





import unittest

from transformers import FunnelConfig, FunnelTokenizer, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_PRETRAINING_MAPPING,
						    FunnelBaseModel,
						    FunnelForMaskedLM,
						    FunnelForMultipleChoice,
						    FunnelForPreTraining,
						    FunnelForQuestionAnswering,
						    FunnelForSequenceClassification,
						    FunnelForTokenClassification,
						    FunnelModel,
						)

class SCREAMING_SNAKE_CASE_			:





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase=13						,	_lowerCAmelCase=7						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=True						,	_lowerCAmelCase=99						,	_lowerCAmelCase=[1, 1, 2]						,	_lowerCAmelCase=1						,	_lowerCAmelCase=32						,	_lowerCAmelCase=4						,	_lowerCAmelCase=8						,	_lowerCAmelCase=37						,	_lowerCAmelCase="gelu_new"						,	_lowerCAmelCase=0.1						,	_lowerCAmelCase=0.1						,	_lowerCAmelCase=0.0						,	_lowerCAmelCase=512						,	_lowerCAmelCase=3						,	_lowerCAmelCase=0.02						,	_lowerCAmelCase=3						,	_lowerCAmelCase=4						,	_lowerCAmelCase=None						,	_lowerCAmelCase=False						,	):
										lowerCamelCase__      =							parent
										lowerCamelCase__      =							batch_size
										lowerCamelCase__      =							seq_length
										lowerCamelCase__      =							is_training
										lowerCamelCase__      =							use_input_mask
										lowerCamelCase__      =							use_token_type_ids
										lowerCamelCase__      =							use_labels
										lowerCamelCase__      =							vocab_size
										lowerCamelCase__      =							block_sizes
										lowerCamelCase__      =							num_decoder_layers
										lowerCamelCase__      =							d_model
										lowerCamelCase__      =							n_head
										lowerCamelCase__      =							d_head
										lowerCamelCase__      =							d_inner
										lowerCamelCase__      =							hidden_act
										lowerCamelCase__      =							hidden_dropout
										lowerCamelCase__      =							attention_dropout
										lowerCamelCase__      =							activation_dropout
										lowerCamelCase__      =							max_position_embeddings
										lowerCamelCase__      =							type_vocab_size
										lowerCamelCase__      =							2
										lowerCamelCase__      =							num_labels
										lowerCamelCase__      =							num_choices
										lowerCamelCase__      =							scope
										lowerCamelCase__      =							initializer_std

										# Used in the tests to check the size of the first attention layer
										lowerCamelCase__      =							n_head
										# Used in the tests to check the size of the first hidden state
										lowerCamelCase__      =							self.d_model
										# Used in the tests to check the number of output hidden states/attentions
										lowerCamelCase__      =							sum(self.block_sizes						) + (0 if base else self.num_decoder_layers)
										# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
										# the last hidden state of the first block (which is the first hidden state of the decoder).
										if not base:
															lowerCamelCase__      =							self.num_hidden_layers + 2





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size						)

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

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

										lowerCamelCase__      =							None
										lowerCamelCase__      =							None
										lowerCamelCase__      =							None
										if self.use_labels:
															lowerCamelCase__      =							ids_tensor([self.batch_size]						,	self.type_sequence_label_size						)
															lowerCamelCase__      =							ids_tensor([self.batch_size, self.seq_length]						,	self.num_labels						)
															lowerCamelCase__      =							ids_tensor([self.batch_size]						,	self.num_choices						)
															lowerCamelCase__      =							ids_tensor([self.batch_size, self.seq_length]						,	1						)

										lowerCamelCase__      =							self.get_config()

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    input_mask,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										    fake_token_labels,
										)





					def        __magic_name__			(			self						):
										return FunnelConfig(
										    vocab_size=self.vocab_size						,	block_sizes=self.block_sizes						,	num_decoder_layers=self.num_decoder_layers						,	d_model=self.d_model						,	n_head=self.n_head						,	d_head=self.d_head						,	d_inner=self.d_inner						,	hidden_act=self.hidden_act						,	hidden_dropout=self.hidden_dropout						,	attention_dropout=self.attention_dropout						,	activation_dropout=self.activation_dropout						,	max_position_embeddings=self.max_position_embeddings						,	type_vocab_size=self.type_vocab_size						,	initializer_std=self.initializer_std						,	)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							FunnelModel(config=_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(_lowerCAmelCase						,	attention_mask=_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.d_model)						)

										lowerCamelCase__      =							False
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.d_model)						)

										lowerCamelCase__      =							False
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.d_model)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							FunnelBaseModel(config=_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(_lowerCAmelCase						,	attention_mask=_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						)
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, 2, self.d_model)						)

										lowerCamelCase__      =							False
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, 3, self.d_model)						)

