import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
   import torch

   from transformers import (
       MODEL_FOR_PRETRAINING_MAPPING,
       ElectraForCausalLM,
       ElectraForMaskedLM,
       ElectraForMultipleChoice,
       ElectraForPreTraining,
       ElectraForQuestionAnswering,
       ElectraForSequenceClassification,
       ElectraForTokenClassification,
       ElectraModel,
   )
   from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST







class __snake_case							:




   def __init__(       self     :							Union[str, Any]			,      _lowercase     :							int			,      _lowercase     :							Dict=13			,      _lowercase     :							List[str]=7			,      _lowercase     :							Any=True			,      _lowercase     :							str=True			,      _lowercase     :							Union[str, Any]=True			,      _lowercase     :							List[Any]=True			,      _lowercase     :							str=99			,      _lowercase     :							Dict=32			,      _lowercase     :							Optional[Any]=5			,      _lowercase     :							Any=4			,      _lowercase     :							List[str]=37			,      _lowercase     :							int="gelu"			,      _lowercase     :							str=0.1			,      _lowercase     :							int=0.1			,      _lowercase     :							Tuple=5_12			,      _lowercase     :							Any=16			,      _lowercase     :							Tuple=2			,      _lowercase     :							List[str]=0.02			,      _lowercase     :							Dict=3			,      _lowercase     :							Union[str, Any]=4			,      _lowercase     :							Tuple=None			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= parent
     SCREAMING_SNAKE_CASE__     				= batch_size
     SCREAMING_SNAKE_CASE__     				= seq_length
     SCREAMING_SNAKE_CASE__     				= is_training
     SCREAMING_SNAKE_CASE__     				= use_input_mask
     SCREAMING_SNAKE_CASE__     				= use_token_type_ids
     SCREAMING_SNAKE_CASE__     				= use_labels
     SCREAMING_SNAKE_CASE__     				= vocab_size
     SCREAMING_SNAKE_CASE__     				= hidden_size
     SCREAMING_SNAKE_CASE__     				= num_hidden_layers
     SCREAMING_SNAKE_CASE__     				= num_attention_heads
     SCREAMING_SNAKE_CASE__     				= intermediate_size
     SCREAMING_SNAKE_CASE__     				= hidden_act
     SCREAMING_SNAKE_CASE__     				= hidden_dropout_prob
     SCREAMING_SNAKE_CASE__     				= attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__     				= max_position_embeddings
     SCREAMING_SNAKE_CASE__     				= type_vocab_size
     SCREAMING_SNAKE_CASE__     				= type_sequence_label_size
     SCREAMING_SNAKE_CASE__     				= initializer_range
     SCREAMING_SNAKE_CASE__     				= num_labels
     SCREAMING_SNAKE_CASE__     				= num_choices
     SCREAMING_SNAKE_CASE__     				= scope




   def       __a		(       self     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size )

     SCREAMING_SNAKE_CASE__     				= None
     if self.use_input_mask:
       SCREAMING_SNAKE_CASE__     				= random_attention_mask([self.batch_size, self.seq_length] )

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

     SCREAMING_SNAKE_CASE__     				= None
     SCREAMING_SNAKE_CASE__     				= None
     SCREAMING_SNAKE_CASE__     				= None
     if self.use_labels:
       SCREAMING_SNAKE_CASE__     				= ids_tensor([self.batch_size]			,      self.type_sequence_label_size )
       SCREAMING_SNAKE_CASE__     				= ids_tensor([self.batch_size, self.seq_length]			,      self.num_labels )
       SCREAMING_SNAKE_CASE__     				= ids_tensor([self.batch_size]			,      self.num_choices )
       SCREAMING_SNAKE_CASE__     				= ids_tensor([self.batch_size, self.seq_length]			,      1 )

     SCREAMING_SNAKE_CASE__     				= self.get_config()

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         fake_token_labels,
     )




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     return ElectraConfig(
         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			,      is_decoder=_lowercase			,      initializer_range=self.initializer_range			,      )




   def       __a		(       self     :							str ):



     """simple docstring"""






     (
         (
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	
     )     				= self.prepare_config_and_inputs()

     SCREAMING_SNAKE_CASE__     				= True
     SCREAMING_SNAKE_CASE__     				= floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
     SCREAMING_SNAKE_CASE__     				= ids_tensor([self.batch_size, self.seq_length]			,      vocab_size=2 )

