import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   a_    					=       'platform'

   import jax
   import jax.numpy as jnp

   from transformers import BlenderbotTokenizer
   from transformers.models.blenderbot.modeling_flax_blenderbot import (
       FlaxBlenderbotForConditionalGeneration,
       FlaxBlenderbotModel,
       shift_tokens_right,
   )





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     List[Any]=None  ,  lowerCamelCase					:     Dict=None  ,  lowerCamelCase					:     str=None  ,  lowerCamelCase					:     Tuple=None  ,  lowerCamelCase					:     str=None  ,  lowerCamelCase					:     Dict=None  ,  ):
 if attention_mask is None:
  UpperCamelCase_					:						Optional[Any] =					np.where(input_ids != config.pad_token_id  ,  1  ,  0	)
 if decoder_attention_mask is None:
  UpperCamelCase_					:						List[str] =					np.where(decoder_input_ids != config.pad_token_id  ,  1  ,  0	)
 if head_mask is None:
  UpperCamelCase_					:						Optional[int] =					np.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  UpperCamelCase_					:						Optional[int] =					np.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  UpperCamelCase_					:						str =					np.ones((config.decoder_layers, config.decoder_attention_heads)	)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": attention_mask,
 }





class 		_lowercase						:

 def __init__( self	:							Any   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							List[Any]=1_3   ,      snake_case	:							int=7   ,      snake_case	:							Dict=True   ,      snake_case	:							int=False   ,      snake_case	:							Any=9_9   ,      snake_case	:							Tuple=1_6   ,      snake_case	:							Optional[int]=2   ,      snake_case	:							Optional[int]=4   ,      snake_case	:							Dict=4   ,      snake_case	:							List[str]="gelu"   ,      snake_case	:							List[Any]=0.1   ,      snake_case	:							List[Any]=0.1   ,      snake_case	:							Any=3_2   ,      snake_case	:							int=2   ,      snake_case	:							Union[str, Any]=1   ,      snake_case	:							int=0   ,      snake_case	:							Dict=0.02   ,      )   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					parent
  UpperCamelCase_					:						List[str] =					batch_size
  UpperCamelCase_					:						int =					seq_length
  UpperCamelCase_					:						List[str] =					is_training
  UpperCamelCase_					:						List[str] =					use_labels
  UpperCamelCase_					:						int =					vocab_size
  UpperCamelCase_					:						int =					hidden_size
  UpperCamelCase_					:						int =					num_hidden_layers
  UpperCamelCase_					:						Tuple =					num_attention_heads
  UpperCamelCase_					:						Union[str, Any] =					intermediate_size
  UpperCamelCase_					:						int =					hidden_act
  UpperCamelCase_					:						str =					hidden_dropout_prob
  UpperCamelCase_					:						int =					attention_probs_dropout_prob
  UpperCamelCase_					:						Any =					max_position_embeddings
  UpperCamelCase_					:						Optional[int] =					eos_token_id
  UpperCamelCase_					:						str =					pad_token_id
  UpperCamelCase_					:						List[str] =					bos_token_id
  UpperCamelCase_					:						List[str] =					initializer_range

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						int =					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]   ,      self.vocab_size			)   ,      3   ,      self.vocab_size			)
  UpperCamelCase_					:						Dict =					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)   ,      dtype=np.intaa			))   ,      -1			)

  UpperCamelCase_					:						Dict =					shift_tokens_right(snake_case   ,      1   ,      2			)

