




'''simple docstring'''
import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType






@require_torch
class        SCREAMING_SNAKE_CASE:
       snake_case_ :     Dict    =					()
       snake_case_ :     Tuple    =					True

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase							=						self.config_class

           # test default config
           __lowercase							=						config_class()
           self.assertIsNotNone(lowerCamelCase__     )
           __lowercase							=						["""stem"""] + [F'stage{idx}' for idx in range(1					,		len(config.depths     ) + 1     )]
           self.assertEqual(config.stage_names					,		lowerCamelCase__     )
           self.assertTrue(set(config.out_features     ).issubset(set(config.stage_names     )     )     )

           # Test out_features and out_indices are correctly set
           # out_features and out_indices both None
           __lowercase							=						config_class(out_features=lowerCamelCase__					,		out_indices=lowerCamelCase__     )
           self.assertEqual(config.out_features					,		[config.stage_names[-1]]     )
           self.assertEqual(config.out_indices					,		[len(config.stage_names     ) - 1]     )

           # out_features and out_indices both set
           __lowercase							=						config_class(out_features=["""stem""", """stage1"""]					,		out_indices=[0, 1]     )
           self.assertEqual(config.out_features					,		["""stem""", """stage1"""]     )
           self.assertEqual(config.out_indices					,		[0, 1]     )

           # Only out_features set
           __lowercase							=						config_class(out_features=["""stage1""", """stage3"""]     )
           self.assertEqual(config.out_features					,		["""stage1""", """stage3"""]     )
           self.assertEqual(config.out_indices					,		[1, 3]     )

           # Only out_indices set
           __lowercase							=						config_class(out_indices=[0, 2]     )
           self.assertEqual(config.out_features					,		[config.stage_names[0], config.stage_names[2]]     )
           self.assertEqual(config.out_indices					,		[0, 2]     )

           # Error raised when out_indices do not correspond to out_features
           with self.assertRaises(lowerCamelCase__     ):
               __lowercase							=						config_class(out_features=["""stage1""", """stage2"""]					,		out_indices=[0, 2]     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __lowercase							=						model_class(lowerCamelCase__     )
               __lowercase							=						inspect.signature(model.forward     )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __lowercase							=						[*signature.parameters.keys()]
               __lowercase							=						["""pixel_values"""]
               self.assertListEqual(arg_names[:1]					,		lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					Tuple:

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __lowercase							=						model_class(lowerCamelCase__     )
               self.assertEqual(len(model.channels     )					,		len(config.out_features     )     )
               __lowercase							=						model.num_features
               __lowercase							=						[config.stage_names.index(lowerCamelCase__     ) for feat in config.out_features]
               __lowercase							=						[num_features[idx] for idx in out_indices]
               self.assertListEqual(model.channels					,		lowerCamelCase__     )

               __lowercase							=						copy.deepcopy(lowerCamelCase__     )
               __lowercase							=						None
               __lowercase							=						model_class(lowerCamelCase__     )
               self.assertEqual(len(model.channels     )					,		1     )
               self.assertListEqual(model.channels					,		[num_features[-1]]     )

               __lowercase							=						copy.deepcopy(lowerCamelCase__     )
               __lowercase							=						None
               __lowercase							=						model_class(lowerCamelCase__     )
               self.assertEqual(len(model.channels     )					,		1     )
               self.assertListEqual(model.channels					,		[num_features[-1]]     )

       def     snake_case__			(    self     )    ->					Dict:

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __lowercase							=						model_class(lowerCamelCase__     )
               model.to(lowerCamelCase__     )
               model.eval()
               __lowercase							=						model(**lowerCamelCase__     )

               self.assertEqual(len(result.feature_maps     )					,		len(config.out_features     )     )
               self.assertEqual(len(model.channels     )					,		len(config.out_features     )     )
               self.assertEqual(len(result.feature_maps     )					,		len(config.out_indices     )     )
               self.assertEqual(len(model.channels     )					,		len(config.out_indices     )     )

               # Check output of last stage is taken if out_features=None, out_indices=None
               __lowercase							=						copy.deepcopy(lowerCamelCase__     )
               __lowercase							=						None
               __lowercase							=						model_class(lowerCamelCase__     )
               model.to(lowerCamelCase__     )
               model.eval()
               __lowercase							=						model(**lowerCamelCase__     )

               self.assertEqual(len(result.feature_maps     )					,		1     )
               self.assertEqual(len(model.channels     )					,		1     )

               __lowercase							=						copy.deepcopy(lowerCamelCase__     )
               __lowercase							=						None
               __lowercase							=						model_class(lowerCamelCase__     )
               model.to(lowerCamelCase__     )
               model.eval()
               __lowercase							=						model(**lowerCamelCase__     )

               self.assertEqual(len(result.feature_maps     )					,		1     )
               self.assertEqual(len(model.channels     )					,		1     )

               # Check backbone can be initialized with fresh weights
               __lowercase							=						copy.deepcopy(lowerCamelCase__     )
               __lowercase							=						False
               __lowercase							=						model_class(lowerCamelCase__     )
               model.to(lowerCamelCase__     )
               model.eval()
               __lowercase							=						model(**lowerCamelCase__     )

