






import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

__lowerCamelCase						= logging.get_logger(__name__)






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase = True     )     ->		List[Any]:







							"""simple docstring"""





							_a  :    Optional[Any]			=						{}
							for name in model.attn_processors.keys():
														_a  :    str			=						name.endswith('''attn2.processor'''     ) and not (
														    name.startswith('''transformer_in'''     ) or '''temp_attentions''' in name.split('''.'''     )
														)
														_a  :    Optional[Any]			=						model.config.cross_attention_dim if has_cross_attention else None
														if name.startswith('''mid_block'''     ):
																					_a  :    Any			=						model.config.block_out_channels[-1]
														elif name.startswith('''up_blocks'''     ):
																					_a  :    Union[str, Any]			=						int(name[len('''up_blocks.'''     )]     )
																					_a  :    Dict			=						list(reversed(model.config.block_out_channels     )     )[block_id]
														elif name.startswith('''down_blocks'''     ):
																					_a  :    Optional[Any]			=						int(name[len('''down_blocks.'''     )]     )
																					_a  :    Optional[int]			=						model.config.block_out_channels[block_id]
														elif name.startswith('''transformer_in'''     ):
																					# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
																					_a  :    Optional[Any]			=						8 * model.config.attention_head_dim

														_a  :    str			=						LoRAAttnProcessor(hidden_size=UpperCAmelCase    ,	cross_attention_dim=UpperCAmelCase     )
														_a  :    Dict			=						lora_attn_procs[name].to(model.device     )

														if mock_weights:
																					# add 1 to weights to mock trained weights
																					with torch.no_grad():
																												lora_attn_procs[name].to_q_lora.up.weight += 1
																												lora_attn_procs[name].to_k_lora.up.weight += 1
																												lora_attn_procs[name].to_v_lora.up.weight += 1
																												lora_attn_procs[name].to_out_lora.up.weight += 1

							return lora_attn_procs







@skip_mps
class      UpperCamelCase_							(				UpperCamelCase ,       UpperCamelCase ,       unittest.TestCase       ):
						lowercase									=  UNetaDConditionModel
						lowercase									=  '''sample'''




						@property
						def      snake_case__(    self       )			->						List[Any]:
													_a  :    Optional[Any]			=						4
													_a  :    Optional[int]			=						4
													_a  :    List[Any]			=						4
													_a  :    List[str]			=						(32, 32)

													_a  :    Union[str, Any]			=						floats_tensor((batch_size, num_channels, num_frames) + sizes       ).to(lowercase       )
													_a  :    int			=						torch.tensor([10]       ).to(lowercase       )
													_a  :    Optional[Any]			=						floats_tensor((batch_size, 4, 32)       ).to(lowercase       )

													return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}




						@property
						def      snake_case__(    self       )			->						List[str]:
													return (4, 4, 32, 32)




						@property
						def      snake_case__(    self       )			->						Optional[int]:
													return (4, 4, 32, 32)




						def      snake_case__(    self       )			->						Optional[Any]:
													_a  :    Union[str, Any]			=						{
													    '''block_out_channels''': (32, 64),
													    '''down_block_types''': (
													        '''CrossAttnDownBlock3D''',
													        '''DownBlock3D''',
													    ),
													    '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
													    '''cross_attention_dim''': 32,
													    '''attention_head_dim''': 8,
													    '''out_channels''': 4,
													    '''in_channels''': 4,
													    '''layers_per_block''': 1,
													    '''sample_size''': 32,
													}
													_a  :    Any			=						self.dummy_input
													return init_dict, inputs_dict




						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()		,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,	)
						def      snake_case__(    self       )			->						List[str]:
													_a		,					_a  :    Any			=						self.prepare_init_args_and_inputs_for_common()
													_a  :    Union[str, Any]			=						self.model_class(**lowercase       )

													model.enable_xformers_memory_efficient_attention()

													assert (
													    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
													    == "XFormersAttnProcessor"
													), "xformers is not enabled"




						def      snake_case__(    self       )			->						Any:
													_a		,					_a  :    int			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    str			=						32

													_a  :    Any			=						self.model_class(**lowercase       )
													model.to(lowercase       )
													model.eval()

													with torch.no_grad():
																				_a  :    int			=						model(**lowercase       )

																				if isinstance(lowercase		,	lowercase       ):
																											_a  :    List[Any]			=						output.sample

													self.assertIsNotNone(lowercase       )
													_a  :    str			=						inputs_dict['''sample'''].shape
													self.assertEqual(output.shape		,	lowercase		,	'''Input and output shapes do not match'''       )




						def      snake_case__(    self       )			->						Optional[Any]:
													_a		,					_a  :    str			=						self.prepare_init_args_and_inputs_for_common()
													_a  :    Any			=						self.model_class(**lowercase       )
													model.to(lowercase       )
													model.eval()

													with torch.no_grad():
																				# Warmup pass when using mps (see #372)
																				if torch_device == "mps" and isinstance(lowercase		,	lowercase       ):
																											model(**self.dummy_input       )

