






'''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 _lowercase      (							_lowercase							, unittest.TestCase						):
							a							 =			KandinskyPipeline
							a							 =			[
							    """prompt""",
							    """image_embeds""",
							    """negative_image_embeds""",
							]
							a							 =			["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
							a							 =			[
							    """generator""",
							    """height""",
							    """width""",
							    """latents""",
							    """guidance_scale""",
							    """negative_prompt""",
							    """num_inference_steps""",
							    """return_dict""",
							    """guidance_scale""",
							    """num_images_per_prompt""",
							    """output_type""",
							    """return_dict""",
							]
							a							 =			False




							@property
							def 						lowerCamelCase_							(				self:							Dict	):
														return 32




							@property
							def 						lowerCamelCase_							(				self:							Tuple	):
														return 32




							@property
							def 						lowerCamelCase_							(				self:							int	):
														return self.time_input_dim




							@property
							def 						lowerCamelCase_							(				self:							Optional[int]	):
														return self.time_input_dim * 4




							@property
							def 						lowerCamelCase_							(				self:							str	):
														return 100




							@property
							def 						lowerCamelCase_							(				self:							List[Any]	):
														lowerCamelCase__   :      List[str]			    =			XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
														return tokenizer




							@property
							def 						lowerCamelCase_							(				self:							int	):
														torch.manual_seed(0	)
														lowerCamelCase__   :      Tuple			    =			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=1_005     ,				)

														lowerCamelCase__   :      List[str]			    =			MultilingualCLIP(UpperCamelCase__	)
														lowerCamelCase__   :      int			    =			text_encoder.eval()

														return text_encoder




							@property
							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														torch.manual_seed(0	)

														lowerCamelCase__   :      Optional[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__   :      Union[str, Any]			    =			UNetaDConditionModel(**UpperCamelCase__	)
														return model




							@property
							def 						lowerCamelCase_							(				self:							Any	):
														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:							Union[str, Any]	):
														torch.manual_seed(0	)
														lowerCamelCase__   :      Union[str, Any]			    =			VQModel(**self.dummy_movq_kwargs	)
														return model




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														lowerCamelCase__   :      Union[str, Any]			    =			self.dummy_text_encoder
														lowerCamelCase__   :      Union[str, Any]			    =			self.dummy_tokenizer
														lowerCamelCase__   :      List[Any]			    =			self.dummy_unet
														lowerCamelCase__   :      int			    =			self.dummy_movq

														lowerCamelCase__   :      Union[str, Any]			    =			DDIMScheduler(
														    num_train_timesteps=1_000     ,				beta_schedule="""linear"""     ,				beta_start=0.00_085     ,				beta_end=0.012     ,				clip_sample=UpperCamelCase__     ,				set_alpha_to_one=UpperCamelCase__     ,				steps_offset=1     ,				prediction_type="""epsilon"""     ,				thresholding=UpperCamelCase__     ,				)

														lowerCamelCase__   :      Union[str, Any]			    =			{
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """movq""": movq,
														}
														return components




							def 						lowerCamelCase_							(				self:							List[str]     ,				UpperCamelCase__:							Optional[int]     ,				UpperCamelCase__:							str=0	):
														lowerCamelCase__   :      List[Any]			    =			floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
														lowerCamelCase__   :      Optional[int]			    =			floats_tensor((1, self.cross_attention_dim)     ,				rng=random.Random(seed + 1	)	).to(UpperCamelCase__	)
														if str(UpperCamelCase__	).startswith("""mps"""	):
																					lowerCamelCase__   :      int			    =			torch.manual_seed(UpperCamelCase__	)
														else:
																					lowerCamelCase__   :      str			    =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
														lowerCamelCase__   :      Union[str, 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 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Union[str, Any]			    =			"""cpu"""

														lowerCamelCase__   :      Any			    =			self.get_dummy_components()

														lowerCamelCase__   :      Optional[int]			    =			self.pipeline_class(**UpperCamelCase__	)
														lowerCamelCase__   :      List[Any]			    =			pipe.to(UpperCamelCase__	)

														pipe.set_progress_bar_config(disable=UpperCamelCase__	)

														lowerCamelCase__   :      List[Any]			    =			pipe(**self.get_dummy_inputs(UpperCamelCase__	)	)
														lowerCamelCase__   :      List[str]			    =			output.images

														lowerCamelCase__   :      int			    =			pipe(
														    **self.get_dummy_inputs(UpperCamelCase__	)     ,				return_dict=UpperCamelCase__     ,				)[0]

														lowerCamelCase__   :      Optional[int]			    =			image[0, -3:, -3:, -1]
														lowerCamelCase__   :      Any			    =			image_from_tuple[0, -3:, -3:, -1]

