import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
						import torch

						from transformers import (
						    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
						    MODEL_MAPPING,
						    EfficientFormerForImageClassification,
						    EfficientFormerForImageClassificationWithTeacher,
						    EfficientFormerModel,
						)
						from transformers.models.efficientformer.modeling_efficientformer import (
						    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						)


if is_vision_available():
						from PIL import Image

						from transformers import EfficientFormerImageProcessor
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = 13       ,  lowerCAmelCase_ = 64       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 3       ,  lowerCAmelCase_ = 3       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = 128       ,  lowerCAmelCase_=[16, 32, 64, 128]       ,  lowerCAmelCase_ = 7       ,  lowerCAmelCase_ = 4       ,  lowerCAmelCase_ = 37       ,  lowerCAmelCase_ = "gelu"       ,  lowerCAmelCase_ = 0.1       ,  lowerCAmelCase_ = 0.1       ,  lowerCAmelCase_ = 10       ,  lowerCAmelCase_ = 0.0_2       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 1       ,  lowerCAmelCase_ = 128       ,  lowerCAmelCase_ = [2, 2, 2, 2]       ,  lowerCAmelCase_ = 2       ,  lowerCAmelCase_ = 2       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =image_size
		_lowercase   =patch_size
		_lowercase   =num_channels
		_lowercase   =is_training
		_lowercase   =use_labels
		_lowercase   =hidden_size
		_lowercase   =num_hidden_layers
		_lowercase   =num_attention_heads
		_lowercase   =intermediate_size
		_lowercase   =hidden_act
		_lowercase   =hidden_dropout_prob
		_lowercase   =attention_probs_dropout_prob
		_lowercase   =type_sequence_label_size
		_lowercase   =initializer_range
		_lowercase   =encoder_stride
		_lowercase   =num_attention_outputs
		_lowercase   =embed_dim
		_lowercase   =embed_dim + 1
		_lowercase   =resolution
		_lowercase   =depths
		_lowercase   =hidden_sizes
		_lowercase   =dim
		_lowercase   =mlp_expansion_ratio


	def __lowerCAmelCase      (   self       ):
		_lowercase   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

		_lowercase   =None
		if self.use_labels:
			_lowercase   =ids_tensor([self.batch_size]       ,  self.type_sequence_label_size       )

		_lowercase   =self.get_config()
		return config, pixel_values, labels


	def __lowerCAmelCase      (   self       ):
		return EfficientFormerConfig(
		    image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  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       ,  is_decoder=lowerCAmelCase_       ,  initializer_range=self.initializer_range       ,  encoder_stride=self.encoder_stride       ,  resolution=self.resolution       ,  depths=self.depths       ,  hidden_sizes=self.hidden_sizes       ,  dim=self.dim       ,  mlp_expansion_ratio=self.mlp_expansion_ratio       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =EfficientFormerModel(config=lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, self.seq_length, self.hidden_size)       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =self.type_sequence_label_size
		_lowercase   =EfficientFormerForImageClassification(lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       ,  labels=lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)       )

		# test greyscale images
		_lowercase   =1
		_lowercase   =EfficientFormerForImageClassification(lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()

		_lowercase   =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.prepare_config_and_inputs()
		(
		    (
		    _lowercase
		)   ,  (
		    _lowercase
		)   ,  (
		    _lowercase
		)   ,  
		)   =config_and_inputs
		_lowercase   ={"pixel_values": pixel_values}
		return config, inputs_dict

@require_torch
class 	_a  (							lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(
	    (
	        EfficientFormerModel,
	        EfficientFormerForImageClassificationWithTeacher,
	        EfficientFormerForImageClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE              =					(
	    {
	        'feature-extraction': EfficientFormerModel,
	        'image-classification': (
	            EfficientFormerForImageClassification,
	            EfficientFormerForImageClassificationWithTeacher,
	        ),
	    }
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE              =					False

	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =EfficientFormerModelTester(self       )
		_lowercase   =ConfigTester(
		    self       ,  config_class=lowerCAmelCase_       ,  has_text_modality=lowerCAmelCase_       ,  hidden_size=37       )


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


	@unittest.skip(reason="EfficientFormer does not use inputs_embeds"       )
	def __lowerCAmelCase      (   self       ):
		pass


	@unittest.skip(reason="EfficientFormer does not support input and output embeddings"       )
	def __lowerCAmelCase      (   self       ):
		pass


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			_lowercase   =model_class(lowerCAmelCase_       )
			_lowercase   =inspect.signature(model.forward       )
			# signature.parameters is an OrderedDict => so arg_names order is deterministic
			_lowercase   =[*signature.parameters.keys()]