  UpperCamelCase_					:						Optional[Any] =					BlenderbotConfig(
      vocab_size=self.vocab_size   ,      d_model=self.hidden_size   ,      encoder_layers=self.num_hidden_layers   ,      decoder_layers=self.num_hidden_layers   ,      encoder_attention_heads=self.num_attention_heads   ,      decoder_attention_heads=self.num_attention_heads   ,      encoder_ffn_dim=self.intermediate_size   ,      decoder_ffn_dim=self.intermediate_size   ,      dropout=self.hidden_dropout_prob   ,      attention_dropout=self.attention_probs_dropout_prob   ,      max_position_embeddings=self.max_position_embeddings   ,      eos_token_id=self.eos_token_id   ,      bos_token_id=self.bos_token_id   ,      pad_token_id=self.pad_token_id   ,      initializer_range=self.initializer_range   ,      use_cache=snake_case   ,      )
  UpperCamelCase_					:						Union[str, Any] =					prepare_blenderbot_inputs_dict(snake_case   ,      snake_case   ,      snake_case			)
  return config, inputs_dict

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						Optional[Any] =					self.prepare_config_and_inputs()
  return config, inputs_dict

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str   ,      snake_case	:							Tuple   ,      snake_case	:							Tuple   ,      snake_case	:							List[str]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Any =					2_0
  UpperCamelCase_					:						List[Any] =					model_class_name(snake_case			)

  UpperCamelCase_					:						Union[str, Any] =					model.encode(inputs_dict['input_ids']			)

  UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					(
      inputs_dict['decoder_input_ids'],
      inputs_dict['decoder_attention_mask'],
  )

  UpperCamelCase_					:						Union[str, Any] =					model.init_cache(decoder_input_ids.shape[0]   ,      snake_case   ,      snake_case			)
  UpperCamelCase_					:						int =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)   ,      dtype='i4'			)

  UpperCamelCase_					:						int =					jnp.broadcast_to(
      jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]   ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,      )
  UpperCamelCase_					:						List[Any] =					model.decode(
      decoder_input_ids[:, :-1]   ,      snake_case   ,      decoder_attention_mask=snake_case   ,      past_key_values=snake_case   ,      decoder_position_ids=snake_case   ,      )

  UpperCamelCase_					:						Any =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,      dtype='i4'			)
  UpperCamelCase_					:						int =					model.decode(
      decoder_input_ids[:, -1:]   ,      snake_case   ,      decoder_attention_mask=snake_case   ,      past_key_values=outputs_cache.past_key_values   ,      decoder_position_ids=snake_case   ,      )

  UpperCamelCase_					:						Union[str, Any] =					model.decode(snake_case   ,      snake_case			)

  UpperCamelCase_					:						List[Any] =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
  self.parent.assertTrue(diff < 1e-3   ,      msg=f"Max diff is {diff}"			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Dict			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					2_0
  UpperCamelCase_					:						List[Any] =					model_class_name(snake_case			)

  UpperCamelCase_					:						int =					model.encode(inputs_dict['input_ids']			)

  UpperCamelCase_, UpperCamelCase_					:						List[str] =					(
      inputs_dict['decoder_input_ids'],
      inputs_dict['decoder_attention_mask'],
  )

  UpperCamelCase_					:						Optional[int] =					jnp.concatenate(
      [
          decoder_attention_mask,
          jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])			),
      ]   ,      axis=-1   ,      )

  UpperCamelCase_					:						int =					model.init_cache(decoder_input_ids.shape[0]   ,      snake_case   ,      snake_case			)
  UpperCamelCase_					:						Optional[int] =					jnp.broadcast_to(
      jnp.arange(decoder_input_ids.shape[-1] - 1			)[None, :]   ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)   ,      )

  UpperCamelCase_					:						Tuple =					model.decode(
      decoder_input_ids[:, :-1]   ,      snake_case   ,      decoder_attention_mask=snake_case   ,      past_key_values=snake_case   ,      decoder_position_ids=snake_case   ,      )
  UpperCamelCase_					:						Dict =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]   ,      dtype='i4'			)
  UpperCamelCase_					:						Tuple =					model.decode(
      decoder_input_ids[:, -1:]   ,      snake_case   ,      past_key_values=outputs_cache.past_key_values   ,      decoder_attention_mask=snake_case   ,      decoder_position_ids=snake_case   ,      )