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()

           for backbone_class in self.all_model_classes:
               __lowercase							=						backbone_class(lowerCamelCase__     )

               self.assertTrue(hasattr(lowerCamelCase__					,		"""backbone_type"""     )     )
               self.assertTrue(hasattr(lowerCamelCase__					,		"""stage_names"""     )     )
               self.assertTrue(hasattr(lowerCamelCase__					,		"""num_features"""     )     )
               self.assertTrue(hasattr(lowerCamelCase__					,		"""out_indices"""     )     )
               self.assertTrue(hasattr(lowerCamelCase__					,		"""out_features"""     )     )
               self.assertTrue(hasattr(lowerCamelCase__					,		"""out_feature_channels"""     )     )
               self.assertTrue(hasattr(lowerCamelCase__					,		"""channels"""     )     )

               self.assertIsInstance(backbone.backbone_type					,		lowerCamelCase__     )
               # Verify num_features has been initialized in the backbone init
               self.assertIsNotNone(backbone.num_features     )
               self.assertTrue(len(backbone.channels     ) == len(backbone.out_indices     )     )
               self.assertTrue(len(backbone.stage_names     ) == len(backbone.num_features     )     )
               self.assertTrue(len(backbone.channels     ) <= len(backbone.num_features     )     )
               self.assertTrue(len(backbone.out_feature_channels     ) == len(backbone.stage_names     )     )




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

           """simple docstring"""



           __lowercase				,__lowercase							=						self.model_tester.prepare_config_and_inputs_for_common()
           __lowercase							=						inputs_dict["""pixel_values"""].shape[0]

           for backbone_class in self.all_model_classes:
               __lowercase							=						backbone_class(lowerCamelCase__     )
               backbone.to(lowerCamelCase__     )
               backbone.eval()

               __lowercase							=						backbone(**lowerCamelCase__     )

               # Test default outputs and verify feature maps
               self.assertIsInstance(outputs.feature_maps					,		lowerCamelCase__     )
               self.assertTrue(len(outputs.feature_maps     ) == len(backbone.channels     )     )
               for feature_map, n_channels in zip(outputs.feature_maps					,		backbone.channels     ):
                   self.assertTrue(feature_map.shape[:2]					,		(batch_size, n_channels)     )
               self.assertIsNone(outputs.hidden_states     )
               self.assertIsNone(outputs.attentions     )

               # Test output_hidden_states=True
               __lowercase							=						backbone(**lowerCamelCase__					,		output_hidden_states=lowerCamelCase__     )
               self.assertIsNotNone(outputs.hidden_states     )
               self.assertTrue(len(outputs.hidden_states     )					,		len(backbone.stage_names     )     )
               for hidden_state, n_channels in zip(outputs.hidden_states					,		backbone.channels     ):
                   self.assertTrue(hidden_state.shape[:2]					,		(batch_size, n_channels)     )

               # Test output_attentions=True
               if self.has_attentions:
                   __lowercase							=						backbone(**lowerCamelCase__					,		output_attentions=lowerCamelCase__     )
                   self.assertIsNotNone(outputs.attentions     )










'''simple docstring'''
import copy
from typing import Dict

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


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






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     List[Any]    =					"""semantic"""

       def __init__(    self					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=True					,		lowerCamelCase__=768					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_0048					,		lowerCamelCase__=12_9595					,		lowerCamelCase__=12_9599					,		lowerCamelCase__=1_0000					,		lowerCamelCase__=256					,		lowerCamelCase__=49.9					,		**lowerCamelCase__					,		)    ->					List[Any]:

           """simple docstring"""



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

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






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[int]    =					"""coarse_acoustics"""

       def __init__(    self					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=0.7					,		lowerCamelCase__=True					,		lowerCamelCase__=1_2048					,		lowerCamelCase__=75					,		lowerCamelCase__=2					,		lowerCamelCase__=1_2050					,		lowerCamelCase__=256					,		lowerCamelCase__ = 630					,		lowerCamelCase__ = 60					,		**lowerCamelCase__					,		)    ->					Dict:

           """simple docstring"""



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

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






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""fine_acoustics"""

       def __init__(    self					,		lowerCamelCase__=0.5					,		lowerCamelCase__=512					,		lowerCamelCase__=1024					,		lowerCamelCase__=8					,		**lowerCamelCase__					,		)    ->					str:

           """simple docstring"""



           super().__init__(temperature=lowerCamelCase__     )

           __lowercase							=						max_fine_history_length
           __lowercase							=						max_fine_input_length
           __lowercase							=						n_fine_codebooks






class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     Optional[Any]    =					"""bark"""
       snake_case_ :     Dict    =					True

       def __init__(    self					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__=2_4000					,		lowerCamelCase__=1024					,		**lowerCamelCase__					,		)    ->					Optional[int]:

           """simple docstring"""



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

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

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

           __lowercase							=						BarkSemanticGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkCoarseGenerationConfig(**lowerCamelCase__     )
           __lowercase							=						BarkFineGenerationConfig(**lowerCamelCase__     )

           __lowercase							=						sample_rate
           __lowercase							=						codebook_size

       @classmethod
       def     snake_case__			(    cls					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		**lowerCamelCase__					,		)    ->					Union[str, Any]:

           """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()					,		**lowerCamelCase__					,		)




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

           """simple docstring"""



           __lowercase							=						copy.deepcopy(self.__dict__     )

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

           __lowercase							=						self.__class__.model_type
           return output




