

"""simple docstring"""

import copy
import logging
import random

import joblib
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AdamW, GPTaLMHeadModel, get_linear_schedule_with_warmup


a__			:						Optional[int]							=			logging.getLogger(__name__)





def 					__UpperCamelCase (     a__:			Any      )					->      Union[str, Any]:




					"""simple docstring"""



					random.seed(__SCREAMING_SNAKE_CASE      )
					np.random.seed(__SCREAMING_SNAKE_CASE      )
					torch.manual_seed(__SCREAMING_SNAKE_CASE      )
					torch.cuda.manual_seed_all(__SCREAMING_SNAKE_CASE      )





def 					__UpperCamelCase (     a__:			Optional[int]			,						a__:			Tuple			,						a__:			Optional[Any]      )					->      Any:




					"""simple docstring"""



					model.eval()
					__lowercase        =      next(model.parameters()      ).device
					__lowercase        =      1
					__lowercase        =      torch.zeros((eval_batch_size, context_len)			,						dtype=torch.long			,						device=__SCREAMING_SNAKE_CASE      )
					__lowercase        =      DataLoader(__SCREAMING_SNAKE_CASE			,						shuffle=__SCREAMING_SNAKE_CASE			,						batch_size=__SCREAMING_SNAKE_CASE      )
					__lowercase        =      torch.zeros(1			,						device=__SCREAMING_SNAKE_CASE      )
					__lowercase        =      0
					for batch in eval_dataloader:
										batch.to(__SCREAMING_SNAKE_CASE      )
										# pad
										context.zero_()
										for i in range(__SCREAMING_SNAKE_CASE      ):
															__lowercase        =      batch[i]
										__lowercase        =      model(__SCREAMING_SNAKE_CASE			,						labels=__SCREAMING_SNAKE_CASE      )
										eval_loss += outputs[0].sum().item()
										nb_eval_examples += batch.size(0      )
					__lowercase        =      eval_loss / nb_eval_examples
					__lowercase        =      torch.exp(__SCREAMING_SNAKE_CASE      )
					model.train()
					return perplexity





def 					__UpperCamelCase (     a__:			Optional[int]="gpt2"      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      GPTaLMHeadModel.from_pretrained(__SCREAMING_SNAKE_CASE			,						output_hidden_states=__SCREAMING_SNAKE_CASE      )
					torch.save(model.state_dict()			,						model_name + '''local.pt'''      )
					return model





def 					__UpperCamelCase (     a__:			Dict			,						a__:			List[str]			,						a__:			List[Any]      )					->      Optional[int]:




					"""simple docstring"""



					__lowercase        =      copy.deepcopy(__SCREAMING_SNAKE_CASE      )
					model.to(__SCREAMING_SNAKE_CASE      )

					__lowercase        =      ["bias", "LayerNorm.weight"]
					__lowercase        =      [
					    {
					        "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay      )],
					        "weight_decay": 0.0,
					    },
					    {"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay      )], "weight_decay": 0.0},
					]
					__lowercase        =      AdamW(__SCREAMING_SNAKE_CASE			,						lr=5e-5			,						eps=1e-8      )
					__lowercase        =      get_linear_schedule_with_warmup(__SCREAMING_SNAKE_CASE			,						0			,						__SCREAMING_SNAKE_CASE      )
					torch.cuda.empty_cache()
					return model, lm_optimizer, lm_scheduler





def 					__UpperCamelCase (     a__:			str			,						a__:			List[str]			,						a__:			Tuple			,						a__:			Optional[int]      )					->      Optional[int]:




					"""simple docstring"""



					__lowercase        =      np.array(__SCREAMING_SNAKE_CASE      ).mean()
					__lowercase        =      np.array(__SCREAMING_SNAKE_CASE      ).std()
					__lowercase        =      (real_perps - avg) / std
					__lowercase        =      list(zip(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )      )
					joblib.dump(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )





def 					__UpperCamelCase (     a__:			Tuple			,						a__:			Union[str, Any]			,						a__:			List[Any]			,						a__:			Optional[Any]			,						a__:			List[Any]			,						a__:			List[str]			,						a__:			int			,						a__:			int="dev.jbl"			,						a__:			str=recopy_gpta			,						)					->      Dict:




					"""simple docstring"""



					__lowercase        =      []
					__lowercase        =      []
					__lowercase        =      []

					# Initialize the transformer model
					__lowercase        =      copy.deepcopy(__SCREAMING_SNAKE_CASE      )
					orig_model.to(device='''cpu'''      )
					torch.cuda.empty_cache()