																				_a  :    int			=						model(**lowercase       )
																				if isinstance(lowercase		,	lowercase       ):
																											_a  :    int			=						first.sample

																				_a  :    Union[str, Any]			=						model(**lowercase       )
																				if isinstance(lowercase		,	lowercase       ):
																											_a  :    Dict			=						second.sample

													_a  :    str			=						first.cpu().numpy()
													_a  :    Tuple			=						second.cpu().numpy()
													_a  :    List[str]			=						out_a[~np.isnan(lowercase       )]
													_a  :    str			=						out_a[~np.isnan(lowercase       )]
													_a  :    List[Any]			=						np.amax(np.abs(out_a - out_a       )       )
													self.assertLessEqual(lowercase		,	1e-5       )




						def      snake_case__(    self       )			->						Tuple:
													_a		,					_a  :    Dict			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    int			=						8

													_a  :    Union[str, Any]			=						self.model_class(**lowercase       )
													model.to(lowercase       )
													model.eval()

													model.set_attention_slice('''auto'''       )
													with torch.no_grad():
																				_a  :    Tuple			=						model(**lowercase       )
													assert output is not None

													model.set_attention_slice('''max'''       )
													with torch.no_grad():
																				_a  :    Optional[int]			=						model(**lowercase       )
													assert output is not None

													model.set_attention_slice(2       )
													with torch.no_grad():
																				_a  :    Dict			=						model(**lowercase       )
													assert output is not None




						def      snake_case__(    self       )			->						Tuple:
													_a		,					_a  :    Union[str, Any]			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    str			=						8

													_a  :    Union[str, Any]			=						self.model_class(**lowercase       )
													model.to(lowercase       )

													with torch.no_grad():
																				_a  :    str			=						model(**lowercase       ).sample

													_a  :    Any			=						create_lora_layers(lowercase       )

													# make sure we can set a list of attention processors
													model.set_attn_processor(lowercase       )
													model.to(lowercase       )

													# test that attn processors can be set to itself
													model.set_attn_processor(model.attn_processors       )

													with torch.no_grad():
																				_a  :    Any			=						model(**lowercase		,	cross_attention_kwargs={'''scale''': 0.0}       ).sample
																				_a  :    Tuple			=						model(**lowercase		,	cross_attention_kwargs={'''scale''': 0.5}       ).sample
																				_a  :    int			=						model(**lowercase		,	cross_attention_kwargs={'''scale''': 0.5}       ).sample

													assert (samplea - samplea).abs().max() < 3e-3
													assert (samplea - samplea).abs().max() < 3e-3

													# sample 2 and sample 3 should be different
													assert (samplea - samplea).abs().max() > 3e-3




						def      snake_case__(    self       )			->						List[str]:
													_a		,					_a  :    Dict			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    int			=						8

													torch.manual_seed(0       )
													_a  :    List[Any]			=						self.model_class(**lowercase       )
													model.to(lowercase       )

													with torch.no_grad():
																				_a  :    Dict			=						model(**lowercase       ).sample

													_a  :    str			=						create_lora_layers(lowercase       )
													model.set_attn_processor(lowercase       )

													with torch.no_grad():
																				_a  :    Optional[Any]			=						model(**lowercase		,	cross_attention_kwargs={'''scale''': 0.5}       ).sample

													with tempfile.TemporaryDirectory() as tmpdirname:
																				model.save_attn_procs(lowercase       )
																				self.assertTrue(os.path.isfile(os.path.join(lowercase		,	'''pytorch_lora_weights.bin'''       )       )       )
																				torch.manual_seed(0       )
																				_a  :    Any			=						self.model_class(**lowercase       )
																				new_model.to(lowercase       )
																				new_model.load_attn_procs(lowercase       )

													with torch.no_grad():
																				_a  :    List[str]			=						new_model(**lowercase		,	cross_attention_kwargs={'''scale''': 0.5}       ).sample

													assert (sample - new_sample).abs().max() < 1e-3

													# LoRA and no LoRA should NOT be the same
													assert (sample - old_sample).abs().max() > 1e-4




						def      snake_case__(    self       )			->						List[str]:
													_a		,					_a  :    List[Any]			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    Optional[Any]			=						8

													torch.manual_seed(0       )
													_a  :    List[Any]			=						self.model_class(**lowercase       )
													model.to(lowercase       )

													with torch.no_grad():
																				_a  :    Optional[Any]			=						model(**lowercase       ).sample