			_lowercase   =["pixel_values"]
			self.assertListEqual(arg_names[:1]       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		def check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()

			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )

			_lowercase   =outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			_lowercase   =getattr(
			    self.model_tester       ,  "expected_num_hidden_layers"       ,  self.model_tester.num_hidden_layers + 1       )
			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			if hasattr(self.model_tester       ,  "encoder_seq_length"       ):
				_lowercase   =self.model_tester.encoder_seq_length
				if hasattr(self.model_tester       ,  "chunk_length"       ) and self.model_tester.chunk_length > 1:
					_lowercase   =seq_length * self.model_tester.chunk_length
			else:
				_lowercase   =self.model_tester.seq_length

			self.assertListEqual(
			    list(hidden_states[-1].shape[-2:]       )       ,  [seq_length, self.model_tester.hidden_size]       ,  )

			if config.is_encoder_decoder:
				_lowercase   =outputs.decoder_hidden_states

				self.assertIsInstance(lowerCAmelCase_       ,  (list, tuple)       )
				self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )
				_lowercase   =getattr(self.model_tester       ,  "seq_length"       ,  lowerCAmelCase_       )
				_lowercase   =getattr(self.model_tester       ,  "decoder_seq_length"       ,  lowerCAmelCase_       )

				self.assertListEqual(
				    list(hidden_states[-1].shape[-2:]       )       ,  [decoder_seq_length, self.model_tester.hidden_size]       ,  )

		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			_lowercase   =True
			check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			_lowercase   =True

			check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_=False       ):
		_lowercase   =super()._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       ,  return_labels=lowerCAmelCase_       )

		if return_labels:
			if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
				del inputs_dict["labels"]

		return inputs_dict


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_       )


	@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"       )
	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		if not self.model_tester.is_training:
			return

		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =True

		for model_class in self.all_model_classes:
			# EfficientFormerForImageClassificationWithTeacher supports inference-only
			if (
			    model_class in get_values(lowerCAmelCase_       )
			    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
			):
				continue
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.train()
			_lowercase   =self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       ,  return_labels=lowerCAmelCase_       )
			_lowercase   =model(**lowerCAmelCase_       ).loss
			loss.backward()


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		_lowercase   =[
		    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
		    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
		    {"title": "regression", "num_labels": 1, "dtype": torch.float},
		]

		for model_class in self.all_model_classes:
			if (
			    model_class
			    not in [
			        *get_values(lowerCAmelCase_       ),
			    ]
			    or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
			):
				continue

			for problem_type in problem_types:
				with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}'''       ):
					_lowercase   =problem_type["title"]
					_lowercase   =problem_type["num_labels"]

					_lowercase   =model_class(lowerCAmelCase_       )
					model.to(lowerCAmelCase_       )
					model.train()

					_lowercase   =self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       ,  return_labels=lowerCAmelCase_       )

					if problem_type["num_labels"] > 1:
						_lowercase   =inputs["labels"].unsqueeze(1       ).repeat(1       ,  problem_type["num_labels"]       )

					_lowercase   =inputs["labels"].to(problem_type["dtype"]       )

					# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
					# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
					# they have the same size." which is a symptom something in wrong for the regression problem.
					# See https://github.com/huggingface/transformers/issues/11780
					with warnings.catch_warnings(record=lowerCAmelCase_       ) as warning_list:
						_lowercase   =model(**lowerCAmelCase_       ).loss
					for w in warning_list:
						if "Using a target size that is different to the input size" in str(w.message       ):
							raise ValueError(
							    F'''Something is going wrong in the regression problem: intercepted {w.message}'''       )

					loss.backward()


	@slow
	def __lowerCAmelCase      (   self       ):
		for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			_lowercase   =EfficientFormerModel.from_pretrained(lowerCAmelCase_       )
			self.assertIsNotNone(lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =True

		_lowercase   =getattr(self.model_tester       ,  "seq_length"       ,  lowerCAmelCase_       )
		_lowercase   =getattr(self.model_tester       ,  "encoder_seq_length"       ,  lowerCAmelCase_       )
		_lowercase   =getattr(self.model_tester       ,  "key_length"       ,  lowerCAmelCase_       )
		_lowercase   =getattr(self.model_tester       ,  "chunk_length"       ,  lowerCAmelCase_       )

		if chunk_length is not None and hasattr(self.model_tester       ,  "num_hashes"       ):
			_lowercase   =encoder_seq_length * self.model_tester.num_hashes

		for model_class in self.all_model_classes:
			_lowercase   =True
			_lowercase   =False
			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
			_lowercase   =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
			self.assertEqual(len(lowerCAmelCase_       )       ,  self.model_tester.num_attention_outputs       )

