



"""simple docstring"""







import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
  )
  from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST







class  lowerCamelCase__ :






    def __init__(			self     ,A     ,A=13     ,A=7     ,A=True     ,A=True     ,A=True     ,A=True     ,A=99     ,A=24     ,A=2     ,A=6     ,A=37     ,A="gelu"     ,A=0.1     ,A=0.1     ,A=512     ,A=16     ,A=2     ,A=0.02     ,A=3     ,A=None     ,A=1_000     ,):
          UpperCAmelCase          =	parent
          UpperCAmelCase          =	batch_size
          UpperCAmelCase          =	seq_length
          UpperCAmelCase          =	is_training
          UpperCAmelCase          =	use_input_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_labels
          UpperCAmelCase          =	scope
          UpperCAmelCase          =	range_bbox






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size )

          UpperCAmelCase          =	ids_tensor([self.batch_size, self.seq_length, 4]     ,self.range_bbox )
          # Ensure that bbox is legal
          for i in range(bbox.shape[0] ):
                for j in range(bbox.shape[1] ):
                      if bbox[i, j, 3] < bbox[i, j, 1]:
                            UpperCAmelCase          =	bbox[i, j, 3]
                            UpperCAmelCase          =	bbox[i, j, 1]
                            UpperCAmelCase          =	t
                      if bbox[i, j, 2] < bbox[i, j, 0]:
                            UpperCAmelCase          =	bbox[i, j, 2]
                            UpperCAmelCase          =	bbox[i, j, 0]
                            UpperCAmelCase          =	t

          UpperCAmelCase          =	None
          if self.use_input_mask:
                UpperCAmelCase          =	ids_tensor([self.batch_size, self.seq_length]     ,vocab_size=2 )

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

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

          UpperCAmelCase          =	self.get_config()

          return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels






    def     _UpperCamelCase							(			self ):
          return LiltConfig(
              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     ,initializer_range=self.initializer_range     ,)






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A     ,):
          UpperCAmelCase          =	LiltModel(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(A     ,bbox=A     ,attention_mask=A     ,token_type_ids=A )
          UpperCAmelCase          =	model(A     ,bbox=A     ,token_type_ids=A )
          UpperCAmelCase          =	model(A     ,bbox=A )

          self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size) )
          self.parent.assertEqual(result.pooler_output.shape     ,(self.batch_size, self.hidden_size) )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A     ,):
          UpperCAmelCase          =	self.num_labels
          UpperCAmelCase          =	LiltForTokenClassification(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(
              A     ,bbox=A     ,attention_mask=A     ,token_type_ids=A     ,labels=A )
          self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels) )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A     ,A     ,A     ,):
          UpperCAmelCase          =	LiltForQuestionAnswering(config=A )
          model.to(A )
          model.eval()
          UpperCAmelCase          =	model(
              A     ,bbox=A     ,attention_mask=A     ,token_type_ids=A     ,start_positions=A     ,end_positions=A     ,)
          self.parent.assertEqual(result.start_logits.shape     ,(self.batch_size, self.seq_length) )
          self.parent.assertEqual(result.end_logits.shape     ,(self.batch_size, self.seq_length) )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase   
          )      , (
              UpperCAmelCase   
          )      , (
              UpperCAmelCase   
          )      , (
              UpperCAmelCase   
          )      , (
              UpperCAmelCase   
          )      , (
              UpperCAmelCase   
          )      , (
              UpperCAmelCase   
          )      ,    
          )          =	config_and_inputs
          UpperCAmelCase          =	{
              """input_ids""": input_ids,
              """bbox""": bbox,
              """token_type_ids""": token_type_ids,
              """attention_mask""": input_mask,
          }
          return config, inputs_dict







@require_torch
class  lowerCamelCase__ (	snake_case_   ,					snake_case_   ,					snake_case_   ,					unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      (
        (
            LiltModel,
            LiltForSequenceClassification,
            LiltForTokenClassification,
            LiltForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    SCREAMING_SNAKE_CASE		=      (
        {
            '''feature-extraction''': LiltModel,
            '''question-answering''': LiltForQuestionAnswering,
            '''text-classification''': LiltForSequenceClassification,
            '''token-classification''': LiltForTokenClassification,
            '''zero-shot''': LiltForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    SCREAMING_SNAKE_CASE		=      False
    SCREAMING_SNAKE_CASE		=      False






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A     ,A ):
          return True






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	LiltModelTester(self )
          UpperCAmelCase          =	ConfigTester(self     ,config_class=A     ,hidden_size=37 )






    def     _UpperCamelCase							(			self ):
          self.config_tester.run_common_tests()






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                UpperCAmelCase          =	type
                self.model_tester.create_and_check_model(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*A )






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*A )






    @slow
    def     _UpperCamelCase							(			self ):
          for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                UpperCAmelCase          =	LiltModel.from_pretrained(A )
                self.assertIsNotNone(A )







@require_torch
@slow
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	LiltModel.from_pretrained("""SCUT-DLVCLab/lilt-roberta-en-base""" ).to(A )

          UpperCAmelCase          =	torch.tensor([[1, 2]]     ,device=A )
          UpperCAmelCase          =	torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]     ,device=A )

          # forward pass
          with torch.no_grad():
                UpperCAmelCase          =	model(input_ids=A     ,bbox=A )

          UpperCAmelCase          =	torch.Size([1, 2, 768] )
          UpperCAmelCase          =	torch.tensor(
              [[-0.0653, 0.0950, -0.0061], [-0.0545, 0.0926, -0.0324]]     ,device=A     ,)

          self.assertTrue(outputs.last_hidden_state.shape     ,A )
          self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]     ,A     ,atol=1e-3 ) )











"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