  UpperCamelCase_					:						int =					model.decode(snake_case   ,      snake_case   ,      decoder_attention_mask=snake_case			)

  UpperCamelCase_					:						Dict =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])			)			)
  self.parent.assertTrue(diff < 1e-3   ,      msg=f"Max diff is {diff}"			)





@require_flax
class 		_lowercase						(					unittest.TestCase       ):
 lowercase				     =   9_9

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					np.array(
      [
          [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
          [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
          [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
          [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
          [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
          [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
          [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
          [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
          [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
          [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
          [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
          [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
          [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
      ]   ,      dtype=np.intaa   ,      )

  UpperCamelCase_					:						Dict =					input_ids.shape[0]
  UpperCamelCase_					:						Dict =					BlenderbotConfig(
      vocab_size=self.vocab_size   ,      d_model=2_4   ,      encoder_layers=2   ,      decoder_layers=2   ,      encoder_attention_heads=2   ,      decoder_attention_heads=2   ,      encoder_ffn_dim=3_2   ,      decoder_ffn_dim=3_2   ,      max_position_embeddings=4_8   ,      eos_token_id=2   ,      pad_token_id=1   ,      bos_token_id=0   ,      )
  return config, input_ids, batch_size

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

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_, UpperCamelCase_					:						str =					self._get_config_and_data()
  UpperCamelCase_					:						Tuple =					FlaxBlenderbotForConditionalGeneration(snake_case			)
  UpperCamelCase_					:						Any =					lm_model(input_ids=snake_case			)
  UpperCamelCase_					:						List[Any] =					(batch_size, input_ids.shape[1], config.vocab_size)
  self.assertEqual(outputs['logits'].shape   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					BlenderbotConfig(
      vocab_size=self.vocab_size   ,      d_model=1_4   ,      encoder_layers=2   ,      decoder_layers=2   ,      encoder_attention_heads=2   ,      decoder_attention_heads=2   ,      encoder_ffn_dim=8   ,      decoder_ffn_dim=8   ,      max_position_embeddings=4_8   ,      )
  UpperCamelCase_					:						Union[str, Any] =					FlaxBlenderbotForConditionalGeneration(snake_case			)
  UpperCamelCase_					:						Any =					np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]   ,      dtype=np.intaa			)
  UpperCamelCase_					:						int =					np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]   ,      dtype=np.intaa			)
  UpperCamelCase_					:						str =					lm_model(input_ids=snake_case   ,      decoder_input_ids=snake_case			)
  UpperCamelCase_					:						str =					(*summary.shape, config.vocab_size)
  self.assertEqual(outputs['logits'].shape   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]   ,      dtype=np.intaa			)
  UpperCamelCase_					:						Dict =					shift_tokens_right(snake_case   ,      1   ,      2			)
  UpperCamelCase_					:						Optional[int] =					np.equal(snake_case   ,      1			).astype(np.floataa			).sum()
  UpperCamelCase_					:						Optional[Any] =					np.equal(snake_case   ,      1			).astype(np.floataa			).sum()
  self.assertEqual(shifted.shape   ,      input_ids.shape			)
  self.assertEqual(snake_case   ,      n_pad_before - 1			)
  self.assertTrue(np.equal(shifted[:, 0]   ,      2			).all()			)





@require_flax
class 		_lowercase						(					snake_case_						,      unittest.TestCase						,      snake_case_       ):
 lowercase				     =   True
 lowercase				     =   (
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowercase				     =   (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_					:						Any =					FlaxBlenderbotModelTester(self			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						Optional[Any] =					self.model_tester.prepare_config_and_inputs()
  for model_class in self.all_model_classes:
   self.model_tester.check_use_cache_forward(snake_case   ,      snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Dict			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						Optional[int] =					self.model_tester.prepare_config_and_inputs()
  for model_class in self.all_model_classes:
   self.model_tester.check_use_cache_forward_with_attn_mask(snake_case   ,      snake_case   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Tuple			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						List[Any] =					self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__			):
    UpperCamelCase_					:						Optional[Any] =					self._prepare_for_class(snake_case   ,      snake_case			)
    UpperCamelCase_					:						Optional[Any] =					model_class(snake_case			)

