


import unittest

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

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 (
							    LayoutLMForMaskedLM,
							    LayoutLMForQuestionAnswering,
							    LayoutLMForSequenceClassification,
							    LayoutLMForTokenClassification,
							    LayoutLMModel,
							)
class _lowerCamelCase :



		'''simple docstring'''







		def __init__(      self	:		int						,  _A	:		Dict						,  _A	:		Dict=13						,  _A	:		Dict=7						,  _A	:		Any=True						,  _A	:		Optional[int]=True						,  _A	:		Any=True						,  _A	:		List[Any]=True						,  _A	:		Optional[int]=99						,  _A	:		Any=32						,  _A	:		Tuple=5						,  _A	:		Optional[Any]=4						,  _A	:		Union[str, Any]=37						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Dict=0.1						,  _A	:		List[Any]=512						,  _A	:		List[Any]=16						,  _A	:		Dict=2						,  _A	:		Any=0.02						,  _A	:		Union[str, Any]=3						,  _A	:		List[Any]=4						,  _A	:		Optional[Any]=None						,  _A	:		Any=1000						,  )						->				int:
			__magic_name__ :					List[Any]  			=  parent
			__magic_name__ :					Union[str, Any]  			=  batch_size
			__magic_name__ :					int  			=  seq_length
			__magic_name__ :					Union[str, Any]  			=  is_training
			__magic_name__ :					Optional[int]  			=  use_input_mask
			__magic_name__ :					Optional[int]  			=  use_token_type_ids
			__magic_name__ :					str  			=  use_labels
			__magic_name__ :					Dict  			=  vocab_size
			__magic_name__ :					List[Any]  			=  hidden_size
			__magic_name__ :					Any  			=  num_hidden_layers
			__magic_name__ :					Union[str, Any]  			=  num_attention_heads
			__magic_name__ :					Tuple  			=  intermediate_size
			__magic_name__ :					int  			=  hidden_act
			__magic_name__ :					List[str]  			=  hidden_dropout_prob
			__magic_name__ :					List[str]  			=  attention_probs_dropout_prob
			__magic_name__ :					Tuple  			=  max_position_embeddings
			__magic_name__ :					List[str]  			=  type_vocab_size
			__magic_name__ :					Optional[Any]  			=  type_sequence_label_size
			__magic_name__ :					Tuple  			=  initializer_range
			__magic_name__ :					List[str]  			=  num_labels
			__magic_name__ :					str  			=  num_choices
			__magic_name__ :					Dict  			=  scope
			__magic_name__ :					Union[str, Any]  			=  range_bbox







		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Any:
			__magic_name__ :					Optional[int]  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size    )

			__magic_name__ :					Optional[int]  			=  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]:
						__magic_name__ :					Any  			=  bbox[i, j, 3]
						__magic_name__ :					Dict  			=  bbox[i, j, 1]
						__magic_name__ :					List[Any]  			=  t
					if bbox[i, j, 2] < bbox[i, j, 0]:
						__magic_name__ :					int  			=  bbox[i, j, 2]
						__magic_name__ :					Tuple  			=  bbox[i, j, 0]
						__magic_name__ :					Tuple  			=  t

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

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

			__magic_name__ :					List[Any]  			=  None
			__magic_name__ :					Optional[int]  			=  None
			__magic_name__ :					Union[str, Any]  			=  None
			if self.use_labels:
				__magic_name__ :					Dict  			=  ids_tensor([self.batch_size]						,  self.type_sequence_label_size    )
				__magic_name__ :					Any  			=  ids_tensor([self.batch_size, self.seq_length]						,  self.num_labels    )
				__magic_name__ :					Tuple  			=  ids_tensor([self.batch_size]						,  self.num_choices    )

			__magic_name__ :					Tuple  			=  self.get_config()

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







		def      __lowerCAmelCase	(      self	:		Dict    )						->				List[Any]:
			return 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						,  )







		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		int						,  _A	:		Optional[Any]						,  _A	:		Tuple						,  _A	:		Tuple						,  _A	:		Tuple						,  _A	:		Any						,  _A	:		str						,  _A	:		List[Any]    )						->				Any:
			__magic_name__ :					Optional[Any]  			=  LayoutLMModel(config=lowerCamelCase__    )
			model.to(lowerCamelCase__    )
			model.eval()
			__magic_name__ :					Tuple  			=  model(lowerCamelCase__						,  lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__    )
			__magic_name__ :					Dict  			=  model(lowerCamelCase__						,  lowerCamelCase__						,  token_type_ids=lowerCamelCase__    )
			__magic_name__ :					Dict  			=  model(lowerCamelCase__						,  lowerCamelCase__    )
			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      __lowerCAmelCase	(      self	:		str						,  _A	:		Tuple						,  _A	:		Union[str, Any]						,  _A	:		Tuple						,  _A	:		str						,  _A	:		int						,  _A	:		List[str]						,  _A	:		List[Any]						,  _A	:		Tuple    )						->				Optional[Any]:
			__magic_name__ :					Optional[Any]  			=  LayoutLMForMaskedLM(config=lowerCamelCase__    )
			model.to(lowerCamelCase__    )
			model.eval()
			__magic_name__ :					Any  			=  model(lowerCamelCase__						,  lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__						,  labels=lowerCamelCase__    )
			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.vocab_size)    )