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							List[str]			,      _lowercase     :							str			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Tuple			,      _lowercase     :							int			,      _lowercase     :							int			,      _lowercase     :							Any			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= ElectraModel(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase )
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      token_type_ids=_lowercase )
     SCREAMING_SNAKE_CASE__     				= model(_lowercase )
     self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size) )




   def       __a		(       self     :							Any			,      _lowercase     :							int			,      _lowercase     :							Any			,      _lowercase     :							List[str]			,      _lowercase     :							int			,      _lowercase     :							Optional[int]			,      _lowercase     :							List[str]			,      _lowercase     :							Optional[Any]			,      _lowercase     :							Dict			,      _lowercase     :							Tuple			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= True
     SCREAMING_SNAKE_CASE__     				= ElectraModel(_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(
         _lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      encoder_hidden_states=_lowercase			,      encoder_attention_mask=_lowercase			,      )
     SCREAMING_SNAKE_CASE__     				= model(
         _lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      encoder_hidden_states=_lowercase			,      )
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase )
     self.parent.assertEqual(result.last_hidden_state.shape			,      (self.batch_size, self.seq_length, self.hidden_size) )




   def       __a		(       self     :							int			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							List[str]			,      _lowercase     :							List[str]			,      _lowercase     :							str			,      _lowercase     :							Dict			,      _lowercase     :							Tuple			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							str			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= ElectraForMaskedLM(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      labels=_lowercase )
     self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size) )




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							int			,      _lowercase     :							Dict			,      _lowercase     :							Optional[int]			,      _lowercase     :							Dict			,      _lowercase     :							List[str]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Dict			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Union[str, Any]			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= ElectraForCausalLM(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      labels=_lowercase )
     self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.vocab_size) )




   def       __a		(       self     :							Tuple			,      _lowercase     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							int			,      _lowercase     :							Optional[Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							Tuple			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.num_labels
     SCREAMING_SNAKE_CASE__     				= ElectraForTokenClassification(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      labels=_lowercase )
     self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length, self.num_labels) )




   def       __a		(       self     :							List[Any]			,      _lowercase     :							int			,      _lowercase     :							Dict			,      _lowercase     :							List[Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							Any			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.num_labels
     SCREAMING_SNAKE_CASE__     				= ElectraForPreTraining(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      labels=_lowercase )
     self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.seq_length) )




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Any			,      _lowercase     :							List[Any]			,      _lowercase     :							str			,      _lowercase     :							Dict			,      _lowercase     :							Optional[int]			,      _lowercase     :							str			,      _lowercase     :							List[Any]			,      _lowercase     :							Tuple			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.num_labels
     SCREAMING_SNAKE_CASE__     				= ElectraForSequenceClassification(_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      labels=_lowercase )
     self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_labels) )




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Dict			,      _lowercase     :							str			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Any			,      _lowercase     :							Optional[Any]			,      _lowercase     :							int			,      _lowercase     :							int			,      _lowercase     :							str			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= ElectraForQuestionAnswering(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= model(
         _lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      start_positions=_lowercase			,      end_positions=_lowercase			,      )
     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     :							Optional[Any]			,      _lowercase     :							int			,      _lowercase     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							List[Any]			,      _lowercase     :							str			,      _lowercase     :							Optional[int]			,      _lowercase     :							List[str]			,      _lowercase     :							List[str]			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.num_choices
     SCREAMING_SNAKE_CASE__     				= ElectraForMultipleChoice(config=_lowercase )
     model.to(_lowercase )
     model.eval()
     SCREAMING_SNAKE_CASE__     				= input_ids.unsqueeze(1 ).expand(-1			,      self.num_choices			,      -1 ).contiguous()
     SCREAMING_SNAKE_CASE__     				= token_type_ids.unsqueeze(1 ).expand(-1			,      self.num_choices			,      -1 ).contiguous()
     SCREAMING_SNAKE_CASE__     				= input_mask.unsqueeze(1 ).expand(-1			,      self.num_choices			,      -1 ).contiguous()
     SCREAMING_SNAKE_CASE__     				= model(
         _lowercase			,      attention_mask=_lowercase			,      token_type_ids=_lowercase			,      labels=_lowercase			,      )
     self.parent.assertEqual(result.logits.shape			,      (self.batch_size, self.num_choices) )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.prepare_config_and_inputs()
     (
         (
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	(
         SCREAMING_SNAKE_CASE__
     )				,	
     )     				= config_and_inputs
     SCREAMING_SNAKE_CASE__     				= {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
     return config, inputs_dict







@require_torch
class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				(
       (
           ElectraModel,
           ElectraForPreTraining,
           ElectraForMaskedLM,
           ElectraForCausalLM,
           ElectraForMultipleChoice,
           ElectraForTokenClassification,
           ElectraForSequenceClassification,
           ElectraForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   lowerCAmelCase_								=				(
       {
           "feature-extraction": ElectraModel,
           "fill-mask": ElectraForMaskedLM,
           "question-answering": ElectraForQuestionAnswering,
           "text-classification": ElectraForSequenceClassification,
           "text-generation": ElectraForCausalLM,
           "token-classification": ElectraForTokenClassification,
           "zero-shot": ElectraForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   lowerCAmelCase_								=				True




   def       __a		(       self     :							Any			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Optional[Any]=False ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= super()._prepare_for_class(_lowercase			,      _lowercase			,      return_labels=_lowercase )

     if return_labels:
       if model_class in get_values(_lowercase ):
         SCREAMING_SNAKE_CASE__     				= torch.zeros(
             (self.model_tester.batch_size, self.model_tester.seq_length)			,      dtype=torch.long			,      device=_lowercase )
     return inputs_dict




