import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class  _UpperCAmelCase       (			A_,       unittest.TestCase   ):



 '''simple docstring'''




 __A           = KandinskyPriorPipeline
 __A           = ["prompt"]
 __A           = ["prompt", "negative_prompt"]
 __A           = [
     "num_images_per_prompt",
     "generator",
     "num_inference_steps",
     "latents",
     "negative_prompt",
     "guidance_scale",
     "output_type",
     "return_dict",
 ]
 __A           = False







 @property
 def 						__UpperCAmelCase							(						self							:					List[str])		->     Union[str, Any]:


   """simple docstring"""







   return 32







 @property
 def 						__UpperCAmelCase							(						self							:					Any)		->     Any:


   """simple docstring"""







   return 32







 @property
 def 						__UpperCAmelCase							(						self							:					int)		->     Dict:


   """simple docstring"""







   return self.time_input_dim







 @property
 def 						__UpperCAmelCase							(						self							:					str)		->     Tuple:


   """simple docstring"""







   return self.time_input_dim * 4







 @property
 def 						__UpperCAmelCase							(						self							:					Dict)		->     Dict:


   """simple docstring"""







   return 100







 @property
 def 						__UpperCAmelCase							(						self							:					Any)		->     int:


   """simple docstring"""







   _UpperCamelCase				=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
   return tokenizer







 @property
 def 						__UpperCAmelCase							(						self							:					Optional[Any])		->     Optional[Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  CLIPTextConfig(
       bos_token_id=0				,      eos_token_id=2				,      hidden_size=self.text_embedder_hidden_size				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=37				,      layer_norm_eps=1e-0_5				,      num_attention_heads=4				,      num_hidden_layers=5				,      pad_token_id=1				,      vocab_size=1000				,      )
   return CLIPTextModelWithProjection(_A)







 @property
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     Tuple:


   """simple docstring"""







   torch.manual_seed(0)

   _UpperCamelCase				=  {
       "num_attention_heads": 2,
       "attention_head_dim": 12,
       "embedding_dim": self.text_embedder_hidden_size,
       "num_layers": 1,
   }

   _UpperCamelCase				=  PriorTransformer(**_A)
   # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
   _UpperCamelCase				=  nn.Parameter(torch.ones(model.clip_std.shape))
   return model







 @property
 def 						__UpperCAmelCase							(						self							:					str)		->     Union[str, Any]:


   """simple docstring"""







   torch.manual_seed(0)
   _UpperCamelCase				=  CLIPVisionConfig(
       hidden_size=self.text_embedder_hidden_size				,      image_size=224				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=37				,      num_attention_heads=4				,      num_channels=3				,      num_hidden_layers=5				,      patch_size=14				,      )

   _UpperCamelCase				=  CLIPVisionModelWithProjection(_A)
   return model







 @property
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     int:


   """simple docstring"""







   _UpperCamelCase				=  CLIPImageProcessor(
       crop_size=224				,      do_center_crop=_A				,      do_normalize=_A				,      do_resize=_A				,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]				,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]				,      resample=3				,      size=224				,      )

   return image_processor







 def 						__UpperCAmelCase							(						self							:					int)		->     Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase				=  self.dummy_prior
   _UpperCamelCase				=  self.dummy_image_encoder
   _UpperCamelCase				=  self.dummy_text_encoder
   _UpperCamelCase				=  self.dummy_tokenizer
   _UpperCamelCase				=  self.dummy_image_processor

   _UpperCamelCase				=  UnCLIPScheduler(
       variance_type="fixed_small_log"				,      prediction_type="sample"				,      num_train_timesteps=1000				,      clip_sample=_A				,      clip_sample_range=10.0				,      )

   _UpperCamelCase				=  {
       "prior": prior,
       "image_encoder": image_encoder,
       "text_encoder": text_encoder,
       "tokenizer": tokenizer,
       "scheduler": scheduler,
       "image_processor": image_processor,
   }

   return components







 def 						__UpperCAmelCase							(						self							:					Optional[int]				,      lowercase_							:					Tuple				,      lowercase_							:					str=0)		->     Union[str, Any]:


   """simple docstring"""







   if str(_A).startswith("mps"):
     _UpperCamelCase				=  torch.manual_seed(_A)
   else:
     _UpperCamelCase				=  torch.Generator(device=_A).manual_seed(_A)
   _UpperCamelCase				=  {
       "prompt": "horse",
       "generator": generator,
       "guidance_scale": 4.0,
       "num_inference_steps": 2,
       "output_type": "np",
   }
   return inputs







 def 						__UpperCAmelCase							(						self							:					List[Any])		->     int:


   """simple docstring"""







   _UpperCamelCase				=  "cpu"

   _UpperCamelCase				=  self.get_dummy_components()

   _UpperCamelCase				=  self.pipeline_class(**_A)
   _UpperCamelCase				=  pipe.to(_A)

   pipe.set_progress_bar_config(disable=_A)

   _UpperCamelCase				=  pipe(**self.get_dummy_inputs(_A))
   _UpperCamelCase				=  output.image_embeds