		def      __lowerCAmelCase	(      self	:		int						,  _A	:		List[str]						,  _A	:		str						,  _A	:		Dict						,  _A	:		Union[str, Any]						,  _A	:		str						,  _A	:		List[str]						,  _A	:		str						,  _A	:		Dict    )						->				List[str]:
			__magic_name__ :					List[str]  			=  self.num_labels
			__magic_name__ :					int  			=  LayoutLMForSequenceClassification(lowerCamelCase__    )
			model.to(lowerCamelCase__    )
			model.eval()
			__magic_name__ :					str  			=  model(
			    lowerCamelCase__						,  lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__						,  labels=lowerCamelCase__    )
			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)    )







		def      __lowerCAmelCase	(      self	:		str						,  _A	:		int						,  _A	:		Tuple						,  _A	:		Any						,  _A	:		Optional[int]						,  _A	:		str						,  _A	:		Optional[Any]						,  _A	:		Optional[int]						,  _A	:		Optional[int]    )						->				Optional[Any]:
			__magic_name__ :					Tuple  			=  self.num_labels
			__magic_name__ :					List[Any]  			=  LayoutLMForTokenClassification(config=lowerCamelCase__    )
			model.to(lowerCamelCase__    )
			model.eval()
			__magic_name__ :					Any  			=  model(lowerCamelCase__						,  lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__						,  labels=lowerCamelCase__    )
			self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.seq_length, self.num_labels)    )







		def      __lowerCAmelCase	(      self	:		Any						,  _A	:		Optional[int]						,  _A	:		Union[str, Any]						,  _A	:		Tuple						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]						,  _A	:		Optional[int]						,  _A	:		Dict						,  _A	:		Union[str, Any]    )						->				List[str]:
			__magic_name__ :					Any  			=  LayoutLMForQuestionAnswering(config=lowerCamelCase__    )
			model.to(lowerCamelCase__    )
			model.eval()
			__magic_name__ :					int  			=  model(
			    lowerCamelCase__						,  bbox=lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__						,  start_positions=lowerCamelCase__						,  end_positions=lowerCamelCase__						,  )
			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      __lowerCAmelCase	(      self	:		str    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  self.prepare_config_and_inputs()
			(
			    (
			    __magic_name__						
			)			,       (
			    __magic_name__						
			)			,       (
			    __magic_name__						
			)			,       (
			    __magic_name__						
			)			,       (
			    __magic_name__						
			)			,       (
			    __magic_name__						
			)			,       (
			    __magic_name__						
			)			,       (
			    __magic_name__						
			)			,       						
			) :					List[str]  			=  config_and_inputs
			__magic_name__ :					int  			=  {
			    'input_ids': input_ids,
			    'bbox': bbox,
			    'token_type_ids': token_type_ids,
			    'attention_mask': input_mask,
			}
			return config, inputs_dict







@require_torch
class _lowerCamelCase (		__UpperCAmelCase	,  __UpperCAmelCase	,  unittest.TestCase							):



		'''simple docstring'''







		A_			:    Tuple   					=       (
		    (
		        LayoutLMModel,
		        LayoutLMForMaskedLM,
		        LayoutLMForSequenceClassification,
		        LayoutLMForTokenClassification,
		        LayoutLMForQuestionAnswering,
		    )
		    if is_torch_available()
		    else None
		)
		A_			:    Optional[Any]   					=       (
		    {
		        """document-question-answering""": LayoutLMForQuestionAnswering,
		        """feature-extraction""": LayoutLMModel,
		        """fill-mask""": LayoutLMForMaskedLM,
		        """text-classification""": LayoutLMForSequenceClassification,
		        """token-classification""": LayoutLMForTokenClassification,
		        """zero-shot""": LayoutLMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)
		A_			:    Optional[Any]   					=       True







		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
			__magic_name__ :					List[str]  			=  LayoutLMModelTester(self    )
			__magic_name__ :					Tuple  			=  ConfigTester(self						,  config_class=lowerCamelCase__						,  hidden_size=37    )







		def      __lowerCAmelCase	(      self	:		List[str]    )						->				Optional[int]:
			self.config_tester.run_common_tests()







		def      __lowerCAmelCase	(      self	:		Dict    )						->				List[Any]:
			__magic_name__ :					Dict  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase__    )







		def      __lowerCAmelCase	(      self	:		Dict    )						->				Optional[Any]:
			__magic_name__ :					Tuple  			=  self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
				__magic_name__ :					Any  			=  type
				self.model_tester.create_and_check_model(*lowerCamelCase__    )







