'''simple docstring'''




import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
  import torch

  from transformers import (
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
  )




class 	__UpperCAmelCase				:





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=13    ,     _SCREAMING_SNAKE_CASE=7    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=99    ,     _SCREAMING_SNAKE_CASE=32    ,     _SCREAMING_SNAKE_CASE=5    ,     _SCREAMING_SNAKE_CASE=4    ,     _SCREAMING_SNAKE_CASE=37    ,     _SCREAMING_SNAKE_CASE="gelu"    ,     _SCREAMING_SNAKE_CASE=0.1    ,     _SCREAMING_SNAKE_CASE=0.1    ,     _SCREAMING_SNAKE_CASE=512    ,     _SCREAMING_SNAKE_CASE=16    ,     _SCREAMING_SNAKE_CASE=2    ,     _SCREAMING_SNAKE_CASE=0.02    ,     _SCREAMING_SNAKE_CASE=3    ,     _SCREAMING_SNAKE_CASE=4    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=1000    ,     )		->     Any:
           A_     =   parent
           A_     =   batch_size
           A_     =   seq_length
           A_     =   is_training
           A_     =   use_input_mask
           A_     =   use_token_type_ids
           A_     =   use_labels
           A_     =   vocab_size
           A_     =   hidden_size
           A_     =   num_hidden_layers
           A_     =   num_attention_heads
           A_     =   intermediate_size
           A_     =   hidden_act
           A_     =   hidden_dropout_prob
           A_     =   attention_probs_dropout_prob
           A_     =   max_position_embeddings
           A_     =   type_vocab_size
           A_     =   type_sequence_label_size
           A_     =   initializer_range
           A_     =   num_labels
           A_     =   num_choices
           A_     =   scope
           A_     =   range_bbox

       def 					__A       (      self )		->     Optional[Any]:
           A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size )

           A_     =   ids_tensor([self.batch_size, self.seq_length, 4]    ,     self.range_bbox )
           # Ensure that bbox is legal
           for i in range(bbox.shape[0] ):
               for j in range(bbox.shape[1] ):
                   if bbox[i, j, 3] < bbox[i, j, 1]:
                       A_     =   bbox[i, j, 3]
                       A_     =   bbox[i, j, 1]
                       A_     =   t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                       A_     =   bbox[i, j, 2]
                       A_     =   bbox[i, j, 0]
                       A_     =   t

           A_     =   None
           if self.use_input_mask:
               A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     vocab_size=2 )

           A_     =   None
           if self.use_token_type_ids:
               A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size )

           A_     =   None
           A_     =   None
           A_     =   None
           if self.use_labels:
               A_     =   ids_tensor([self.batch_size]    ,     self.type_sequence_label_size )
               A_     =   ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels )
               A_     =   ids_tensor([self.batch_size]    ,     self.num_choices )

           A_     =   self.get_config()

           return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

       def 					__A       (      self )		->     Any:
           return LayoutLMConfig(
               vocab_size=self.vocab_size    ,     hidden_size=self.hidden_size    ,     num_hidden_layers=self.num_hidden_layers    ,     num_attention_heads=self.num_attention_heads    ,     intermediate_size=self.intermediate_size    ,     hidden_act=self.hidden_act    ,     hidden_dropout_prob=self.hidden_dropout_prob    ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,     max_position_embeddings=self.max_position_embeddings    ,     type_vocab_size=self.type_vocab_size    ,     initializer_range=self.initializer_range    ,     )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Optional[int]:
           A_     =   LayoutLMModel(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           A_     =   model(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE )
           A_     =   model(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE )
           A_     =   model(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )
           self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size) )
           self.parent.assertEqual(result.pooler_output.shape    ,     (self.batch_size, self.hidden_size) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     List[str]:
           A_     =   LayoutLMForMaskedLM(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           A_     =   model(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     labels=_SCREAMING_SNAKE_CASE )
           self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Optional[Any]:
           A_     =   self.num_labels
           A_     =   LayoutLMForSequenceClassification(_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           A_     =   model(
               _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     labels=_SCREAMING_SNAKE_CASE )
           self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     Union[str, Any]:
           A_     =   self.num_labels
           A_     =   LayoutLMForTokenClassification(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           A_     =   model(_SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     labels=_SCREAMING_SNAKE_CASE )
           self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels) )

