'''simple docstring'''





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
 import jax
 import jax.numpy as jnp

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch






class 				UpperCAmelCase :




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=14    ,							lowercase__=7    ,							lowercase__=True    ,							lowercase__=True    ,							lowercase__=False    ,							lowercase__=True    ,							lowercase__=99    ,							lowercase__=32    ,							lowercase__=4    ,							lowercase__=4    ,							lowercase__=4    ,							lowercase__=37    ,							lowercase__="gelu"    ,							lowercase__=0.1    ,							lowercase__=0.1    ,							lowercase__=512    ,							lowercase__=0.0_2    ,							)							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       parent
         SCREAMING_SNAKE_CASE					:			Any    	=       batch_size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       seq_length
         SCREAMING_SNAKE_CASE					:			int    	=       is_training
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       use_input_mask
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       use_token_type_ids
         SCREAMING_SNAKE_CASE					:			str    	=       use_labels
         SCREAMING_SNAKE_CASE					:			Any    	=       vocab_size
         SCREAMING_SNAKE_CASE					:			Dict    	=       hidden_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       rotary_dim
         SCREAMING_SNAKE_CASE					:			int    	=       num_hidden_layers
         SCREAMING_SNAKE_CASE					:			List[str]    	=       num_attention_heads
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       intermediate_size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       hidden_act
         SCREAMING_SNAKE_CASE					:			int    	=       hidden_dropout_prob
         SCREAMING_SNAKE_CASE					:			str    	=       attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       max_position_embeddings
         SCREAMING_SNAKE_CASE					:			List[str]    	=       initializer_range
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       None
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       vocab_size - 1
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       vocab_size - 1
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       vocab_size - 1






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			str    	=       ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       random_attention_mask([self.batch_size, self.seq_length] )

         SCREAMING_SNAKE_CASE					:			int    	=       GPTJConfig(
             vocab_size=self.vocab_size    ,							n_embd=self.hidden_size    ,							n_layer=self.num_hidden_layers    ,							n_head=self.num_attention_heads    ,							n_positions=self.max_position_embeddings    ,							use_cache=lowercase__    ,							bos_token_id=self.bos_token_id    ,							eos_token_id=self.eos_token_id    ,							pad_token_id=self.pad_token_id    ,							rotary_dim=self.rotary_dim    ,							)

         return (config, input_ids, input_mask)






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.prepare_config_and_inputs()
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       config_and_inputs
         SCREAMING_SNAKE_CASE					:			List[str]    	=       {'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> str:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       20
         SCREAMING_SNAKE_CASE					:			int    	=       model_class_name(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model.init_cache(input_ids.shape[0]    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       jnp.ones((input_ids.shape[0], max_decoder_length)    ,							dtype='i4' )

         SCREAMING_SNAKE_CASE					:			Any    	=       jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,							(input_ids.shape[0], input_ids.shape[-1] - 1) )
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       model(
             input_ids[:, :-1]    ,							attention_mask=lowercase__    ,							past_key_values=lowercase__    ,							position_ids=lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,							dtype='i4' )
         SCREAMING_SNAKE_CASE					:			str    	=       model(
             input_ids[:, -1:]    ,							attention_mask=lowercase__    ,							past_key_values=outputs_cache.past_key_values    ,							position_ids=lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[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       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )							-> int:
         SCREAMING_SNAKE_CASE					:			List[str]    	=       20
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_class_name(lowercase__ )

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

         SCREAMING_SNAKE_CASE					:			str    	=       model.init_cache(input_ids.shape[0]    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,							(input_ids.shape[0], input_ids.shape[-1] - 1) )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       model(
             input_ids[:, :-1]    ,							attention_mask=lowercase__    ,							past_key_values=lowercase__    ,							position_ids=lowercase__    ,							)
         SCREAMING_SNAKE_CASE					:			str    	=       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,							dtype='i4' )
         SCREAMING_SNAKE_CASE					:			str    	=       model(
             input_ids[:, -1:]    ,							past_key_values=outputs_cache.past_key_values    ,							attention_mask=lowercase__    ,							position_ids=lowercase__    ,							)

