






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 				__a				:





		'''simple docstring'''



		def __init__(    self			,     UpperCamelCase__			,     UpperCamelCase__=13			,     UpperCamelCase__=32			,     UpperCamelCase__=2			,     UpperCamelCase__=3			,     UpperCamelCase__=16			,     UpperCamelCase__=[1, 2, 1]			,     UpperCamelCase__=[2, 2, 4]			,     UpperCamelCase__=2			,     UpperCamelCase__=2.0			,     UpperCamelCase__=True			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.1			,     UpperCamelCase__="gelu"			,     UpperCamelCase__=False			,     UpperCamelCase__=True			,     UpperCamelCase__=0.02			,     UpperCamelCase__=1E-5			,     UpperCamelCase__=True			,     UpperCamelCase__=None			,     UpperCamelCase__=True			,     UpperCamelCase__=10			,     UpperCamelCase__=8			,     ):
						SCREAMING_SNAKE_CASE_ :						str    				= parent
						SCREAMING_SNAKE_CASE_ :						List[str]    				= batch_size
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= image_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= patch_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= num_channels
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= embed_dim
						SCREAMING_SNAKE_CASE_ :						Dict    				= depths
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= num_heads
						SCREAMING_SNAKE_CASE_ :						Dict    				= window_size
						SCREAMING_SNAKE_CASE_ :						List[str]    				= mlp_ratio
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= qkv_bias
						SCREAMING_SNAKE_CASE_ :						Any    				= hidden_dropout_prob
						SCREAMING_SNAKE_CASE_ :						Any    				= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_ :						Any    				= drop_path_rate
						SCREAMING_SNAKE_CASE_ :						str    				= hidden_act
						SCREAMING_SNAKE_CASE_ :						List[str]    				= use_absolute_embeddings
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= patch_norm
						SCREAMING_SNAKE_CASE_ :						str    				= layer_norm_eps
						SCREAMING_SNAKE_CASE_ :						Tuple    				= initializer_range
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= is_training
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= scope
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= use_labels
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= type_sequence_label_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= encoder_stride


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= self.get_config()

						return config, pixel_values, labels


		def 	__snake_case      (    self							):
						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 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						Tuple    				= DonutSwinModel(config=a_							)
						model.to(a_							)
						model.eval()
						SCREAMING_SNAKE_CASE_ :						List[str]    				= model(a_							)

						SCREAMING_SNAKE_CASE_ :						List[Any]    				= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
						SCREAMING_SNAKE_CASE_ :						Any    				= 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 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Dict    				= self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE_		
						)				,							(
						    SCREAMING_SNAKE_CASE_		
						)				,							(
						    SCREAMING_SNAKE_CASE_		
						)				,									
						) :						Optional[int]    				= config_and_inputs
						SCREAMING_SNAKE_CASE_ :						Dict    				= {'pixel_values': pixel_values}
						return config, inputs_dict






@require_torch
class 				__a				(	SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						unittest.TestCase    ):





		'''simple docstring'''



		UpperCAmelCase__   :					List[str]					     =    (DonutSwinModel,) if is_torch_available() else ()
		UpperCAmelCase__   :					Tuple					     =    {'feature-extraction': DonutSwinModel} if is_torch_available() else {}
		UpperCAmelCase__   :					str					     =    True

		UpperCAmelCase__   :					Optional[int]					     =    False
		UpperCAmelCase__   :					Tuple					     =    False
		UpperCAmelCase__   :					List[Any]					     =    False


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= DonutSwinModelTester(self							)
						SCREAMING_SNAKE_CASE_ :						Any    				= ConfigTester(self			,     config_class=a_			,     embed_dim=37							)


		def 	__snake_case      (    self							):
						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 	__snake_case      (    self							):
						return


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a_							)


		def 	__snake_case      (    self							):
						pass


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_ :						List[str]    				= model_class(a_							)
										self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)							)
										SCREAMING_SNAKE_CASE_ :						str    				= model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(a_			,     nn.Linear							)							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Dict    				= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_ :						int    				= model_class(a_							)
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= inspect.signature(model.forward							)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										SCREAMING_SNAKE_CASE_ :						Any    				= [*signature.parameters.keys()]