		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[int]:
			__magic_name__ :					Tuple  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__    )







		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[Any]:
			__magic_name__ :					str  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__    )







		def      __lowerCAmelCase	(      self	:		str    )						->				Any:
			__magic_name__ :					Optional[int]  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__    )







		def      __lowerCAmelCase	(      self	:		Any    )						->				List[str]:
			__magic_name__ :					Any  			=  self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__    )







		@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'    )
		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				Optional[int]:
			pass







def lowerCamelCase    (    ):





	"""simple docstring"""







	__magic_name__ :					Optional[int]  			=  torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]]	,	device=lowercase__	)  # noqa: E231
	__magic_name__ :					int  			=  torch.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],]	,	device=lowercase__	)  # noqa: E231
	__magic_name__ :					Optional[int]  			=  torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]	,	device=lowercase__	)  # noqa: E231
	__magic_name__ :					List[str]  			=  torch.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]]	,	device=lowercase__	)  # noqa: E231
	# these are sequence labels (i.e. at the token level)
	__magic_name__ :					Optional[int]  			=  torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]	,	device=lowercase__	)  # noqa: E231
	# fmt: on

	return input_ids, attention_mask, bbox, token_type_ids, labels





@require_torch
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







		@slow
		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				Optional[int]:
			__magic_name__ :					Union[str, Any]  			=  LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(lowerCamelCase__    )

			__magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__ :					int  			=  prepare_layoutlm_batch_inputs()

			# forward pass
			__magic_name__ :					Any  			=  model(input_ids=lowerCamelCase__						,  bbox=lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__    )

			# test the sequence output on [0, :3, :3]
			__magic_name__ :					List[str]  			=  torch.tensor(
			    [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]						,  device=lowerCamelCase__						,  )

			self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]						,  lowerCamelCase__						,  atol=1E-3    )    )

			# test the pooled output on [1, :3]
			__magic_name__ :					Optional[Any]  			=  torch.tensor([-0.6580, -0.0214, 0.8552]						,  device=lowerCamelCase__    )

			self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]						,  lowerCamelCase__						,  atol=1E-3    )    )







		@slow
		def      __lowerCAmelCase	(      self	:		int    )						->				Union[str, Any]:
			__magic_name__ :					int  			=  LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,  num_labels=2    ).to(
			    lowerCamelCase__    )

			__magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__ :					Optional[Any]  			=  prepare_layoutlm_batch_inputs()

			# forward pass
			__magic_name__ :					Union[str, Any]  			=  model(
			    input_ids=lowerCamelCase__						,  bbox=lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__						,  labels=torch.tensor([1, 1]						,  device=lowerCamelCase__    )						,  )

			# test whether we get a loss as a scalar
			__magic_name__ :					Optional[Any]  			=  outputs.loss
			__magic_name__ :					List[str]  			=  torch.Size([]    )
			self.assertEqual(loss.shape						,  lowerCamelCase__    )

			# test the shape of the logits
			__magic_name__ :					str  			=  outputs.logits
			__magic_name__ :					int  			=  torch.Size((2, 2)    )
			self.assertEqual(logits.shape						,  lowerCamelCase__    )







		@slow
		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[str]:
			__magic_name__ :					List[str]  			=  LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'						,  num_labels=13    ).to(
			    lowerCamelCase__    )

			__magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__ :					Union[str, Any]  			=  prepare_layoutlm_batch_inputs()

			# forward pass
			__magic_name__ :					Any  			=  model(
			    input_ids=lowerCamelCase__						,  bbox=lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__						,  labels=lowerCamelCase__    )

			# test the loss calculation to be around 2.65
			# expected_loss = torch.tensor(2.65, device=torch_device)

			# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
			# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

			# test the shape of the logits
			__magic_name__ :					Union[str, Any]  			=  outputs.logits
			__magic_name__ :					Tuple  			=  torch.Size((2, 25, 13)    )
			self.assertEqual(logits.shape						,  lowerCamelCase__    )







		@slow
		def      __lowerCAmelCase	(      self	:		Dict    )						->				Any:
			__magic_name__ :					int  			=  LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(lowerCamelCase__    )

			__magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__			,       __magic_name__ :					List[Any]  			=  prepare_layoutlm_batch_inputs()

			# forward pass
			__magic_name__ :					int  			=  model(input_ids=lowerCamelCase__						,  bbox=lowerCamelCase__						,  attention_mask=lowerCamelCase__						,  token_type_ids=lowerCamelCase__    )

