

'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, 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 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						KandinskyVaaControlnetImgaImgPipeline
				lowerCamelCase_							=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
				lowerCamelCase_							=						['image_embeds', 'negative_image_embeds', 'image', 'hint']
				lowerCamelCase_							=						[
				    'generator',
				    'height',
				    'width',
				    'strength',
				    'guidance_scale',
				    'num_inference_steps',
				    'return_dict',
				    'guidance_scale',
				    'num_images_per_prompt',
				    'output_type',
				    'return_dict',
				]
				lowerCamelCase_							=						False







				@property
				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										return 32







				@property
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										return 32







				@property
				def    lowerCamelCase_  (					self    :		Optional[Any]      ):
										'''simple docstring'''
										return self.time_input_dim







				@property
				def    lowerCamelCase_  (					self    :		str      ):
										'''simple docstring'''
										return self.time_input_dim * 4







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										return 100







				@property
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										torch.manual_seed(0      )

										lowercase   :       Union[str, Any]								={
										    '''in_channels''': 8,
										    # Out channels is double in channels because predicts mean and variance
										    '''out_channels''': 8,
										    '''addition_embed_type''': '''image_hint''',
										    '''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''': '''image_proj''',
										    '''cross_attention_dim''': self.cross_attention_dim,
										    '''attention_head_dim''': 4,
										    '''resnet_time_scale_shift''': '''scale_shift''',
										    '''class_embed_type''': None,
										}

										lowercase   :       Any								=UNetaDConditionModel(**UpperCAmelCase__      )
										return model







				@property
				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										return {
										    "block_out_channels": [32, 32, 64, 64],
										    "down_block_types": [
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										        "DownEncoderBlock2D",
										        "AttnDownEncoderBlock2D",
										    ],
										    "in_channels": 3,
										    "latent_channels": 4,
										    "layers_per_block": 1,
										    "norm_num_groups": 8,
										    "norm_type": "spatial",
										    "num_vq_embeddings": 12,
										    "out_channels": 3,
										    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
										    "vq_embed_dim": 4,
										}







				@property
				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										torch.manual_seed(0      )
										lowercase   :       str								=VQModel(**self.dummy_movq_kwargs      )
										return model







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       Dict								=self.dummy_unet
										lowercase   :       List[str]								=self.dummy_movq

										lowercase   :       str								={
										    '''num_train_timesteps''': 1000,
										    '''beta_schedule''': '''linear''',
										    '''beta_start''': 0.0_00_85,
										    '''beta_end''': 0.0_12,
										    '''clip_sample''': False,
										    '''set_alpha_to_one''': False,
										    '''steps_offset''': 0,
										    '''prediction_type''': '''epsilon''',
										    '''thresholding''': False,
										}

										lowercase   :       Union[str, Any]								=DDIMScheduler(**UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								={
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''movq''': movq,
										}

										return components







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				UpperCAmelCase__    :		Optional[Any]		,				UpperCAmelCase__    :		Any=0      ):
										'''simple docstring'''
										lowercase   :       str								=floats_tensor((1, self.text_embedder_hidden_size)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
										lowercase   :       List[str]								=floats_tensor((1, self.text_embedder_hidden_size)		,				rng=random.Random(seed + 1      )      ).to(
										    UpperCAmelCase__      )
										# create init_image
										lowercase   :       Union[str, Any]								=floats_tensor((1, 3, 64, 64)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )
										lowercase   :       str								=image.cpu().permute(0		,				2		,				3		,				1      )[0]
										lowercase   :       Union[str, Any]								=Image.fromarray(np.uinta(UpperCAmelCase__      )      ).convert('''RGB'''      ).resize((256, 256)      )
										# create hint
										lowercase   :       Dict								=floats_tensor((1, 3, 64, 64)		,				rng=random.Random(UpperCAmelCase__      )      ).to(UpperCAmelCase__      )

										if str(UpperCAmelCase__      ).startswith('''mps'''      ):
																lowercase   :       List[str]								=torch.manual_seed(UpperCAmelCase__      )
										else:
																lowercase   :       Optional[int]								=torch.Generator(device=UpperCAmelCase__      ).manual_seed(UpperCAmelCase__      )
										lowercase   :       int								={
										    '''image''': init_image,
										    '''image_embeds''': image_embeds,
										    '''negative_image_embeds''': negative_image_embeds,
										    '''hint''': hint,
										    '''generator''': generator,
										    '''height''': 64,
										    '''width''': 64,
										    '''num_inference_steps''': 10,
										    '''guidance_scale''': 7.0,
										    '''strength''': 0.2,
										    '''output_type''': '''np''',
										}
										return inputs







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       List[Any]								='''cpu'''

										lowercase   :       Any								=self.get_dummy_components()

										lowercase   :       List[Any]								=self.pipeline_class(**UpperCAmelCase__      )
										lowercase   :       Union[str, Any]								=pipe.to(UpperCAmelCase__      )

										pipe.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       List[str]								=pipe(**self.get_dummy_inputs(UpperCAmelCase__      )      )
										lowercase   :       List[str]								=output.images

										lowercase   :       Any								=pipe(
										    **self.get_dummy_inputs(UpperCAmelCase__      )		,				return_dict=UpperCAmelCase__		,				)[0]

										lowercase   :       Union[str, Any]								=image[0, -3:, -3:, -1]
										lowercase   :       str								=image_from_tuple[0, -3:, -3:, -1]

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

										lowercase   :       Tuple								=np.array(
										    [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]      )
										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 		__SCREAMING_SNAKE_CASE       ( unittest.TestCase			):







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







				def    lowerCamelCase_  (					self    :		Tuple      ):
										'''simple docstring'''
										lowercase   :       Any								=load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''      )

										lowercase   :       Union[str, Any]								=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
										lowercase   :       List[Any]								=init_image.resize((512, 512)      )

										lowercase   :       List[Any]								=load_image(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
										    '''/kandinskyv22/hint_image_cat.png'''      )
										lowercase   :       List[str]								=torch.from_numpy(np.array(UpperCAmelCase__      )      ).float() / 255.0
										lowercase   :       Any								=hint.permute(2		,				0		,				1      ).unsqueeze(0      )

										lowercase   :       Tuple								='''A robot, 4k photo'''

										lowercase   :       Optional[int]								=KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-prior'''		,				torch_dtype=torch.floataa      )
										pipe_prior.to(UpperCAmelCase__      )

										lowercase   :       str								=KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
										    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''		,				torch_dtype=torch.floataa      )
										lowercase   :       Union[str, Any]								=pipeline.to(UpperCAmelCase__      )

										pipeline.set_progress_bar_config(disable=UpperCAmelCase__      )

										lowercase   :       List[str]								=torch.Generator(device='''cpu'''      ).manual_seed(0      )

										lowercase   :       Tuple								=pipe_prior(
										    UpperCAmelCase__		,				image=UpperCAmelCase__		,				strength=0.85		,				generator=UpperCAmelCase__		,				negative_prompt=''''''		,				).to_tuple()

										lowercase   :       Optional[int]								=pipeline(
										    image=UpperCAmelCase__		,				image_embeds=UpperCAmelCase__		,				negative_image_embeds=UpperCAmelCase__		,				hint=UpperCAmelCase__		,				generator=UpperCAmelCase__		,				num_inference_steps=100		,				height=512		,				width=512		,				strength=0.5		,				output_type='''np'''		,				)

										lowercase   :       Any								=output.images[0]

										assert image.shape == (512, 512, 3)

										assert_mean_pixel_difference(UpperCAmelCase__		,				UpperCAmelCase__      )








'''simple docstring'''





import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 		__SCREAMING_SNAKE_CASE       ( lowercase__	,       unittest.TestCase			):
				lowerCamelCase_							=						None
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						BloomTokenizerFast
				lowerCamelCase_							=						True
				lowerCamelCase_							=						False
				lowerCamelCase_							=						'tokenizer_file'
				lowerCamelCase_							=						{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}







				def    lowerCamelCase_  (					self    :		List[str]      ):
										'''simple docstring'''
										super().setUp()
										lowercase   :       Union[str, Any]								=BloomTokenizerFast.from_pretrained('''bigscience/tokenizer'''      )
										tokenizer.save_pretrained(self.tmpdirname      )







				def    lowerCamelCase_  (					self    :		Union[str, Any]		,				**UpperCAmelCase__    :		Any      ):
										'''simple docstring'''
										kwargs.update(self.special_tokens_map      )
										return BloomTokenizerFast.from_pretrained(self.tmpdirname		,				**UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Any      ):
										'''simple docstring'''
										lowercase   :       str								=self.get_rust_tokenizer()

										lowercase   :       List[str]								=['''The quick brown fox</s>''', '''jumps over the lazy dog</s>''']
										lowercase   :       Any								=[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

										lowercase   :       Any								=tokenizer.batch_encode_plus(UpperCAmelCase__      )['''input_ids''']
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )

										lowercase   :       int								=tokenizer.batch_decode(UpperCAmelCase__      )
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		Tuple		,				UpperCAmelCase__    :		Any=6      ):
										'''simple docstring'''
										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																						lowercase   :       Optional[int]								=self.rust_tokenizer_class.from_pretrained(UpperCAmelCase__		,				**UpperCAmelCase__      )
																						# tokenizer_r.pad_token = None # Hotfixing padding = None
																						# Simple input
																						lowercase   :       Tuple								='''This is a simple input'''
																						lowercase   :       int								=['''This is a simple input 1''', '''This is a simple input 2''']
																						lowercase   :       Optional[Any]								=('''This is a simple input''', '''This is a pair''')
																						lowercase   :       int								=[
																						    ('''This is a simple input 1''', '''This is a simple input 2'''),
																						    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																						]

																						# Simple input tests
																						try:
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )

																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.encode(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																												tokenizer_r.batch_encode_plus(UpperCAmelCase__		,				max_length=UpperCAmelCase__      )
																						except ValueError:
																												self.fail('''Bloom Tokenizer should be able to deal with padding'''      )

																						lowercase   :       Optional[int]								=None  # Hotfixing padding = None
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Simple input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(UpperCAmelCase__		,				tokenizer_r.encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''      )

																						# Pair input
																						self.assertRaises(
																						    UpperCAmelCase__		,				tokenizer_r.batch_encode_plus		,				UpperCAmelCase__		,				max_length=UpperCAmelCase__		,				padding='''max_length'''		,				)







				def    lowerCamelCase_  (					self    :		Union[str, Any]      ):
										'''simple docstring'''
										lowercase   :       Union[str, Any]								=self.get_rust_tokenizer()
										lowercase   :       Dict								=load_dataset('''xnli'''		,				'''all_languages'''		,				split='''test'''		,				streaming=UpperCAmelCase__      )

										lowercase   :       Union[str, Any]								=next(iter(UpperCAmelCase__      )      )['''premise''']  # pick up one data
										lowercase   :       int								=list(sample_data.values()      )

										lowercase   :       Any								=list(map(tokenizer.encode		,				UpperCAmelCase__      )      )
										lowercase   :       List[str]								=[tokenizer.decode(UpperCAmelCase__		,				clean_up_tokenization_spaces=UpperCAmelCase__      ) for x in output_tokens]
										self.assertListEqual(UpperCAmelCase__		,				UpperCAmelCase__      )







				def    lowerCamelCase_  (					self    :		List[Any]      ):
										'''simple docstring'''
										# The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
										# any sequence length constraints. This test of the parent class will fail since it relies on the
										# maximum sequence length of the positoonal embeddings.
										self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map      )		,				1      )
										self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()      )[0]      )		,				1      )





