




'''simple docstring'''



import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


lowerCamelCase_	 =		logging.getLogger(__name__)







class 	lowercase_					(				A     ):


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			Optional[Any]						,   __lowerCamelCase      :			int						,   __lowerCamelCase      :			Optional[int]=None						,   __lowerCamelCase      :			int=None					):

      """simple docstring"""
      if current_layer == 0:
         _SCREAMING_SNAKE_CASE =   self.embedding_hidden_mapping_in(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE =   hidden_states[0]

      _SCREAMING_SNAKE_CASE =   int(self.config.num_hidden_layers / self.config.num_hidden_groups					)

      # Index of the hidden group
      _SCREAMING_SNAKE_CASE =   int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)					)

      _SCREAMING_SNAKE_CASE =   self.albert_layer_groups[group_idx](
          __lowerCamelCase						,   __lowerCamelCase						,   head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]						,   )
      _SCREAMING_SNAKE_CASE =   layer_group_output[0]

      return (hidden_states,)







@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''  ,						A  ,						)
class 	lowercase_					(				A     ):


   """simple docstring"""

   def __init__(					self      :			List[str]						,   __lowerCamelCase      :			List[Any]					):

      """simple docstring"""
      super().__init__(__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   AlbertTransformerWithPabee(__lowerCamelCase					)

      self.init_weights()
      _SCREAMING_SNAKE_CASE =   0
      _SCREAMING_SNAKE_CASE =   0
      _SCREAMING_SNAKE_CASE =   0

      _SCREAMING_SNAKE_CASE =   0



   def 							lowerCAmelCase_    (					self      :			List[Any]						,   __lowerCamelCase      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   threshold



   def 							lowerCAmelCase_    (					self      :			Union[str, Any]						,   __lowerCamelCase      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   patience



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   0
      _SCREAMING_SNAKE_CASE =   0



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.inference_layers_num / self.inference_instances_num
      _SCREAMING_SNAKE_CASE =   (
          F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
          F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
      )
      print(__lowerCamelCase					)



   @add_start_docstrings_to_model_forward(__lowerCamelCase					)
   def 							lowerCAmelCase_    (					self      :			Dict						,   __lowerCamelCase      :			Optional[Any]=None						,   __lowerCamelCase      :			Optional[int]=None						,   __lowerCamelCase      :			Any=None						,   __lowerCamelCase      :			Any=None						,   __lowerCamelCase      :			Tuple=None						,   __lowerCamelCase      :			Any=None						,   __lowerCamelCase      :			List[Any]=None						,   __lowerCamelCase      :			List[Any]=None						,   __lowerCamelCase      :			Optional[int]=False						,   ):

      """simple docstring"""

      if input_ids is not None and inputs_embeds is not None:
         raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"					)
      elif input_ids is not None:
         _SCREAMING_SNAKE_CASE =   input_ids.size()
      elif inputs_embeds is not None:
         _SCREAMING_SNAKE_CASE =   inputs_embeds.size()[:-1]
      else:
         raise ValueError("You have to specify either input_ids or inputs_embeds"					)

      _SCREAMING_SNAKE_CASE =   input_ids.device if input_ids is not None else inputs_embeds.device

      if attention_mask is None:
         _SCREAMING_SNAKE_CASE =   torch.ones(__lowerCamelCase						,   device=__lowerCamelCase					)
      if token_type_ids is None:
         _SCREAMING_SNAKE_CASE =   torch.zeros(__lowerCamelCase						,   dtype=torch.long						,   device=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   attention_mask.unsqueeze(1					).unsqueeze(2					)
      _SCREAMING_SNAKE_CASE =   extended_attention_mask.to(dtype=self.dtype					)  # fp16 compatibility
      _SCREAMING_SNAKE_CASE =   (1.0 - extended_attention_mask) * -1_0_0_0_0.0
      _SCREAMING_SNAKE_CASE =   self.get_head_mask(__lowerCamelCase						,   self.config.num_hidden_layers					)

      _SCREAMING_SNAKE_CASE =   self.embeddings(
          __lowerCamelCase						,   position_ids=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   inputs_embeds=__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   embedding_output

      if self.training:
         _SCREAMING_SNAKE_CASE =   []
         for i in range(self.config.num_hidden_layers					):
            _SCREAMING_SNAKE_CASE =   self.encoder.adaptive_forward(
                __lowerCamelCase						,   current_layer=__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   head_mask=__lowerCamelCase						,   )

            _SCREAMING_SNAKE_CASE =   self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]					)					)
            _SCREAMING_SNAKE_CASE =   output_layers[i](output_dropout(__lowerCamelCase					)					)
            res.append(__lowerCamelCase					)
      elif self.patience == 0:  # Use all layers for inference
         _SCREAMING_SNAKE_CASE =   self.encoder(__lowerCamelCase						,   __lowerCamelCase						,   head_mask=__lowerCamelCase					)
         _SCREAMING_SNAKE_CASE =   self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]					)					)
         _SCREAMING_SNAKE_CASE =   [output_layers[self.config.num_hidden_layers - 1](__lowerCamelCase					)]
      else:
         _SCREAMING_SNAKE_CASE =   0
         _SCREAMING_SNAKE_CASE =   None
         _SCREAMING_SNAKE_CASE =   0
         for i in range(self.config.num_hidden_layers					):
            calculated_layer_num += 1
            _SCREAMING_SNAKE_CASE =   self.encoder.adaptive_forward(
                __lowerCamelCase						,   current_layer=__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   head_mask=__lowerCamelCase						,   )