    @jax.jit
    def encode_jitted(snake_case	:							Optional[Any]   ,      snake_case	:							Optional[Any]=None   ,      **snake_case	:							List[Any]			):
     return model.encode(input_ids=snake_case   ,      attention_mask=snake_case			)

    with self.subTest('JIT Enabled'			):
     UpperCamelCase_					:						Any =					encode_jitted(**snake_case			).to_tuple()

    with self.subTest('JIT Disabled'			):
     with jax.disable_jit():
      UpperCamelCase_					:						List[str] =					encode_jitted(**snake_case			).to_tuple()

    self.assertEqual(len(snake_case			)   ,      len(snake_case			)			)
    for jitted_output, output in zip(snake_case   ,      snake_case			):
     self.assertEqual(jitted_output.shape   ,      output.shape			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						int:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__			):
    UpperCamelCase_					:						Union[str, Any] =					model_class(snake_case			)
    UpperCamelCase_					:						Dict =					model.encode(inputs_dict['input_ids']   ,      inputs_dict['attention_mask']			)

    UpperCamelCase_					:						List[Any] =					{
        'decoder_input_ids': inputs_dict['decoder_input_ids'],
        'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
        'encoder_outputs': encoder_outputs,
    }

    @jax.jit
    def decode_jitted(snake_case	:							List[Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Any			):
     return model.decode(
         decoder_input_ids=snake_case   ,      decoder_attention_mask=snake_case   ,      encoder_outputs=snake_case   ,      )

    with self.subTest('JIT Enabled'			):
     UpperCamelCase_					:						List[str] =					decode_jitted(**snake_case			).to_tuple()

    with self.subTest('JIT Disabled'			):
     with jax.disable_jit():
      UpperCamelCase_					:						Tuple =					decode_jitted(**snake_case			).to_tuple()

    self.assertEqual(len(snake_case			)   ,      len(snake_case			)			)
    for jitted_output, output in zip(snake_case   ,      snake_case			):
     self.assertEqual(jitted_output.shape   ,      output.shape			)

 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Union[str, Any]:

  """simple docstring"""





  for model_class_name in self.all_model_classes:
   UpperCamelCase_					:						int =					model_class_name.from_pretrained('facebook/blenderbot-400M-distill'			)
   # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
   UpperCamelCase_					:						List[str] =					np.ones((1, 1)			) * model.config.eos_token_id
   UpperCamelCase_					:						List[Any] =					model(snake_case			)
   self.assertIsNotNone(snake_case			)


 @unittest.skipUnless(jax_device != 'cpu'   ,      '3B test too slow on CPU.'			)
 @slow
 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						List[str] =					{'num_beams': 1, 'early_stopping': True, 'min_length': 1_5, 'max_length': 2_5}
  UpperCamelCase_					:						Tuple =					{'skip_special_tokens': True, 'clean_up_tokenization_spaces': True}

  UpperCamelCase_					:						Any =					FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B'   ,      from_pt=snake_case			)
  UpperCamelCase_					:						Optional[int] =					BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'			)

  UpperCamelCase_					:						str =					['Sam']
  UpperCamelCase_					:						Tuple =					tokenizer(snake_case   ,      return_tensors='jax'			)

  UpperCamelCase_					:						Dict =					model.generate(**snake_case   ,      **snake_case			)
  UpperCamelCase_					:						List[str] =					'Sam is a great name. It means "sun" in Gaelic.'