			# test the shape of the logits
			__magic_name__ :					str  			=  torch.Size((2, 25)    )
			self.assertEqual(outputs.start_logits.shape						,  lowerCamelCase__    )
			self.assertEqual(outputs.end_logits.shape						,  lowerCamelCase__    )



'''simple docstring'''

import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, LDMSuperResolutionPipeline, UNetaDModel, VQModel
from diffusers.utils import PIL_INTERPOLATION, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch


enable_full_determinism()
class _lowerCamelCase (		unittest.TestCase							):



		'''simple docstring'''







		@property
		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  1
			__magic_name__ :					str  			=  3
			__magic_name__ :					Optional[Any]  			=  (32, 32)

			__magic_name__ :					Dict  			=  floats_tensor((batch_size, num_channels) + sizes						,  rng=random.Random(0    )    ).to(_A    )
			return image







		@property
		def      __lowerCAmelCase	(      self	:		Optional[Any]    )						->				List[str]:
			torch.manual_seed(0    )
			__magic_name__ :					Dict  			=  UNetaDModel(
			    block_out_channels=(32, 64)						,  layers_per_block=2						,  sample_size=32						,  in_channels=6						,  out_channels=3						,  down_block_types=('DownBlock2D', 'AttnDownBlock2D')						,  up_block_types=('AttnUpBlock2D', 'UpBlock2D')						,  )
			return model







		@property
		def      __lowerCAmelCase	(      self	:		Any    )						->				Optional[int]:
			torch.manual_seed(0    )
			__magic_name__ :					Union[str, Any]  			=  VQModel(
			    block_out_channels=[32, 64]						,  in_channels=3						,  out_channels=3						,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,  latent_channels=3						,  )
			return model







		def      __lowerCAmelCase	(      self	:		Optional[int]    )						->				int:
			__magic_name__ :					Dict  			=  'cpu'
			__magic_name__ :					str  			=  self.dummy_uncond_unet
			__magic_name__ :					Optional[int]  			=  DDIMScheduler()
			__magic_name__ :					Optional[Any]  			=  self.dummy_vq_model

			__magic_name__ :					Tuple  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
			ldm.to(_A    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					Optional[int]  			=  self.dummy_image.to(_A    )

			__magic_name__ :					List[Any]  			=  torch.Generator(device=_A    ).manual_seed(0    )
			__magic_name__ :					str  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=2						,  output_type='numpy'    ).images

			__magic_name__ :					List[Any]  			=  image[0, -3:, -3:, -1]

			assert image.shape == (1, 64, 64, 3)
			__magic_name__ :					Optional[Any]  			=  np.array([0.8678, 0.8245, 0.6381, 0.6830, 0.4385, 0.5599, 0.4641, 0.6201, 0.5150]    )

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







		@unittest.skipIf(torch_device != 'cuda'						,  'This test requires a GPU'    )
		def      __lowerCAmelCase	(      self	:		List[Any]    )						->				List[Any]:
			__magic_name__ :					Union[str, Any]  			=  self.dummy_uncond_unet
			__magic_name__ :					Tuple  			=  DDIMScheduler()
			__magic_name__ :					Union[str, Any]  			=  self.dummy_vq_model

			# put models in fp16
			__magic_name__ :					Optional[int]  			=  unet.half()
			__magic_name__ :					int  			=  vqvae.half()

			__magic_name__ :					Optional[Any]  			=  LDMSuperResolutionPipeline(unet=_A						,  vqvae=_A						,  scheduler=_A    )
			ldm.to(_A    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					str  			=  self.dummy_image.to(_A    )

			__magic_name__ :					Union[str, Any]  			=  ldm(_A						,  num_inference_steps=2						,  output_type='numpy'    ).images

			assert image.shape == (1, 64, 64, 3)







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



		'''simple docstring'''







		def      __lowerCAmelCase	(      self	:		str    )						->				Dict:
			__magic_name__ :					Tuple  			=  load_image(
			    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
			    '/vq_diffusion/teddy_bear_pool.png'    )
			__magic_name__ :					Dict  			=  init_image.resize((64, 64)						,  resample=PIL_INTERPOLATION['lanczos']    )

			__magic_name__ :					List[Any]  			=  LDMSuperResolutionPipeline.from_pretrained('duongna/ldm-super-resolution'						,  device_map='auto'    )
			ldm.set_progress_bar_config(disable=_A    )

			__magic_name__ :					int  			=  torch.manual_seed(0    )
			__magic_name__ :					Optional[Any]  			=  ldm(image=_A						,  generator=_A						,  num_inference_steps=20						,  output_type='numpy'    ).images

			__magic_name__ :					List[str]  			=  image[0, -3:, -3:, -1]

			assert image.shape == (1, 256, 256, 3)
			__magic_name__ :					int  			=  np.array([0.7644, 0.7679, 0.7642, 0.7633, 0.7666, 0.7560, 0.7425, 0.7257, 0.6907]    )

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