					# Compute perplexity of initial transformer model for comparison
					model.train()
					__lowercase        =      recopy_model(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )

					for step in tqdm(range(__SCREAMING_SNAKE_CASE      )      ):
										__lowercase        =      torch.zeros((1, context_len)			,						dtype=torch.long			,						device=__SCREAMING_SNAKE_CASE      )
										__lowercase        =      random.choice(__SCREAMING_SNAKE_CASE      )
										__lowercase        =      random.randint(0			,						len(story[0]      ) - context_len - 1      )
										__lowercase        =      story[0][start : start + context_len]
										lm_optimizer.zero_grad()
										__lowercase        =      model(__SCREAMING_SNAKE_CASE			,						labels=__SCREAMING_SNAKE_CASE      )
										__lowercase        =      outputs[0]
										__lowercase        =      compute_perplexity(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )
										model.train()
										lm_loss.backward()
										# Do LM backprop
										torch.nn.utils.clip_grad_norm_(model.parameters()			,						3.0      )
										lm_optimizer.step()
										lm_scheduler.step()  # Update learning rate schedule

										# Compute perplexity after back-propagating on the selected context
										__lowercase        =      compute_perplexity(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )

										# Periodically save the stored (X, IG(X)) pairs
										if step % 10_00 == 0 and step > 1:
															intermittent_save(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )

										# Reset the pretrained model to the original pretrained GPT-2 weights after each iteration
										__lowercase        =      recopy_model(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )

										past_perps.append(past_perp.item()      )
										real_perps.append(orig_perp - real_perp.item()      )
										contexts.append(np.array(context.cpu()      )      )

					intermittent_save(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      )





def 					__UpperCamelCase (     a__:			str			,						a__:			Dict="data/tokenized_stories_train_wikitext103.jbl"			,						a__:			int=1_00			,						a__:			Optional[int]=10_26			,						a__:			List[Any]=True      )					->      str:




					"""simple docstring"""



					__lowercase        =      joblib.load(__SCREAMING_SNAKE_CASE      )
					print('''data loaded'''      )
					__lowercase        =      []
					if trim:
										for i, example in enumerate(__SCREAMING_SNAKE_CASE      ):
															if len(example[0]      ) > min_len:
																				__lowercase        =      random.randint(0			,						len(example[0]      ) - context_len - 1      )
																				objective_set.append(example[0, start : start + context_len]      )
															if len(__SCREAMING_SNAKE_CASE      ) >= number:
																				break
										__lowercase        =      []
										for j in range(i + 1			,						len(__SCREAMING_SNAKE_CASE      )      ):
															if len(data[j][0]      ) > min_len:
																				train_data.append(data[j]      )
					else:
										__lowercase        =      data[0:number]
										__lowercase        =      data[number:]

					joblib.dump(__SCREAMING_SNAKE_CASE			,						'''objective_set.jbl'''      )
					print('''objective set saved'''      )
					return train_data, objective_set





def 					__UpperCamelCase (     a__:			str			,						a__:			int			,						a__:			Optional[Any]			,						a__:			Optional[Any]			,						a__:			str=50			,						a__:			List[str]="secondary_learner.pt"      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )
					# We will use the first 512 pairs from our dataset as a test set for
					# our secondary learner and the rest to train
					__lowercase        =      train_dataset[:5_12]
					__lowercase        =      train_dataset[5_12:]
					__lowercase        =      DataLoader(__SCREAMING_SNAKE_CASE			,						shuffle=__SCREAMING_SNAKE_CASE			,						batch_size=__SCREAMING_SNAKE_CASE      )
					__lowercase        =      DataLoader(__SCREAMING_SNAKE_CASE			,						shuffle=__SCREAMING_SNAKE_CASE			,						batch_size=__SCREAMING_SNAKE_CASE      )

					# secondary learner model set up
					__lowercase        =      nn.MSELoss()
					__lowercase        =      nn.MSELoss(reduction='''sum'''      )
					secondary_learner.to(__SCREAMING_SNAKE_CASE      )
					__lowercase        =      torch.optim.Adam(secondary_learner.parameters()			,						lr=0.0_00_01      )
					secondary_learner.train()