  UpperCamelCase_					:						List[Any] =					tokenizer.batch_decode(snake_case   ,      **snake_case			)
  assert generated_txt[0].strip() == tgt_text




import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class 		_lowercase						(					snake_case_						,      snake_case_						,      unittest.TestCase       ):
 lowercase				     =   StableDiffusionDiffEditPipeline
 lowercase				     =   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'height', 'width', 'image'} | {'image_latents'}
 lowercase				     =   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {'image'} | {'image_latents'}
 lowercase				     =   frozenset(
     []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowercase				     =   frozenset([]       )

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]			)   ->						Dict:

  """simple docstring"""





  torch.manual_seed(0			)
  UpperCamelCase_					:						Dict =					UNetaDConditionModel(
      block_out_channels=(3_2, 6_4)   ,      layers_per_block=2   ,      sample_size=3_2   ,      in_channels=4   ,      out_channels=4   ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,      cross_attention_dim=3_2   ,      attention_head_dim=(2, 4)   ,      use_linear_projection=snake_case   ,      )
  UpperCamelCase_					:						Optional[int] =					DDIMScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      beta_schedule='scaled_linear'   ,      clip_sample=snake_case   ,      set_alpha_to_one=snake_case   ,      )
  UpperCamelCase_					:						Tuple =					DDIMInverseScheduler(
      beta_start=0.00085   ,      beta_end=0.012   ,      beta_schedule='scaled_linear'   ,      clip_sample=snake_case   ,      set_alpha_to_zero=snake_case   ,      )
  torch.manual_seed(0			)
  UpperCamelCase_					:						List[Any] =					AutoencoderKL(
      block_out_channels=[3_2, 6_4]   ,      in_channels=3   ,      out_channels=3   ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,      latent_channels=4   ,      sample_size=1_2_8   ,      )
  torch.manual_seed(0			)
  UpperCamelCase_					:						List[Any] =					CLIPTextConfig(
      bos_token_id=0   ,      eos_token_id=2   ,      hidden_size=3_2   ,      intermediate_size=3_7   ,      layer_norm_eps=1e-05   ,      num_attention_heads=4   ,      num_hidden_layers=5   ,      pad_token_id=1   ,      vocab_size=1_0_0_0   ,      hidden_act='gelu'   ,      projection_dim=5_1_2   ,      )
  UpperCamelCase_					:						List[str] =					CLIPTextModel(snake_case			)
  UpperCamelCase_					:						List[str] =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

  UpperCamelCase_					:						Any =					{
      'unet': unet,
      'scheduler': scheduler,
      'inverse_scheduler': inverse_scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }

  return components

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]   ,      snake_case	:							Dict   ,      snake_case	:							Tuple=0			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					floats_tensor((1, 1_6, 1_6)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						Optional[int] =					floats_tensor((1, 2, 4, 1_6, 1_6)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						int =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						Any =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					{
      'prompt': 'a dog and a newt',
      'mask_image': mask,
      'image_latents': latents,
      'generator': generator,
      'num_inference_steps': 2,
      'inpaint_strength': 1.0,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }

  return inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]   ,      snake_case	:							Union[str, Any]   ,      snake_case	:							Dict=0			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						int =					image.cpu().permute(0   ,      2   ,      3   ,      1			)[0]
  UpperCamelCase_					:						Optional[int] =					Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						int =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						Optional[int] =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						List[str] =					{
      'image': image,
      'source_prompt': 'a cat and a frog',
      'target_prompt': 'a dog and a newt',
      'generator': generator,
      'num_inference_steps': 2,
      'num_maps_per_mask': 2,
      'mask_encode_strength': 1.0,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
  }

  return inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							str   ,      snake_case	:							Dict=0			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					floats_tensor((1, 3, 3_2, 3_2)   ,      rng=random.Random(snake_case			)			).to(snake_case			)
  UpperCamelCase_					:						Any =					image.cpu().permute(0   ,      2   ,      3   ,      1			)[0]
  UpperCamelCase_					:						List[str] =					Image.fromarray(np.uinta(snake_case			)			).convert('RGB'			)
  if str(snake_case			).startswith('mps'			):
   UpperCamelCase_					:						Any =					torch.manual_seed(snake_case			)
  else:
   UpperCamelCase_					:						int =					torch.Generator(device=snake_case			).manual_seed(snake_case			)
  UpperCamelCase_					:						int =					{
      'image': image,
      'prompt': 'a cat and a frog',
      'generator': generator,
      'num_inference_steps': 2,
      'inpaint_strength': 1.0,
      'guidance_scale': 6.0,
      'decode_latents': True,
      'output_type': 'numpy',
  }
  return inputs

