



from __future__ import annotations

import unittest

import numpy as np

from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers.models.layoutlm.modeling_tf_layoutlm import (
       TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
       TFLayoutLMForMaskedLM,
       TFLayoutLMForQuestionAnswering,
       TFLayoutLMForSequenceClassification,
       TFLayoutLMForTokenClassification,
       TFLayoutLMModel,
   )



class      _UpperCAmelCase      :




     def __init__(					self			,    a__			,    a__=1_3			,    a__=7			,    a__=True			,    a__=True			,    a__=True			,    a__=True			,    a__=9_9			,    a__=3_2			,    a__=2			,    a__=4			,    a__=3_7			,    a__="gelu"			,    a__=0.1			,    a__=0.1			,    a__=5_1_2			,    a__=1_6			,    a__=2			,    a__=0.0_2			,    a__=3			,    a__=4			,    a__=None			,    a__=1_0_0_0			,    ):
         A__					=       parent
         A__					=       batch_size
         A__					=       seq_length
         A__					=       is_training
         A__					=       use_input_mask
         A__					=       use_token_type_ids
         A__					=       use_labels
         A__					=       vocab_size
         A__					=       hidden_size
         A__					=       num_hidden_layers
         A__					=       num_attention_heads
         A__					=       intermediate_size
         A__					=       hidden_act
         A__					=       hidden_dropout_prob
         A__					=       attention_probs_dropout_prob
         A__					=       max_position_embeddings
         A__					=       type_vocab_size
         A__					=       type_sequence_label_size
         A__					=       initializer_range
         A__					=       num_labels
         A__					=       num_choices
         A__					=       scope
         A__					=       range_bbox




     def 		snake_case_		(					self):
         A__					=       ids_tensor([self.batch_size, self.seq_length]			,    self.vocab_size)

         # convert bbox to numpy since TF does not support item assignment
         A__					=       ids_tensor([self.batch_size, self.seq_length, 4]			,    self.range_bbox).numpy()
         # 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]:
                     A__					=       bbox[i, j, 3]
                     A__					=       bbox[i, j, 1]
                     A__					=       t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                     A__					=       bbox[i, j, 2]
                     A__					=       bbox[i, j, 0]
                     A__					=       t
         A__					=       tf.convert_to_tensor(a__)

         A__					=       None
         if self.use_input_mask:
             A__					=       random_attention_mask([self.batch_size, self.seq_length])

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

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

         A__					=       LayoutLMConfig(
             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			,    )

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




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFLayoutLMModel(config=a__)

         A__					=       model(a__			,    a__			,    attention_mask=a__			,    token_type_ids=a__)
         A__					=       model(a__			,    a__			,    token_type_ids=a__)
         A__					=       model(a__			,    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 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFLayoutLMForMaskedLM(config=a__)

         A__					=       model(a__			,    a__			,    attention_mask=a__			,    token_type_ids=a__			,    labels=a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.seq_length, self.vocab_size))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       TFLayoutLMForSequenceClassification(config=a__)

         A__					=       model(a__			,    a__			,    attention_mask=a__			,    token_type_ids=a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels))




     def 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       TFLayoutLMForTokenClassification(config=a__)

         A__					=       model(a__			,    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 		snake_case_		(					self			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__			,    a__):
         A__					=       TFLayoutLMForQuestionAnswering(config=a__)

         A__					=       model(a__			,    a__			,    attention_mask=a__			,    token_type_ids=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 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         (
             (
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						(
             A__
         )			,						
         )					=       config_and_inputs
         A__					=       {
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict







@require_tf
class      _UpperCAmelCase      (							A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					(
         (
             TFLayoutLMModel,
             TFLayoutLMForMaskedLM,
             TFLayoutLMForTokenClassification,
             TFLayoutLMForSequenceClassification,
             TFLayoutLMForQuestionAnswering,
         )
         if is_tf_available()
         else ()
     )
     UpperCamelCase__		     =					(
         {
             '''feature-extraction''': TFLayoutLMModel,
             '''fill-mask''': TFLayoutLMForMaskedLM,
             '''text-classification''': TFLayoutLMForSequenceClassification,
             '''token-classification''': TFLayoutLMForTokenClassification,
             '''zero-shot''': TFLayoutLMForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					True
     UpperCamelCase__		     =					10




     def 		snake_case_		(					self):
         A__					=       TFLayoutLMModelTester(self)
         A__					=       ConfigTester(self			,    config_class=a__			,    hidden_size=3_7)




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




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a__)




