"""simple docstring"""




import os
import warnings
from pathlib import Path
from typing import Optional, Union

from .dynamic_module_utils import custom_object_save
from .tokenization_utils_base import PreTrainedTokenizerBase
from .utils import PushToHubMixin, copy_func, direct_transformers_import, logging


__lowerCamelCase									=	logging.get_logger(__name__)

# Dynamically import the Transformers module to grab the attribute classes of the processor form their names.
__lowerCamelCase									=	direct_transformers_import(Path(__file__).parent)


__lowerCamelCase									=	{
    "AutoTokenizer": "PreTrainedTokenizerBase",
    "AutoFeatureExtractor": "FeatureExtractionMixin",
    "AutoImageProcessor": "ImageProcessingMixin",
}
class      UpperCamelCase__(      __A			):
						lowerCAmelCase__	:    int    				=		['feature_extractor', 'tokenizer']
						# Names need to be attr_class for attr in attributes
						lowerCAmelCase__	:    int    				=		None
						lowerCAmelCase__	:    Optional[Any]    				=		None
						lowerCAmelCase__	:    Union[str, Any]    				=		None





						def __init__(							self		,*__UpperCAmelCase		,**__UpperCAmelCase  )       ->					Optional[Any]:
													# Sanitize args and kwargs
													for key in kwargs:
																				if key not in self.attributes:
																											raise TypeError(f'''Unexpected keyword argument {key}.'''  )
													for arg, attribute_name in zip(__UpperCAmelCase		,self.attributes  ):
																				if attribute_name in kwargs:
																											raise TypeError(f'''Got multiple values for argument {attribute_name}.'''  )
																				else:
																											A__						  =				arg

													if len(__UpperCAmelCase  ) != len(self.attributes  ):
																				raise ValueError(
																				    f'''This processor requires {len(self.attributes  )} arguments: {", ".join(self.attributes  )}. Got '''
																				    f'''{len(__UpperCAmelCase  )} arguments instead.'''  )

													# Check each arg is of the proper class (this will also catch a user initializing in the wrong order)
													for attribute_name, arg in kwargs.items():
																				A__						  =				getattr(self		,f'''{attribute_name}_class'''  )
																				# Nothing is ever going to be an instance of "AutoXxx", in that case we check the base class.
																				A__						  =				AUTO_TO_BASE_CLASS_MAPPING.get(__UpperCAmelCase		,__UpperCAmelCase  )
																				if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																											A__						  =				tuple(getattr(__UpperCAmelCase		,__UpperCAmelCase  ) for n in class_name if n is not None  )
																				else:
																											A__						  =				getattr(__UpperCAmelCase		,__UpperCAmelCase  )

																				if not isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																											raise ValueError(
																											    f'''Received a {type(__UpperCAmelCase  ).__name__} for argument {attribute_name}, but a {class_name} was expected.'''  )

																				setattr(self		,__UpperCAmelCase		,__UpperCAmelCase  )





						def __repr__(							self  )       ->					Optional[Any]:
													A__						  =				[f'''- {name}: {repr(getattr(self		,__UpperCAmelCase  )  )}''' for name in self.attributes]
													A__						  =				'\n'.join(__UpperCAmelCase  )
													return f'''{self.__class__.__name__}:\n{attributes_repr}'''





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase = False		,**__UpperCAmelCase  )       ->					Optional[int]:
													os.makedirs(__UpperCAmelCase		,exist_ok=__UpperCAmelCase  )

													if push_to_hub:
																				A__						  =				kwargs.pop('commit_message'		,__UpperCAmelCase  )
																				A__						  =				kwargs.pop('repo_id'		,save_directory.split(os.path.sep  )[-1]  )
																				A__						  =				self._create_repo(__UpperCAmelCase		,**__UpperCAmelCase  )
																				A__						  =				self._get_files_timestamps(__UpperCAmelCase  )
													# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
													# loaded from the Hub.
													if self._auto_class is not None:
																				A__						  =				[getattr(self		,__UpperCAmelCase  ) for attribute_name in self.attributes]
																				A__						  =				[(a.init_kwargs if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ) else a) for a in attrs]
																				custom_object_save(self		,__UpperCAmelCase		,config=__UpperCAmelCase  )

