


'''simple docstring'''

import copy
from typing import Dict

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


lowercase__					  =		logging.get_logger(__name__)





class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					'''semantic'''






     def __init__(      self  ,				__UpperCamelCase=10_000  ,				__UpperCamelCase=True  ,				__UpperCamelCase=768  ,				__UpperCamelCase=False  ,				__UpperCamelCase=False  ,				__UpperCamelCase=False  ,				__UpperCamelCase=False  ,				__UpperCamelCase=0.7  ,				__UpperCamelCase=True  ,				__UpperCamelCase=10_048  ,				__UpperCamelCase=129_595  ,				__UpperCamelCase=129_599  ,				__UpperCamelCase=10_000  ,				__UpperCamelCase=256  ,				__UpperCamelCase=49.9  ,				**__UpperCamelCase  ,				)							->     Tuple:
          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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					'''coarse_acoustics'''






     def __init__(      self  ,				__UpperCamelCase=True  ,				__UpperCamelCase=False  ,				__UpperCamelCase=False  ,				__UpperCamelCase=False  ,				__UpperCamelCase=False  ,				__UpperCamelCase=0.7  ,				__UpperCamelCase=True  ,				__UpperCamelCase=12_048  ,				__UpperCamelCase=75  ,				__UpperCamelCase=2  ,				__UpperCamelCase=12_050  ,				__UpperCamelCase=256  ,				__UpperCamelCase = 630  ,				__UpperCamelCase = 60  ,				**__UpperCamelCase  ,				)							->     Tuple:
          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     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					'''fine_acoustics'''






     def __init__(      self  ,				__UpperCamelCase=0.5  ,				__UpperCamelCase=512  ,				__UpperCamelCase=1_024  ,				__UpperCamelCase=8  ,				**__UpperCamelCase  ,				)							->     Union[str, Any]:
          super().__init__(temperature=__UpperCamelCase		)

          _a            =		max_fine_history_length
          _a            =		max_fine_input_length
          _a            =		n_fine_codebooks







class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			):
     UpperCAmelCase			       =					'''bark'''
     UpperCAmelCase			       =					True






     def __init__(      self  ,				__UpperCamelCase = None  ,				__UpperCamelCase = None  ,				__UpperCamelCase = None  ,				__UpperCamelCase=24_000  ,				__UpperCamelCase=1_024  ,				**__UpperCamelCase  ,				)							->     List[str]:
          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 			a_       (      cls  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				__UpperCamelCase  ,				**__UpperCamelCase  ,				)							->     Optional[int]:
          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 			a_       (      self		)							->     int:
          _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





'''simple docstring'''

import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin





@skip_mps
class     __SCREAMING_SNAKE_CASE       (   lowerCamelCase__			,      lowerCamelCase__			,      unittest.TestCase			):
     UpperCAmelCase			       =					IFImgaImgPipeline
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCAmelCase			       =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     UpperCAmelCase			       =					PipelineTesterMixin.required_optional_params - {'''latents'''}






     def 			a_       (      self		)							->     Optional[Any]:
          return self._get_dummy_components()






     def 			a_       (      self  ,				__UpperCamelCase  ,				__UpperCamelCase=0		)							->     Any:
          if str(__UpperCamelCase		).startswith("mps"		):
               _a            =		torch.manual_seed(__UpperCamelCase		)
          else:
               _a            =		torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

          _a            =		floats_tensor((1, 3, 32, 32)  ,				rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)

          _a            =		{
              "prompt": "A painting of a squirrel eating a burger",
              "image": image,
              "generator": generator,
              "num_inference_steps": 2,
              "output_type": "numpy",
          }

          return inputs






     def 			a_       (      self		)							->     Any:
          self._test_save_load_optional_components()






     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()  ,				reason="XFormers attention is only available with CUDA and `xformers` installed"  ,				)
     def 			a_       (      self		)							->     Dict:
          self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     List[Any]:
          # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
          super().test_save_load_floataa(expected_max_diff=1e-1		)






     @unittest.skipIf(torch_device != "cuda"  ,				reason="float16 requires CUDA"		)
     def 			a_       (      self		)							->     int:
          super().test_floataa_inference(expected_max_diff=1e-1		)






     def 			a_       (      self		)							->     Dict:
          self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)






     def 			a_       (      self		)							->     Optional[Any]:
          self._test_save_load_local()






     def 			a_       (      self		)							->     Union[str, Any]:
          self._test_inference_batch_single_identical(
              expected_max_diff=1e-2  ,				)