										lowerCamelCase__      =							False
										lowerCamelCase__      =							model(_lowerCAmelCase						)
										self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, 2, self.d_model)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							FunnelForPreTraining(config=_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(_lowerCAmelCase						,	attention_mask=_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						,	labels=_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							FunnelForMaskedLM(config=_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(_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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							FunnelForSequenceClassification(_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(_lowerCAmelCase						,	attention_mask=_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						,	labels=_lowerCAmelCase						)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							self.num_choices
										lowerCamelCase__      =							FunnelForMultipleChoice(config=_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							input_ids.unsqueeze(1						).expand(-1						,	self.num_choices						,	-1						).contiguous()
										lowerCamelCase__      =							token_type_ids.unsqueeze(1						).expand(-1						,	self.num_choices						,	-1						).contiguous()
										lowerCamelCase__      =							input_mask.unsqueeze(1						).expand(-1						,	self.num_choices						,	-1						).contiguous()
										lowerCamelCase__      =							model(
										    _lowerCAmelCase						,	attention_mask=_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						,	labels=_lowerCAmelCase						,	)
										self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)						)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							self.num_labels
										lowerCamelCase__      =							FunnelForTokenClassification(config=_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(_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        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase						,	):
										lowerCamelCase__      =							FunnelForQuestionAnswering(config=_lowerCAmelCase						)
										model.to(_lowerCAmelCase						)
										model.eval()
										lowerCamelCase__      =							model(
										    _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        __magic_name__			(			self						):
										lowerCamelCase__      =							self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase__
										)     ,		(
										    lowerCamelCase__
										)     ,		(
										    lowerCamelCase__
										)     ,		(
										    lowerCamelCase__
										)     ,		(
										    lowerCamelCase__
										)     ,		(
										    lowerCamelCase__
										)     ,		(
										    lowerCamelCase__
										)     ,		(
										    lowerCamelCase__
										)     ,		
										)      =							config_and_inputs
										lowerCamelCase__      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
										return config, inputs_dict

@require_torch
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     False
					A__								=     False
					A__								=     (
					    (
					        FunnelModel,
					        FunnelForMaskedLM,
					        FunnelForPreTraining,
					        FunnelForQuestionAnswering,
					        FunnelForTokenClassification,
					    )
					    if is_torch_available()
					    else ()
					)
					A__								=     (
					    {
					        "feature-extraction": (FunnelBaseModel, FunnelModel),
					        "fill-mask": FunnelForMaskedLM,
					        "question-answering": FunnelForQuestionAnswering,
					        "text-classification": FunnelForSequenceClassification,
					        "token-classification": FunnelForTokenClassification,
					        "zero-shot": FunnelForSequenceClassification,
					    }
					    if is_torch_available()
					    else {}
					)





					def        __magic_name__			(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						,	_lowerCAmelCase=False						):
										lowerCamelCase__      =							super()._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)

										if return_labels:
															if model_class in get_values(_lowerCAmelCase						):
																				lowerCamelCase__      =							torch.zeros(
																				    (self.model_tester.batch_size, self.model_tester.seq_length)						,	dtype=torch.long						,	device=_lowerCAmelCase						)
										return inputs_dict





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							FunnelModelTester(self						)
										lowerCamelCase__      =							ConfigTester(self						,	config_class=_lowerCAmelCase						)





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





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase						)






					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										if hasattr(_lowerCAmelCase						,	"weight"						) and module.weight is not None:
															module.weight.data.fill_(3						)
										if hasattr(_lowerCAmelCase						,	"bias"						) and module.bias is not None:
															module.bias.data.fill_(3						)

										for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
															if hasattr(_lowerCAmelCase						,	_lowerCAmelCase						) and getattr(_lowerCAmelCase						,	_lowerCAmelCase						) is not None:
																				lowerCamelCase__      =							getattr(_lowerCAmelCase						,	_lowerCAmelCase						)
																				weight.data.fill_(3						)

@require_torch
class SCREAMING_SNAKE_CASE_			(       lowercase_				,							unittest.TestCase ):





					"""simple docstring"""


					A__								=     False
					A__								=     False
					A__								=     (
					    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
					)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							FunnelModelTester(self						,	base=_lowerCAmelCase						)
										lowerCamelCase__      =							ConfigTester(self						,	config_class=_lowerCAmelCase						)





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





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_base_model(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*_lowerCAmelCase						)





					def        __magic_name__			(			self						):
										lowerCamelCase__     ,		lowerCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()
										lowerCamelCase__      =							True

										for model_class in self.all_model_classes:
															if model_class.__name__ == "FunnelBaseModel":
																				continue
															lowerCamelCase__      =							model_class(_lowerCAmelCase						)
															model.to(_lowerCAmelCase						)
															model.train()
															lowerCamelCase__      =							self._prepare_for_class(_lowerCAmelCase						,	_lowerCAmelCase						,	return_labels=_lowerCAmelCase						)
															lowerCamelCase__      =							model(**_lowerCAmelCase						).loss
															loss.backward()