										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= ['pixel_values']
										self.assertListEqual(arg_names[:1]			,     a_							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						str    				= self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE_ :						List[str]    				= True

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= True
										SCREAMING_SNAKE_CASE_ :						Dict    				= False
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= True
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= model_class(a_							)
										model.to(a_							)
										model.eval()
										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= model(**self._prepare_for_class(a_			,     a_							)							)
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= outputs.attentions
										SCREAMING_SNAKE_CASE_ :						str    				= len(self.model_tester.depths							)
										self.assertEqual(len(a_							)			,     a_							)

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= True
										SCREAMING_SNAKE_CASE_ :						Dict    				= config.window_size**2
										SCREAMING_SNAKE_CASE_ :						List[Any]    				= model_class(a_							)
										model.to(a_							)
										model.eval()
										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						Optional[int]    				= model(**self._prepare_for_class(a_			,     a_							)							)
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= outputs.attentions
										self.assertEqual(len(a_							)			,     a_							)

										self.assertListEqual(
										    list(attentions[0].shape[-3:]							)			,     [self.model_tester.num_heads[0], window_size_squared, window_size_squared]			,     )
										SCREAMING_SNAKE_CASE_ :						List[str]    				= len(a_							)

										# Check attention is always last and order is fine
										SCREAMING_SNAKE_CASE_ :						str    				= True
										SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= True
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= model_class(a_							)
										model.to(a_							)
										model.eval()
										with torch.no_grad():
														SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= model(**self._prepare_for_class(a_			,     a_							)							)

										if hasattr(self.model_tester			,     'num_hidden_states_types'							):
														SCREAMING_SNAKE_CASE_ :						Dict    				= self.model_tester.num_hidden_states_types
										else:
														# also another +1 for reshaped_hidden_states
														SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= 2
										self.assertEqual(out_len + added_hidden_states			,     len(a_							)							)

										SCREAMING_SNAKE_CASE_ :						List[str]    				= outputs.attentions

										self.assertEqual(len(a_							)			,     a_							)

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


		def 	__snake_case      (    self			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__			,     UpperCamelCase__							):
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= model_class(a_							)
						model.to(a_							)
						model.eval()

						with torch.no_grad():
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= model(**self._prepare_for_class(a_			,     a_							)							)

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= outputs.hidden_states

						SCREAMING_SNAKE_CASE_ :						Tuple    				= getattr(
						    self.model_tester			,     'expected_num_hidden_layers'			,     len(self.model_tester.depths							) + 1							)
						self.assertEqual(len(a_							)			,     a_							)

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

						SCREAMING_SNAKE_CASE_ :						List[str]    				= (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]			,     )

						SCREAMING_SNAKE_CASE_ :						int    				= outputs.reshaped_hidden_states
						self.assertEqual(len(a_							)			,     a_							)

						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[int]    				= reshaped_hidden_states[0].shape
						SCREAMING_SNAKE_CASE_ :						Tuple    				= (
						    reshaped_hidden_states[0].view(a_			,     a_			,     height * width							).permute(0			,     2			,     1							)
						)
						self.assertListEqual(
						    list(reshaped_hidden_states.shape[-2:]							)			,     [num_patches, self.model_tester.embed_dim]			,     )


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Optional[int]    				= self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= (
						    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:
										SCREAMING_SNAKE_CASE_ :						Any    				= True
										self.check_hidden_states_output(a_			,     a_			,     a_			,     a_							)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										SCREAMING_SNAKE_CASE_ :						Any    				= True

										self.check_hidden_states_output(a_			,     a_			,     a_			,     a_							)


		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						Any    				= self.model_tester.prepare_config_and_inputs_for_common()
						SCREAMING_SNAKE_CASE_ :						List[str]    				= 3

						SCREAMING_SNAKE_CASE_ :						List[str]    				= (
						    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)
						)
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= (
						    config.patch_size
						    if isinstance(config.patch_size			,     collections.abc.Iterable							)
						    else (config.patch_size, config.patch_size)
						)