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= ElectraModelTester(self )
     SCREAMING_SNAKE_CASE__     				= ConfigTester(self			,      config_class=_lowercase			,      hidden_size=37 )




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_electra_model(*_lowercase )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_electra_model_as_decoder(*_lowercase )




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     for type in ["absolute", "relative_key", "relative_key_query"]:
       SCREAMING_SNAKE_CASE__     				= type
       self.model_tester.create_and_check_electra_model(*_lowercase )




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_electra_for_masked_lm(*_lowercase )




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_electra_for_token_classification(*_lowercase )




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_electra_for_pretraining(*_lowercase )




   def       __a		(       self     :							int ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_electra_for_sequence_classification(*_lowercase )




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_electra_for_question_answering(*_lowercase )




   def       __a		(       self     :							Optional[int] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_electra_for_multiple_choice(*_lowercase )




   @slow
   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       SCREAMING_SNAKE_CASE__     				= ElectraModel.from_pretrained(_lowercase )
       self.assertIsNotNone(_lowercase )




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_electra_for_causal_lm(*_lowercase )







@require_torch
class __snake_case							(		unittest.TestCase						):




   @slow
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= ElectraModel.from_pretrained("""google/electra-small-discriminator""" )
     SCREAMING_SNAKE_CASE__     				= torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
     SCREAMING_SNAKE_CASE__     				= torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
     SCREAMING_SNAKE_CASE__     				= model(_lowercase			,      attention_mask=_lowercase )[0]
     SCREAMING_SNAKE_CASE__     				= torch.Size((1, 11, 2_56) )
     self.assertEqual(output.shape			,      _lowercase )
     SCREAMING_SNAKE_CASE__     				= torch.tensor(
         [[[0.44_71, 0.68_21, -0.32_65], [0.46_27, 0.52_55, -0.36_68], [0.45_32, 0.33_13, -0.43_44]]] )

     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]			,      _lowercase			,      atol=1E-4 ) )

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







@skip_mps
class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				StableDiffusionModelEditingPipeline
   lowerCAmelCase_								=				TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= UNetaDConditionModel(
         block_out_channels=(32, 64)			,      layers_per_block=2			,      sample_size=32			,      in_channels=4			,      out_channels=4			,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,      cross_attention_dim=32			,      )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler()
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= AutoencoderKL(
         block_out_channels=[32, 64]			,      in_channels=3			,      out_channels=3			,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,      latent_channels=4			,      )
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= CLIPTextConfig(
         bos_token_id=0			,      eos_token_id=2			,      hidden_size=32			,      intermediate_size=37			,      layer_norm_eps=1E-05			,      num_attention_heads=4			,      num_hidden_layers=5			,      pad_token_id=1			,      vocab_size=10_00			,      )
     SCREAMING_SNAKE_CASE__     				= CLIPTextModel(_lowercase )
     SCREAMING_SNAKE_CASE__     				= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

     SCREAMING_SNAKE_CASE__     				= {
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Union[str, Any]=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         # Setting height and width to None to prevent OOMs on CPU.
         """height""": None,
         """width""": None,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]
     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= """french fries"""
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase			,      negative_prompt=_lowercase )
     SCREAMING_SNAKE_CASE__     				= output.images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85			,      beta_end=0.0_12			,      beta_schedule="""scaled_linear""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= PNDMScheduler()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     # the pipeline does not expect pndm so test if it raises error.
     with self.assertRaises(_lowercase ):
       SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     super().test_inference_batch_single_identical(expected_max_diff=5E-3 )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     super().test_attention_slicing_forward_pass(expected_max_diff=5E-3 )







@slow
@require_torch_gpu
class __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							str ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Dict=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 7.5,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(_lowercase			,      safety_checker=_lowercase )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     SCREAMING_SNAKE_CASE__     				= np.array(
         [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58] )

     assert np.abs(expected_slice - image_slice ).max() < 1E-2

     # make sure image changes after editing
     pipe.edit_model("""A pack of roses"""			,      """A pack of blue roses""" )

     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     assert np.abs(expected_slice - image_slice ).max() > 1E-1




   def       __a		(       self     :							Any ):



     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_pretrained(_lowercase			,      subfolder="""scheduler""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(
         _lowercase			,      scheduler=_lowercase			,      safety_checker=_lowercase )
     SCREAMING_SNAKE_CASE__     				= pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing(1 )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase )

     SCREAMING_SNAKE_CASE__     				= torch.cuda.max_memory_allocated()
     # make sure that less than 4.4 GB is allocated
     assert mem_bytes < 4.4 * 10**9