     @slow
     def 		snake_case_		(					self):
         for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A__					=       TFLayoutLMModel.from_pretrained(a__)
             self.assertIsNotNone(a__)




     @unittest.skip('''Onnx compliancy broke with TF 2.10''')
     def 		snake_case_		(					self):
         pass







def 		lowerCAmelCase__     (       )->	str:
    # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
    # fmt: off
    A__					=       tf.convert_to_tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]						)  # noqa: E231
    A__					=       tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]						)  # noqa: E231
    A__					=       tf.convert_to_tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]						)  # noqa: E231
    A__					=       tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]						)  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    A__					=       tf.convert_to_tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]						)  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels



@require_tf
class      _UpperCAmelCase      (							unittest.TestCase  ):




     @slow
     def 		snake_case_		(					self):
         A__					=       TFLayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased''')

         A__			,						A__			,						A__			,						A__			,						A__					=       prepare_layoutlm_batch_inputs()

         # forward pass
         A__					=       model(input_ids=a__			,    bbox=a__			,    attention_mask=a__			,    token_type_ids=a__)

         # test the sequence output on [0, :3, :3]
         A__					=       tf.convert_to_tensor(
             [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]			,    )

         self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]			,    a__			,    atol=1e-3))

         # test the pooled output on [1, :3]
         A__					=       tf.convert_to_tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2])

         self.assertTrue(np.allclose(outputs.pooler_output[1, :3]			,    a__			,    atol=1e-3))




     @slow
     def 		snake_case_		(					self):
         # initialize model with randomly initialized sequence classification head
         A__					=       TFLayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''			,    num_labels=2)

         A__			,						A__			,						A__			,						A__			,						A__					=       prepare_layoutlm_batch_inputs()

         # forward pass
         A__					=       model(
             input_ids=a__			,    bbox=a__			,    attention_mask=a__			,    token_type_ids=a__			,    labels=tf.convert_to_tensor([1, 1])			,    )

         # test whether we get a loss as a scalar
         A__					=       outputs.loss
         A__					=       (2,)
         self.assertEqual(loss.shape			,    a__)

         # test the shape of the logits
         A__					=       outputs.logits
         A__					=       (2, 2)
         self.assertEqual(logits.shape			,    a__)




     @slow
     def 		snake_case_		(					self):
         # initialize model with randomly initialized token classification head
         A__					=       TFLayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''			,    num_labels=1_3)

         A__			,						A__			,						A__			,						A__			,						A__					=       prepare_layoutlm_batch_inputs()

         # forward pass
         A__					=       model(
             input_ids=a__			,    bbox=a__			,    attention_mask=a__			,    token_type_ids=a__			,    labels=a__)

         # test the shape of the logits
         A__					=       outputs.logits
         A__					=       tf.convert_to_tensor((2, 2_5, 1_3))
         self.assertEqual(logits.shape			,    a__)




     @slow
     def 		snake_case_		(					self):
         # initialize model with randomly initialized token classification head
         A__					=       TFLayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased''')

         A__			,						A__			,						A__			,						A__			,						A__					=       prepare_layoutlm_batch_inputs()

         # forward pass
         A__					=       model(input_ids=a__			,    bbox=a__			,    attention_mask=a__			,    token_type_ids=a__)

         # test the shape of the logits
         A__					=       tf.convert_to_tensor((2, 2_5))
         self.assertEqual(outputs.start_logits.shape			,    a__)
         self.assertEqual(outputs.end_logits.shape			,    a__)





import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]						)->	List[str]:
    A__					=       [tensor.shape for tensor in tensor_list]
    return all(shape == shapes[0] for shape in shapes[1:]						)



class      _UpperCAmelCase      (							A__      ,   A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					StableDiffusionLatentUpscalePipeline
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     UpperCamelCase__		     =					PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     UpperCamelCase__		     =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCamelCase__		     =					frozenset(
         []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     UpperCamelCase__		     =					frozenset([]  )

     UpperCamelCase__		     =					True




     @property
     def 		snake_case_		(					self):
         A__					=       1
         A__					=       4
         A__					=       (1_6, 1_6)

