



from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


if is_tf_available():
 import numpy as np
 import tensorflow as tf

 from transformers import (
     TF_MODEL_FOR_CAUSAL_LM_MAPPING,
     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_MASKED_LM_MAPPING,
     TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
     TF_MODEL_FOR_PRETRAINING_MAPPING,
     TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
     TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     TFSharedEmbeddings,
 )

 if _tf_gpu_memory_limit is not None:
  __A       					=		tf.config.list_physical_devices('''GPU''')
  for gpu in gpus:
   # Restrict TensorFlow to only allocate x GB of memory on the GPUs
   try:
    tf.config.set_logical_device_configuration(
        gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
    )
    __A       					=		tf.config.list_logical_devices('''GPU''')
    print('''Logical GPUs''', logical_gpus)
   except RuntimeError as e:
    # Virtual devices must be set before GPUs have been initialized
    print(e)






@require_tf
class 	lowercase		:






 """simple docstring"""


 a__   :      List[str]	     =							None
 a__   :      Any	     =							()
 a__   :      Optional[int]	     =							()
 a__   :      List[Any]	     =							True
 a__   :      List[str]	     =							True
 a__   :      str	     =							True
 a__   :      List[Any]	     =							False




 def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Any=False  )    ->				int:
       UpperCAmelCase_=     copy.deepcopy(__UpperCAmelCase  )

       if model_class in get_values(__UpperCAmelCase  ):
             UpperCAmelCase_=     {
                 k: tf.tile(tf.expand_dims(__UpperCAmelCase		,						1  )		,						(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)  )
                 if isinstance(__UpperCAmelCase		,						tf.Tensor  ) and v.ndim > 0
                 else v
                 for k, v in inputs_dict.items()
             }

       if return_labels:
             if model_class in get_values(__UpperCAmelCase  ):
                   UpperCAmelCase_=     tf.ones(self.model_tester.batch_size		,						dtype=tf.intaa  )
             elif model_class in get_values(__UpperCAmelCase  ):
                   UpperCAmelCase_=     tf.zeros(self.model_tester.batch_size		,						dtype=tf.intaa  )
                   UpperCAmelCase_=     tf.zeros(self.model_tester.batch_size		,						dtype=tf.intaa  )
             elif model_class in [
                 *get_values(__UpperCAmelCase  ),
                 *get_values(__UpperCAmelCase  ),
             ]:
                   UpperCAmelCase_=     tf.zeros(self.model_tester.batch_size		,						dtype=tf.intaa  )
             elif model_class in get_values(__UpperCAmelCase  ):
                   UpperCAmelCase_=     tf.zeros(self.model_tester.batch_size		,						dtype=tf.intaa  )
             elif model_class in [
                 *get_values(__UpperCAmelCase  ),
                 *get_values(__UpperCAmelCase  ),
                 *get_values(__UpperCAmelCase  ),
                 *get_values(__UpperCAmelCase  ),
                 *get_values(__UpperCAmelCase  ),
             ]:
                   UpperCAmelCase_=     tf.zeros(
                       (self.model_tester.batch_size, self.model_tester.seq_length)		,						dtype=tf.intaa  )
       return inputs_dict




 @slow
 def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Optional[int]:
       UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
             UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )

             @tf.function
             def run_in_graph_mode():
                   return model(__UpperCAmelCase  )

             UpperCAmelCase_=     run_in_graph_mode()
             self.assertIsNotNone(__UpperCAmelCase  )




 @slow
 def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]  )    ->				Optional[int]:
       UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
             UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )

             @tf.function(experimental_compile=__UpperCAmelCase  )
             def run_in_graph_mode():
                   return model(__UpperCAmelCase  )

             UpperCAmelCase_=     run_in_graph_mode()
             self.assertIsNotNone(__UpperCAmelCase  )




 @slow
 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Tuple:
       UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes[:2]:
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )
             if getattr(__UpperCAmelCase		,						"""hf_compute_loss"""		,						__UpperCAmelCase  ):
                   # Test that model correctly compute the loss with kwargs
                   UpperCAmelCase_=     self._prepare_for_class(inputs_dict.copy()		,						__UpperCAmelCase		,						return_labels=__UpperCAmelCase  )
                   # Is there a better way to remove these decoder inputs?
                   UpperCAmelCase_=     {
                       key: val
                       for key, val in prepared_for_class.items()
                       if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
                   }

                   UpperCAmelCase_=     {
                       "labels",
                       "label",
                       "label_ids",
                       "start_positions",
                       "start_position",
                       "end_positions",
                       "end_position",
                       "next_sentence_label",
                   }
                   UpperCAmelCase_=     possible_label_cols.intersection(set(__UpperCAmelCase  )  )
                   self.assertGreater(len(__UpperCAmelCase  )		,						0		,						msg="""No matching label names found!"""  )
                   UpperCAmelCase_=     {key: val for key, val in prepared_for_class.items() if key in label_names}
                   UpperCAmelCase_=     {key: val for key, val in prepared_for_class.items() if key not in label_names}
                   self.assertGreater(len(__UpperCAmelCase  )		,						0  )

