


'''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_						(_UpperCAmelCase      ,	unittest.TestCase						):





    """simple docstring"""






    lowerCamelCase						:      Dict							  =    KandinskyPipeline
    lowerCamelCase						:      Tuple							  =    [
        'prompt',
        'image_embeds',
        'negative_image_embeds',
    ]
    lowerCamelCase						:      Optional[Any]							  =    ['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
    lowerCamelCase						:      Tuple							  =    [
        '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    lowercase_       (							self			)       ->  int:
         return 32







    @property
    def    lowercase_       (							self			)       ->  Dict:
         return 32







    @property
    def    lowercase_       (							self			)       ->  str:
         return self.time_input_dim







    @property
    def    lowercase_       (							self			)       ->  Tuple:
         return self.time_input_dim * 4







    @property
    def    lowercase_       (							self			)       ->  Dict:
         return 1_00







    @property
    def    lowercase_       (							self			)       ->  str:
         __lowerCamelCase     :			int              =	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'			)
         return tokenizer







    @property
    def    lowercase_       (							self			)       ->  int:
         torch.manual_seed(0			)
         __lowerCamelCase     :			int              =	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    ,  )

         __lowerCamelCase     :			Any              =	MultilingualCLIP(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			str              =	text_encoder.eval()

         return text_encoder







    @property
    def    lowercase_       (							self			)       ->  Union[str, Any]:
         torch.manual_seed(0			)

         __lowerCamelCase     :			Optional[int]              =	{
             '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     :			str              =	UNetaDConditionModel(**SCREAMING_SNAKE_CASE_			)
         return model







    @property
    def    lowercase_       (							self			)       ->  List[str]:
         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    lowercase_       (							self			)       ->  Any:
         torch.manual_seed(0			)
         __lowerCamelCase     :			Optional[int]              =	VQModel(**self.dummy_movq_kwargs			)
         return model







    def    lowercase_       (							self			)       ->  Tuple:
         __lowerCamelCase     :			List[str]              =	self.dummy_text_encoder
         __lowerCamelCase     :			int              =	self.dummy_tokenizer
         __lowerCamelCase     :			List[str]              =	self.dummy_unet
         __lowerCamelCase     :			Dict              =	self.dummy_movq

         __lowerCamelCase     :			Optional[Any]              =	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=SCREAMING_SNAKE_CASE_    ,  set_alpha_to_one=SCREAMING_SNAKE_CASE_    ,  steps_offset=1    ,  prediction_type='epsilon'    ,  thresholding=SCREAMING_SNAKE_CASE_    ,  )

         __lowerCamelCase     :			List[str]              =	{
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }
         return components







    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_=0			)       ->  Dict:
         __lowerCamelCase     :			Any              =	floats_tensor((1, self.cross_attention_dim)    ,  rng=random.Random(SCREAMING_SNAKE_CASE_			)			).to(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Optional[int]              =	floats_tensor((1, self.cross_attention_dim)    ,  rng=random.Random(seed + 1			)			).to(SCREAMING_SNAKE_CASE_			)
         if str(SCREAMING_SNAKE_CASE_			).startswith('mps'			):
              __lowerCamelCase     :			Tuple              =	torch.manual_seed(SCREAMING_SNAKE_CASE_			)
         else:
              __lowerCamelCase     :			str              =	torch.Generator(device=SCREAMING_SNAKE_CASE_			).manual_seed(SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Optional[Any]              =	{
             '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    lowercase_       (							self			)       ->  Optional[int]:
         __lowerCamelCase     :			Any              =	'cpu'

         __lowerCamelCase     :			List[str]              =	self.get_dummy_components()

         __lowerCamelCase     :			List[str]              =	self.pipeline_class(**SCREAMING_SNAKE_CASE_			)
         __lowerCamelCase     :			Tuple              =	pipe.to(SCREAMING_SNAKE_CASE_			)

         pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			str              =	pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)			)
         __lowerCamelCase     :			List[str]              =	output.images

         __lowerCamelCase     :			Optional[int]              =	pipe(
             **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_			)    ,  return_dict=SCREAMING_SNAKE_CASE_    ,  )[0]

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

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

         __lowerCamelCase     :			Tuple              =	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						):





    """simple docstring"""






    def    lowercase_       (							self			)       ->  List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







    def    lowercase_       (							self			)       ->  Dict:
         __lowerCamelCase     :			str              =	load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinsky/kandinsky_text2img_cat_fp16.npy'			)

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

         __lowerCamelCase     :			Dict              =	KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'    ,  torch_dtype=torch.floataa			)
         __lowerCamelCase     :			int              =	pipeline.to(SCREAMING_SNAKE_CASE_			)
         pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_			)

         __lowerCamelCase     :			Optional[int]              =	'red cat, 4k photo'

         __lowerCamelCase     :			Any              =	torch.Generator(device='cuda'			).manual_seed(0			)
         __lowerCamelCase       , __lowerCamelCase     :			int              =	pipe_prior(
             SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_    ,  num_inference_steps=5    ,  negative_prompt=''    ,  ).to_tuple()

         __lowerCamelCase     :			Tuple              =	torch.Generator(device='cuda'			).manual_seed(0			)
         __lowerCamelCase     :			Tuple              =	pipeline(
             SCREAMING_SNAKE_CASE_    ,  image_embeds=SCREAMING_SNAKE_CASE_    ,  negative_image_embeds=SCREAMING_SNAKE_CASE_    ,  generator=SCREAMING_SNAKE_CASE_    ,  num_inference_steps=1_00    ,  output_type='np'    ,  )

         __lowerCamelCase     :			Optional[int]              =	output.images[0]

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

         assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_    ,  SCREAMING_SNAKE_CASE_			)










'''simple docstring'''



from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__	:      Any				=						logging.get_logger(__name__)

A__	:      str				=						{
    """microsoft/prophetnet-large-uncased""": (
        """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
    ),
}





class      UpperCAmelCase_						(_UpperCAmelCase						):





    """simple docstring"""






    lowerCamelCase						:      Dict							  =    'prophetnet'
    lowerCamelCase						:      List[str]							  =    ['past_key_values']
    lowerCamelCase						:      List[str]							  =    {
        'num_attention_heads': 'num_encoder_attention_heads',
    }







    def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
         __lowerCamelCase     :			List[Any]              =	vocab_size
         __lowerCamelCase     :			Union[str, Any]              =	hidden_size
         __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
         __lowerCamelCase     :			Any              =	num_encoder_layers
         __lowerCamelCase     :			int              =	num_encoder_attention_heads
         __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
         __lowerCamelCase     :			str              =	num_decoder_layers
         __lowerCamelCase     :			int              =	num_decoder_attention_heads
         __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
         __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
         __lowerCamelCase     :			Union[str, Any]              =	activation_function

         # parameters for prophetnet
         __lowerCamelCase     :			int              =	ngram
         __lowerCamelCase     :			Optional[int]              =	num_buckets
         __lowerCamelCase     :			str              =	relative_max_distance
         __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
         __lowerCamelCase     :			str              =	eps

         # 3 Types of Dropout
         __lowerCamelCase     :			Optional[Any]              =	attention_dropout
         __lowerCamelCase     :			str              =	activation_dropout
         __lowerCamelCase     :			Optional[Any]              =	dropout

         __lowerCamelCase     :			Optional[Any]              =	use_cache

         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )







    @property
    def    lowercase_       (							self			)       ->  int:
         return self.num_encoder_layers + self.num_decoder_layers







    @num_hidden_layers.setter
    def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
         raise NotImplementedError(
             'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
             ' `num_decoder_layers`.'			)






