




"""simple docstring"""




import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


__A       :						Optional[int]												=			logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
__A       :						str												=			direct_transformers_import(Path(__file__).parent)


__A       :						Dict												=			{
    '''AutoTokenizer''': '''PreTrainedTokenizerBase''',
    '''AutoFeatureExtractor''': '''FeatureExtractionMixin''',
    '''AutoImageProcessor''': '''ImageProcessingMixin''',
}
class 							lowerCamelCase						(       a_ ):
   lowercase				:     int            = ['feature_extractor', 'tokenizer']
   # Names need to be attr_class for attr in attributes
   lowercase				:     Any            = None
   lowercase				:     str            = None
   lowercase				:     Tuple            = None







   def __init__(							self			,					*SCREAMING_SNAKE_CASE_			,					**SCREAMING_SNAKE_CASE_       ):





       for key in kwargs:
           if key not in self.attributes:
               raise TypeError(f'Unexpected keyword argument {key}.'       )
       for arg, attribute_name in zip(snake_case__			,					self.attributes       ):
           if attribute_name in kwargs:
               raise TypeError(f'Got multiple values for argument {attribute_name}.'       )
           else:
               UpperCamelCase :      Optional[Any]      				=							arg

       if len(snake_case__       ) != len(self.attributes       ):
           raise ValueError(
               f'This processor requires {len(self.attributes       )} arguments: {", ".join(self.attributes       )}. Got '
               f'{len(snake_case__       )} arguments instead.'       )

       # Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
       for attribute_name, arg in kwargs.items():
           UpperCamelCase :      str      				=							getattr(self			,					f'{attribute_name}_class'       )
           # Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
           UpperCamelCase :      Optional[int]      				=							AUTO_TO_BASE_CLASS_MAPPING.get(snake_case__			,					snake_case__       )
           if isinstance(snake_case__			,					snake_case__       ):
               UpperCamelCase :      List[str]      				=							tuple(getattr(snake_case__			,					snake_case__       ) for n in class_name if n is not None       )
           else:
               UpperCamelCase :      Dict      				=							getattr(snake_case__			,					snake_case__       )

           if not isinstance(snake_case__			,					snake_case__       ):
               raise ValueError(
                   f'Received a {type(snake_case__       ).__name__} for argument {attribute_name}, but a {class_name} was expected.'       )

           setattr(self			,					snake_case__			,					snake_case__       )







   def __repr__(							self       ):
       UpperCamelCase :      str      				=							[f'- {name}: {repr(getattr(self			,					snake_case__       )       )}' for name in self.attributes]
       UpperCamelCase :      str      				=							"""\n""".join(snake_case__       )
       return f'{self.__class__.__name__}:\n{attributes_repr}'







   def 					a_							(							self			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_ = False			,					**SCREAMING_SNAKE_CASE_       ):
       os.makedirs(snake_case__			,					exist_ok=snake_case__       )

       if push_to_hub:
           UpperCamelCase :      str      				=							kwargs.pop("""commit_message"""			,					snake_case__       )
           UpperCamelCase :      List[str]      				=							kwargs.pop("""repo_id"""			,					save_directory.split(os.path.sep       )[-1]       )
           UpperCamelCase :      Tuple      				=							self._create_repo(snake_case__			,					**snake_case__       )
           UpperCamelCase :      str      				=							self._get_files_timestamps(snake_case__       )
       # If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
       # loaded from the Hub.
       if self._auto_class is not None:
           UpperCamelCase :      Tuple      				=							[getattr(self			,					snake_case__       ) for attribute_name in self.attributes]
           UpperCamelCase :      Optional[Any]      				=							[(a.init_kwargs if isinstance(snake_case__			,					snake_case__       ) else a) for a in attrs]
           custom_object_save(self			,					snake_case__			,					config=snake_case__       )

       for attribute_name in self.attributes:
           UpperCamelCase :      Optional[Any]      				=							getattr(self			,					snake_case__       )
           # Include the processor class in the attribute config so this processor can then be reloaded with the
           # `AutoProcessor` API.
           if hasattr(snake_case__			,					"""_set_processor_class"""       ):
               attribute._set_processor_class(self.__class__.__name__       )
           attribute.save_pretrained(snake_case__       )

       if self._auto_class is not None:
           # We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
           for attribute_name in self.attributes:
               UpperCamelCase :      Optional[int]      				=							getattr(self			,					snake_case__       )
               if isinstance(snake_case__			,					snake_case__       ):
                   del attribute.init_kwargs["auto_map"]

       if push_to_hub:
           self._upload_modified_files(
               snake_case__			,					snake_case__			,					snake_case__			,					commit_message=snake_case__			,					token=kwargs.get("""use_auth_token"""       )			,					)







