
'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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 			lowerCAmelCase__       (  lowerCamelCase_   ,						unittest.TestCase					):
					lowerCAmelCase_					       =					KandinskyImgaImgPipeline
					lowerCAmelCase_					       =					['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
					lowerCAmelCase_					       =					[
					    '''prompt''',
					    '''negative_prompt''',
					    '''image_embeds''',
					    '''negative_image_embeds''',
					    '''image''',
					]
					lowerCAmelCase_					       =					[
					    '''generator''',
					    '''height''',
					    '''width''',
					    '''strength''',
					    '''guidance_scale''',
					    '''negative_prompt''',
					    '''num_inference_steps''',
					    '''return_dict''',
					    '''guidance_scale''',
					    '''num_images_per_prompt''',
					    '''output_type''',
					    '''return_dict''',
					]
					lowerCAmelCase_					       =					False






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return 32






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return 32






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return self.time_input_dim






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return self.time_input_dim * 4






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return 1_00






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''		)
											return tokenizer






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_     :					Optional[Any]													=	MCLIPConfig(
											    numDims=self.cross_attention_dim    ,							transformerDimensions=self.text_embedder_hidden_size    ,							hidden_size=self.text_embedder_hidden_size    ,							intermediate_size=37    ,							num_attention_heads=4    ,							num_hidden_layers=5    ,							vocab_size=10_05    ,							)

											lowercase_     :					List[Any]													=	MultilingualCLIP(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Union[str, Any]													=	text_encoder.eval()

											return text_encoder






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											torch.manual_seed(0		)

											lowercase_     :					Any													=	{
											    '''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,
											}

											lowercase_     :					int													=	UNetaDConditionModel(**__SCREAMING_SNAKE_CASE		)
											return model






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											return {
											    "block_out_channels": [32, 64],
											    "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": 12,
											    "out_channels": 3,
											    "up_block_types": [
											        "AttnUpDecoderBlock2D",
											        "UpDecoderBlock2D",
											    ],
											    "vq_embed_dim": 4,
											}






					@property
					def      _snake_case   (       self		):


											"""simple docstring"""





											torch.manual_seed(0		)
											lowercase_     :					Optional[Any]													=	VQModel(**self.dummy_movq_kwargs		)
											return model






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.dummy_text_encoder
											lowercase_     :					Optional[int]													=	self.dummy_tokenizer
											lowercase_     :					int													=	self.dummy_unet
											lowercase_     :					Any													=	self.dummy_movq

											lowercase_     :					Dict													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_schedule''': '''linear''',
											    '''beta_start''': 0.00_085,
											    '''beta_end''': 0.012,
											    '''clip_sample''': False,
											    '''set_alpha_to_one''': False,
											    '''steps_offset''': 0,
											    '''prediction_type''': '''epsilon''',
											    '''thresholding''': False,
											}

											lowercase_     :					Dict													=	DDIMScheduler(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	{
											    '''text_encoder''': text_encoder,
											    '''tokenizer''': tokenizer,
											    '''unet''': unet,
											    '''scheduler''': scheduler,
											    '''movq''': movq,
											}

											return components






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=0		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	floats_tensor((1, self.cross_attention_dim)    ,							rng=random.Random(__SCREAMING_SNAKE_CASE		)		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					List[str]													=	floats_tensor((1, self.cross_attention_dim)    ,							rng=random.Random(seed + 1		)		).to(__SCREAMING_SNAKE_CASE		)
											# create init_image
											lowercase_     :					Optional[Any]													=	floats_tensor((1, 3, 64, 64)    ,							rng=random.Random(__SCREAMING_SNAKE_CASE		)		).to(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Any													=	image.cpu().permute(0    ,							2    ,							3    ,							1		)[0]
											lowercase_     :					Tuple													=	Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE		)		).convert('''RGB'''		).resize((2_56, 2_56)		)

