
'''simple docstring'''


import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


__lowercase							:				Dict					=       namedtuple('''ExampleNamedTuple''', '''a b c''')

class 		__lowercase			(					unittest.TestCase      ):

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Optional[int]       =						torch.randn(5     ,				2 )
								lowerCamelCase_   : Any       =						torch.device('''cuda''' ) if torch.cuda.is_available() else torch.device('''cpu''' )

								lowerCamelCase_   : str       =						send_to_device(A     ,				A )
								self.assertTrue(torch.equal(resulta.cpu()     ,				A ) )

								lowerCamelCase_   : Union[str, Any]       =						send_to_device((tensor, [tensor, tensor], 1)     ,				A )
								self.assertIsInstance(A     ,				A )
								self.assertTrue(torch.equal(resulta[0].cpu()     ,				A ) )
								self.assertIsInstance(resulta[1]     ,				A )
								self.assertTrue(torch.equal(resulta[1][0].cpu()     ,				A ) )
								self.assertTrue(torch.equal(resulta[1][1].cpu()     ,				A ) )
								self.assertEqual(resulta[2]     ,				1 )

								lowerCamelCase_   : Tuple       =						send_to_device({'''a''': tensor, '''b''': [tensor, tensor], '''c''': 1}     ,				A )
								self.assertIsInstance(A     ,				A )
								self.assertTrue(torch.equal(resulta['''a'''].cpu()     ,				A ) )
								self.assertIsInstance(resulta['''b''']     ,				A )
								self.assertTrue(torch.equal(resulta['''b'''][0].cpu()     ,				A ) )
								self.assertTrue(torch.equal(resulta['''b'''][1].cpu()     ,				A ) )
								self.assertEqual(resulta['''c''']     ,				1 )

								lowerCamelCase_   : str       =						send_to_device(ExampleNamedTuple(a=A     ,				b=[tensor, tensor]     ,				c=1 )     ,				A )
								self.assertIsInstance(A     ,				A )
								self.assertTrue(torch.equal(resulta.a.cpu()     ,				A ) )
								self.assertIsInstance(resulta.b     ,				A )
								self.assertTrue(torch.equal(resulta.b[0].cpu()     ,				A ) )
								self.assertTrue(torch.equal(resulta.b[1].cpu()     ,				A ) )
								self.assertEqual(resulta.c     ,				1 )

								lowerCamelCase_   : List[str]       =						send_to_device(UserDict({'''a''': tensor, '''b''': [tensor, tensor], '''c''': 1} )     ,				A )
								self.assertIsInstance(A     ,				A )
								self.assertTrue(torch.equal(resulta['''a'''].cpu()     ,				A ) )
								self.assertIsInstance(resulta['''b''']     ,				A )
								self.assertTrue(torch.equal(resulta['''b'''][0].cpu()     ,				A ) )
								self.assertTrue(torch.equal(resulta['''b'''][1].cpu()     ,				A ) )
								self.assertEqual(resulta['''c''']     ,				1 )

			def     UpperCAmelCase__	(self ):
								with self.assertRaises(A ) as cm:
													lowerCamelCase_   : Optional[int]       =						recursively_apply(torch.tensor     ,				(torch.tensor(1 ), 1)     ,				error_on_other_type=A )
								self.assertEqual(
								    str(cm.exception )     ,				'''Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'''     ,				)

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						torch.tensor([1, 2, 3, 4, 5] )
								self.assertEqual(listify(A )     ,				[1, 2, 3, 4, 5] )

								lowerCamelCase_   : List[str]       =						torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]] )
								self.assertEqual(listify(A )     ,				[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]] )

								lowerCamelCase_   : Tuple       =						torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]] )
								self.assertEqual(
								    listify(A )     ,				[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]] )

			def     UpperCAmelCase__	(self ):
								with patch_environment(aa=1     ,				BB=2 ):
													self.assertEqual(os.environ.get('''AA''' )     ,				'''1''' )
													self.assertEqual(os.environ.get('''BB''' )     ,				'''2''' )

								self.assertNotIn('''AA'''     ,				os.environ )
								self.assertNotIn('''BB'''     ,				os.environ )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : str       =						RegressionModel()
								lowerCamelCase_   : Dict       =						model.forward
								lowerCamelCase_   : Any       =						convert_outputs_to_fpaa(model.forward )
								lowerCamelCase_   : Optional[Any]       =						extract_model_from_parallel(A     ,				keep_fpaa_wrapper=A )
								lowerCamelCase_   : Union[str, Any]       =						pickle.dumps(A )