													_a  :    Tuple			=						create_lora_layers(lowercase       )
													model.set_attn_processor(lowercase       )

													with torch.no_grad():
																				_a  :    str			=						model(**lowercase		,	cross_attention_kwargs={'''scale''': 0.5}       ).sample

													with tempfile.TemporaryDirectory() as tmpdirname:
																				model.save_attn_procs(lowercase		,	safe_serialization=lowercase       )
																				self.assertTrue(os.path.isfile(os.path.join(lowercase		,	'''pytorch_lora_weights.safetensors'''       )       )       )
																				torch.manual_seed(0       )
																				_a  :    Optional[int]			=						self.model_class(**lowercase       )
																				new_model.to(lowercase       )
																				new_model.load_attn_procs(lowercase       )

													with torch.no_grad():
																				_a  :    Optional[int]			=						new_model(**lowercase		,	cross_attention_kwargs={'''scale''': 0.5}       ).sample

													assert (sample - new_sample).abs().max() < 3e-3

													# LoRA and no LoRA should NOT be the same
													assert (sample - old_sample).abs().max() > 1e-4




						def      snake_case__(    self       )			->						Any:
													# enable deterministic behavior for gradient checkpointing
													_a		,					_a  :    Tuple			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    Dict			=						8

													torch.manual_seed(0       )
													_a  :    Union[str, Any]			=						self.model_class(**lowercase       )
													model.to(lowercase       )

													_a  :    str			=						create_lora_layers(lowercase		,	mock_weights=lowercase       )
													model.set_attn_processor(lowercase       )
													# Saving as torch, properly reloads with directly filename
													with tempfile.TemporaryDirectory() as tmpdirname:
																				model.save_attn_procs(lowercase       )
																				self.assertTrue(os.path.isfile(os.path.join(lowercase		,	'''pytorch_lora_weights.bin'''       )       )       )
																				torch.manual_seed(0       )
																				_a  :    Union[str, Any]			=						self.model_class(**lowercase       )
																				new_model.to(lowercase       )
																				new_model.load_attn_procs(lowercase		,	weight_name='''pytorch_lora_weights.bin'''       )




						def      snake_case__(    self       )			->						Any:
													_a		,					_a  :    Union[str, Any]			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    Optional[int]			=						8

													torch.manual_seed(0       )
													_a  :    Optional[int]			=						self.model_class(**lowercase       )
													model.to(lowercase       )

													_a  :    Tuple			=						create_lora_layers(lowercase		,	mock_weights=lowercase       )
													model.set_attn_processor(lowercase       )
													# Saving as torch, properly reloads with directly filename
													with tempfile.TemporaryDirectory() as tmpdirname:
																				model.save_attn_procs(lowercase       )
																				self.assertTrue(os.path.isfile(os.path.join(lowercase		,	'''pytorch_lora_weights.bin'''       )       )       )
																				torch.manual_seed(0       )
																				_a  :    str			=						self.model_class(**lowercase       )
																				new_model.to(lowercase       )
																				with self.assertRaises(lowercase       ) as e:
																											new_model.load_attn_procs(lowercase		,	use_safetensors=lowercase       )
																				self.assertIn('''Error no file named pytorch_lora_weights.safetensors'''		,	str(e.exception       )       )




						def      snake_case__(    self       )			->						Optional[Any]:
													_a		,					_a  :    Tuple			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    Tuple			=						8

													torch.manual_seed(0       )
													_a  :    List[Any]			=						self.model_class(**lowercase       )
													model.to(lowercase       )

													with torch.no_grad():
																				_a  :    int			=						model(**lowercase       ).sample

													_a  :    Tuple			=						create_lora_layers(lowercase       )
													model.set_attn_processor(lowercase       )

													with torch.no_grad():
																				_a  :    Union[str, Any]			=						model(**lowercase		,	cross_attention_kwargs={'''scale''': 0.0}       ).sample

													model.set_attn_processor(AttnProcessor()       )

													with torch.no_grad():
																				_a  :    Dict			=						model(**lowercase       ).sample

													assert (sample - new_sample).abs().max() < 1e-4
													assert (sample - old_sample).abs().max() < 3e-3




						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()		,	reason='''XFormers attention is only available with CUDA and `xformers` installed'''		,	)
						def      snake_case__(    self       )			->						Optional[Any]:
													# enable deterministic behavior for gradient checkpointing
													_a		,					_a  :    Optional[int]			=						self.prepare_init_args_and_inputs_for_common()

													_a  :    List[Any]			=						4

													torch.manual_seed(0       )
													_a  :    List[Any]			=						self.model_class(**lowercase       )
													model.to(lowercase       )
													_a  :    Union[str, Any]			=						create_lora_layers(lowercase       )
													model.set_attn_processor(lowercase       )