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Optional[int]:

  """simple docstring"""





  if not hasattr(self.pipeline_class   ,      '_optional_components'			):
   return

  UpperCamelCase_					:						int =					self.get_dummy_components()
  UpperCamelCase_					:						str =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  # set all optional components to None and update pipeline config accordingly
  for optional_component in pipe._optional_components:
   setattr(snake_case   ,      snake_case   ,      snake_case			)
  pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

  UpperCamelCase_					:						Any =					self.get_dummy_inputs(snake_case			)
  UpperCamelCase_					:						Any =					pipe(**snake_case			)[0]

  with tempfile.TemporaryDirectory() as tmpdir:
   pipe.save_pretrained(snake_case			)
   UpperCamelCase_					:						int =					self.pipeline_class.from_pretrained(snake_case			)
   pipe_loaded.to(snake_case			)
   pipe_loaded.set_progress_bar_config(disable=snake_case			)

  for optional_component in pipe._optional_components:
   self.assertTrue(
       getattr(snake_case   ,      snake_case			) is None   ,      f"`{optional_component}` did not stay set to None after loading."   ,      )

  UpperCamelCase_					:						Dict =					self.get_dummy_inputs(snake_case			)
  UpperCamelCase_					:						int =					pipe_loaded(**snake_case			)[0]

  UpperCamelCase_					:						int =					np.abs(output - output_loaded			).max()
  self.assertLess(snake_case   ,      1e-4			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					'cpu'

  UpperCamelCase_					:						Any =					self.get_dummy_components()
  UpperCamelCase_					:						int =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Union[str, Any] =					self.get_dummy_mask_inputs(snake_case			)
  UpperCamelCase_					:						Optional[int] =					pipe.generate_mask(**snake_case			)
  UpperCamelCase_					:						List[str] =					mask[0, -3:, -3:]

  self.assertEqual(mask.shape   ,      (1, 1_6, 1_6)			)
  UpperCamelCase_					:						Tuple =					np.array([0] * 9			)
  UpperCamelCase_					:						int =					np.abs(mask_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)
  self.assertEqual(mask[0, -3, -4]   ,      0			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						List[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					'cpu'

  UpperCamelCase_					:						List[str] =					self.get_dummy_components()
  UpperCamelCase_					:						List[Any] =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Optional[int] =					self.get_dummy_inversion_inputs(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					pipe.invert(**snake_case			).images
  UpperCamelCase_					:						Tuple =					image[0, -1, -3:, -3:]

  self.assertEqual(image.shape   ,      (2, 3_2, 3_2, 3)			)
  UpperCamelCase_					:						str =					np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,      )
  UpperCamelCase_					:						Any =					np.abs(image_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any			)   ->						Any:

  """simple docstring"""





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


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					'cpu'

  UpperCamelCase_					:						Tuple =					self.get_dummy_components()

  UpperCamelCase_					:						int =					{'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
  UpperCamelCase_					:						str =					DPMSolverMultistepScheduler(**snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					DPMSolverMultistepInverseScheduler(**snake_case			)

  UpperCamelCase_					:						Optional[int] =					self.pipeline_class(**snake_case			)
  pipe.to(snake_case			)
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Any =					self.get_dummy_inversion_inputs(snake_case			)
  UpperCamelCase_					:						Union[str, Any] =					pipe.invert(**snake_case			).images
  UpperCamelCase_					:						Any =					image[0, -1, -3:, -3:]

  self.assertEqual(image.shape   ,      (2, 3_2, 3_2, 3)			)
  UpperCamelCase_					:						int =					np.array(
      [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]   ,      )
  UpperCamelCase_					:						Union[str, Any] =					np.abs(image_slice.flatten() - expected_slice			).max()
  self.assertLessEqual(snake_case   ,      1e-3			)





