
"""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


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)  # pylint: disable=invalid-name




def     lowercase__						(					lowerCAmelCase				:					Union[List, PIL.Image.Image, torch.Tensor] ) ->      List[Any]:






    """simple docstring"""




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

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

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




def     lowercase__						(					lowerCAmelCase				:					Union[List, PIL.Image.Image, torch.Tensor] ) ->      Union[str, Any]:






    """simple docstring"""




    if isinstance(lowerCAmelCase ,				torch.Tensor ):
        return mask
    elif isinstance(lowerCAmelCase ,				PIL.Image.Image ):
        UpperCAmelCase									=					[mask]

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







class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):
      __SCREAMING_SNAKE_CASE   :							UNetaDModel
      __SCREAMING_SNAKE_CASE   :							RePaintScheduler




      def __init__(							self   ,       lowercase_   ,       lowercase_							)						->					List[Any]:
          super().__init__()
          self.register_modules(unet=lowercase_   ,       scheduler=lowercase_							)




      @torch.no_grad()
      def __call__(							self   ,       lowercase_   ,       lowercase_   ,       lowercase_ = 2_5_0   ,       lowercase_ = 0.0   ,       lowercase_ = 1_0   ,       lowercase_ = 1_0   ,       lowercase_ = None   ,       lowercase_ = "pil"   ,       lowercase_ = True   ,       )						->					Union[ImagePipelineOutput, Tuple]:
          UpperCAmelCase									=					image

          UpperCAmelCase									=					_preprocess_image(lowercase_							)
          UpperCAmelCase									=					original_image.to(device=self.device   ,       dtype=self.unet.dtype							)
          UpperCAmelCase									=					_preprocess_mask(lowercase_							)
          UpperCAmelCase									=					mask_image.to(device=self.device   ,       dtype=self.unet.dtype							)

          UpperCAmelCase									=					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."							)

          UpperCAmelCase									=					original_image.shape
          UpperCAmelCase									=					randn_tensor(lowercase_   ,       generator=lowercase_   ,       device=self.device   ,       dtype=self.unet.dtype							)

          # set step values
          self.scheduler.set_timesteps(lowercase_   ,       lowercase_   ,       lowercase_   ,       self.device							)
          UpperCAmelCase									=					eta

          UpperCAmelCase									=					self.scheduler.timesteps[0] + 1
          UpperCAmelCase									=					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
                  UpperCAmelCase									=					self.unet(lowercase_   ,       lowercase_							).sample
                  # compute previous image: x_t -> x_t-1
                  UpperCAmelCase									=					self.scheduler.step(lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_							).prev_sample

              else:
                  # compute the reverse: x_t-1 -> x_t
                  UpperCAmelCase									=					self.scheduler.undo_step(lowercase_   ,       lowercase_   ,       lowercase_							)
              UpperCAmelCase									=					t

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

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=lowercase_							)





"""simple docstring"""

import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=1_3   ,       lowercase_=7   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=9_9   ,       lowercase_=3_2   ,       lowercase_=5   ,       lowercase_=4   ,       lowercase_=3_7   ,       lowercase_="gelu"   ,       lowercase_=0.1   ,       lowercase_=0.1   ,       lowercase_=5_1_2   ,       lowercase_=1_6   ,       lowercase_=2   ,       lowercase_=0.0_2   ,       lowercase_=4   ,       )						->					Dict:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					seq_length
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_attention_mask
          UpperCAmelCase									=					use_token_type_ids
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					vocab_size
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					num_hidden_layers
          UpperCAmelCase									=					num_attention_heads
          UpperCAmelCase									=					intermediate_size
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					hidden_dropout_prob
          UpperCAmelCase									=					attention_probs_dropout_prob
          UpperCAmelCase									=					max_position_embeddings
          UpperCAmelCase									=					type_vocab_size
          UpperCAmelCase									=					type_sequence_label_size
          UpperCAmelCase									=					initializer_range
          UpperCAmelCase									=					num_choices




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					ids_tensor([self.batch_size, self.seq_length]   ,       self.vocab_size							)

          UpperCAmelCase									=					None
          if self.use_attention_mask:
              UpperCAmelCase									=					random_attention_mask([self.batch_size, self.seq_length]							)

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

          UpperCAmelCase									=					RobertaPreLayerNormConfig(
              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   ,       )

          return config, input_ids, token_type_ids, attention_mask




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




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase			,							UpperCAmelCase									=					config_and_inputs

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

          return (
              config,
              input_ids,
              token_type_ids,
              encoder_hidden_states,
              encoder_attention_mask,
          )

@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	True

      __SCREAMING_SNAKE_CASE   :							int							      =	(
          (
              FlaxRobertaPreLayerNormModel,
              FlaxRobertaPreLayerNormForCausalLM,
              FlaxRobertaPreLayerNormForMaskedLM,
              FlaxRobertaPreLayerNormForSequenceClassification,
              FlaxRobertaPreLayerNormForTokenClassification,
              FlaxRobertaPreLayerNormForMultipleChoice,
              FlaxRobertaPreLayerNormForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModelTester(self							)




      @slow
      def     a_      (							self							)						->					Dict:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase									=					model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)
              UpperCAmelCase									=					model(np.ones((1, 1)							)							)
              self.assertIsNotNone(lowercase_							)

@require_flax
class      _UpperCAmelCase							(		unittest.TestCase							):




      @slow
      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          UpperCAmelCase									=					[1, 1_1, 5_0_2_6_5]
          self.assertEqual(list(output.shape							)   ,       lowercase_							)
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)




      @slow
      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'   ,       from_pt=lowercase_							)

          UpperCAmelCase									=					np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]   ,       dtype=jnp.intaa							)
          UpperCAmelCase									=					model(lowercase_							)[0]
          # compare the actual values for a slice.
          UpperCAmelCase									=					np.array(
              [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]   ,       dtype=np.floataa							)
          self.assertTrue(np.allclose(output[:, :3, :3]   ,       lowercase_   ,       atol=1E-4							)							)