													# default
													with torch.no_grad():
																				_a  :    str			=						model(**lowercase       ).sample

																				model.enable_xformers_memory_efficient_attention()
																				_a  :    Optional[Any]			=						model(**lowercase       ).sample

																				model.disable_xformers_memory_efficient_attention()
																				_a  :    Dict			=						model(**lowercase       ).sample

													assert (sample - on_sample).abs().max() < 1e-4
													assert (sample - off_sample).abs().max() < 1e-4







						def      snake_case__(    self       )			->						Optional[int]:
													_a		,					_a  :    Optional[int]			=						self.prepare_init_args_and_inputs_for_common()
													_a  :    Tuple			=						32

													_a  :    Tuple			=						self.model_class(**lowercase       )
													model.to(lowercase       )
													model.eval()

													with torch.no_grad():
																				_a  :    Union[str, Any]			=						model(**lowercase       )[0]

													model.enable_forward_chunking()
													with torch.no_grad():
																				_a  :    List[Any]			=						model(**lowercase       )[0]

													self.assertEqual(output.shape		,	output_a.shape		,	'''Shape doesn\'t match'''       )
													assert np.abs(output.cpu() - output_a.cpu()       ).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.







import copy
from typing import Dict

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


__lowerCamelCase						= logging.get_logger(__name__)

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''semantic'''




						def __init__(    self		,	lowercase=10_000		,	lowercase=True		,	lowercase=768		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=10_048		,	lowercase=129_595		,	lowercase=129_599		,	lowercase=10_000		,	lowercase=256		,	lowercase=49.9		,	**lowercase		,	)			->						int:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	eos_token_id=lowercase		,	renormalize_logits=lowercase		,	max_new_tokens=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    Optional[int]			=						text_encoding_offset
													_a  :    str			=						text_pad_token
													_a  :    List[str]			=						eos_token_id
													_a  :    Tuple			=						semantic_infer_token
													_a  :    int			=						semantic_vocab_size
													_a  :    List[str]			=						max_input_semantic_length
													_a  :    Union[str, Any]			=						semantic_rate_hz

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''coarse_acoustics'''




						def __init__(    self		,	lowercase=True		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	lowercase=0.7		,	lowercase=True		,	lowercase=12_048		,	lowercase=75		,	lowercase=2		,	lowercase=12_050		,	lowercase=256		,	lowercase = 630		,	lowercase = 60		,	**lowercase		,	)			->						List[Any]:
													super().__init__(
													    temperature=lowercase		,	do_sample=lowercase		,	renormalize_logits=lowercase		,	output_scores=lowercase		,	return_dict_in_generate=lowercase		,	output_hidden_states=lowercase		,	output_attentions=lowercase		,	**lowercase		,	)

													_a  :    str			=						coarse_semantic_pad_token
													_a  :    Any			=						coarse_rate_hz
													_a  :    Dict			=						n_coarse_codebooks
													_a  :    str			=						coarse_infer_token
													_a  :    List[Any]			=						max_coarse_input_length
													_a  :    Union[str, Any]			=						max_coarse_history
													_a  :    Any			=						sliding_window_len

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''fine_acoustics'''




						def __init__(    self		,	lowercase=0.5		,	lowercase=512		,	lowercase=1_024		,	lowercase=8		,	**lowercase		,	)			->						str:
													super().__init__(temperature=lowercase       )

													_a  :    Tuple			=						max_fine_history_length
													_a  :    Tuple			=						max_fine_input_length
													_a  :    Any			=						n_fine_codebooks

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''bark'''
						lowercase									=  True




						def __init__(    self		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase=24_000		,	lowercase=1_024		,	**lowercase		,	)			->						List[str]:
													if semantic_config is None:
																				_a  :    str			=						{}
																				logger.info('''semantic_config is None. initializing the semantic model with default values.'''       )

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

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

													_a  :    List[Any]			=						BarkSemanticGenerationConfig(**lowercase       )
													_a  :    Union[str, Any]			=						BarkCoarseGenerationConfig(**lowercase       )
													_a  :    str			=						BarkFineGenerationConfig(**lowercase       )

													_a  :    List[str]			=						sample_rate
													_a  :    str			=						codebook_size




						@classmethod
						def      snake_case__(    cls		,	lowercase		,	lowercase		,	lowercase		,	**lowercase		,	)			->						List[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()		,	**lowercase		,	)







						def      snake_case__(    self       )			->						List[str]:
													_a  :    Any			=						copy.deepcopy(self.__dict__       )

													_a  :    str			=						self.semantic_config.to_dict()
													_a  :    Any			=						self.coarse_acoustics_config.to_dict()
													_a  :    Union[str, Any]			=						self.fine_acoustics_config.to_dict()

													_a  :    List[str]			=						self.__class__.model_type
													return output