






"""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					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			KandinskyPipeline
     SCREAMING_SNAKE_CASE				  =			[
         "prompt",
         "image_embeds",
         "negative_image_embeds",
     ]
     SCREAMING_SNAKE_CASE				  =			["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
     SCREAMING_SNAKE_CASE				  =			[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     SCREAMING_SNAKE_CASE				  =			False




     @property
     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         return 32




     @property
     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         return 32




     @property
     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         return self.time_input_dim




     @property
     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         return self.time_input_dim * 4




     @property
     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         return 100




     @property
     def 						lowerCAmelCase_       (			self    :     List[Any]			):


         """simple docstring"""



         __A             =		XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
         return tokenizer




     @property
     def 						lowerCAmelCase_       (			self    :     Dict			):


         """simple docstring"""



         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=1_005	,		)

         __A             =		MultilingualCLIP(UpperCamelCase_			)
         __A             =		text_encoder.eval()

         return text_encoder




     @property
     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         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 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """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 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         torch.manual_seed(0			)
         __A             =		VQModel(**self.dummy_movq_kwargs			)
         return model




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         __A             =		self.dummy_text_encoder
         __A             =		self.dummy_tokenizer
         __A             =		self.dummy_unet
         __A             =		self.dummy_movq

         __A             =		DDIMScheduler(
             num_train_timesteps=1_000	,		beta_schedule="""linear"""	,		beta_start=0.00085	,		beta_end=0.012	,		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 						lowerCAmelCase_       (			self    :     List[Any]	,		UpperCamelCase_    :     Optional[Any]	,		UpperCamelCase_    :     List[Any]=0			):


         """simple docstring"""



         __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 						lowerCAmelCase_       (			self    :     List[Any]			):


         """simple docstring"""



         __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(
             [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953]			)

         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					):







     '''simple docstring'''




     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



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





     def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


         """simple docstring"""



         __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=100	,		output_type="""np"""	,		)

         __A             =		output.images[0]

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

         assert_mean_pixel_difference(UpperCamelCase_	,		UpperCamelCase_			)












import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__a :    str								=				logging.get_logger(__name__)


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"semantic"




     def __init__(			self    :     Optional[int]	,		UpperCamelCase_    :     List[Any]=10_000	,		UpperCamelCase_    :     int=True	,		UpperCamelCase_    :     Dict=768	,		UpperCamelCase_    :     str=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     List[Any]=False	,		UpperCamelCase_    :     Any=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     Dict=10_048	,		UpperCamelCase_    :     Tuple=129_595	,		UpperCamelCase_    :     List[str]=129_599	,		UpperCamelCase_    :     Optional[int]=10_000	,		UpperCamelCase_    :     Dict=256	,		UpperCamelCase_    :     int=49.9	,		**UpperCamelCase_    :     Any	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		eos_token_id=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		max_new_tokens=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		text_encoding_offset
         __A             =		text_pad_token
         __A             =		eos_token_id
         __A             =		semantic_infer_token
         __A             =		semantic_vocab_size
         __A             =		max_input_semantic_length
         __A             =		semantic_rate_hz


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"coarse_acoustics"




     def __init__(			self    :     int	,		UpperCamelCase_    :     List[Any]=True	,		UpperCamelCase_    :     int=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Dict=False	,		UpperCamelCase_    :     Union[str, Any]=False	,		UpperCamelCase_    :     Any=0.7	,		UpperCamelCase_    :     Optional[Any]=True	,		UpperCamelCase_    :     str=12_048	,		UpperCamelCase_    :     int=75	,		UpperCamelCase_    :     Dict=2	,		UpperCamelCase_    :     List[str]=12_050	,		UpperCamelCase_    :     str=256	,		UpperCamelCase_    :     int = 630	,		UpperCamelCase_    :     int = 60	,		**UpperCamelCase_    :     Union[str, Any]	,		):


         """simple docstring"""



         super().__init__(
             temperature=UpperCamelCase_	,		do_sample=UpperCamelCase_	,		renormalize_logits=UpperCamelCase_	,		output_scores=UpperCamelCase_	,		return_dict_in_generate=UpperCamelCase_	,		output_hidden_states=UpperCamelCase_	,		output_attentions=UpperCamelCase_	,		**UpperCamelCase_	,		)

         __A             =		coarse_semantic_pad_token
         __A             =		coarse_rate_hz
         __A             =		n_coarse_codebooks
         __A             =		coarse_infer_token
         __A             =		max_coarse_input_length
         __A             =		max_coarse_history
         __A             =		sliding_window_len


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"fine_acoustics"




     def __init__(			self    :     Any	,		UpperCamelCase_    :     Dict=0.5	,		UpperCamelCase_    :     List[Any]=512	,		UpperCamelCase_    :     int=1_024	,		UpperCamelCase_    :     Union[str, Any]=8	,		**UpperCamelCase_    :     int	,		):


         """simple docstring"""



         super().__init__(temperature=UpperCamelCase_			)

         __A             =		max_fine_history_length
         __A             =		max_fine_input_length
         __A             =		n_fine_codebooks


class  __lowercase (		lowercase_					):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE				  =			"bark"
     SCREAMING_SNAKE_CASE				  =			True




     def __init__(			self    :     Dict	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     Dict = None	,		UpperCamelCase_    :     List[str]=24_000	,		UpperCamelCase_    :     Union[str, Any]=1_024	,		**UpperCamelCase_    :     List[str]	,		):


         """simple docstring"""



         if semantic_config is None:
             __A             =		{}
             logger.info("""semantic_config is None. initializing the semantic model with default values."""			)

         if coarse_acoustics_config is None:
             __A             =		{}
             logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""			)

         if fine_acoustics_config is None:
             __A             =		{}
             logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""			)

         __A             =		BarkSemanticGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkCoarseGenerationConfig(**UpperCamelCase_			)
         __A             =		BarkFineGenerationConfig(**UpperCamelCase_			)

         __A             =		sample_rate
         __A             =		codebook_size




     @classmethod
     def 						lowerCAmelCase_       (			cls    :     int	,		UpperCamelCase_    :     BarkSemanticGenerationConfig	,		UpperCamelCase_    :     BarkCoarseGenerationConfig	,		UpperCamelCase_    :     BarkFineGenerationConfig	,		**UpperCamelCase_    :     Tuple	,		):


         """simple docstring"""



         return cls(
             semantic_config=semantic_config.to_dict()	,		coarse_acoustics_config=coarse_acoustics_config.to_dict()	,		fine_acoustics_config=fine_acoustics_config.to_dict()	,		**UpperCamelCase_	,		)





     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		copy.deepcopy(self.__dict__			)

         __A             =		self.semantic_config.to_dict()
         __A             =		self.coarse_acoustics_config.to_dict()
         __A             =		self.fine_acoustics_config.to_dict()

         __A             =		self.__class__.model_type
         return output




