
"""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 				A__ (			_lowerCamelCase ,  unittest.TestCase):
  A_		: Tuple            =			KandinskyImgaImgPipeline
  A_		: Optional[Any]            =			['prompt', 'image_embeds', 'negative_image_embeds', 'image']
  A_		: Optional[int]            =			[
      'prompt',
      'negative_prompt',
      'image_embeds',
      'negative_image_embeds',
      'image',
  ]
  A_		: int            =			[
      'generator',
      'height',
      'width',
      'strength',
      'guidance_scale',
      'negative_prompt',
      'num_inference_steps',
      'return_dict',
      'guidance_scale',
      'num_images_per_prompt',
      'output_type',
      'return_dict',
  ]
  A_		: Any            =			False

  @property
  def 							__lowerCamelCase							(   self				):
   return 32

  @property
  def 							__lowerCamelCase							(   self				):
   return 32

  @property
  def 							__lowerCamelCase							(   self				):
   return self.time_input_dim

  @property
  def 							__lowerCamelCase							(   self				):
   return self.time_input_dim * 4

  @property
  def 							__lowerCamelCase							(   self				):
   return 1_00

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

  @property
  def 							__lowerCamelCase							(   self				):
   torch.manual_seed(0				)
   __lowerCAmelCase    :		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 ,	)

   __lowerCAmelCase    :		str		    =   MultilingualCLIP(_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		str		    =   text_encoder.eval()

   return text_encoder

  @property
  def 							__lowerCamelCase							(   self				):
   torch.manual_seed(0				)

   __lowerCAmelCase    :		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,
   }

   __lowerCAmelCase    :		int		    =   UNetaDConditionModel(**_SCREAMING_SNAKE_CASE				)
   return model

  @property
  def 							__lowerCamelCase							(   self				):
   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 							__lowerCamelCase							(   self				):
   torch.manual_seed(0				)
   __lowerCAmelCase    :		Any		    =   VQModel(**self.dummy_movq_kwargs				)
   return model

  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		int		    =   self.dummy_text_encoder
   __lowerCAmelCase    :		Union[str, Any]		    =   self.dummy_tokenizer
   __lowerCAmelCase    :		Any		    =   self.dummy_unet
   __lowerCAmelCase    :		Dict		    =   self.dummy_movq

   __lowerCAmelCase    :		Optional[Any]		    =   {
       'num_train_timesteps': 10_00,
       '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,
   }

   __lowerCAmelCase    :		Optional[Any]		    =   DDIMScheduler(**_SCREAMING_SNAKE_CASE				)

   __lowerCAmelCase    :		Union[str, Any]		    =   {
       'text_encoder': text_encoder,
       'tokenizer': tokenizer,
       'unet': unet,
       'scheduler': scheduler,
       'movq': movq,
   }

   return components

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

   if str(_SCREAMING_SNAKE_CASE				).startswith('mps'				):
    __lowerCAmelCase    :		int		    =   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': 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 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		str		    =   'cpu'

   __lowerCAmelCase    :		Dict		    =   self.get_dummy_components()

   __lowerCAmelCase    :		List[str]		    =   self.pipeline_class(**_SCREAMING_SNAKE_CASE				)
   __lowerCAmelCase    :		Optional[int]		    =   pipe.to(_SCREAMING_SNAKE_CASE				)

   pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

   __lowerCAmelCase    :		List[Any]		    =   pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				)				)
   __lowerCAmelCase    :		Optional[Any]		    =   output.images

   __lowerCAmelCase    :		Union[str, Any]		    =   pipe(
       **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE				) ,	return_dict=_SCREAMING_SNAKE_CASE ,	)[0]

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

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

   __lowerCAmelCase    :		Optional[Any]		    =   np.array(
       [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]				)
   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 				A__ (			unittest.TestCase):

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







  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		Optional[Any]		    =   load_numpy(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
       '/kandinsky/kandinsky_img2img_frog.npy'				)

   __lowerCAmelCase    :		Dict		    =   load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'				)
   __lowerCAmelCase    :		Any		    =   'A red cartoon frog, 4k'

   __lowerCAmelCase    :		Any		    =   KandinskyPriorPipeline.from_pretrained(
       'kandinsky-community/kandinsky-2-1-prior' ,	torch_dtype=torch.floataa				)
   pipe_prior.to(_SCREAMING_SNAKE_CASE				)

   __lowerCAmelCase    :		Optional[Any]		    =   KandinskyImgaImgPipeline.from_pretrained(
       'kandinsky-community/kandinsky-2-1' ,	torch_dtype=torch.floataa				)
   __lowerCAmelCase    :		Any		    =   pipeline.to(_SCREAMING_SNAKE_CASE				)

   pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE				)

   __lowerCAmelCase    :		int		    =   torch.Generator(device='cpu'				).manual_seed(0				)
   __lowerCAmelCase    :		List[str]		    =   pipe_prior(
       _SCREAMING_SNAKE_CASE ,	generator=_SCREAMING_SNAKE_CASE ,	num_inference_steps=5 ,	negative_prompt='' ,	).to_tuple()