   @classmethod
   def 					a_							(							cls			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_ = None			,					SCREAMING_SNAKE_CASE_ = False			,					SCREAMING_SNAKE_CASE_ = False			,					SCREAMING_SNAKE_CASE_ = None			,					SCREAMING_SNAKE_CASE_ = "main"			,					**SCREAMING_SNAKE_CASE_			,					):
       UpperCamelCase :      List[Any]      				=							cache_dir
       UpperCamelCase :      List[str]      				=							force_download
       UpperCamelCase :      Optional[Any]      				=							local_files_only
       UpperCamelCase :      int      				=							revision

       UpperCamelCase :      Dict      				=							kwargs.pop("""use_auth_token"""			,					snake_case__       )
       if use_auth_token is not None:
           warnings.warn(
               """The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers."""			,					snake_case__       )
           if token is not None:
               raise ValueError(
                   """`token` and `use_auth_token` are both specified. Please set only the argument `token`."""       )
           UpperCamelCase :      Union[str, Any]      				=							use_auth_token

       if token is not None:
           # change to `token` in a follow-up PR
           UpperCamelCase :      Any      				=							token

       UpperCamelCase :      Optional[int]      				=							cls._get_arguments_from_pretrained(snake_case__			,					**snake_case__       )
       return cls(*snake_case__       )







   @classmethod
   def 					a_							(							cls			,					SCREAMING_SNAKE_CASE_="AutoProcessor"       ):





       if not isinstance(snake_case__			,					snake_case__       ):
           UpperCamelCase :      List[Any]      				=							auto_class.__name__

       import transformers.models.auto as auto_module

       if not hasattr(snake_case__			,					snake_case__       ):
           raise ValueError(f'{auto_class} is not a valid auto class.'       )

       UpperCamelCase :      Optional[Any]      				=							auto_class







   @classmethod
   def 					a_							(							cls			,					SCREAMING_SNAKE_CASE_			,					**SCREAMING_SNAKE_CASE_       ):
       UpperCamelCase :      Dict      				=							[]
       for attribute_name in cls.attributes:
           UpperCamelCase :      Optional[int]      				=							getattr(cls			,					f'{attribute_name}_class'       )
           if isinstance(snake_case__			,					snake_case__       ):
               UpperCamelCase :      Dict      				=							tuple(getattr(snake_case__			,					snake_case__       ) if n is not None else None for n in class_name       )
               UpperCamelCase :      List[Any]      				=							kwargs.get("""use_fast"""			,					snake_case__       )
               if use_fast and classes[1] is not None:
                   UpperCamelCase :      Optional[Any]      				=							classes[1]
               else:
                   UpperCamelCase :      str      				=							classes[0]
           else:
               UpperCamelCase :      str      				=							getattr(snake_case__			,					snake_case__       )

           args.append(attribute_class.from_pretrained(snake_case__			,					**snake_case__       )       )
       return args







   @property
   def 					a_							(							self       ):
       UpperCamelCase :      Dict      				=							getattr(self			,					self.attributes[0]       )
       return getattr(snake_case__			,					"""model_input_names"""			,					snake_case__       )







__A       :						Optional[Any]												=			copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
  __A       :						Tuple												=			ProcessorMixin.push_to_hub.__doc__.format(
      object='''processor''', object_class='''AutoProcessor''', object_files='''processor files'''
  )












"""simple docstring"""







import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import DonutSwinModel
 from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST





class      UpperCamelCase_       :



  def __init__(					self       ,						snake_case__       ,						snake_case__=13       ,						snake_case__=32       ,						snake_case__=2       ,						snake_case__=3       ,						snake_case__=16       ,						snake_case__=[1, 2, 1]       ,						snake_case__=[2, 2, 4]       ,						snake_case__=2       ,						snake_case__=2.0       ,						snake_case__=True       ,						snake_case__=0.0       ,						snake_case__=0.0       ,						snake_case__=0.1       ,						snake_case__="gelu"       ,						snake_case__=False       ,						snake_case__=True       ,						snake_case__=0.02       ,						snake_case__=1e-5       ,						snake_case__=True       ,						snake_case__=None       ,						snake_case__=True       ,						snake_case__=10       ,						snake_case__=8       ,						)	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	parent
         UpperCAmelCase					=	batch_size
         UpperCAmelCase					=	image_size
         UpperCAmelCase					=	patch_size
         UpperCAmelCase					=	num_channels
         UpperCAmelCase					=	embed_dim
         UpperCAmelCase					=	depths
         UpperCAmelCase					=	num_heads
         UpperCAmelCase					=	window_size
         UpperCAmelCase					=	mlp_ratio
         UpperCAmelCase					=	qkv_bias
         UpperCAmelCase					=	hidden_dropout_prob
         UpperCAmelCase					=	attention_probs_dropout_prob
         UpperCAmelCase					=	drop_path_rate
         UpperCAmelCase					=	hidden_act
         UpperCAmelCase					=	use_absolute_embeddings
         UpperCAmelCase					=	patch_norm
         UpperCAmelCase					=	layer_norm_eps
         UpperCAmelCase					=	initializer_range
         UpperCAmelCase					=	is_training
         UpperCAmelCase					=	scope
         UpperCAmelCase					=	use_labels
         UpperCAmelCase					=	type_sequence_label_size
         UpperCAmelCase					=	encoder_stride