			@require_cuda
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[Any]       =						RegressionModel()
								lowerCamelCase_   : Any       =						model.forward
								lowerCamelCase_   : Optional[Any]       =						torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
								lowerCamelCase_   : str       =						convert_outputs_to_fpaa(model.forward )
								lowerCamelCase_   : List[str]       =						extract_model_from_parallel(A     ,				keep_fpaa_wrapper=A )
								lowerCamelCase_   : Dict       =						pickle.dumps(A )

			@require_cuda
			@require_torch_min_version(version='''2.0''' )
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						RegressionModel()
								lowerCamelCase_   : Optional[int]       =						model.forward
								lowerCamelCase_   : int       =						torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
								lowerCamelCase_   : Union[str, Any]       =						convert_outputs_to_fpaa(model.forward )
								lowerCamelCase_   : List[str]       =						torch.compile(model.forward     ,				backend='''inductor''' )
								lowerCamelCase_   : List[Any]       =						torch.randn(4     ,				1_0 ).cuda()
								lowerCamelCase_   : int       =						model(A )

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						RegressionModel()
								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								lowerCamelCase_   : Dict       =						torch.nn.parallel.DataParallel(A )
								lowerCamelCase_   : List[Any]       =						extract_model_from_parallel(A )

								self.assertEqual(A     ,				A )

			@require_torch_min_version(version='''2.0''' )
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Any       =						RegressionModel()
								lowerCamelCase_   : str       =						torch.compile(A )

								# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
								lowerCamelCase_   : int       =						torch.nn.parallel.DataParallel(A )
								lowerCamelCase_   : Union[str, Any]       =						torch.compile(A )
								lowerCamelCase_   : Any       =						extract_model_from_parallel(A )

								self.assertEqual(compiled_model._orig_mod     ,				compiled_model_unwrapped._orig_mod )






			def     UpperCAmelCase__	(self ):
								self.assertEqual(find_device([1, '''a''', torch.tensor([1, 2, 3] )] )     ,				torch.device('''cpu''' ) )
								self.assertEqual(find_device({'''a''': 1, '''b''': torch.tensor([1, 2, 3] )} )     ,				torch.device('''cpu''' ) )
								self.assertIsNone(find_device([1, '''a'''] ) )




'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 		__lowercase			(					_lowercase ,       unittest.TestCase      ):
			lowerCamelCase     :						List[str]		 =     KandinskyPipeline
			lowerCamelCase     :						List[str]		 =     [
			    "prompt",
			    "image_embeds",
			    "negative_image_embeds",
			]
			lowerCamelCase     :						str		 =     ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
			lowerCamelCase     :						Dict		 =     [
			    "generator",
			    "height",
			    "width",
			    "latents",
			    "guidance_scale",
			    "negative_prompt",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			lowerCamelCase     :						Union[str, Any]		 =     False

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

			@property
			def     UpperCAmelCase__	(self ):
								return 3_2

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

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

			@property
			def     UpperCAmelCase__	(self ):
								return 1_0_0

			@property
			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
								return tokenizer

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Any       =						MCLIPConfig(
								    numDims=self.cross_attention_dim     ,				transformerDimensions=self.text_embedder_hidden_size     ,				hidden_size=self.text_embedder_hidden_size     ,				intermediate_size=3_7     ,				num_attention_heads=4     ,				num_hidden_layers=5     ,				vocab_size=1_0_0_5     ,				)

								lowerCamelCase_   : Union[str, Any]       =						MultilingualCLIP(A )
								lowerCamelCase_   : Dict       =						text_encoder.eval()

								return text_encoder

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

								lowerCamelCase_   : Dict       =						{
								    '''in_channels''': 4,
								    # Out channels is double in channels because predicts mean and variance
								    '''out_channels''': 8,
								    '''addition_embed_type''': '''text_image''',
								    '''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,
								    '''encoder_hid_dim''': self.text_embedder_hidden_size,
								    '''encoder_hid_dim_type''': '''text_image_proj''',
								    '''cross_attention_dim''': self.cross_attention_dim,
								    '''attention_head_dim''': 4,
								    '''resnet_time_scale_shift''': '''scale_shift''',
								    '''class_embed_type''': None,
								}

								lowerCamelCase_   : Union[str, Any]       =						UNetaDConditionModel(**A )
								return model

