



"""simple docstring"""







import math
import os
import unittest

from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_PRETRAINING_MAPPING,
			    MegatronBertForCausalLM,
			    MegatronBertForMaskedLM,
			    MegatronBertForMultipleChoice,
			    MegatronBertForNextSentencePrediction,
			    MegatronBertForPreTraining,
			    MegatronBertForQuestionAnswering,
			    MegatronBertForSequenceClassification,
			    MegatronBertForTokenClassification,
			    MegatronBertModel,
			)

class       _a  :



			def __init__(			self							:				List[Any]   ,	_lowercase							:				Dict   ,	_lowercase							:				Optional[int]=13   ,	_lowercase							:				Tuple=7   ,	_lowercase							:				Optional[int]=True   ,	_lowercase							:				Dict=True   ,	_lowercase							:				Optional[int]=True   ,	_lowercase							:				Optional[int]=True   ,	_lowercase							:				List[str]=99   ,	_lowercase							:				Tuple=64   ,	_lowercase							:				Any=32   ,	_lowercase							:				List[Any]=5   ,	_lowercase							:				List[Any]=4   ,	_lowercase							:				Optional[Any]=37   ,	_lowercase							:				Optional[Any]="gelu"   ,	_lowercase							:				List[Any]=0.1   ,	_lowercase							:				Tuple=0.1   ,	_lowercase							:				Tuple=512   ,	_lowercase							:				Tuple=16   ,	_lowercase							:				List[str]=2   ,	_lowercase							:				int=0.02   ,	_lowercase							:				int=3   ,	_lowercase							:				Tuple=4   ,	_lowercase							:				Dict=None   ,	)							->					int:
									snake_case   :			List[str]						    = parent
									snake_case   :			List[Any]						    = batch_size
									snake_case   :			str						    = seq_length
									snake_case   :			str						    = is_training
									snake_case   :			Optional[int]						    = use_input_mask
									snake_case   :			Tuple						    = use_token_type_ids
									snake_case   :			Tuple						    = use_labels
									snake_case   :			Tuple						    = vocab_size
									snake_case   :			Union[str, Any]						    = hidden_size
									snake_case   :			Union[str, Any]						    = embedding_size
									snake_case   :			int						    = num_hidden_layers
									snake_case   :			Dict						    = num_attention_heads
									snake_case   :			List[str]						    = intermediate_size
									snake_case   :			int						    = hidden_act
									snake_case   :			List[Any]						    = hidden_dropout_prob
									snake_case   :			int						    = attention_probs_dropout_prob
									snake_case   :			str						    = max_position_embeddings
									snake_case   :			Union[str, Any]						    = type_vocab_size
									snake_case   :			List[str]						    = type_sequence_label_size
									snake_case   :			Tuple						    = initializer_range
									snake_case   :			List[Any]						    = num_labels
									snake_case   :			Any						    = num_choices
									snake_case   :			Tuple						    = scope



			def 				__lowercase					(			self							:				str		)							->					List[str]:
									snake_case   :			Optional[int]						    = ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size		)

									snake_case   :			Optional[int]						    = None
									if self.use_input_mask:
															snake_case   :			Tuple						    = random_attention_mask([self.batch_size, self.seq_length]		)

									snake_case   :			Tuple						    = None
									if self.use_token_type_ids:
															snake_case   :			Union[str, Any]						    = ids_tensor([self.batch_size, self.seq_length]   ,	self.type_vocab_size		)

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

									snake_case   :			str						    = self.get_config()

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



			def 				__lowercase					(			self							:				Any		)							->					Optional[int]:
									return MegatronBertConfig(
									    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   ,	embedding_size=self.embedding_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   ,	)



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				List[str]   ,	_lowercase							:				str   ,	_lowercase							:				Tuple   ,	_lowercase							:				str   ,	_lowercase							:				Any   ,	_lowercase							:				Any   ,	_lowercase							:				Dict		)							->					List[str]:
									snake_case   :			Union[str, Any]						    = MegatronBertModel(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			Optional[int]						    = model(_lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase		)
									snake_case   :			int						    = model(_lowercase   ,	token_type_ids=_lowercase		)
									snake_case   :			Tuple						    = model(_lowercase		)