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       model(lowercase__    ,							attention_mask=lowercase__ )

         SCREAMING_SNAKE_CASE					:			int    	=       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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   List[Any]							=						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
    snake_case__							:   Any							=						(FlaxGPTJForCausalLM,) if is_flax_available() else ()






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       FlaxGPTJModelTester(self )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> str:
         for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ )






    @tooslow
    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       GPTaTokenizer.from_pretrained('gpt2'    ,							pad_token='<|endoftext|>'    ,							padding_side='left' )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer(['Hello this is a long string', 'Hey']    ,							return_tensors='np'    ,							padding=lowercase__    ,							truncation=lowercase__ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
         SCREAMING_SNAKE_CASE					:			str    	=       False
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model.config.eos_token_id

         SCREAMING_SNAKE_CASE					:			Any    	=       jax.jit(model.generate )

         SCREAMING_SNAKE_CASE					:			Any    	=       jit_generate(
             inputs['input_ids']    ,							attention_mask=inputs['attention_mask']    ,							pad_token_id=tokenizer.pad_token_id ).sequences

         SCREAMING_SNAKE_CASE					:			int    	=       tokenizer.batch_decode(lowercase__    ,							skip_special_tokens=lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       [
             'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
             'Hey, I\'m a little late to the party. I\'m going to',
         ]

         self.assertListEqual(lowercase__    ,							lowercase__ )






    @is_pt_flax_cross_test
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                   # prepare inputs
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self._prepare_for_class(lowercase__    ,							lowercase__ )
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       getattr(lowercase__    ,							lowercase__ )

                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       pt_inputs['input_ids'].shape
                   SCREAMING_SNAKE_CASE					:			str    	=       np.random.randint(0    ,							seq_length - 1    ,							size=(batch_size,) )
                   for batch_idx, start_index in enumerate(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       0
                        SCREAMING_SNAKE_CASE					:			int    	=       1
                        SCREAMING_SNAKE_CASE					:			int    	=       0
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       1
                   SCREAMING_SNAKE_CASE					:			int    	=       pt_model_class(lowercase__ ).eval()
                   SCREAMING_SNAKE_CASE					:			Dict    	=       model_class(lowercase__    ,							dtype=jnp.floataa )

                   SCREAMING_SNAKE_CASE					:			Dict    	=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,							lowercase__ )
                   SCREAMING_SNAKE_CASE					:			int    	=       fx_state

                   with torch.no_grad():
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       pt_model(**lowercase__ ).to_tuple()

                   SCREAMING_SNAKE_CASE					:			str    	=       fx_model(**lowercase__ ).to_tuple()
                   self.assertEqual(len(lowercase__ )    ,							len(lowercase__ )    ,							'Output lengths differ between Flax and PyTorch' )
                   for fx_output, pt_output in zip(lowercase__    ,							lowercase__ ):
                        self.assert_almost_equals(fx_output[:, -1]    ,							pt_output[:, -1].numpy()    ,							4E-2 )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                        pt_model.save_pretrained(lowercase__ )
                        SCREAMING_SNAKE_CASE					:			List[Any]    	=       model_class.from_pretrained(lowercase__    ,							from_pt=lowercase__ )

                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       fx_model_loaded(**lowercase__ ).to_tuple()
                   self.assertEqual(
                       len(lowercase__ )    ,							len(lowercase__ )    ,							'Output lengths differ between Flax and PyTorch' )
                   for fx_output_loaded, pt_output in zip(lowercase__    ,							lowercase__ ):
                        self.assert_almost_equals(fx_output_loaded[:, -1]    ,							pt_output[:, -1].numpy()    ,							4E-2 )