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
			_lowercase   =outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
			self.assertEqual(len(lowerCAmelCase_       )       ,  self.model_tester.num_attention_outputs       )

			if chunk_length is not None:
				self.assertListEqual(
				    list(attentions[0].shape[-4:]       )       ,  [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]       ,  )
			else:
				self.assertListEqual(
				    list(attentions[0].shape[-3:]       )       ,  [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]       ,  )

def 	__lowerCamelCase							(				)      ->       str:
	_lowercase   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
	return image
@require_torch
@require_vision
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	@cached_property
	def __lowerCAmelCase      (   self       ):
		return (
		    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"       )
		    if is_vision_available()
		    else None
		)


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"       ).to(
		    lowerCAmelCase_       )

		_lowercase   =self.default_image_processor
		_lowercase   =prepare_img()
		_lowercase   =image_processor(images=lowerCAmelCase_       ,  return_tensors="pt"       ).to(lowerCAmelCase_       )

		# forward pass
		with torch.no_grad():
			_lowercase   =model(**lowerCAmelCase_       )

		# verify the logits
		_lowercase   =(1, 1000)
		self.assertEqual(outputs.logits.shape       ,  lowerCAmelCase_       )

		_lowercase   =torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]       ).to(lowerCAmelCase_       )
		self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,  lowerCAmelCase_       ,  atol=1e-4       )       )


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =EfficientFormerForImageClassificationWithTeacher.from_pretrained(
		    "snap-research/efficientformer-l1-300"       ).to(lowerCAmelCase_       )

		_lowercase   =self.default_image_processor
		_lowercase   =prepare_img()
		_lowercase   =image_processor(images=lowerCAmelCase_       ,  return_tensors="pt"       ).to(lowerCAmelCase_       )

		# forward pass
		with torch.no_grad():
			_lowercase   =model(**lowerCAmelCase_       )

		# verify the logits
		_lowercase   =(1, 1000)
		self.assertEqual(outputs.logits.shape       ,  lowerCAmelCase_       )

		_lowercase   =torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]       ).to(lowerCAmelCase_       )
		self.assertTrue(torch.allclose(outputs.logits[0][:3]       ,  lowerCAmelCase_       ,  atol=1e-4       )       )





import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 	_a  (							lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					UnCLIPPipeline
	__SCREAMING_SNAKE_CASE              =					TEXT_TO_IMAGE_PARAMS - {
	    'negative_prompt',
	    'height',
	    'width',
	    'negative_prompt_embeds',
	    'guidance_scale',
	    'prompt_embeds',
	    'cross_attention_kwargs',
	}
	__SCREAMING_SNAKE_CASE              =					TEXT_TO_IMAGE_BATCH_PARAMS
	__SCREAMING_SNAKE_CASE              =					[
	    'generator',
	    'return_dict',
	    'prior_num_inference_steps',
	    'decoder_num_inference_steps',
	    'super_res_num_inference_steps',
	]
	__SCREAMING_SNAKE_CASE              =					False


	@property
	def __lowerCAmelCase      (   self       ):
		return 32


	@property
	def __lowerCAmelCase      (   self       ):
		return 32


	@property
	def __lowerCAmelCase      (   self       ):
		return self.time_input_dim


	@property
	def __lowerCAmelCase      (   self       ):
		return self.time_input_dim * 4


	@property
	def __lowerCAmelCase      (   self       ):
		return 100


	@property
	def __lowerCAmelCase      (   self       ):
		_lowercase   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )
		return tokenizer


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )
		_lowercase   =CLIPTextConfig(
		    bos_token_id=0       ,  eos_token_id=2       ,  hidden_size=self.text_embedder_hidden_size       ,  projection_dim=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 CLIPTextModelWithProjection(lowerCAmelCase_       )


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "num_attention_heads": 2,
		    "attention_head_dim": 12,
		    "embedding_dim": self.text_embedder_hidden_size,
		    "num_layers": 1,
		}

		_lowercase   =PriorTransformer(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "clip_embeddings_dim": self.text_embedder_hidden_size,
		    "time_embed_dim": self.time_embed_dim,
		    "cross_attention_dim": self.cross_attention_dim,
		}

		_lowercase   =UnCLIPTextProjModel(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   ={
		    "sample_size": 32,
		    # RGB in channels
		    "in_channels": 3,
		    # Out channels is double in channels because predicts mean and variance
		    "out_channels": 6,
		    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
		    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
		    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
		    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
		    "layers_per_block": 1,
		    "cross_attention_dim": self.cross_attention_dim,
		    "attention_head_dim": 4,
		    "resnet_time_scale_shift": "scale_shift",
		    "class_embed_type": "identity",
		}