									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 				__lowercase					(			self							:				Union[str, Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				Any   ,	_lowercase							:				str   ,	_lowercase							:				Any		)							->					Union[str, Any]:
									snake_case   :			int						    = MegatronBertForMaskedLM(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			List[Any]						    = model(_lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase   ,	labels=_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)		)



			def 				__lowercase					(			self							:				str   ,	_lowercase							:				int   ,	_lowercase							:				str   ,	_lowercase							:				Any   ,	_lowercase							:				Any   ,	_lowercase							:				Tuple   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Tuple		)							->					Optional[int]:
									snake_case   :			List[Any]						    = MegatronBertForCausalLM(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			Any						    = model(_lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase   ,	labels=_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)		)



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				str   ,	_lowercase							:				str   ,	_lowercase							:				Tuple   ,	_lowercase							:				Dict   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				List[str]   ,	_lowercase							:				int		)							->					int:
									snake_case   :			Dict						    = MegatronBertForNextSentencePrediction(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			Optional[int]						    = model(
									    _lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase   ,	labels=_lowercase   ,	)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, 2)		)



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Dict   ,	_lowercase							:				List[str]   ,	_lowercase							:				Dict   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				List[str]   ,	_lowercase							:				Optional[Any]   ,	_lowercase							:				Dict		)							->					Dict:
									snake_case   :			Optional[int]						    = MegatronBertForPreTraining(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			Tuple						    = model(
									    _lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase   ,	labels=_lowercase   ,	next_sentence_label=_lowercase   ,	)
									self.parent.assertEqual(result.prediction_logits.shape   ,	(self.batch_size, self.seq_length, self.vocab_size)		)
									self.parent.assertEqual(result.seq_relationship_logits.shape   ,	(self.batch_size, 2)		)



			def 				__lowercase					(			self							:				Any   ,	_lowercase							:				Tuple   ,	_lowercase							:				int   ,	_lowercase							:				Any   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Any   ,	_lowercase							:				str   ,	_lowercase							:				Optional[Any]		)							->					str:
									snake_case   :			Union[str, Any]						    = MegatronBertForQuestionAnswering(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			Any						    = model(
									    _lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase   ,	start_positions=_lowercase   ,	end_positions=_lowercase   ,	)
									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 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Any   ,	_lowercase							:				Any   ,	_lowercase							:				int   ,	_lowercase							:				Dict   ,	_lowercase							:				int   ,	_lowercase							:				Dict   ,	_lowercase							:				List[str]		)							->					List[Any]:
									snake_case   :			List[Any]						    = self.num_labels
									snake_case   :			int						    = MegatronBertForSequenceClassification(_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			str						    = model(_lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase   ,	labels=_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_labels)		)



			def 				__lowercase					(			self							:				Tuple   ,	_lowercase							:				List[str]   ,	_lowercase							:				List[str]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				str   ,	_lowercase							:				Tuple		)							->					Union[str, Any]:
									snake_case   :			Dict						    = self.num_labels
									snake_case   :			str						    = MegatronBertForTokenClassification(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			int						    = model(_lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase   ,	labels=_lowercase		)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.seq_length, self.num_labels)		)



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				Union[str, Any]   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Tuple   ,	_lowercase							:				int   ,	_lowercase							:				List[Any]   ,	_lowercase							:				List[str]   ,	_lowercase							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = self.num_choices
									snake_case   :			Union[str, Any]						    = MegatronBertForMultipleChoice(config=_lowercase		)
									model.to(_lowercase		)
									model.eval()
									snake_case   :			Optional[Any]						    = input_ids.unsqueeze(1		).expand(-1   ,	self.num_choices   ,	-1		).contiguous()
									snake_case   :			Tuple						    = token_type_ids.unsqueeze(1		).expand(-1   ,	self.num_choices   ,	-1		).contiguous()
									snake_case   :			Tuple						    = input_mask.unsqueeze(1		).expand(-1   ,	self.num_choices   ,	-1		).contiguous()
									snake_case   :			int						    = model(
									    _lowercase   ,	attention_mask=_lowercase   ,	token_type_ids=_lowercase   ,	labels=_lowercase   ,	)
									self.parent.assertEqual(result.logits.shape   ,	(self.batch_size, self.num_choices)		)



			def 				__lowercase					(			self							:				Optional[Any]		)							->					int:
									snake_case   :			Union[str, Any]						    = self.prepare_config_and_inputs()
									(
									    (
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			(
									    snake_case
									)    ,			
									)   :			Optional[Any]						    = config_and_inputs
									snake_case   :			Optional[Any]						    = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
									return config, inputs_dict