			@property
			def     UpperCAmelCase__	(self ):
								return {
								    "block_out_channels": [3_2, 6_4],
								    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
								    "in_channels": 3,
								    "latent_channels": 4,
								    "layers_per_block": 1,
								    "norm_num_groups": 8,
								    "norm_type": "spatial",
								    "num_vq_embeddings": 1_2,
								    "out_channels": 3,
								    "up_block_types": [
								        "AttnUpDecoderBlock2D",
								        "UpDecoderBlock2D",
								    ],
								    "vq_embed_dim": 4,
								}

			@property
			def     UpperCAmelCase__	(self ):
								torch.manual_seed(0 )
								lowerCamelCase_   : Dict       =						VQModel(**self.dummy_movq_kwargs )
								return model

			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : List[str]       =						self.dummy_text_encoder
								lowerCamelCase_   : Tuple       =						self.dummy_tokenizer
								lowerCamelCase_   : int       =						self.dummy_unet
								lowerCamelCase_   : str       =						self.dummy_movq

								lowerCamelCase_   : str       =						DDIMScheduler(
								    num_train_timesteps=1_0_0_0     ,				beta_schedule='''linear'''     ,				beta_start=0.0_00_85     ,				beta_end=0.0_12     ,				clip_sample=A     ,				set_alpha_to_one=A     ,				steps_offset=1     ,				prediction_type='''epsilon'''     ,				thresholding=A     ,				)

								lowerCamelCase_   : int       =						{
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''movq''': movq,
								}
								return components

			def     UpperCAmelCase__	(self     ,				A     ,				A=0 ):
								lowerCamelCase_   : Union[str, Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(A ) ).to(A )
								lowerCamelCase_   : List[Any]       =						floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(seed + 1 ) ).to(A )
								if str(A ).startswith('''mps''' ):
													lowerCamelCase_   : List[str]       =						torch.manual_seed(A )
								else:
													lowerCamelCase_   : Optional[Any]       =						torch.Generator(device=A ).manual_seed(A )
								lowerCamelCase_   : Any       =						{
								    '''prompt''': '''horse''',
								    '''image_embeds''': image_embeds,
								    '''negative_image_embeds''': negative_image_embeds,
								    '''generator''': generator,
								    '''height''': 6_4,
								    '''width''': 6_4,
								    '''guidance_scale''': 4.0,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''np''',
								}
								return inputs






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : int       =						'''cpu'''

								lowerCamelCase_   : Optional[Any]       =						self.get_dummy_components()

								lowerCamelCase_   : Tuple       =						self.pipeline_class(**A )
								lowerCamelCase_   : List[str]       =						pipe.to(A )

								pipe.set_progress_bar_config(disable=A )

								lowerCamelCase_   : Optional[Any]       =						pipe(**self.get_dummy_inputs(A ) )
								lowerCamelCase_   : int       =						output.images

								lowerCamelCase_   : List[Any]       =						pipe(
								    **self.get_dummy_inputs(A )     ,				return_dict=A     ,				)[0]

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

								assert image.shape == (1, 6_4, 6_4, 3)

								lowerCamelCase_   : int       =						np.array(
								    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53] )

								assert (
								    np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
								assert (
								    np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
								), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""

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

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






			def     UpperCAmelCase__	(self ):
								lowerCamelCase_   : Tuple       =						load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/kandinsky/kandinsky_text2img_cat_fp16.npy''' )

								lowerCamelCase_   : str       =						KandinskyPriorPipeline.from_pretrained(
								    '''kandinsky-community/kandinsky-2-1-prior'''     ,				torch_dtype=torch.floataa )
								pipe_prior.to(A )

								lowerCamelCase_   : List[Any]       =						KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''     ,				torch_dtype=torch.floataa )
								lowerCamelCase_   : Tuple       =						pipeline.to(A )
								pipeline.set_progress_bar_config(disable=A )

								lowerCamelCase_   : List[str]       =						'''red cat, 4k photo'''

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_, lowerCamelCase_   : Any       =						pipe_prior(
								    A     ,				generator=A     ,				num_inference_steps=5     ,				negative_prompt=''''''     ,				).to_tuple()

								lowerCamelCase_   : Optional[Any]       =						torch.Generator(device='''cuda''' ).manual_seed(0 )
								lowerCamelCase_   : Optional[Any]       =						pipeline(
								    A     ,				image_embeds=A     ,				negative_image_embeds=A     ,				generator=A     ,				num_inference_steps=1_0_0     ,				output_type='''np'''     ,				)

								lowerCamelCase_   : Union[str, Any]       =						output.images[0]

								assert image.shape == (5_1_2, 5_1_2, 3)

								assert_mean_pixel_difference(A     ,				A )