		_lowercase   =UNetaDConditionModel(**lowerCAmelCase_       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		return {
		    "sample_size": 64,
		    "layers_per_block": 1,
		    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
		    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
		    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
		    "in_channels": 6,
		    "out_channels": 3,
		}


	@property
	def __lowerCAmelCase      (   self       ):
		torch.manual_seed(0       )

		_lowercase   =UNetaDModel(**self.dummy_super_res_kwargs       )
		return model


	@property
	def __lowerCAmelCase      (   self       ):
		# seeded differently to get different unet than `self.dummy_super_res_first`
		torch.manual_seed(1       )

		_lowercase   =UNetaDModel(**self.dummy_super_res_kwargs       )
		return model


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.dummy_prior
		_lowercase   =self.dummy_decoder
		_lowercase   =self.dummy_text_proj
		_lowercase   =self.dummy_text_encoder
		_lowercase   =self.dummy_tokenizer
		_lowercase   =self.dummy_super_res_first
		_lowercase   =self.dummy_super_res_last

		_lowercase   =UnCLIPScheduler(
		    variance_type="fixed_small_log"       ,  prediction_type="sample"       ,  num_train_timesteps=1000       ,  clip_sample_range=5.0       ,  )

		_lowercase   =UnCLIPScheduler(
		    variance_type="learned_range"       ,  prediction_type="epsilon"       ,  num_train_timesteps=1000       ,  )

		_lowercase   =UnCLIPScheduler(
		    variance_type="fixed_small_log"       ,  prediction_type="epsilon"       ,  num_train_timesteps=1000       ,  )

		_lowercase   ={
		    "prior": prior,
		    "decoder": decoder,
		    "text_proj": text_proj,
		    "text_encoder": text_encoder,
		    "tokenizer": tokenizer,
		    "super_res_first": super_res_first,
		    "super_res_last": super_res_last,
		    "prior_scheduler": prior_scheduler,
		    "decoder_scheduler": decoder_scheduler,
		    "super_res_scheduler": super_res_scheduler,
		}

		return components


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=0       ):
		if str(lowerCAmelCase_       ).startswith("mps"       ):
			_lowercase   =torch.manual_seed(lowerCAmelCase_       )
		else:
			_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(lowerCAmelCase_       )
		_lowercase   ={
		    "prompt": "horse",
		    "generator": generator,
		    "prior_num_inference_steps": 2,
		    "decoder_num_inference_steps": 2,
		    "super_res_num_inference_steps": 2,
		    "output_type": "numpy",
		}
		return inputs


	def __lowerCAmelCase      (   self       ):
		_lowercase   ="cpu"

		_lowercase   =self.get_dummy_components()

		_lowercase   =self.pipeline_class(**lowerCAmelCase_       )
		_lowercase   =pipe.to(lowerCAmelCase_       )

		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =pipe(**self.get_dummy_inputs(lowerCAmelCase_       )       )
		_lowercase   =output.images

		_lowercase   =pipe(
		    **self.get_dummy_inputs(lowerCAmelCase_       )       ,  return_dict=lowerCAmelCase_       ,  )[0]

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

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

		_lowercase   =np.array(
		    [
		        0.9_9_9_7,
		        0.9_9_8_8,
		        0.0_0_2_8,
		        0.9_9_9_7,
		        0.9_9_8_4,
		        0.9_9_6_5,
		        0.0_0_2_9,
		        0.9_9_8_6,
		        0.0_0_2_5,
		    ]       )

		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 __lowerCAmelCase      (   self       ):
		_lowercase   =torch.device("cpu"       )
		class 	_a  :



			"""simple docstring"""



			__SCREAMING_SNAKE_CASE              =					1

		_lowercase   =self.get_dummy_components()

		_lowercase   =self.pipeline_class(**lowerCAmelCase_       )
		_lowercase   =pipe.to(lowerCAmelCase_       )

		_lowercase   =components["prior"]
		_lowercase   =components["decoder"]
		_lowercase   =components["super_res_first"]
		_lowercase   =components["tokenizer"]
		_lowercase   =components["text_encoder"]

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =prior.dtype
		_lowercase   =1

		_lowercase   =(batch_size, prior.config.embedding_dim)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )
		_lowercase   =(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )

		_lowercase   =(
		    batch_size,
		    super_res_first.config.in_channels // 2,
		    super_res_first.config.sample_size,
		    super_res_first.config.sample_size,
		)
		_lowercase   =pipe.prepare_latents(
		    lowerCAmelCase_       ,  dtype=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  generator=lowerCAmelCase_       ,  latents=lowerCAmelCase_       ,  scheduler=DummyScheduler()       )