						SCREAMING_SNAKE_CASE_ :						List[str]    				= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_ :						Optional[int]    				= True
										self.check_hidden_states_output(a_			,     a_			,     a_			,     (padded_height, padded_width)							)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= True
										self.check_hidden_states_output(a_			,     a_			,     a_			,     (padded_height, padded_width)							)


		@slow
		def 	__snake_case      (    self							):



						for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_ :						Dict    				= DonutSwinModel.from_pretrained(a_							)
										self.assertIsNotNone(a_							)






		def 	__snake_case      (    self							):
						SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_ :						int    				= self.model_tester.prepare_config_and_inputs_for_common()

						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= _config_zero_init(a_							)
						for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= model_class(config=a_							)
										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'''			,     )



'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

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


__lowercase:		List[str]    						=  logging.get_logger(__name__)

__lowercase:		List[Any]    						=  {
    "uclanlp/plbart-base": "https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json",
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):
 _lowerCamelCase			:       Optional[int]            =       'plbart'
 _lowerCamelCase			:       Union[str, Any]            =       ['past_key_values']
 _lowerCamelCase			:       Tuple            =       {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}

 def __init__(			self			:						Optional[Any],       a_			:						Union[str, Any]=5_0005,       a_			:						int=1024,       a_			:						str=6,       a_			:						Union[str, Any]=3072,       a_			:						Optional[int]=12,       a_			:						Optional[Any]=6,       a_			:						Dict=3072,       a_			:						Dict=12,       a_			:						int=0.0,       a_			:						List[Any]=0.0,       a_			:						Optional[Any]=True,       a_			:						List[str]=True,       a_			:						Tuple="gelu",       a_			:						Any=768,       a_			:						Dict=0.1,       a_			:						List[Any]=0.1,       a_			:						Union[str, Any]=0.0,       a_			:						Optional[Any]=0.02,       a_			:						Union[str, Any]=0.0,       a_			:						int=True,       a_			:						Dict=1,       a_			:						Optional[int]=0,       a_			:						Optional[int]=2,       a_			:						Any=2,       **a_			:						Optional[Any],       ):





    """simple docstring"""



    UpperCamelCase__			    =    vocab_size
    UpperCamelCase__			    =    max_position_embeddings
    UpperCamelCase__			    =    d_model
    UpperCamelCase__			    =    encoder_ffn_dim
    UpperCamelCase__			    =    encoder_layers
    UpperCamelCase__			    =    encoder_attention_heads
    UpperCamelCase__			    =    decoder_ffn_dim
    UpperCamelCase__			    =    decoder_layers
    UpperCamelCase__			    =    decoder_attention_heads
    UpperCamelCase__			    =    dropout
    UpperCamelCase__			    =    attention_dropout
    UpperCamelCase__			    =    activation_dropout
    UpperCamelCase__			    =    activation_function
    UpperCamelCase__			    =    init_std
    UpperCamelCase__			    =    encoder_layerdrop
    UpperCamelCase__			    =    decoder_layerdrop
    UpperCamelCase__			    =    classifier_dropout
    UpperCamelCase__			    =    use_cache
    UpperCamelCase__			    =    encoder_layers
    UpperCamelCase__			    =    scale_embedding  # scale factor will be sqrt(d_model) if True
    super().__init__(
        pad_token_id=a_,       bos_token_id=a_,       eos_token_id=a_,       is_encoder_decoder=a_,       forced_eos_token_id=a_,       **a_,       )







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 @property
 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    return OrderedDict(
        [
            ("input_ids", {0: "batch", 1: "sequence"}),
            ("attention_mask", {0: "batch", 1: "sequence"}),
        ]       )

 @property
 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    if self.use_past:
       return OrderedDict(
           [
               ("last_hidden_state", {0: "batch", 1: "sequence"}),
               ("past_keys", {0: "batch", 2: "sequence"}),
               ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
           ]       )
    else:
       return OrderedDict(
           [
               ("last_hidden_state", {0: "batch", 1: "sequence"}),
               ("encoder_last_hidden_state", {0: "batch", 1: "sequence"}),
           ]       )