													for attribute_name in self.attributes:
																				A__						  =				getattr(self		,__UpperCAmelCase  )
																				# Include the processor class in the attribute config so this processor can then be reloaded with the
																				# `AutoProcessor` API.
																				if hasattr(__UpperCAmelCase		,'_set_processor_class'  ):
																											attribute._set_processor_class(self.__class__.__name__  )
																				attribute.save_pretrained(__UpperCAmelCase  )

													if self._auto_class is not None:
																				# We added an attribute to the init_kwargs of the tokenizers, which needs to be cleaned up.
																				for attribute_name in self.attributes:
																											A__						  =				getattr(self		,__UpperCAmelCase  )
																											if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																																		del attribute.init_kwargs["auto_map"]

													if push_to_hub:
																				self._upload_modified_files(
																				    __UpperCAmelCase		,__UpperCAmelCase		,__UpperCAmelCase		,commit_message=__UpperCAmelCase		,token=kwargs.get('use_auth_token'  )		,)





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,__UpperCAmelCase = None		,__UpperCAmelCase = False		,__UpperCAmelCase = False		,__UpperCAmelCase = None		,__UpperCAmelCase = "main"		,**__UpperCAmelCase		,)       ->					Tuple:
													A__						  =				cache_dir
													A__						  =				force_download
													A__						  =				local_files_only
													A__						  =				revision

													A__						  =				kwargs.pop('use_auth_token'		,__UpperCAmelCase  )
													if use_auth_token is not None:
																				warnings.warn(
																				    'The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers.'		,__UpperCAmelCase  )
																				if token is not None:
																											raise ValueError(
																											    '`token` and `use_auth_token` are both specified. Please set only the argument `token`.'  )
																				A__						  =				use_auth_token

													if token is not None:
																				# change to `token` in a follow-up PR
																				A__						  =				token

													A__						  =				cls._get_arguments_from_pretrained(__UpperCAmelCase		,**__UpperCAmelCase  )
													return cls(*__UpperCAmelCase  )





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase="AutoProcessor"  )       ->					int:
													if not isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																				A__						  =				auto_class.__name__

													import transformers.models.auto as auto_module

													if not hasattr(__UpperCAmelCase		,__UpperCAmelCase  ):
																				raise ValueError(f'''{auto_class} is not a valid auto class.'''  )

													A__						  =				auto_class





						@classmethod
						def 					snake_case__						(							cls		,__UpperCAmelCase		,**__UpperCAmelCase  )       ->					str:
													A__						  =				[]
													for attribute_name in cls.attributes:
																				A__						  =				getattr(cls		,f'''{attribute_name}_class'''  )
																				if isinstance(__UpperCAmelCase		,__UpperCAmelCase  ):
																											A__						  =				tuple(getattr(__UpperCAmelCase		,__UpperCAmelCase  ) if n is not None else None for n in class_name  )
																											A__						  =				kwargs.get('use_fast'		,__UpperCAmelCase  )
																											if use_fast and classes[1] is not None:
																																		A__						  =				classes[1]
																											else:
																																		A__						  =				classes[0]
																				else:
																											A__						  =				getattr(__UpperCAmelCase		,__UpperCAmelCase  )

																				args.append(attribute_class.from_pretrained(__UpperCAmelCase		,**__UpperCAmelCase  )  )
													return args





						@property
						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				getattr(self		,self.attributes[0]  )
													return getattr(__UpperCAmelCase		,'model_input_names'		,__UpperCAmelCase  )






__lowerCamelCase									=	copy_func(ProcessorMixin.push_to_hub)
if ProcessorMixin.push_to_hub.__doc__ is not None:
				__lowerCamelCase									=	ProcessorMixin.push_to_hub.__doc__.format(
				    object="processor", object_class="AutoProcessor", object_files="processor files"
				)






"""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      UpperCamelCase__(      __A		,    unittest.TestCase			):
						lowerCAmelCase__	:    Tuple    				=		KandinskyPipeline
						lowerCAmelCase__	:    int    				=		[
						    'prompt',
						    'image_embeds',
						    'negative_image_embeds',
						]
						lowerCAmelCase__	:    str    				=		['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
						lowerCAmelCase__	:    Any    				=		[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						lowerCAmelCase__	:    Dict    				=		False





						@property
						def 					snake_case__						(							self  )       ->					List[Any]:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Any:
													return 32





						@property
						def 					snake_case__						(							self  )       ->					Dict:
													return self.time_input_dim