		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   ="this is a prompt example"

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =pipe(
		    [prompt]       ,  generator=lowerCAmelCase_       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  prior_latents=lowerCAmelCase_       ,  decoder_latents=lowerCAmelCase_       ,  super_res_latents=lowerCAmelCase_       ,  output_type="np"       ,  )
		_lowercase   =output.images

		_lowercase   =tokenizer(
		    lowerCAmelCase_       ,  padding="max_length"       ,  max_length=tokenizer.model_max_length       ,  return_tensors="pt"       ,  )
		_lowercase   =text_encoder(text_inputs.input_ids       )
		_lowercase   =text_inputs.attention_mask

		_lowercase   =torch.Generator(device=lowerCAmelCase_       ).manual_seed(0       )
		_lowercase   =pipe(
		    generator=lowerCAmelCase_       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  prior_latents=lowerCAmelCase_       ,  decoder_latents=lowerCAmelCase_       ,  super_res_latents=lowerCAmelCase_       ,  text_model_output=lowerCAmelCase_       ,  text_attention_mask=lowerCAmelCase_       ,  output_type="np"       ,  )[0]

		# make sure passing text embeddings manually is identical
		assert np.abs(image - image_from_text       ).max() < 1e-4


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		_lowercase   =torch_device == "cpu"

		self._test_attention_slicing_forward_pass(test_max_difference=lowerCAmelCase_       ,  expected_max_diff=0.0_1       )


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		_lowercase   =torch_device == "cpu"
		_lowercase   =True
		_lowercase   =[
		    "prior_num_inference_steps",
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]

		self._test_inference_batch_single_identical(
		    test_max_difference=lowerCAmelCase_       ,  relax_max_difference=lowerCAmelCase_       ,  additional_params_copy_to_batched_inputs=lowerCAmelCase_       ,  )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =[
		    "prior_num_inference_steps",
		    "decoder_num_inference_steps",
		    "super_res_num_inference_steps",
		]

		if torch_device == "mps":
			# TODO: MPS errors with larger batch sizes
			_lowercase   =[2, 3]
			self._test_inference_batch_consistent(
			    batch_sizes=lowerCAmelCase_       ,  additional_params_copy_to_batched_inputs=lowerCAmelCase_       ,  )
		else:
			self._test_inference_batch_consistent(
			    additional_params_copy_to_batched_inputs=lowerCAmelCase_       )


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_dict_tuple_outputs_equivalent()


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_save_load_local()


	@skip_mps
	def __lowerCAmelCase      (   self       ):
		return super().test_save_load_optional_components()

@nightly
class 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/unclip/karlo_v1_alpha_horse_cpu.npy"       )

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       )
		pipeline.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =torch.manual_seed(0       )
		_lowercase   =pipeline(
		    "horse"       ,  num_images_per_prompt=1       ,  generator=lowerCAmelCase_       ,  output_type="np"       ,  )

		_lowercase   =output.images[0]

		assert image.shape == (256, 256, 3)
		assert np.abs(expected_image - image       ).max() < 1e-1

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



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		# clean up the VRAM after each test
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =load_numpy(
		    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
		    "/unclip/karlo_v1_alpha_horse_fp16.npy"       )

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       ,  torch_dtype=torch.floataa       )
		_lowercase   =pipeline.to(lowerCAmelCase_       )
		pipeline.set_progress_bar_config(disable=lowerCAmelCase_       )

		_lowercase   =torch.Generator(device="cpu"       ).manual_seed(0       )
		_lowercase   =pipeline(
		    "horse"       ,  generator=lowerCAmelCase_       ,  output_type="np"       ,  )

		_lowercase   =output.images[0]

		assert image.shape == (256, 256, 3)

		assert_mean_pixel_difference(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		_lowercase   =UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"       ,  torch_dtype=torch.floataa       )
		_lowercase   =pipe.to(lowerCAmelCase_       )
		pipe.set_progress_bar_config(disable=lowerCAmelCase_       )
		pipe.enable_attention_slicing()
		pipe.enable_sequential_cpu_offload()

		_lowercase   =pipe(
		    "horse"       ,  num_images_per_prompt=1       ,  prior_num_inference_steps=2       ,  decoder_num_inference_steps=2       ,  super_res_num_inference_steps=2       ,  output_type="np"       ,  )

		_lowercase   =torch.cuda.max_memory_allocated()
		# make sure that less than 7 GB is allocated
		assert mem_bytes < 7 * 10**9




