stage_names     )     )
									self.assertEqual(timm_model.channels			,			transformers_model.channels     )
									# Out indices are set to the last layer by default. For timm models, we don't know
									# the number of layers in advance, so we set it to (-1,), whereas for transformers
									# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
									self.assertEqual(timm_model.out_indices			,			(-1,)     )
									self.assertEqual(transformers_model.out_indices			,			[len(timm_model.stage_names     ) - 1]     )

									__UpperCamelCase     :     Union[str, Any]										=   AutoBackbone.from_pretrained(__UpperCAmelCase			,			use_timm_backbone=__UpperCAmelCase			,			out_indices=[1, 2, 3]     )
									__UpperCamelCase     :     Optional[int]										=   AutoBackbone.from_pretrained(__UpperCAmelCase			,			out_indices=[1, 2, 3]     )

									self.assertEqual(timm_model.out_indices			,			transformers_model.out_indices     )
									self.assertEqual(len(timm_model.out_features     )			,			len(transformers_model.out_features     )     )
									self.assertEqual(timm_model.channels			,			transformers_model.channels     )

						@unittest.skip("TimmBackbone doesn't support feed forward chunking"     )
						def __lowerCamelCase       (					self     )				->						Optional[int]:




									'''simple docstring'''


									pass

						@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"     )
						def __lowerCamelCase       (					self     )				->						str:




									'''simple docstring'''


									pass

						@unittest.skip("TimmBackbone initialization is managed on the timm side"     )
						def __lowerCamelCase       (					self     )				->						List[Any]:




									'''simple docstring'''


									pass

						@unittest.skip("TimmBackbone models doesn't have inputs_embeds"     )
						def __lowerCamelCase       (					self     )				->						str:




									'''simple docstring'''


									pass

						@unittest.skip("TimmBackbone models doesn't have inputs_embeds"     )
						def __lowerCamelCase       (					self     )				->						str:




									'''simple docstring'''


									pass

						@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"     )
						def __lowerCamelCase       (					self     )				->						str:




									'''simple docstring'''


									pass

						@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"     )
						def __lowerCamelCase       (					self     )				->						Any:




									'''simple docstring'''


									pass

						@unittest.skip("model weights aren't tied in TimmBackbone."     )
						def __lowerCamelCase       (					self     )				->						List[Any]:




									'''simple docstring'''


									pass

						@unittest.skip("model weights aren't tied in TimmBackbone."     )
						def __lowerCamelCase       (					self     )				->						List[str]:




									'''simple docstring'''


									pass

						@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"     )
						def __lowerCamelCase       (					self     )				->						List[Any]:




									'''simple docstring'''


									pass

						@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"     )
						def __lowerCamelCase       (					self     )				->						Union[str, Any]:




									'''simple docstring'''


									pass

						@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."     )
						def __lowerCamelCase       (					self     )				->						Optional[int]:




									'''simple docstring'''


									pass

						@unittest.skip("TimmBackbone doesn't support output_attentions."     )
						def __lowerCamelCase       (					self     )				->						str:




									'''simple docstring'''


									pass

						@unittest.skip("Safetensors is not supported by timm."     )
						def __lowerCamelCase       (					self     )				->						List[str]:




									'''simple docstring'''


									pass

						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
						def __lowerCamelCase       (					self     )				->						str:




									'''simple docstring'''


									pass

						def __lowerCamelCase       (					self     )				->						Dict:




									'''simple docstring'''


									__UpperCamelCase     :     int										=   self.model_tester.prepare_config_and_inputs_for_common()

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

												__UpperCamelCase     :     Optional[Any]										=   ["pixel_values"]
												self.assertListEqual(arg_names[:1]			,			__UpperCAmelCase     )

						def __lowerCamelCase       (					self     )				->						Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase     :     Optional[Any]										=   self.model_tester.prepare_config_and_inputs_for_common()
									__UpperCamelCase     :     Any										=   True
									__UpperCamelCase     :     str										=   self.has_attentions

									# no need to test all models as different heads yield the same functionality
									__UpperCamelCase     :     List[Any]										=   self.all_model_classes[0]
									__UpperCamelCase     :     Optional[int]										=   model_class(__UpperCAmelCase     )
									model.to(__UpperCAmelCase     )

									__UpperCamelCase     :     Optional[int]										=   self._prepare_for_class(__UpperCAmelCase			,			__UpperCAmelCase     )
									__UpperCamelCase     :     Tuple										=   model(**__UpperCAmelCase     )
									__UpperCamelCase     :     int										=   outputs[0][-1]