            _SCREAMING_SNAKE_CASE =   self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]					)					)
            _SCREAMING_SNAKE_CASE =   output_layers[i](__lowerCamelCase					)
            if regression:
               _SCREAMING_SNAKE_CASE =   logits.detach()
               if patient_result is not None:
                  _SCREAMING_SNAKE_CASE =   patient_result.detach()
               if (patient_result is not None) and torch.abs(patient_result - labels					) < self.regression_threshold:
                  patient_counter += 1
               else:
                  _SCREAMING_SNAKE_CASE =   0
            else:
               _SCREAMING_SNAKE_CASE =   logits.detach().argmax(dim=1					)
               if patient_result is not None:
                  _SCREAMING_SNAKE_CASE =   patient_result.detach().argmax(dim=1					)
               if (patient_result is not None) and torch.all(labels.eq(__lowerCamelCase					)					):
                  patient_counter += 1
               else:
                  _SCREAMING_SNAKE_CASE =   0

            _SCREAMING_SNAKE_CASE =   logits
            if patient_counter == self.patience:
               break
         _SCREAMING_SNAKE_CASE =   [patient_result]
         self.inference_layers_num += calculated_layer_num
         self.inference_instances_num += 1

      return res







@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''  ,						A  ,						)
class 	lowercase_					(				A     ):


   """simple docstring"""

   def __init__(					self      :			Dict						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      super().__init__(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   config.num_labels

      _SCREAMING_SNAKE_CASE =   AlbertModelWithPabee(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   nn.Dropout(config.classifier_dropout_prob					)
      _SCREAMING_SNAKE_CASE =   nn.ModuleList(
          [nn.Linear(config.hidden_size						,   self.config.num_labels					) for _ in range(config.num_hidden_layers					)]					)

      self.init_weights()



   @add_start_docstrings_to_model_forward(__lowerCamelCase					)
   def 							lowerCAmelCase_    (					self      :			str						,   __lowerCamelCase      :			int=None						,   __lowerCamelCase      :			Union[str, Any]=None						,   __lowerCamelCase      :			Optional[Any]=None						,   __lowerCamelCase      :			Dict=None						,   __lowerCamelCase      :			Optional[Any]=None						,   __lowerCamelCase      :			int=None						,   __lowerCamelCase      :			Any=None						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.albert(
          input_ids=__lowerCamelCase						,   attention_mask=__lowerCamelCase						,   token_type_ids=__lowerCamelCase						,   position_ids=__lowerCamelCase						,   head_mask=__lowerCamelCase						,   inputs_embeds=__lowerCamelCase						,   output_dropout=self.dropout						,   output_layers=self.classifiers						,   regression=self.num_labels == 1						,   )

      _SCREAMING_SNAKE_CASE =   (logits[-1],)

      if labels is not None:
         _SCREAMING_SNAKE_CASE =   None
         _SCREAMING_SNAKE_CASE =   0
         for ix, logits_item in enumerate(__lowerCamelCase					):
            if self.num_labels == 1:
               #  We are doing regression
               _SCREAMING_SNAKE_CASE =   MSELoss()
               _SCREAMING_SNAKE_CASE =   loss_fct(logits_item.view(-1					)						,   labels.view(-1					)					)
            else:
               _SCREAMING_SNAKE_CASE =   CrossEntropyLoss()
               _SCREAMING_SNAKE_CASE =   loss_fct(logits_item.view(-1						,   self.num_labels					)						,   labels.view(-1					)					)
            if total_loss is None:
               _SCREAMING_SNAKE_CASE =   loss
            else:
               total_loss += loss * (ix + 1)
            total_weights += ix + 1
         _SCREAMING_SNAKE_CASE =   (total_loss / total_weights,) + outputs

      return outputs







'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 	lowercase_					(				A  ,						unittest.TestCase     ):


   """simple docstring"""

   lowerCamelCase_       						=		PaintByExamplePipeline
   lowerCamelCase_       						=		IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   lowerCamelCase_       						=		IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCamelCase_       						=		frozenset([]     )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)						,   layers_per_block=2						,   sample_size=3_2						,   in_channels=9						,   out_channels=4						,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")						,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")						,   cross_attention_dim=3_2						,   )
      _SCREAMING_SNAKE_CASE =   PNDMScheduler(skip_prk_steps=__lowerCamelCase					)
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   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						,   )
      torch.manual_seed(0					)
      _SCREAMING_SNAKE_CASE =   CLIPVisionConfig(
          hidden_size=3_2						,   projection_dim=3_2						,   intermediate_size=3_7						,   layer_norm_eps=1e-05						,   num_attention_heads=4						,   num_hidden_layers=5						,   image_size=3_2						,   patch_size=4						,   )
      _SCREAMING_SNAKE_CASE =   PaintByExampleImageEncoder(__lowerCamelCase						,   proj_size=3_2					)
      _SCREAMING_SNAKE_CASE =   CLIPImageProcessor(crop_size=3_2						,   size=3_2					)