         A__					=       floats_tensor((batch_size, num_channels) + sizes			,    rng=random.Random(0)).to(a__)
         return image




     def 		snake_case_		(					self):
         torch.manual_seed(0)
         A__					=       UNetaDConditionModel(
             act_fn='''gelu'''			,    attention_head_dim=8			,    norm_num_groups=a__			,    block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    time_cond_proj_dim=1_6_0			,    conv_in_kernel=1			,    conv_out_kernel=1			,    cross_attention_dim=3_2			,    down_block_types=(
                 '''KDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
                 '''KCrossAttnDownBlock2D''',
             )			,    in_channels=8			,    mid_block_type=a__			,    only_cross_attention=a__			,    out_channels=5			,    resnet_time_scale_shift='''scale_shift'''			,    time_embedding_type='''fourier'''			,    timestep_post_act='''gelu'''			,    up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,    )
         A__					=       AutoencoderKL(
             block_out_channels=[3_2, 3_2, 6_4, 6_4]			,    in_channels=3			,    out_channels=3			,    down_block_types=[
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
                 '''DownEncoderBlock2D''',
             ]			,    up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']			,    latent_channels=4			,    )
         A__					=       EulerDiscreteScheduler(prediction_type='''sample''')
         A__					=       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='''quick_gelu'''			,    projection_dim=5_1_2			,    )
         A__					=       CLIPTextModel(a__)
         A__					=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

         A__					=       {
             '''unet''': model.eval(),
             '''vae''': vae.eval(),
             '''scheduler''': scheduler,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
         }

         return components




     def 		snake_case_		(					self			,    a__			,    a__=0):
         if str(a__).startswith('''mps'''):
             A__					=       torch.manual_seed(a__)
         else:
             A__					=       torch.Generator(device=a__).manual_seed(a__)
         A__					=       {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': self.dummy_image.cpu(),
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs




     def 		snake_case_		(					self):
         A__					=       '''cpu'''

         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)
         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)

         A__					=       self.get_dummy_inputs(a__)
         A__					=       pipe(**a__).images
         A__					=       image[0, -3:, -3:, -1]

         self.assertEqual(image.shape			,    (1, 2_5_6, 2_5_6, 3))
         A__					=       np.array(
             [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5])
         A__					=       np.abs(image_slice.flatten() - expected_slice).max()
         self.assertLessEqual(a__			,    1e-3)




     def 		snake_case_		(					self):
         super().test_attention_slicing_forward_pass(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_cpu_offload_forward_pass(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_inference_batch_single_identical(expected_max_diff=7e-3)




     def 		snake_case_		(					self):
         super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_local(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         super().test_save_load_optional_components(expected_max_difference=3e-3)




     def 		snake_case_		(					self):
         A__					=       [
             '''DDIMScheduler''',
             '''DDPMScheduler''',
             '''PNDMScheduler''',
             '''HeunDiscreteScheduler''',
             '''EulerAncestralDiscreteScheduler''',
             '''KDPM2DiscreteScheduler''',
             '''KDPM2AncestralDiscreteScheduler''',
             '''DPMSolverSDEScheduler''',
         ]
         A__					=       self.get_dummy_components()
         A__					=       self.pipeline_class(**a__)

         # make sure that PNDM does not need warm-up
         pipe.scheduler.register_to_config(skip_prk_steps=a__)

         pipe.to(a__)
         pipe.set_progress_bar_config(disable=a__)
         A__					=       self.get_dummy_inputs(a__)
         A__					=       2

         A__					=       []
         for scheduler_enum in KarrasDiffusionSchedulers:
             if scheduler_enum.name in skip_schedulers:
                 # no sigma schedulers are not supported
                 # no schedulers
                 continue

             A__					=       getattr(a__			,    scheduler_enum.name)
             A__					=       scheduler_cls.from_config(pipe.scheduler.config)
             A__					=       pipe(**a__)[0]
             outputs.append(a__)

         assert check_same_shape(a__)







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




     def 		snake_case_		(					self):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''			,    torch_dtype=torch.floataa)
         pipe.to('''cuda''')

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''a photo of an astronaut high resolution, unreal engine, ultra realistic'''

         A__					=       pipe(a__			,    generator=a__			,    output_type='''latent''').images

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy''')
         assert np.abs((expected_image - image).mean()) < 5e-2




     def 		snake_case_		(					self):
         A__					=       torch.manual_seed(3_3)

         A__					=       StableDiffusionLatentUpscalePipeline.from_pretrained(
             '''stabilityai/sd-x2-latent-upscaler'''			,    torch_dtype=torch.floataa)
         upscaler.to('''cuda''')

         A__					=       '''the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'''

         A__					=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png''')

         A__					=       upscaler(
             prompt=a__			,    image=a__			,    num_inference_steps=2_0			,    guidance_scale=0			,    generator=a__			,    output_type='''np'''			,    ).images[0]

         A__					=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy''')
         assert np.abs((expected_image - image).max()) < 5e-2