@require_torch
class       _a  ( SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	unittest.TestCase):
			__magic_name__     					=     (
			    (
			        MegatronBertModel,
			        MegatronBertForMaskedLM,
			        MegatronBertForCausalLM,
			        MegatronBertForMultipleChoice,
			        MegatronBertForNextSentencePrediction,
			        MegatronBertForPreTraining,
			        MegatronBertForQuestionAnswering,
			        MegatronBertForSequenceClassification,
			        MegatronBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			__magic_name__     					=     (
			    {
			        """feature-extraction""": MegatronBertModel,
			        """fill-mask""": MegatronBertForMaskedLM,
			        """question-answering""": MegatronBertForQuestionAnswering,
			        """text-classification""": MegatronBertForSequenceClassification,
			        """text-generation""": MegatronBertForCausalLM,
			        """token-classification""": MegatronBertForTokenClassification,
			        """zero-shot""": MegatronBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			__magic_name__     					=     True
			# test_resize_embeddings = False
			__magic_name__     					=     False



			def 				__lowercase					(			self							:				Dict   ,	_lowercase							:				Optional[int]   ,	_lowercase							:				Any   ,	_lowercase							:				Union[str, Any]=False		)							->					Optional[int]:
									snake_case   :			Union[str, Any]						    = super()._prepare_for_class(_lowercase   ,	_lowercase   ,	return_labels=_lowercase		)

									if return_labels:
															if model_class in get_values(_lowercase		):
																					snake_case   :			Union[str, Any]						    = torch.zeros(
																					    (self.model_tester.batch_size, self.model_tester.seq_length)   ,	dtype=torch.long   ,	device=_lowercase		)
																					snake_case   :			Dict						    = torch.zeros(
																					    self.model_tester.batch_size   ,	dtype=torch.long   ,	device=_lowercase		)
									return inputs_dict



			def 				__lowercase					(			self							:				Any		)							->					Tuple:
									snake_case   :			Any						    = MegatronBertModelTester(self		)
									snake_case   :			List[str]						    = ConfigTester(self   ,	config_class=_lowercase   ,	hidden_size=37		)



			def 				__lowercase					(			self							:				Optional[int]		)							->					Any:
									self.config_tester.run_common_tests()



			def 				__lowercase					(			self							:				Any		)							->					str:
									snake_case   :			Union[str, Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_megatron_bert_model(*_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					List[str]:
									snake_case   :			Union[str, Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_megatron_bert_for_masked_lm(*_lowercase		)



			def 				__lowercase					(			self							:				Optional[Any]		)							->					List[Any]:
									snake_case   :			Union[str, Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*_lowercase		)



			def 				__lowercase					(			self							:				List[Any]		)							->					Union[str, Any]:
									snake_case   :			Tuple						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*_lowercase		)



			def 				__lowercase					(			self							:				str		)							->					int:
									snake_case   :			List[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_megatron_bert_for_pretraining(*_lowercase		)



			def 				__lowercase					(			self							:				Dict		)							->					str:
									snake_case   :			int						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_megatron_bert_for_question_answering(*_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					List[Any]:
									snake_case   :			Tuple						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*_lowercase		)



			def 				__lowercase					(			self							:				Tuple		)							->					Any:
									snake_case   :			List[Any]						    = self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_megatron_bert_for_token_classification(*_lowercase		)



def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
						"""simple docstring"""






						return torch.tensor(
						    lowerCamelCase_     ,		dtype=torch.long     ,		device=lowerCamelCase_     ,		)


A		=						1e-4

@require_torch
@require_sentencepiece
@require_tokenizers
class       _a  ( unittest.TestCase):