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

														lowerCamelCase__   :      str			    =			np.array(
														    [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]	)

														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 _lowercase      (							unittest.TestCase						):




							def 						lowerCamelCase_							(				self:							Optional[int]	):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()




							def 						lowerCamelCase_							(				self:							Optional[Any]	):
														lowerCamelCase__   :      Any			    =			load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""	)

														lowerCamelCase__   :      List[Any]			    =			KandinskyPriorPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-1-prior"""     ,				torch_dtype=torch.floataa	)
														pipe_prior.to(UpperCamelCase__	)

														lowerCamelCase__   :      str			    =			KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""     ,				torch_dtype=torch.floataa	)
														lowerCamelCase__   :      Tuple			    =			pipeline.to(UpperCamelCase__	)
														pipeline.set_progress_bar_config(disable=UpperCamelCase__	)

														lowerCamelCase__   :      str			    =			"""red cat, 4k photo"""

														lowerCamelCase__   :      str			    =			torch.Generator(device="""cuda"""	).manual_seed(0	)
														lowerCamelCase__    ,						lowerCamelCase__   :      Optional[int]			    =			pipe_prior(
														    UpperCamelCase__     ,				generator=UpperCamelCase__     ,				num_inference_steps=5     ,				negative_prompt=""""""     ,				).to_tuple()

														lowerCamelCase__   :      int			    =			torch.Generator(device="""cuda"""	).manual_seed(0	)
														lowerCamelCase__   :      Any			    =			pipeline(
														    UpperCamelCase__     ,				image_embeds=UpperCamelCase__     ,				negative_image_embeds=UpperCamelCase__     ,				generator=UpperCamelCase__     ,				num_inference_steps=100     ,				output_type="""np"""     ,				)

														lowerCamelCase__   :      Dict			    =			output.images[0]

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

														assert_mean_pixel_difference(UpperCamelCase__     ,				UpperCamelCase__	)









'''simple docstring'''







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


_A      :     Union[str, Any]       	=logging.get_logger(__name__)

_A      :     Tuple       	={
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class _lowercase      (							_lowercase						):
							a							 =			"""splinter"""




							def __init__(				self:							List[str]     ,				UpperCamelCase__:							List[Any]=30_522     ,				UpperCamelCase__:							Union[str, Any]=768     ,				UpperCamelCase__:							str=12     ,				UpperCamelCase__:							Dict=12     ,				UpperCamelCase__:							Tuple=3_072     ,				UpperCamelCase__:							Optional[int]="gelu"     ,				UpperCamelCase__:							Optional[int]=0.1     ,				UpperCamelCase__:							str=0.1     ,				UpperCamelCase__:							List[Any]=512     ,				UpperCamelCase__:							List[str]=2     ,				UpperCamelCase__:							Any=0.02     ,				UpperCamelCase__:							Optional[Any]=1e-12     ,				UpperCamelCase__:							Dict=True     ,				UpperCamelCase__:							str=0     ,				UpperCamelCase__:							int=104     ,				**UpperCamelCase__:							Any     ,				):
														super().__init__(pad_token_id=UpperCamelCase__     ,				**UpperCamelCase__	)

														lowerCamelCase__   :      Union[str, Any]			    =			vocab_size
														lowerCamelCase__   :      str			    =			max_position_embeddings
														lowerCamelCase__   :      Tuple			    =			hidden_size
														lowerCamelCase__   :      Dict			    =			num_hidden_layers
														lowerCamelCase__   :      Tuple			    =			num_attention_heads
														lowerCamelCase__   :      Tuple			    =			intermediate_size
														lowerCamelCase__   :      Any			    =			hidden_act
														lowerCamelCase__   :      int			    =			hidden_dropout_prob
														lowerCamelCase__   :      List[str]			    =			attention_probs_dropout_prob
														lowerCamelCase__   :      Dict			    =			initializer_range
														lowerCamelCase__   :      List[Any]			    =			type_vocab_size
														lowerCamelCase__   :      Tuple			    =			layer_norm_eps
														lowerCamelCase__   :      Optional[Any]			    =			use_cache
														lowerCamelCase__   :      Optional[Any]			    =			question_token_id