  def 						UpperCamelCase_ (					self )	->	List[Any]:



         """simple docstring"""





         UpperCAmelCase					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

         UpperCAmelCase					=	None
         if self.use_labels:
                UpperCAmelCase					=	ids_tensor([self.batch_size]       ,						self.type_sequence_label_size )

         UpperCAmelCase					=	self.get_config()

         return config, pixel_values, labels



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         return DonutSwinConfig(
             image_size=self.image_size       ,						patch_size=self.patch_size       ,						num_channels=self.num_channels       ,						embed_dim=self.embed_dim       ,						depths=self.depths       ,						num_heads=self.num_heads       ,						window_size=self.window_size       ,						mlp_ratio=self.mlp_ratio       ,						qkv_bias=self.qkv_bias       ,						hidden_dropout_prob=self.hidden_dropout_prob       ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,						drop_path_rate=self.drop_path_rate       ,						hidden_act=self.hidden_act       ,						use_absolute_embeddings=self.use_absolute_embeddings       ,						path_norm=self.patch_norm       ,						layer_norm_eps=self.layer_norm_eps       ,						initializer_range=self.initializer_range       ,						encoder_stride=self.encoder_stride       ,						)



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	DonutSwinModel(config=snake_case__ )
         model.to(snake_case__ )
         model.eval()
         UpperCAmelCase					=	model(snake_case__ )

         UpperCAmelCase					=	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
         UpperCAmelCase					=	int(config.embed_dim * 2 ** (len(config.depths ) - 1) )

         self.parent.assertEqual(result.last_hidden_state.shape       ,						(self.batch_size, expected_seq_len, expected_dim) )



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



         """simple docstring"""





         UpperCAmelCase					=	self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase
         )			,				(
             UpperCAmelCase
         )			,				(
             UpperCAmelCase
         )			,				
         )					=	config_and_inputs
         UpperCAmelCase					=	{"""pixel_values""": pixel_values}
         return config, inputs_dict





@require_torch
class      UpperCamelCase_       (				a_      ,    a_      ,    unittest.TestCase     ):
  _A				:    Tuple =		(DonutSwinModel,) if is_torch_available() else ()
  _A				:    Any =		{'feature-extraction': DonutSwinModel} if is_torch_available() else {}
  _A				:    Optional[Any] =		True

  _A				:    Dict =		False
  _A				:    Any =		False
  _A				:    Tuple =		False



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase					=	DonutSwinModelTester(self )
         UpperCAmelCase					=	ConfigTester(self       ,						config_class=snake_case__       ,						embed_dim=37 )



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         self.create_and_test_config_common_properties()
         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 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         return



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



         """simple docstring"""





         UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__ )



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         pass



  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
                UpperCAmelCase					=	model_class(snake_case__ )
                self.assertIsInstance(model.get_input_embeddings()       ,						(nn.Module) )
                UpperCAmelCase					=	model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case__       ,						nn.Linear ) )



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



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

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

                UpperCAmelCase					=	["""pixel_values"""]
                self.assertListEqual(arg_names[:1]       ,						snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Tuple:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase					=	True

         for model_class in self.all_model_classes:
                UpperCAmelCase					=	True
                UpperCAmelCase					=	False
                UpperCAmelCase					=	True
                UpperCAmelCase					=	model_class(snake_case__ )
                model.to(snake_case__ )
                model.eval()
                with torch.no_grad():
                       UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )
                UpperCAmelCase					=	outputs.attentions
                UpperCAmelCase					=	len(self.model_tester.depths )
                self.assertEqual(len(snake_case__ )       ,						snake_case__ )

                # check that output_attentions also work using config
                del inputs_dict["output_attentions"]
                UpperCAmelCase					=	True
                UpperCAmelCase					=	config.window_size**2
                UpperCAmelCase					=	model_class(snake_case__ )
                model.to(snake_case__ )
                model.eval()
                with torch.no_grad():
                       UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )
                UpperCAmelCase					=	outputs.attentions
                self.assertEqual(len(snake_case__ )       ,						snake_case__ )

                self.assertListEqual(
                    list(attentions[0].shape[-3:] )       ,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,						)
                UpperCAmelCase					=	len(snake_case__ )