			@slow
			@unittest.skip("Model is not available."		)
			def 				__lowercase					(			self							:				int		)							->					Tuple:
									snake_case   :			int						    = "nvidia/megatron-bert-uncased-345m"
									if "MYDIR" in os.environ:
															snake_case   :			Dict						    = os.path.join(os.environ["MYDIR"]   ,	_lowercase		)
									snake_case   :			int						    = MegatronBertModel.from_pretrained(_lowercase		)
									model.to(_lowercase		)
									model.half()
									snake_case   :			Dict						    = _long_tensor([[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]		)
									with torch.no_grad():
															snake_case   :			Optional[int]						    = model(_lowercase		)[0]
									snake_case   :			Union[str, Any]						    = torch.Size((1, 9, 1024)		)
									self.assertEqual(output.shape   ,	_lowercase		)

									snake_case   :			Dict						    = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
									for ii in range(3		):
															for jj in range(3		):
																					snake_case   :			int						    = output[0, ii, jj]
																					snake_case   :			Any						    = expected[3 * ii + jj]
																					snake_case   :			List[Any]						    = "ii={} jj={} a={} b={}".format(_lowercase   ,	_lowercase   ,	_lowercase   ,	_lowercase		)
																					self.assertTrue(math.isclose(_lowercase   ,	_lowercase   ,	rel_tol=_lowercase   ,	abs_tol=_lowercase		)   ,	msg=_lowercase		)








"""simple docstring"""







import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A		=						False

class       _a  ( unittest.TestCase):



			def 				__lowercase					(			self							:				str		)							->					Optional[int]:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Any:
									return 12



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Dict:
									return 12



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Union[str, Any]:
									return 32



			@property
			def 				__lowercase					(			self							:				int		)							->					Dict:
									torch.manual_seed(0		)
									snake_case   :			List[str]						    = 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   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
									return model



			@property
			def 				__lowercase					(			self							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
									return tokenizer



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Any:
									torch.manual_seed(0		)
									snake_case   :			Optional[int]						    = CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1E-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1000   ,	)
									return CLIPTextModel(_lowercase		)



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									torch.manual_seed(0		)

									snake_case   :			List[Any]						    = 12
									snake_case   :			Dict						    = 12

									snake_case   :			Tuple						    = {
									    "attention_bias": True,
									    "cross_attention_dim": 32,
									    "attention_head_dim": height * width,
									    "num_attention_heads": 1,
									    "num_vector_embeds": self.num_embed,
									    "num_embeds_ada_norm": self.num_embeds_ada_norm,
									    "norm_num_groups": 32,
									    "sample_size": width,
									    "activation_fn": "geglu-approximate",
									}

									snake_case   :			Dict						    = TransformeraDModel(**_lowercase		)
									return model



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = "cpu"

									snake_case   :			Optional[int]						    = self.dummy_vqvae
									snake_case   :			Dict						    = self.dummy_text_encoder
									snake_case   :			Tuple						    = self.dummy_tokenizer
									snake_case   :			List[Any]						    = self.dummy_transformer
									snake_case   :			List[Any]						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			List[Any]						    = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase		)

									snake_case   :			Dict						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = "teddy bear playing in the pool"

									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Tuple						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[int]						    = output.images

									snake_case   :			int						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			List[str]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			List[Any]						    = image[0, -3:, -3:, -1]
									snake_case   :			Tuple						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			str						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

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



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = "cpu"

									snake_case   :			Dict						    = self.dummy_vqvae
									snake_case   :			List[Any]						    = self.dummy_text_encoder
									snake_case   :			Optional[Any]						    = self.dummy_tokenizer
									snake_case   :			int						    = self.dummy_transformer
									snake_case   :			str						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			Optional[int]						    = LearnedClassifierFreeSamplingEmbeddings(
									    learnable=_lowercase   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length		)

									snake_case   :			List[str]						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Optional[Any]						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = "teddy bear playing in the pool"

									snake_case   :			Union[str, Any]						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[Any]						    = output.images

									snake_case   :			Dict						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

									snake_case   :			Any						    = image[0, -3:, -3:, -1]
									snake_case   :			Any						    = image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			Optional[Any]						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2



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



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()



			def 				__lowercase					(			self							:				Dict		)							->					Tuple:
									snake_case   :			Union[str, Any]						    = load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"		)

									snake_case   :			Tuple						    = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"		)
									snake_case   :			Union[str, Any]						    = pipeline.to(_lowercase		)
									pipeline.set_progress_bar_config(disable=_lowercase		)

									# requires GPU generator for gumbel softmax
									# don't use GPU generator in tests though
									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Optional[int]						    = pipeline(
									    "teddy bear playing in the pool"   ,	num_images_per_prompt=1   ,	generator=_lowercase   ,	output_type="np"   ,	)

									snake_case   :			List[str]						    = output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image		).max() < 2.0