					# TODO in original code this is written as number of actual batches seen
					# not number of items seen but other places it is number of items instead.
					# improve consistency! changed this to epochs for clarity
					__lowercase        =      float('''inf'''      )
					# Iterate through batches until we've used max_steps batches
					for epoch in range(int(__SCREAMING_SNAKE_CASE      )      ):
										__lowercase        =      0.0
										secondary_learner.train()
										for step, batch in enumerate(__SCREAMING_SNAKE_CASE      ):
															__lowercase        =      batch[0].to(__SCREAMING_SNAKE_CASE      )
															__lowercase        =      batch[1].to(__SCREAMING_SNAKE_CASE      )
															__lowercase        =      secondary_learner(__SCREAMING_SNAKE_CASE      )
															q_optimizer.zero_grad()
															__lowercase        =      loss(__SCREAMING_SNAKE_CASE			,						real_q.float()      )
															q_loss.backward()
															q_optimizer.step()
															tr_q_loss += q_loss.item()

															# model trains fairly quickly so we won't wait for a full epoch
															# eval is triggered at eval_freq and end of epochs
															if (step % eval_freq == 0 and step > 0) or ((step + 1) == len(__SCREAMING_SNAKE_CASE      )):
																				__lowercase        =      tr_q_loss / (step + 1)

																				secondary_learner.eval()
																				__lowercase        =      0.0
																				__lowercase        =      0.0
																				__lowercase        =      []
																				__lowercase        =      []
																				# Compute performance of the secondary learner after this batch
																				for stepa, batcha in enumerate(__SCREAMING_SNAKE_CASE      ):
																									__lowercase        =      batcha[0].to(__SCREAMING_SNAKE_CASE      )
																									__lowercase        =      batcha[1].to(__SCREAMING_SNAKE_CASE      )
																									__lowercase        =      secondary_learner(__SCREAMING_SNAKE_CASE      )
																									q_lossa += test_loss(__SCREAMING_SNAKE_CASE			,						__SCREAMING_SNAKE_CASE      ).item()
																									sum_qa += torch.sum(__SCREAMING_SNAKE_CASE      ).item()
																									for ei, i in enumerate(predicted_qa.cpu().detach().numpy()      ):
																														predicted.append(i.item()      )
																									for ei, i in enumerate(real_qa.cpu().detach().numpy()      ):
																														actual.append(i.item()      )

																				q_lossa /= len(__SCREAMING_SNAKE_CASE      )
																				print(
																				    '''Epoch: '''			,						__SCREAMING_SNAKE_CASE			,						'''step: '''			,						__SCREAMING_SNAKE_CASE			,						'''Avg. q:'''			,						sum_qa / len(__SCREAMING_SNAKE_CASE      )			,						'''Train Loss: '''			,						__SCREAMING_SNAKE_CASE			,						'''Test Loss: '''			,						__SCREAMING_SNAKE_CASE			,						)
																				if q_lossa < best_test_loss:
																									joblib.dump((predicted, actual)			,						'''pred_vs_actual.jbl'''      )
																									torch.save(secondary_learner.state_dict()			,						__SCREAMING_SNAKE_CASE      )
																									__lowercase        =      q_lossa

															secondary_learner.train()
					return secondary_learner


class       _snake_case  (    nn.Module							):







						def __init__(self  ,				UpperCAmelCase_				)      ->    int:
											super(lowercase_  ,				self				).__init__()
											__lowercase        =      model.transformer.wte
											__lowercase        =      copy.deepcopy(model.transformer.wte.weight				)
											__lowercase        =      nn.Convad(self.embeddings.weight.size(1				)  ,				2_5_6  ,				3  ,				padding=1				)
											__lowercase        =      nn.Sequential(nn.Linear(2_5_6  ,				3_2				)  ,				nn.Dropout(p=0.1				)  ,				nn.Linear(3_2  ,				3_2				)  ,				nn.Linear(3_2  ,				1				)				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    Union[str, Any]:
											__lowercase        =      torch.max(self.conv(self.embeddings(lowercase_				).squeeze(1				).transpose(1  ,				2				)				)  ,				2				)[0]
											__lowercase        =      self.fc(lowercase_				)
											return qs.squeeze(1				)







						@classmethod
						def 							_UpperCamelCase     (cls  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Union[str, Any]:
											__lowercase        =      cls(lowercase_				)  # this calls __init__
											__lowercase        =      torch.load(lowercase_				)
											secondary_learner.load_state_dict(lowercase_				)
											__lowercase        =      model.transformer.wte
											__lowercase        =      copy.deepcopy(model.transformer.wte.weight				)
											return secondary_learner