                # Check attention is always last and order is fine
                UpperCAmelCase					=	True
                UpperCAmelCase					=	True
                UpperCAmelCase					=	model_class(snake_case__ )
                model.to(snake_case__ )
                model.eval()
                with torch.no_grad():
                       UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )

                if hasattr(self.model_tester       ,						"""num_hidden_states_types""" ):
                       UpperCAmelCase					=	self.model_tester.num_hidden_states_types
                else:
                       # also another +1 for reshaped_hidden_states
                       UpperCAmelCase					=	2
                self.assertEqual(out_len + added_hidden_states       ,						len(snake_case__ ) )

                UpperCAmelCase					=	outputs.attentions

                self.assertEqual(len(snake_case__ )       ,						snake_case__ )

                self.assertListEqual(
                    list(self_attentions[0].shape[-3:] )       ,						[self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,						)



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )	->	Optional[int]:



         """simple docstring"""





         UpperCAmelCase					=	model_class(snake_case__ )
         model.to(snake_case__ )
         model.eval()

         with torch.no_grad():
                UpperCAmelCase					=	model(**self._prepare_for_class(snake_case__       ,						snake_case__ ) )

         UpperCAmelCase					=	outputs.hidden_states

         UpperCAmelCase					=	getattr(
             self.model_tester       ,						"""expected_num_hidden_layers"""       ,						len(self.model_tester.depths ) + 1 )
         self.assertEqual(len(snake_case__ )       ,						snake_case__ )

         # DonutSwin has a different seq_length
         UpperCAmelCase					=	(
             config.patch_size
             if isinstance(config.patch_size       ,						collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase					=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

         self.assertListEqual(
             list(hidden_states[0].shape[-2:] )       ,						[num_patches, self.model_tester.embed_dim]       ,						)

         UpperCAmelCase					=	outputs.reshaped_hidden_states
         self.assertEqual(len(snake_case__ )       ,						snake_case__ )

         UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase			,				UpperCAmelCase					=	reshaped_hidden_states[0].shape
         UpperCAmelCase					=	(
             reshaped_hidden_states[0].view(snake_case__       ,						snake_case__       ,						height * width ).permute(0       ,						2       ,						1 )
         )
         self.assertListEqual(
             list(reshaped_hidden_states.shape[-2:] )       ,						[num_patches, self.model_tester.embed_dim]       ,						)



  def 						UpperCamelCase_ (					self )	->	str:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase					=	(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size       ,						collections.abc.Iterable )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )

         for model_class in self.all_model_classes:
                UpperCAmelCase					=	True
                self.check_hidden_states_output(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCAmelCase					=	True

                self.check_hidden_states_output(snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Dict:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()
         UpperCAmelCase					=	3

         UpperCAmelCase					=	(
             self.model_tester.image_size
             if isinstance(self.model_tester.image_size       ,						collections.abc.Iterable )
             else (self.model_tester.image_size, self.model_tester.image_size)
         )
         UpperCAmelCase					=	(
             config.patch_size
             if isinstance(config.patch_size       ,						collections.abc.Iterable )
             else (config.patch_size, config.patch_size)
         )

         UpperCAmelCase					=	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
         UpperCAmelCase					=	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

         for model_class in self.all_model_classes:
                UpperCAmelCase					=	True
                self.check_hidden_states_output(snake_case__       ,						snake_case__       ,						snake_case__       ,						(padded_height, padded_width) )

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                UpperCAmelCase					=	True
                self.check_hidden_states_output(snake_case__       ,						snake_case__       ,						snake_case__       ,						(padded_height, padded_width) )



  @slow
  def 						UpperCamelCase_ (					self )	->	List[str]:



         """simple docstring"""





         for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase					=	DonutSwinModel.from_pretrained(snake_case__ )
                self.assertIsNotNone(snake_case__ )



  def 						UpperCamelCase_ (					self )	->	Any:



         """simple docstring"""





         UpperCAmelCase			,				UpperCAmelCase					=	self.model_tester.prepare_config_and_inputs_for_common()

         UpperCAmelCase					=	_config_zero_init(snake_case__ )
         for model_class in self.all_model_classes:
                UpperCAmelCase					=	model_class(config=snake_case__ )
                for name, param in model.named_parameters():
                       if "embeddings" not in name and param.requires_grad:
                              self.assertIn(
                                  ((param.data.mean() * 1e9).round() / 1e9).item()       ,						[0.0, 1.0]       ,						msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''       ,						)