						@property
						def 					snake_case__						(							self  )       ->					Optional[Any]:
													return self.time_input_dim * 4





						@property
						def 					snake_case__						(							self  )       ->					List[str]:
													return 1_00





						@property
						def 					snake_case__						(							self  )       ->					Any:
													A__						  =				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
													return tokenizer





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													torch.manual_seed(0  )
													A__						  =				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		,)

													A__						  =				MultilingualCLIP(__UpperCAmelCase  )
													A__						  =				text_encoder.eval()

													return text_encoder





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

													A__						  =				{
													    '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,
													}

													A__						  =				UNetaDConditionModel(**__UpperCAmelCase  )
													return model





						@property
						def 					snake_case__						(							self  )       ->					Tuple:
													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  )       ->					Any:
													torch.manual_seed(0  )
													A__						  =				VQModel(**self.dummy_movq_kwargs  )
													return model





						def 					snake_case__						(							self  )       ->					Optional[int]:
													A__						  =				self.dummy_text_encoder
													A__						  =				self.dummy_tokenizer
													A__						  =				self.dummy_unet
													A__						  =				self.dummy_movq

													A__						  =				DDIMScheduler(
													    num_train_timesteps=10_00		,beta_schedule='linear'		,beta_start=0.0_0_0_8_5		,beta_end=0.0_1_2		,clip_sample=__UpperCAmelCase		,set_alpha_to_one=__UpperCAmelCase		,steps_offset=1		,prediction_type='epsilon'		,thresholding=__UpperCAmelCase		,)

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





						def 					snake_case__						(							self		,__UpperCAmelCase		,__UpperCAmelCase=0  )       ->					Optional[Any]:
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(__UpperCAmelCase  )  ).to(__UpperCAmelCase  )
													A__						  =				floats_tensor((1, self.cross_attention_dim)		,rng=random.Random(seed + 1  )  ).to(__UpperCAmelCase  )
													if str(__UpperCAmelCase  ).startswith('mps'  ):
																				A__						  =				torch.manual_seed(__UpperCAmelCase  )
													else:
																				A__						  =				torch.Generator(device=__UpperCAmelCase  ).manual_seed(__UpperCAmelCase  )
													A__						  =				{
													    'prompt': 'horse',
													    'image_embeds': image_embeds,
													    'negative_image_embeds': negative_image_embeds,
													    'generator': generator,
													    'height': 64,
													    'width': 64,
													    'guidance_scale': 4.0,
													    'num_inference_steps': 2,
													    'output_type': 'np',
													}
													return inputs





						def 					snake_case__						(							self  )       ->					Optional[Any]:
													A__						  =				'cpu'

													A__						  =				self.get_dummy_components()

													A__						  =				self.pipeline_class(**__UpperCAmelCase  )
													A__						  =				pipe.to(__UpperCAmelCase  )

													pipe.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				pipe(**self.get_dummy_inputs(__UpperCAmelCase  )  )
													A__						  =				output.images

													A__						  =				pipe(
													    **self.get_dummy_inputs(__UpperCAmelCase  )		,return_dict=__UpperCAmelCase		,)[0]

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

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

													A__						  =				np.array(
													    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]  )

													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      UpperCamelCase__(      unittest.TestCase			):





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





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

													A__						  =				KandinskyPriorPipeline.from_pretrained(
													    'kandinsky-community/kandinsky-2-1-prior'		,torch_dtype=torch.floataa  )
													pipe_prior.to(__UpperCAmelCase  )

													A__						  =				KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'		,torch_dtype=torch.floataa  )
													A__						  =				pipeline.to(__UpperCAmelCase  )
													pipeline.set_progress_bar_config(disable=__UpperCAmelCase  )

													A__						  =				'red cat, 4k photo'

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__  ,	A__						  =				pipe_prior(
													    __UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=5		,negative_prompt=''		,).to_tuple()

													A__						  =				torch.Generator(device='cuda'  ).manual_seed(0  )
													A__						  =				pipeline(
													    __UpperCAmelCase		,image_embeds=__UpperCAmelCase		,negative_image_embeds=__UpperCAmelCase		,generator=__UpperCAmelCase		,num_inference_steps=1_00		,output_type='np'		,)

													A__						  =				output.images[0]

													assert image.shape == (5_12, 5_12, 3)

													assert_mean_pixel_difference(__UpperCAmelCase		,__UpperCAmelCase  )