   __lowerCAmelCase    :		str		    =   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' ,	)

   __lowerCAmelCase    :		int		    =   output.images[0]

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

   assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				)

"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							from transformers.models.roberta.modeling_flax_roberta import (
							    FlaxRobertaForCausalLM,
							    FlaxRobertaForMaskedLM,
							    FlaxRobertaForMultipleChoice,
							    FlaxRobertaForQuestionAnswering,
							    FlaxRobertaForSequenceClassification,
							    FlaxRobertaForTokenClassification,
							    FlaxRobertaModel,
							)




class 				A__ (			unittest.TestCase):

		def __init__(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=13 ,	_SCREAMING_SNAKE_CASE=7 ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=99 ,	_SCREAMING_SNAKE_CASE=32 ,	_SCREAMING_SNAKE_CASE=5 ,	_SCREAMING_SNAKE_CASE=4 ,	_SCREAMING_SNAKE_CASE=37 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=5_12 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=2 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=4 ,	):
			__lowerCAmelCase    :		List[Any]		    =   parent
			__lowerCAmelCase    :		int		    =   batch_size
			__lowerCAmelCase    :		List[str]		    =   seq_length
			__lowerCAmelCase    :		str		    =   is_training
			__lowerCAmelCase    :		Optional[Any]		    =   use_attention_mask
			__lowerCAmelCase    :		Union[str, Any]		    =   use_token_type_ids
			__lowerCAmelCase    :		Dict		    =   use_labels
			__lowerCAmelCase    :		Union[str, Any]		    =   vocab_size
			__lowerCAmelCase    :		List[str]		    =   hidden_size
			__lowerCAmelCase    :		List[str]		    =   num_hidden_layers
			__lowerCAmelCase    :		Optional[int]		    =   num_attention_heads
			__lowerCAmelCase    :		Dict		    =   intermediate_size
			__lowerCAmelCase    :		Dict		    =   hidden_act
			__lowerCAmelCase    :		Optional[int]		    =   hidden_dropout_prob
			__lowerCAmelCase    :		Dict		    =   attention_probs_dropout_prob
			__lowerCAmelCase    :		Union[str, Any]		    =   max_position_embeddings
			__lowerCAmelCase    :		Any		    =   type_vocab_size
			__lowerCAmelCase    :		Tuple		    =   type_sequence_label_size
			__lowerCAmelCase    :		Union[str, Any]		    =   initializer_range
			__lowerCAmelCase    :		int		    =   num_choices

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Union[str, Any]		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.vocab_size				)

			__lowerCAmelCase    :		List[str]		    =   None
			if self.use_attention_mask:
				__lowerCAmelCase    :		str		    =   random_attention_mask([self.batch_size, self.seq_length]				)

			__lowerCAmelCase    :		Dict		    =   None
			if self.use_token_type_ids:
				__lowerCAmelCase    :		Tuple		    =   ids_tensor([self.batch_size, self.seq_length] ,	self.type_vocab_size				)

			__lowerCAmelCase    :		str		    =   RobertaConfig(
			    vocab_size=self.vocab_size ,	hidden_size=self.hidden_size ,	num_hidden_layers=self.num_hidden_layers ,	num_attention_heads=self.num_attention_heads ,	intermediate_size=self.intermediate_size ,	hidden_act=self.hidden_act ,	hidden_dropout_prob=self.hidden_dropout_prob ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob ,	max_position_embeddings=self.max_position_embeddings ,	type_vocab_size=self.type_vocab_size ,	is_decoder=_SCREAMING_SNAKE_CASE ,	initializer_range=self.initializer_range ,	)

			return config, input_ids, token_type_ids, attention_mask

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		List[Any]		    =   config_and_inputs
			__lowerCAmelCase    :		List[Any]		    =   {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
			return config, inputs_dict







		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    ,     __lowerCAmelCase    :		Any		    =   config_and_inputs

			__lowerCAmelCase    :		int		    =   True
			__lowerCAmelCase    :		Any		    =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
			__lowerCAmelCase    :		int		    =   ids_tensor([self.batch_size, self.seq_length] ,	vocab_size=2				)

			return (
			    config,
			    input_ids,
			    token_type_ids,
			    encoder_hidden_states,
			    encoder_attention_mask,
			)




@require_flax
class 				A__ (			_lowerCamelCase ,  unittest.TestCase):
		A_		: List[Any]            =			True

		A_		: Optional[Any]            =			(
		    (
		        FlaxRobertaModel,
		        FlaxRobertaForCausalLM,
		        FlaxRobertaForMaskedLM,
		        FlaxRobertaForSequenceClassification,
		        FlaxRobertaForTokenClassification,
		        FlaxRobertaForMultipleChoice,
		        FlaxRobertaForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   FlaxRobertaModelTester(self				)







		@slow
		def 							__lowerCamelCase							(   self				):
			for model_class_name in self.all_model_classes:
				__lowerCAmelCase    :		str		    =   model_class_name.from_pretrained('roberta-base' ,	from_pt=_SCREAMING_SNAKE_CASE				)
				__lowerCAmelCase    :		Optional[int]		    =   model(np.ones((1, 1)				)				)
				self.assertIsNotNone(_SCREAMING_SNAKE_CASE				)