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





 def 							snake_case__		(     self):
     snake_case_			:     str        =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

     snake_case_			:     Tuple        =    None
     if self.use_labels:
         snake_case_			:     Union[str, Any]        =    ids_tensor([self.batch_size]			,							self.type_sequence_label_size)

     snake_case_			:     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			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     str        =    DonutSwinModel(config=lowercase_)
     model.to(lowercase_)
     model.eval()
     snake_case_			:     Union[str, Any]        =    model(lowercase_)

     snake_case_			:     Tuple        =    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
     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):
     snake_case_			:     Tuple        =    self.prepare_config_and_inputs()
     (
         (
         snake_case_
     )      ,			(
         snake_case_
     )      ,			(
         snake_case_
     )      ,			
     )			:     Optional[int]        =    config_and_inputs
     snake_case_			:     Dict        =    {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class  UpperCAmelCase_					(						snake_case__     ,    snake_case__     ,    unittest.TestCase       ):
 UpperCAmelCase_		     =    (DonutSwinModel,) if is_torch_available() else ()
 UpperCAmelCase_		     =    {"""feature-extraction""": DonutSwinModel} if is_torch_available() else {}
 UpperCAmelCase_		     =    True

 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False
 UpperCAmelCase_		     =    False





 def 							snake_case__		(     self):
     snake_case_			:     Union[str, Any]        =    DonutSwinModelTester(self)
     snake_case_			:     Optional[Any]        =    ConfigTester(self			,							config_class=lowercase_			,							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):
     snake_case_			:     int        =    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_)





 def 							snake_case__		(     self):
     # DonutSwin does not use inputs_embeds
     pass





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Tuple        =    self.model_tester.prepare_config_and_inputs_for_common()

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





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Optional[Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

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

         snake_case_			:     Any        =    ["pixel_values"]
         self.assertListEqual(arg_names[:1]			,							lowercase_)





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.model_tester.prepare_config_and_inputs_for_common()
     snake_case_			:     Union[str, Any]        =    True

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

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

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

         # Check attention is always last and order is fine
         snake_case_			:     Union[str, Any]        =    True
         snake_case_			:     Union[str, Any]        =    True
         snake_case_			:     List[Any]        =    model_class(lowercase_)
         model.to(lowercase_)
         model.eval()
         with torch.no_grad():
             snake_case_			:     Tuple        =    model(**self._prepare_for_class(lowercase_			,							lowercase_))

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

         snake_case_			:     Any        =    outputs.attentions

         self.assertEqual(len(lowercase_)			,							lowercase_)

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





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Union[str, Any]        =    model_class(lowercase_)
     model.to(lowercase_)
     model.eval()

     with torch.no_grad():
         snake_case_			:     List[str]        =    model(**self._prepare_for_class(lowercase_			,							lowercase_))

     snake_case_			:     Dict        =    outputs.hidden_states

     snake_case_			:     Dict        =    getattr(
         self.model_tester			,							"expected_num_hidden_layers"			,							len(self.model_tester.depths) + 1)
     self.assertEqual(len(lowercase_)			,							lowercase_)

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

     snake_case_			:     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]			,							)

     snake_case_			:     str        =    outputs.reshaped_hidden_states
     self.assertEqual(len(lowercase_)			,							lowercase_)

     snake_case_      ,			snake_case_      ,			snake_case_      ,			snake_case_			:     Dict        =    reshaped_hidden_states[0].shape
     snake_case_			:     Union[str, Any]        =    (
         reshaped_hidden_states[0].view(lowercase_			,							lowercase_			,							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):
     snake_case_      ,			snake_case_			:     int        =    self.model_tester.prepare_config_and_inputs_for_common()

     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:
         snake_case_			:     List[str]        =    True
         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)

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

         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)





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

     snake_case_			:     int        =    (
         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)
     )
     snake_case_			:     int        =    (
         config.patch_size
         if isinstance(config.patch_size			,							collections.abc.Iterable)
         else (config.patch_size, config.patch_size)
     )

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

     for model_class in self.all_model_classes:
         snake_case_			:     str        =    True
         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							(padded_height, padded_width))

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         snake_case_			:     Dict        =    True
         self.check_hidden_states_output(lowercase_			,							lowercase_			,							lowercase_			,							(padded_height, padded_width))





 @slow
 def 							snake_case__		(     self):
     for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_			:     List[str]        =    DonutSwinModel.from_pretrained(lowercase_)
         self.assertIsNotNone(lowercase_)





 def 							snake_case__		(     self):
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.model_tester.prepare_config_and_inputs_for_common()

     snake_case_			:     Dict        =    _config_zero_init(lowercase_)
     for model_class in self.all_model_classes:
         snake_case_			:     str        =    model_class(config=lowercase_)
         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'			,							)