      _SCREAMING_SNAKE_CASE =   {
          "unet": unet,
          "scheduler": scheduler,
          "vae": vae,
          "image_encoder": image_encoder,
          "safety_checker": None,
          "feature_extractor": feature_extractor,
      }
      return components



   def 							lowerCAmelCase_    (					self      :			Any						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   np.array(image.convert("RGB"					)					)
      _SCREAMING_SNAKE_CASE =   image[None].transpose(0						,   3						,   1						,   2					)
      _SCREAMING_SNAKE_CASE =   torch.from_numpy(__lowerCamelCase					).to(dtype=torch.floataa					) / 1_2_7.5 - 1.0
      return image



   def 							lowerCAmelCase_    (					self      :			List[str]						,   __lowerCamelCase      :			Optional[int]="cpu"						,   __lowerCamelCase      :			Union[str, Any]=0					):

      """simple docstring"""
      # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
      _SCREAMING_SNAKE_CASE =   floats_tensor((1, 3, 3_2, 3_2)						,   rng=random.Random(__lowerCamelCase					)					).to(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   image.cpu().permute(0						,   2						,   3						,   1					)[0]
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(image + 4					)					).convert("RGB"					).resize((6_4, 6_4)					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					).resize((3_2, 3_2)					)

      if str(__lowerCamelCase					).startswith("mps"					):
         _SCREAMING_SNAKE_CASE =   torch.manual_seed(__lowerCamelCase					)
      else:
         _SCREAMING_SNAKE_CASE =   torch.Generator(device=__lowerCamelCase					).manual_seed(__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   {
          "example_image": example_image,
          "image": init_image,
          "mask_image": mask_image,
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs



   def 							lowerCAmelCase_    (					self      :			List[str]					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   self.get_dummy_components()

      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   pipe.to("cpu"					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   self.get_dummy_inputs()
      _SCREAMING_SNAKE_CASE =   pipe(**__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)
      _SCREAMING_SNAKE_CASE =   np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]					)

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



   def 							lowerCAmelCase_    (					self      :			Tuple					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   "cpu"
      _SCREAMING_SNAKE_CASE =   self.get_dummy_inputs()
      inputs.pop("mask_image"					)
      _SCREAMING_SNAKE_CASE =   self.convert_to_pt(inputs.pop("image"					)					)
      _SCREAMING_SNAKE_CASE =   image.clamp(0						,   1					) / 2

      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline(**self.get_dummy_components()					)
      _SCREAMING_SNAKE_CASE =   pipe.to(__lowerCamelCase					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   pipe(image=__lowerCamelCase						,   mask_image=mask_image[:, 0]						,   **__lowerCamelCase					)
      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image.cpu().permute(0						,   2						,   3						,   1					)[0]
      _SCREAMING_SNAKE_CASE =   mask_image.cpu().permute(0						,   2						,   3						,   1					)[0]

      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					)
      _SCREAMING_SNAKE_CASE =   Image.fromarray(np.uinta(__lowerCamelCase					)					).convert("RGB"					)

      _SCREAMING_SNAKE_CASE =   pipe(**self.get_dummy_inputs()					)
      _SCREAMING_SNAKE_CASE =   output.images

      assert out_a.shape == (1, 6_4, 6_4, 3)
      assert np.abs(out_a.flatten() - out_a.flatten()					).max() < 5e-2



   def 							lowerCAmelCase_    (					self      :			List[Any]					):

      """simple docstring"""
      super().test_inference_batch_single_identical(expected_max_diff=3e-3					)







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


   """simple docstring"""

   def 							lowerCAmelCase_    (					self      :			Optional[int]					):

      """simple docstring"""
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      # make sure here that pndm scheduler skips prk
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/dog_in_bucket.png"					)
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/mask.png"					)
      _SCREAMING_SNAKE_CASE =   load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/paint_by_example/panda.jpg"					)

      _SCREAMING_SNAKE_CASE =   PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"					)
      _SCREAMING_SNAKE_CASE =   pipe.to(__lowerCamelCase					)
      pipe.set_progress_bar_config(disable=__lowerCamelCase					)

      _SCREAMING_SNAKE_CASE =   torch.manual_seed(3_2_1					)
      _SCREAMING_SNAKE_CASE =   pipe(
          image=__lowerCamelCase						,   mask_image=__lowerCamelCase						,   example_image=__lowerCamelCase						,   generator=__lowerCamelCase						,   guidance_scale=5.0						,   num_inference_steps=5_0						,   output_type="np"						,   )

      _SCREAMING_SNAKE_CASE =   output.images

      _SCREAMING_SNAKE_CASE =   image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      _SCREAMING_SNAKE_CASE =   np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]					)

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