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

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Any:

  """simple docstring"""





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

 @classmethod
 def 						SCREAMING_SNAKE_CASE__   ( cls	:							Any			)   ->						Any:

  """simple docstring"""





  UpperCamelCase_					:						Optional[int] =					load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'			)

  UpperCamelCase_					:						Any =					raw_image.convert('RGB'			).resize((7_6_8, 7_6_8)			)

  UpperCamelCase_					:						Optional[Any] =					raw_image

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Optional[int]:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					torch.manual_seed(0			)

  UpperCamelCase_					:						List[str] =					StableDiffusionDiffEditPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2-1'   ,      safety_checker=snake_case   ,      torch_dtype=torch.floataa			)
  UpperCamelCase_					:						int =					DDIMScheduler.from_config(pipe.scheduler.config			)
  UpperCamelCase_					:						Tuple =					DDIMInverseScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						Optional[int] =					'a bowl of fruit'
  UpperCamelCase_					:						int =					'a bowl of pears'

  UpperCamelCase_					:						Any =					pipe.generate_mask(
      image=self.raw_image   ,      source_prompt=snake_case   ,      target_prompt=snake_case   ,      generator=snake_case   ,      )

  UpperCamelCase_					:						int =					pipe.invert(
      prompt=snake_case   ,      image=self.raw_image   ,      inpaint_strength=0.7   ,      generator=snake_case			).latents

  UpperCamelCase_					:						Optional[int] =					pipe(
      prompt=snake_case   ,      mask_image=snake_case   ,      image_latents=snake_case   ,      generator=snake_case   ,      negative_prompt=snake_case   ,      inpaint_strength=0.7   ,      output_type='numpy'   ,      ).images[0]

  UpperCamelCase_					:						Any =					(
      np.array(
          load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/diffedit/pears.png'			).resize((7_6_8, 7_6_8)			)			)
      / 2_5_5
  )
  assert np.abs((expected_image - image).max()			) < 5e-1


 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						int:

  """simple docstring"""





  UpperCamelCase_					:						Union[str, Any] =					torch.manual_seed(0			)

  UpperCamelCase_					:						List[Any] =					StableDiffusionDiffEditPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2-1'   ,      safety_checker=snake_case   ,      torch_dtype=torch.floataa			)
  UpperCamelCase_					:						Optional[int] =					DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
  UpperCamelCase_					:						Union[str, Any] =					DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=snake_case			)

  UpperCamelCase_					:						str =					'a bowl of fruit'
  UpperCamelCase_					:						int =					'a bowl of pears'

  UpperCamelCase_					:						Union[str, Any] =					pipe.generate_mask(
      image=self.raw_image   ,      source_prompt=snake_case   ,      target_prompt=snake_case   ,      generator=snake_case   ,      )

  UpperCamelCase_					:						Any =					pipe.invert(
      prompt=snake_case   ,      image=self.raw_image   ,      inpaint_strength=0.7   ,      generator=snake_case   ,      num_inference_steps=2_5   ,      ).latents

  UpperCamelCase_					:						int =					pipe(
      prompt=snake_case   ,      mask_image=snake_case   ,      image_latents=snake_case   ,      generator=snake_case   ,      negative_prompt=snake_case   ,      inpaint_strength=0.7   ,      num_inference_steps=2_5   ,      output_type='numpy'   ,      ).images[0]

  UpperCamelCase_					:						str =					(
      np.array(
          load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/diffedit/pears.png'			).resize((7_6_8, 7_6_8)			)			)
      / 2_5_5
  )
  assert np.abs((expected_image - image).max()			) < 5e-1