   _UpperCamelCase				=  pipe(
       **self.get_dummy_inputs(_A)				,      return_dict=_A				,      )[0]

   _UpperCamelCase				=  image[0, -10:]
   _UpperCamelCase				=  image_from_tuple[0, -10:]

   assert image.shape == (1, 32)

   _UpperCamelCase				=  np.array(
       [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56])

   assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2







 @skip_mps
 def 						__UpperCAmelCase							(						self							:					List[Any])		->     str:


   """simple docstring"""







   _UpperCamelCase				=  torch_device == "cpu"
   _UpperCamelCase				=  True
   _UpperCamelCase				=  False

   self._test_inference_batch_single_identical(
       test_max_difference=_A				,      relax_max_difference=_A				,      test_mean_pixel_difference=_A				,      )







 @skip_mps
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     List[str]:


   """simple docstring"""







   _UpperCamelCase				=  torch_device == "cpu"
   _UpperCamelCase				=  False

   self._test_attention_slicing_forward_pass(
       test_max_difference=_A				,      test_mean_pixel_difference=_A				,      )






import copy
from typing import Dict

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


_A  					= logging.get_logger(__name__)







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Tuple           =		"semantic"





     def __init__(self      :    Dict				,    _A      :    Optional[Any]=1_0_0_0_0				,    _A      :    List[Any]=True				,    _A      :    Any=7_6_8				,    _A      :    Union[str, Any]=False				,    _A      :    List[Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    Union[str, Any]=False				,    _A      :    str=0.7				,    _A      :    Optional[int]=True				,    _A      :    Any=1_0_0_4_8				,    _A      :    Optional[int]=1_2_9_5_9_5				,    _A      :    Union[str, Any]=1_2_9_5_9_9				,    _A      :    Any=1_0_0_0_0				,    _A      :    str=2_5_6				,    _A      :    int=49.9				,    **_A      :    Union[str, Any]				,    )						->						Any:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    eos_token_id=_A				,    renormalize_logits=_A				,    max_new_tokens=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

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







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							List[Any]           =		"coarse_acoustics"





     def __init__(self      :    Any				,    _A      :    Union[str, Any]=True				,    _A      :    List[Any]=False				,    _A      :    Any=False				,    _A      :    Union[str, Any]=False				,    _A      :    Optional[Any]=False				,    _A      :    str=0.7				,    _A      :    int=True				,    _A      :    List[str]=1_2_0_4_8				,    _A      :    Optional[int]=7_5				,    _A      :    Any=2				,    _A      :    Optional[Any]=1_2_0_5_0				,    _A      :    Tuple=2_5_6				,    _A      :    int = 6_3_0				,    _A      :    int = 6_0				,    **_A      :    int				,    )						->						Union[str, Any]:
         super().__init__(
             temperature=_A				,    do_sample=_A				,    renormalize_logits=_A				,    output_scores=_A				,    return_dict_in_generate=_A				,    output_hidden_states=_A				,    output_attentions=_A				,    **_A				,    )

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







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							int           =		"fine_acoustics"





     def __init__(self      :    Tuple				,    _A      :    Optional[int]=0.5				,    _A      :    List[str]=5_1_2				,    _A      :    Union[str, Any]=1_0_2_4				,    _A      :    Tuple=8				,    **_A      :    Tuple				,    )						->						Tuple:
         super().__init__(temperature=_A   )

         snake_case      =					max_fine_history_length
         snake_case      =					max_fine_input_length
         snake_case      =					n_fine_codebooks







class      lowerCamelCase		(  A_  ):
     UpperCAmelCase__				:							Optional[Any]           =		"bark"
     UpperCAmelCase__				:							Union[str, Any]           =		True





     def __init__(self      :    Union[str, Any]				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Dict = None				,    _A      :    Optional[int]=2_4_0_0_0				,    _A      :    Any=1_0_2_4				,    **_A      :    List[str]				,    )						->						Optional[Any]:
         if semantic_config is None:
             snake_case      =					{}
             logger.info("semantic_config is None. initializing the semantic model with default values."   )

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

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

         snake_case      =					BarkSemanticGenerationConfig(**_A   )
         snake_case      =					BarkCoarseGenerationConfig(**_A   )
         snake_case      =					BarkFineGenerationConfig(**_A   )

         snake_case      =					sample_rate
         snake_case      =					codebook_size





     @classmethod
     def 		UpperCAmelCase(cls      :    List[Any]				,    _A      :    BarkSemanticGenerationConfig				,    _A      :    BarkCoarseGenerationConfig				,    _A      :    BarkFineGenerationConfig				,    **_A      :    Optional[int]				,    )						->						Union[str, Any]:
         return cls(
             semantic_config=semantic_config.to_dict()				,    coarse_acoustics_config=coarse_acoustics_config.to_dict()				,    fine_acoustics_config=fine_acoustics_config.to_dict()				,    **_A				,    )





     def 		UpperCAmelCase(self      :    List[str]   )						->						int:
         snake_case      =					copy.deepcopy(self.__dict__   )

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

         snake_case      =					self.__class__.model_type
         return output