											if str(__SCREAMING_SNAKE_CASE		).startswith('''mps'''		):
																	lowercase_     :					List[Any]													=	torch.manual_seed(__SCREAMING_SNAKE_CASE		)
											else:
																	lowercase_     :					Tuple													=	torch.Generator(device=__SCREAMING_SNAKE_CASE		).manual_seed(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					str													=	{
											    '''prompt''': '''horse''',
											    '''image''': init_image,
											    '''image_embeds''': image_embeds,
											    '''negative_image_embeds''': negative_image_embeds,
											    '''generator''': generator,
											    '''height''': 64,
											    '''width''': 64,
											    '''num_inference_steps''': 10,
											    '''guidance_scale''': 7.0,
											    '''strength''': 0.2,
											    '''output_type''': '''np''',
											}
											return inputs






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	'''cpu'''

											lowercase_     :					Optional[int]													=	self.get_dummy_components()

											lowercase_     :					List[Any]													=	self.pipeline_class(**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Dict													=	pipe.to(__SCREAMING_SNAKE_CASE		)

											pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Union[str, Any]													=	output.images

											lowercase_     :					Tuple													=	pipe(
											    **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE		)    ,							return_dict=__SCREAMING_SNAKE_CASE    ,							)[0]

											lowercase_     :					str													=	image[0, -3:, -3:, -1]
											lowercase_     :					Optional[int]													=	image_from_tuple[0, -3:, -3:, -1]

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

											lowercase_     :					Any													=	np.array(
											    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]		)
											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 			lowerCAmelCase__       (  unittest.TestCase					):






					def      _snake_case   (       self		):


											"""simple docstring"""





											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	load_numpy(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
											    '''/kandinsky/kandinsky_img2img_frog.npy'''		)

											lowercase_     :					Optional[Any]													=	load_image(
											    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''		)
											lowercase_     :					Union[str, Any]													=	'''A red cartoon frog, 4k'''

											lowercase_     :					Optional[int]													=	KandinskyPriorPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1-prior'''    ,							torch_dtype=torch.floataa		)
											pipe_prior.to(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	KandinskyImgaImgPipeline.from_pretrained(
											    '''kandinsky-community/kandinsky-2-1'''    ,							torch_dtype=torch.floataa		)
											lowercase_     :					List[str]													=	pipeline.to(__SCREAMING_SNAKE_CASE		)

											pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	torch.Generator(device='''cpu'''		).manual_seed(0		)
											lowercase_     :					Tuple													=	pipe_prior(
											    __SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE    ,							num_inference_steps=5    ,							negative_prompt=''''''    ,							).to_tuple()

											lowercase_     :					Dict													=	pipeline(
											    __SCREAMING_SNAKE_CASE    ,							image=__SCREAMING_SNAKE_CASE    ,							image_embeds=__SCREAMING_SNAKE_CASE    ,							negative_image_embeds=__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE    ,							num_inference_steps=1_00    ,							height=7_68    ,							width=7_68    ,							strength=0.2    ,							output_type='''np'''    ,							)

											lowercase_     :					List[Any]													=	output.images[0]

											assert image.shape == (7_68, 7_68, 3)

											assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)


'''simple docstring'''







import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_lowercase			:  Tuple   							=	logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__					)
class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					field(default=lowerCamelCase_   ,						metadata={'''help''': '''Whether to use SortishSampler or not.'''}					)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}					)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={
					        '''help''': (
					            '''The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
					            '''to the `max_length` value of the model configuration.'''
					        )
					    }   ,						)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={
					        '''help''': (
					            '''The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default '''
					            '''to the `num_beams` value of the model configuration.'''
					        )
					    }   ,						)
					lowerCAmelCase_					       =					field(
					    default=lowerCamelCase_   ,						metadata={
					        '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
					    }   ,						)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Dict													=	super().to_dict()
											for k, v in d.items():
																	if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																							lowercase_     :					str													=	v.to_dict()
											return d