    @is_pt_flax_cross_test
    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__ ):
                   # prepare inputs
                   SCREAMING_SNAKE_CASE					:			Any    	=       self._prepare_for_class(lowercase__    ,							lowercase__ )
                   SCREAMING_SNAKE_CASE					:			str    	=       {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       getattr(lowercase__    ,							lowercase__ )

                   SCREAMING_SNAKE_CASE					:			int    	=       pt_model_class(lowercase__ ).eval()
                   SCREAMING_SNAKE_CASE					:			Dict    	=       model_class(lowercase__    ,							dtype=jnp.floataa )

                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       load_flax_weights_in_pytorch_model(lowercase__    ,							fx_model.params )
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       pt_inputs['input_ids'].shape
                   SCREAMING_SNAKE_CASE					:			str    	=       np.random.randint(0    ,							seq_length - 1    ,							size=(batch_size,) )
                   for batch_idx, start_index in enumerate(lowercase__ ):
                        SCREAMING_SNAKE_CASE					:			Dict    	=       0
                        SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       1
                        SCREAMING_SNAKE_CASE					:			Any    	=       0
                        SCREAMING_SNAKE_CASE					:			Any    	=       1

                   # make sure weights are tied in PyTorch
                   pt_model.tie_weights()

                   with torch.no_grad():
                        SCREAMING_SNAKE_CASE					:			Optional[int]    	=       pt_model(**lowercase__ ).to_tuple()

                   SCREAMING_SNAKE_CASE					:			int    	=       fx_model(**lowercase__ ).to_tuple()
                   self.assertEqual(len(lowercase__ )    ,							len(lowercase__ )    ,							'Output lengths differ between Flax and PyTorch' )
                   for fx_output, pt_output in zip(lowercase__    ,							lowercase__ ):
                        self.assert_almost_equals(fx_output[:, -1]    ,							pt_output[:, -1].numpy()    ,							4E-2 )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                        fx_model.save_pretrained(lowercase__ )
                        SCREAMING_SNAKE_CASE					:			Any    	=       pt_model_class.from_pretrained(lowercase__    ,							from_flax=lowercase__ )

                   with torch.no_grad():
                        SCREAMING_SNAKE_CASE					:			Tuple    	=       pt_model_loaded(**lowercase__ ).to_tuple()

                   self.assertEqual(
                       len(lowercase__ )    ,							len(lowercase__ )    ,							'Output lengths differ between Flax and PyTorch' )
                   for fx_output, pt_output in zip(lowercase__    ,							lowercase__ ):
                        self.assert_almost_equals(fx_output[:, -1]    ,							pt_output[:, -1].numpy()    ,							4E-2 )






    @tooslow
    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE					:			int    	=       model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
              SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       model(np.ones((1, 1) ) )
              self.assertIsNotNone(lowercase__ )




'''simple docstring'''





import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase  :Union[str, Any]  				=    logging.get_logger(__name__)  # pylint: disable=invalid-name


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     warnings.warn(
         'The preprocess method is deprecated and will be removed in a future version. Please'
         ' use VaeImageProcessor.preprocess instead'       ,      a_       ,      )
     if isinstance(a_       ,      torch.Tensor		):
          return image
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			str    	=       [image]

     if isinstance(image[0]       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[str]    	=       image[0].size
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

          SCREAMING_SNAKE_CASE					:			Any    	=       [np.array(i.resize((w, h)       ,      resample=PIL_INTERPOLATION['lanczos']		)		)[None, :] for i in image]
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       np.concatenate(a_       ,      axis=0		)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       np.array(a_		).astype(np.floataa		) / 255.0
          SCREAMING_SNAKE_CASE					:			Tuple    	=       image.transpose(0       ,      3       ,      1       ,      2		)
          SCREAMING_SNAKE_CASE					:			Dict    	=       2.0 * image - 1.0
          SCREAMING_SNAKE_CASE					:			int    	=       torch.from_numpy(a_		)
     elif isinstance(image[0]       ,      torch.Tensor		):
          SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.cat(a_       ,      dim=0		)
     return image


def     __lowerCAmelCase						(       a_		)      ->	List[Any]:





     '''simple docstring'''





     if isinstance(a_       ,      torch.Tensor		):
          return mask
     elif isinstance(a_       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [mask]

     if isinstance(mask[0]       ,      PIL.Image.Image		):
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			List[Any]    	=       mask[0].size
          SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
          SCREAMING_SNAKE_CASE					:			Tuple    	=       [np.array(m.convert('L'		).resize((w, h)       ,      resample=PIL_INTERPOLATION['nearest']		)		)[None, :] for m in mask]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       np.concatenate(a_       ,      axis=0		)
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       mask.astype(np.floataa		) / 255.0
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       0
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       1
          SCREAMING_SNAKE_CASE					:			str    	=       torch.from_numpy(a_		)
     elif isinstance(mask[0]       ,      torch.Tensor		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.cat(a_       ,      dim=0		)
     return mask






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   UNetaDModel
    snake_case__							:   RePaintScheduler






    def __init__(       self    ,							lowercase__    ,							lowercase__ )							-> Union[str, Any]:
         super().__init__()
         self.register_modules(unet=lowercase__    ,							scheduler=lowercase__ )






    @torch.no_grad()
    def __call__(       self    ,							lowercase__    ,							lowercase__    ,							lowercase__ = 250    ,							lowercase__ = 0.0    ,							lowercase__ = 10    ,							lowercase__ = 10    ,							lowercase__ = None    ,							lowercase__ = "pil"    ,							lowercase__ = True    ,							)							-> Union[ImagePipelineOutput, Tuple]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image

         SCREAMING_SNAKE_CASE					:			List[str]    	=       _preprocess_image(lowercase__ )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       original_image.to(device=self.device    ,							dtype=self.unet.dtype )
         SCREAMING_SNAKE_CASE					:			Any    	=       _preprocess_mask(lowercase__ )
         SCREAMING_SNAKE_CASE					:			str    	=       mask_image.to(device=self.device    ,							dtype=self.unet.dtype )

         SCREAMING_SNAKE_CASE					:			int    	=       original_image.shape[0]

         # sample gaussian noise to begin the loop
         if isinstance(lowercase__    ,							lowercase__ ) and len(lowercase__ ) != batch_size:
              raise ValueError(
                  F"""You have passed a list of generators of length {len(lowercase__ )}, but requested an effective batch"""
                  F""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         SCREAMING_SNAKE_CASE					:			Dict    	=       original_image.shape
         SCREAMING_SNAKE_CASE					:			Any    	=       randn_tensor(lowercase__    ,							generator=lowercase__    ,							device=self.device    ,							dtype=self.unet.dtype )

         # set step values
         self.scheduler.set_timesteps(lowercase__    ,							lowercase__    ,							lowercase__    ,							self.device )
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       eta

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.scheduler.timesteps[0] + 1
         SCREAMING_SNAKE_CASE					:			Tuple    	=       generator[0] if isinstance(lowercase__    ,							lowercase__ ) else generator
         for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
              if t < t_last:
                   # predict the noise residual
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.unet(lowercase__    ,							lowercase__ ).sample
                   # compute previous image: x_t -> x_t-1
                   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.scheduler.step(lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__ ).prev_sample

              else:
                   # compute the reverse: x_t-1 -> x_t
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.scheduler.undo_step(lowercase__    ,							lowercase__    ,							lowercase__ )
              SCREAMING_SNAKE_CASE					:			int    	=       t

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       (image / 2 + 0.5).clamp(0    ,							1 )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image.cpu().permute(0    ,							2    ,							3    ,							1 ).numpy()
         if output_type == "pil":
              SCREAMING_SNAKE_CASE					:			Dict    	=       self.numpy_to_pil(lowercase__ )

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=lowercase__ )



