






'''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       (       SCREAMING_SNAKE_CASE_						, unittest.TestCase							):
			snake_case__	=					KandinskyVaaControlnetImgaImgPipeline
			snake_case__	=					["image_embeds", "negative_image_embeds", "image", "hint"]
			snake_case__	=					["image_embeds", "negative_image_embeds", "image", "hint"]
			snake_case__	=					[
			    "generator",
			    "height",
			    "width",
			    "strength",
			    "guidance_scale",
			    "num_inference_steps",
			    "return_dict",
			    "guidance_scale",
			    "num_images_per_prompt",
			    "output_type",
			    "return_dict",
			]
			snake_case__	=					False






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Tuple:
							return 32






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






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							return self.time_input_dim






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      List[Any]:
							return self.time_input_dim * 4






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Dict       )      ->      List[Any]:
							return 100






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      str:
							torch.manual_seed(0       )

							a_		:	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,
							}

							a_		:	Tuple									=   UNetaDConditionModel(**__SCREAMING_SNAKE_CASE       )
							return model






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      Tuple:
							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       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      List[str]:
							torch.manual_seed(0       )
							a_		:	str									=   VQModel(**self.dummy_movq_kwargs       )
							return model






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      Union[str, Any]:
							a_		:	List[str]									=   self.dummy_unet
							a_		:	Optional[Any]									=   self.dummy_movq

							a_		:	Union[str, Any]									=   {
							    '''num_train_timesteps''': 1000,
							    '''beta_schedule''': '''linear''',
							    '''beta_start''': 0.0_0085,
							    '''beta_end''': 0.012,
							    '''clip_sample''': False,
							    '''set_alpha_to_one''': False,
							    '''steps_offset''': 0,
							    '''prediction_type''': '''epsilon''',
							    '''thresholding''': False,
							}

							a_		:	Dict									=   DDIMScheduler(**__SCREAMING_SNAKE_CASE       )

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

							return components






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

							if str(__SCREAMING_SNAKE_CASE       ).startswith('''mps'''       ):
											a_		:	Dict									=   torch.manual_seed(__SCREAMING_SNAKE_CASE       )
							else:
											a_		:	Tuple									=   torch.Generator(device=__SCREAMING_SNAKE_CASE       ).manual_seed(__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   {
							    '''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       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      str:
							a_		:	List[Any]									=   '''cpu'''

							a_		:	Dict									=   self.get_dummy_components()

							a_		:	Tuple									=   self.pipeline_class(**__SCREAMING_SNAKE_CASE       )
							a_		:	List[Any]									=   pipe.to(__SCREAMING_SNAKE_CASE       )

							pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

							a_		:	Any									=   pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )       )
							a_		:	Optional[Any]									=   output.images

							a_		:	Optional[Any]									=   pipe(
							    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE       )	,    return_dict=__SCREAMING_SNAKE_CASE	,    )[0]

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

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

							a_		:	str									=   np.array(
							    [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]       )
							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       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()







			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      List[Any]:
							a_		:	str									=   load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''       )

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

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

							a_		:	Optional[int]									=   '''A robot, 4k photo'''

							a_		:	Optional[Any]									=   KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-prior'''	,    torch_dtype=torch.floataa       )
							pipe_prior.to(__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''	,    torch_dtype=torch.floataa       )
							a_		:	int									=   pipeline.to(__SCREAMING_SNAKE_CASE       )

							pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

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

							a_					,      a_		:	Optional[int]									=   pipe_prior(
							    __SCREAMING_SNAKE_CASE	,    image=__SCREAMING_SNAKE_CASE	,    strength=0.85	,    generator=__SCREAMING_SNAKE_CASE	,    negative_prompt=''''''	,    ).to_tuple()

							a_		:	List[Any]									=   pipeline(
							    image=__SCREAMING_SNAKE_CASE	,    image_embeds=__SCREAMING_SNAKE_CASE	,    negative_image_embeds=__SCREAMING_SNAKE_CASE	,    hint=__SCREAMING_SNAKE_CASE	,    generator=__SCREAMING_SNAKE_CASE	,    num_inference_steps=100	,    height=512	,    width=512	,    strength=0.5	,    output_type='''np'''	,    )

							a_		:	str									=   output.images[0]

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

							assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )












'''simple docstring'''







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
		import torch


if is_vision_available():
		from PIL import Image

		from transformers import MgpstrProcessor, ViTImageProcessor





@require_torch
@require_vision
class 						SCREAMING_SNAKE_CASE       (       unittest.TestCase							):
			snake_case__	=					ViTImageProcessor if is_vision_available() else None






			@property
			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      List[str]:
							return self.image_processor_tester.prepare_image_processor_dict()






			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Optional[int]:
							a_		:	Optional[Any]									=   (3, 32, 128)
							a_		:	Optional[Any]									=   tempfile.mkdtemp()

							# fmt: off
							a_		:	Union[str, Any]									=   ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
							# fmt: on
							a_		:	List[Any]									=   dict(zip(__SCREAMING_SNAKE_CASE	,    range(len(__SCREAMING_SNAKE_CASE       )       )       )       )

							a_		:	Union[str, Any]									=   os.path.join(self.tmpdirname	,    VOCAB_FILES_NAMES['''vocab_file''']       )
							with open(self.vocab_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											fp.write(json.dumps(__SCREAMING_SNAKE_CASE       ) + '''\n'''       )

							a_		:	Union[str, Any]									=   {
							    '''do_normalize''': False,
							    '''do_resize''': True,
							    '''image_processor_type''': '''ViTImageProcessor''',
							    '''resample''': 3,
							    '''size''': {'''height''': 32, '''width''': 128},
							}
							a_		:	Any									=   os.path.join(self.tmpdirname	,    __SCREAMING_SNAKE_CASE       )
							with open(self.image_processor_file	,    '''w'''	,    encoding='''utf-8'''       ) as fp:
											json.dump(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[Any]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      Dict:
							return MgpstrTokenizer.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]	,    **__SCREAMING_SNAKE_CASE   :      Union[str, Any]       )      ->      List[Any]:
							return ViTImageProcessor.from_pretrained(self.tmpdirname	,    **__SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[Any]       )      ->      Union[str, Any]:
							shutil.rmtree(self.tmpdirname       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      str:
							a_		:	Optional[Any]									=   np.random.randint(255	,    size=(3, 30, 400)	,    dtype=np.uinta       )

							a_		:	Optional[int]									=   Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE	,    0	,    -1       )       )

							return image_input






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	str									=   self.get_tokenizer()
							a_		:	List[Any]									=   self.get_image_processor()

							a_		:	Union[str, Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )
							a_		:	Tuple									=   MgpstrProcessor.from_pretrained(self.tmpdirname	,    use_fast=__SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Optional[int]       )      ->      int:
							a_		:	Any									=   self.get_tokenizer()
							a_		:	Dict									=   self.get_image_processor()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )
							processor.save_pretrained(self.tmpdirname       )

							a_		:	str									=   self.get_tokenizer(bos_token='''(BOS)'''	,    eos_token='''(EOS)'''       )
							a_		:	str									=   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							a_		:	Optional[int]									=   MgpstrProcessor.from_pretrained(
							    self.tmpdirname	,    bos_token='''(BOS)'''	,    eos_token='''(EOS)'''	,    do_normalize=__SCREAMING_SNAKE_CASE	,    padding_value=1.0       )

							self.assertEqual(processor.char_tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()       )
							self.assertIsInstance(processor.char_tokenizer	,    __SCREAMING_SNAKE_CASE       )

							self.assertEqual(processor.image_processor.to_json_string()	,    image_processor_add_kwargs.to_json_string()       )
							self.assertIsInstance(processor.image_processor	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[Any]									=   self.prepare_image_inputs()

							a_		:	List[Any]									=   image_processor(__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )
							a_		:	int									=   processor(images=__SCREAMING_SNAKE_CASE	,    return_tensors='''np'''       )

							for key in input_image_proc.keys():
											self.assertAlmostEqual(input_image_proc[key].sum()	,    input_processor[key].sum()	,    delta=1e-2       )






			def       SCREAMING_SNAKE_CASE   (			self   :      str       )      ->      Optional[Any]:
							a_		:	Dict									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	int									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   '''test'''

							a_		:	Any									=   processor(text=__SCREAMING_SNAKE_CASE       )

							a_		:	Optional[int]									=   tokenizer(__SCREAMING_SNAKE_CASE       )
							for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]       )






			def       SCREAMING_SNAKE_CASE   (			self   :      int       )      ->      int:
							a_		:	Union[str, Any]									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Optional[Any]									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Union[str, Any]									=   '''test'''
							a_		:	str									=   self.prepare_image_inputs()

							a_		:	Tuple									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

							self.assertListEqual(list(inputs.keys()       )	,    ['''pixel_values''', '''labels''']       )

							# test if it raises when no input is passed
							with pytest.raises(__SCREAMING_SNAKE_CASE       ):
											processor()






			def       SCREAMING_SNAKE_CASE   (			self   :      List[str]       )      ->      int:
							a_		:	int									=   self.get_image_processor()
							a_		:	List[Any]									=   self.get_tokenizer()

							a_		:	Dict									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Tuple									=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

							a_		:	List[Any]									=   processor.char_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Optional[int]									=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   [seq.replace(''' '''	,    ''''''       ) for seq in decoded_tok]

							self.assertListEqual(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )






			def       SCREAMING_SNAKE_CASE   (			self   :      Any       )      ->      List[Any]:
							a_		:	int									=   self.get_image_processor()
							a_		:	Tuple									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   None
							a_		:	Tuple									=   self.prepare_image_inputs()

							a_		:	Optional[int]									=   processor(text=__SCREAMING_SNAKE_CASE	,    images=__SCREAMING_SNAKE_CASE       )

							self.assertListEqual(list(inputs.keys()       )	,    processor.model_input_names       )







			def       SCREAMING_SNAKE_CASE   (			self   :      Tuple       )      ->      Union[str, Any]:
							a_		:	List[str]									=   self.get_image_processor()
							a_		:	str									=   self.get_tokenizer()

							a_		:	Any									=   MgpstrProcessor(tokenizer=__SCREAMING_SNAKE_CASE	,    image_processor=__SCREAMING_SNAKE_CASE       )

							a_		:	str									=   torch.randn(1	,    27	,    38       )
							a_		:	int									=   torch.randn(1	,    27	,    5_0257       )
							a_		:	Union[str, Any]									=   torch.randn(1	,    27	,    3_0522       )

							a_		:	Tuple									=   processor.batch_decode([char_input, bpe_input, wp_input]       )

							self.assertListEqual(list(results.keys()       )	,    ['''generated_text''', '''scores''', '''char_preds''', '''bpe_preds''', '''wp_preds''']       )