									# Encoder-/Decoder-only models
									__UpperCamelCase     :     str										=   outputs.hidden_states[0]
									hidden_states.retain_grad()

									if self.has_attentions:
												__UpperCamelCase     :     str										=   outputs.attentions[0]
												attentions.retain_grad()

									output.flatten()[0].backward(retain_graph=__UpperCAmelCase     )

									self.assertIsNotNone(hidden_states.grad     )

									if self.has_attentions:
												self.assertIsNotNone(attentions.grad     )




						def __lowerCamelCase       (					self     )				->						int:




									'''simple docstring'''


									__UpperCamelCase     :     Union[str, Any]										=   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												__UpperCamelCase     :     Tuple										=   model_class(__UpperCAmelCase     )
												model.to(__UpperCAmelCase     )
												model.eval()
												__UpperCamelCase     :     int										=   model(**__UpperCAmelCase     )

												self.assertEqual(len(result.feature_maps     )			,			len(config.out_indices     )     )
												self.assertEqual(len(model.channels     )			,			len(config.out_indices     )     )

												# Check output of last stage is taken if out_features=None, out_indices=None
												__UpperCamelCase     :     Any										=   copy.deepcopy(__UpperCAmelCase     )
												__UpperCamelCase     :     Union[str, Any]										=   None
												__UpperCamelCase     :     Dict										=   model_class(__UpperCAmelCase     )
												model.to(__UpperCAmelCase     )
												model.eval()
												__UpperCamelCase     :     int										=   model(**__UpperCAmelCase     )

												self.assertEqual(len(result.feature_maps     )			,			1     )
												self.assertEqual(len(model.channels     )			,			1     )

												# Check backbone can be initialized with fresh weights
												__UpperCamelCase     :     Union[str, Any]										=   copy.deepcopy(__UpperCAmelCase     )
												__UpperCamelCase     :     Optional[Any]										=   False
												__UpperCamelCase     :     Tuple										=   model_class(__UpperCAmelCase     )
												model.to(__UpperCAmelCase     )
												model.eval()
												__UpperCamelCase     :     Optional[Any]										=   model(**__UpperCAmelCase     )




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:int =       ["image_processor", "tokenizer"]
							A				:Any =       "LayoutLMv3ImageProcessor"
							A				:str =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



							def __init__(							self ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							str      						= None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead." ,					__UpperCAmelCase ,					)
															a__	:							List[str]      						= kwargs.pop("feature_extractor"				)

											a__	:							Optional[int]      						= image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError("You need to specify an `image_processor`."				)
											if tokenizer is None:
															raise ValueError("You need to specify a `tokenizer`."				)

											super().__init__(__UpperCAmelCase ,					__UpperCAmelCase				)



							def __call__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 0 ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											if self.image_processor.apply_ocr and (boxes is not None):
															raise ValueError(
															    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."				)

											if self.image_processor.apply_ocr and (word_labels is not None):
															raise ValueError(
															    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."				)

											# first, apply the image processor
											a__	:							List[str]      						= self.image_processor(images=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase				)

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
															if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				):
																			a__	:							Union[str, Any]      						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
															a__	:							Optional[Any]      						= features["words"]

											a__	:							Union[str, Any]      						= self.tokenizer(
											    text=text if text is not None else features["words"] ,					text_pair=text_pair if text_pair is not None else None ,					boxes=boxes if boxes is not None else features["boxes"] ,					word_labels=__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					max_length=__UpperCAmelCase ,					stride=__UpperCAmelCase ,					pad_to_multiple_of=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase ,					return_attention_mask=__UpperCAmelCase ,					return_overflowing_tokens=__UpperCAmelCase ,					return_special_tokens_mask=__UpperCAmelCase ,					return_offsets_mapping=__UpperCAmelCase ,					return_length=__UpperCAmelCase ,					verbose=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

											# add pixel values
											a__	:							Dict      						= features.pop("pixel_values"				)
											if return_overflowing_tokens is True:
															a__	:							str      						= self.get_overflowing_images(__UpperCAmelCase ,					encoded_inputs["overflow_to_sample_mapping"]				)
											a__	:							Tuple      						= images

											return encoded_inputs



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= []
											for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]				)

											if len(__UpperCAmelCase				) != len(__UpperCAmelCase				):
															raise ValueError(
															    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
															    f' {len(__UpperCAmelCase				)} and {len(__UpperCAmelCase				)}'				)

											return images_with_overflow



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return self.tokenizer.batch_decode(*__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return self.tokenizer.decode(*__UpperCAmelCase ,					**__UpperCAmelCase				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return ["input_ids", "bbox", "attention_mask", "pixel_values"]



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor_class




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor