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     List[Any]:
           A_     =   LayoutLMForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
           model.to(_SCREAMING_SNAKE_CASE )
           model.eval()
           A_     =   model(
               _SCREAMING_SNAKE_CASE    ,     bbox=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     start_positions=_SCREAMING_SNAKE_CASE    ,     end_positions=_SCREAMING_SNAKE_CASE    ,     )
           self.parent.assertEqual(result.start_logits.shape    ,     (self.batch_size, self.seq_length) )
           self.parent.assertEqual(result.end_logits.shape    ,     (self.batch_size, self.seq_length) )

       def 					__A       (      self )		->     List[str]:
           A_     =   self.prepare_config_and_inputs()
           (
               (
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,(
               A_
           )   ,
           )     =   config_and_inputs
           A_     =   {
               '''input_ids''': input_ids,
               '''bbox''': bbox,
               '''token_type_ids''': token_type_ids,
               '''attention_mask''': input_mask,
           }
           return config, inputs_dict




@require_torch
class 	__UpperCAmelCase				(				_UpperCamelCase						,      _UpperCamelCase						,      unittest.TestCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[int]    =				(
           (
               LayoutLMModel,
               LayoutLMForMaskedLM,
               LayoutLMForSequenceClassification,
               LayoutLMForTokenClassification,
               LayoutLMForQuestionAnswering,
           )
           if is_torch_available()
           else None
       )
       __lowercase					:					List[str]    =				(
           {
               'document-question-answering': LayoutLMForQuestionAnswering,
               'feature-extraction': LayoutLMModel,
               'fill-mask': LayoutLMForMaskedLM,
               'text-classification': LayoutLMForSequenceClassification,
               'token-classification': LayoutLMForTokenClassification,
               'zero-shot': LayoutLMForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __lowercase					:					Any    =				True

       def 					__A       (      self )		->     int:
           A_     =   LayoutLMModelTester(self )
           A_     =   ConfigTester(self    ,     config_class=_SCREAMING_SNAKE_CASE    ,     hidden_size=37 )

       def 					__A       (      self )		->     List[str]:
           self.config_tester.run_common_tests()

       def 					__A       (      self )		->     List[Any]:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Optional[int]:
           A_     =   self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
               A_     =   type
               self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Dict:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Any:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     List[Any]:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE )

       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
       def 					__A       (      self )		->     Tuple:
           pass





def      _UpperCAmelCase    (							)	->      int:
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    A_     =   torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]],   device=_UpperCamelCase   )  # noqa: E231
    A_     =   torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],   device=_UpperCamelCase   )  # noqa: E231
    A_     =   torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]],   device=_UpperCamelCase   )  # noqa: E231
    A_     =   torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],   device=_UpperCamelCase   )  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    A_     =   torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]],   device=_UpperCamelCase   )  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels




@require_torch
class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       @slow
       def 					__A       (      self )		->     str:
           A_     =   LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''' ).to(_SCREAMING_SNAKE_CASE )

           A_   ,A_   ,A_   ,A_   ,A_     =   prepare_layoutlm_batch_inputs()

           # forward pass
           A_     =   model(input_ids=_SCREAMING_SNAKE_CASE    ,     bbox=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE )

           # test the sequence output on [0, :3, :3]
           A_     =   torch.tensor(
               [[0.1_785, -0.1_947, -0.0_425], [-0.3_254, -0.2_807, 0.2_553], [-0.5_391, -0.3_322, 0.3_364]]    ,     device=_SCREAMING_SNAKE_CASE    ,     )

           self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]    ,     _SCREAMING_SNAKE_CASE    ,     atol=1E-3 ) )

           # test the pooled output on [1, :3]
           A_     =   torch.tensor([-0.6_580, -0.0_214, 0.8_552]    ,     device=_SCREAMING_SNAKE_CASE )

           self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]    ,     _SCREAMING_SNAKE_CASE    ,     atol=1E-3 ) )

       @slow
       def 					__A       (      self )		->     Tuple:
           # initialize model with randomly initialized sequence classification head
           A_     =   LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,     num_labels=2 ).to(
               _SCREAMING_SNAKE_CASE )

           A_   ,A_   ,A_   ,A_   ,A_     =   prepare_layoutlm_batch_inputs()

           # forward pass
           A_     =   model(
               input_ids=_SCREAMING_SNAKE_CASE    ,     bbox=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     labels=torch.tensor([1, 1]    ,     device=_SCREAMING_SNAKE_CASE )    ,     )

           # test whether we get a loss as a scalar
           A_     =   outputs.loss
           A_     =   torch.Size([] )
           self.assertEqual(loss.shape    ,     _SCREAMING_SNAKE_CASE )

           # test the shape of the logits
           A_     =   outputs.logits
           A_     =   torch.Size((2, 2) )
           self.assertEqual(logits.shape    ,     _SCREAMING_SNAKE_CASE )

       @slow
       def 					__A       (      self )		->     Union[str, Any]:
           # initialize model with randomly initialized token classification head
           A_     =   LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,     num_labels=13 ).to(
               _SCREAMING_SNAKE_CASE )

           A_   ,A_   ,A_   ,A_   ,A_     =   prepare_layoutlm_batch_inputs()

           # forward pass
           A_     =   model(
               input_ids=_SCREAMING_SNAKE_CASE    ,     bbox=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE    ,     labels=_SCREAMING_SNAKE_CASE )

           # test the loss calculation to be around 2.65
           # expected_loss = torch.tensor(2.65, device=torch_device)

           # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
           # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

           # test the shape of the logits
           A_     =   outputs.logits
           A_     =   torch.Size((2, 25, 13) )
           self.assertEqual(logits.shape    ,     _SCREAMING_SNAKE_CASE )

       @slow
       def 					__A       (      self )		->     str:
           # initialize model with randomly initialized token classification head
           A_     =   LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''' ).to(_SCREAMING_SNAKE_CASE )

           A_   ,A_   ,A_   ,A_   ,A_     =   prepare_layoutlm_batch_inputs()

           # forward pass
           A_     =   model(input_ids=_SCREAMING_SNAKE_CASE    ,     bbox=_SCREAMING_SNAKE_CASE    ,     attention_mask=_SCREAMING_SNAKE_CASE    ,     token_type_ids=_SCREAMING_SNAKE_CASE )

           # test the shape of the logits
           A_     =   torch.Size((2, 25) )
           self.assertEqual(outputs.start_logits.shape    ,     _SCREAMING_SNAKE_CASE )
           self.assertEqual(outputs.end_logits.shape    ,     _SCREAMING_SNAKE_CASE )










# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class   A__							(						UpperCamelCase__     ):




						def __init__(				self    :					Any ,			_a    :					Optional[Any] ,			_a    :					List[str]     )							->   Tuple:


										"""simple docstring"""





										super().__init__()
										self.register_modules(unet=_a ,			scheduler=_a     )




						@torch.no_grad()
						def __call__(				self    :					Any ,			_a    :					int = 1 ,			_a    :					Optional[torch.Generator] = None ,			_a    :					int = 50 ,			_a    :					Optional[str] = "pil" ,			_a    :					bool = True ,			**_a    :					Dict ,			)							->   Union[ImagePipelineOutput, Tuple]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =torch.randn(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=_a ,			)
										_SCREAMING_SNAKE_CASE	     =image.to(self.device     )

										# set step values
										self.scheduler.set_timesteps(_a     )

										for t in self.progress_bar(self.scheduler.timesteps     ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE	     =self.unet(_a ,			_a     ).sample

														# 2. predict previous mean of image x_t-1 and add variance depending on eta
														# eta corresponds to η in paper and should be between [0, 1]
														# do x_t -> x_t-1
														_SCREAMING_SNAKE_CASE	     =self.scheduler.step(_a ,			_a ,			_a     ).prev_sample

										_SCREAMING_SNAKE_CASE	     =(image / 2 + 0.5).clamp(0 ,			1     )
										_SCREAMING_SNAKE_CASE	     =image.cpu().permute(0 ,			2 ,			3 ,			1     ).numpy()
										if output_type == "pil":
														_SCREAMING_SNAKE_CASE	     =self.numpy_to_pil(_a     )

										if not return_dict:
														return (image,), "This is a local test"

										return ImagePipelineOutput(images=_a     ), "This is a local test"