					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										if hasattr(_lowerCAmelCase						,	"weight"						) and module.weight is not None:
															module.weight.data.fill_(3						)
										if hasattr(_lowerCAmelCase						,	"bias"						) and module.bias is not None:
															module.bias.data.fill_(3						)

										for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
															if hasattr(_lowerCAmelCase						,	_lowerCAmelCase						) and getattr(_lowerCAmelCase						,	_lowerCAmelCase						) is not None:
																				lowerCamelCase__      =							getattr(_lowerCAmelCase						,	_lowerCAmelCase						)
																				weight.data.fill_(3						)

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





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							13
										lowerCamelCase__      =							7
										lowerCamelCase__      =							torch.arange(0						,	batch_size * sequence_length						).long().reshape(_lowerCAmelCase						,	_lowerCAmelCase						)
										lowerCamelCase__      =							[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
										lowerCamelCase__      =							torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]						)

										lowerCamelCase__      =							FunnelModel.from_pretrained("sgugger/funnel-random-tiny"						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						)[0].abs()

										lowerCamelCase__      =							torch.tensor(2_344.8_352						)
										lowerCamelCase__      =							torch.tensor(0.8_052						)
										self.assertTrue(torch.allclose(output.sum()						,	_lowerCAmelCase						,	atol=1E-4						)						)
										self.assertTrue(torch.allclose(output.mean()						,	_lowerCAmelCase						,	atol=1E-4						)						)

										lowerCamelCase__      =							torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]						)
										lowerCamelCase__      =							model(_lowerCAmelCase						,	attention_mask=_lowerCAmelCase						,	token_type_ids=_lowerCAmelCase						)[0].abs()

										lowerCamelCase__      =							torch.tensor(2_343.8_425						)
										lowerCamelCase__      =							torch.tensor(0.8_049						)
										self.assertTrue(torch.allclose(output.sum()						,	_lowerCAmelCase						,	atol=1E-4						)						)
										self.assertTrue(torch.allclose(output.mean()						,	_lowerCAmelCase						,	atol=1E-4						)						)






					@slow
					def        __magic_name__			(			self						):
										lowerCamelCase__      =							FunnelTokenizer.from_pretrained("huggingface/funnel-small"						)
										lowerCamelCase__      =							FunnelModel.from_pretrained("huggingface/funnel-small"						)
										lowerCamelCase__      =							tokenizer("Hello! I am the Funnel Transformer model."						,	return_tensors="pt"						)
										lowerCamelCase__      =							model(**_lowerCAmelCase						)[0]

										lowerCamelCase__      =							torch.tensor(235.7_246						)
										lowerCamelCase__      =							torch.tensor(0.0_256						)
										self.assertTrue(torch.allclose(output.sum()						,	_lowerCAmelCase						,	atol=1E-4						)						)
										self.assertTrue(torch.allclose(output.mean()						,	_lowerCAmelCase						,	atol=1E-4						)						)








import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()

class SCREAMING_SNAKE_CASE_			(       unittest.TestCase ):





					"""simple docstring"""


					@property
					def        __magic_name__			(			self						):
										torch.manual_seed(0						)
										lowerCamelCase__      =							UNetaDModel(
										    block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=3						,	out_channels=3						,	down_block_types=("DownBlock2D", "AttnDownBlock2D")						,	up_block_types=("AttnUpBlock2D", "UpBlock2D")						,	)
										return model





					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"cpu"
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler()

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.Generator(device=_lowerCAmelCase						).manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						,	return_dict=_lowerCAmelCase						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array(
										    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]						)

										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        __magic_name__			(			self						):
										lowerCamelCase__      =							self.dummy_uncond_unet
										lowerCamelCase__      =							DDPMScheduler(prediction_type="sample"						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						).images

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	num_inference_steps=2						,	output_type="numpy"						)[0]

										lowerCamelCase__      =							image[0, -3:, -3:, -1]
										lowerCamelCase__      =							image_eps[0, -3:, -3:, -1]

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							1E-2 if torch_device != "mps" else 3E-2
										assert np.abs(image_slice.flatten() - image_eps_slice.flatten()						).max() < tolerance

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





					"""simple docstring"""


					def        __magic_name__			(			self						):
										lowerCamelCase__      =							"google/ddpm-cifar10-32"

										lowerCamelCase__      =							UNetaDModel.from_pretrained(_lowerCAmelCase						)
										lowerCamelCase__      =							DDPMScheduler.from_pretrained(_lowerCAmelCase						)

										lowerCamelCase__      =							DDPMPipeline(unet=_lowerCAmelCase						,	scheduler=_lowerCAmelCase						)
										ddpm.to(_lowerCAmelCase						)
										ddpm.set_progress_bar_config(disable=_lowerCAmelCase						)

										lowerCamelCase__      =							torch.manual_seed(0						)
										lowerCamelCase__      =							ddpm(generator=_lowerCAmelCase						,	output_type="numpy"						).images

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

										assert image.shape == (1, 32, 32, 3)
										lowerCamelCase__      =							np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]						)
										assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-2