                   # Make sure it works with XLA!
                   model.compile(optimizer=tf.keras.optimizers.SGD(0.0  )		,						jit_compile=__UpperCAmelCase  )
                   # Make sure the model fits without crashing regardless of where we pass the labels
                   UpperCAmelCase_=     model.fit(
                       __UpperCAmelCase		,						validation_data=__UpperCAmelCase		,						steps_per_epoch=1		,						validation_steps=1		,						shuffle=__UpperCAmelCase		,						verbose=0		,						)
                   UpperCAmelCase_=     history.history["loss"][0]
                   self.assertTrue(not isnan(__UpperCAmelCase  )  )
                   UpperCAmelCase_=     history.history["val_loss"][0]
                   self.assertTrue(not isnan(__UpperCAmelCase  )  )

                   # Now test it with separate labels, to make sure that path works in XLA too.
                   UpperCAmelCase_=     model_class(__UpperCAmelCase  )
                   model.compile(optimizer=tf.keras.optimizers.SGD(0.0  )		,						jit_compile=__UpperCAmelCase  )
                   UpperCAmelCase_=     model.fit(
                       __UpperCAmelCase		,						__UpperCAmelCase		,						validation_data=(inputs_minus_labels, labels)		,						steps_per_epoch=1		,						validation_steps=1		,						shuffle=__UpperCAmelCase		,						verbose=0		,						)

                   UpperCAmelCase_=     history.history["loss"][0]
                   self.assertTrue(not isnan(__UpperCAmelCase  )  )
                   UpperCAmelCase_=     history.history["val_loss"][0]
                   self.assertTrue(not isnan(__UpperCAmelCase  )  )




 @slow
 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				List[Any]:
       UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_=     True
       UpperCAmelCase_=     True

       if hasattr(__UpperCAmelCase		,						"""use_cache"""  ):
             UpperCAmelCase_=     True

       UpperCAmelCase_=     getattr(self.model_tester		,						"""encoder_seq_length"""		,						self.model_tester.seq_length  )
       UpperCAmelCase_=     getattr(self.model_tester		,						"""key_length"""		,						__UpperCAmelCase  )

       for model_class in self.all_model_classes[:2]:
             UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )
             model.build()
             UpperCAmelCase_=     len(model(__UpperCAmelCase  )  )

             for key in list(class_inputs_dict.keys()  ):
                   # Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
                   if key not in model.input_signature:
                         del class_inputs_dict[key]
                   # Check it's a tensor, in case the inputs dict has some bools in it too
                   elif isinstance(class_inputs_dict[key]		,						tf.Tensor  ) and class_inputs_dict[key].dtype.is_integer:
                         UpperCAmelCase_=     tf.cast(class_inputs_dict[key]		,						tf.intaa  )

             if set(class_inputs_dict.keys()  ) != set(model.input_signature.keys()  ):
                   continue  # Some models have inputs that the preparation functions don't create, we skip those

             with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(__UpperCAmelCase		,						saved_model=__UpperCAmelCase  )
                   UpperCAmelCase_=     os.path.join(__UpperCAmelCase		,						"""saved_model"""		,						"""1"""  )
                   UpperCAmelCase_=     tf.keras.models.load_model(__UpperCAmelCase  )
                   UpperCAmelCase_=     model(__UpperCAmelCase  )

                   if self.is_encoder_decoder:
                         UpperCAmelCase_=     outputs["encoder_hidden_states"]
                         UpperCAmelCase_=     outputs["encoder_attentions"]
                   else:
                         UpperCAmelCase_=     outputs["hidden_states"]
                         UpperCAmelCase_=     outputs["attentions"]

                   self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )

                   UpperCAmelCase_=     getattr(
                       self.model_tester		,						"""expected_num_hidden_layers"""		,						self.model_tester.num_hidden_layers + 1  )

                   self.assertEqual(len(__UpperCAmelCase  )		,						__UpperCAmelCase  )
                   self.assertListEqual(
                       list(output_hidden_states[0].shape[-2:]  )		,						[self.model_tester.seq_length, self.model_tester.hidden_size]		,						)

                   self.assertEqual(len(__UpperCAmelCase  )		,						self.model_tester.num_hidden_layers  )
                   self.assertListEqual(
                       list(output_attentions[0].shape[-3:]  )		,						[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]		,						)




 @slow
 def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				Union[str, Any]:
       tf.keras.mixed_precision.set_global_policy("""mixed_float16"""  )

       # try/finally block to ensure subsequent tests run in float32
       try:
             UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
             for model_class in self.all_model_classes[:2]:
                   UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )
                   UpperCAmelCase_=     model_class(__UpperCAmelCase  )
                   UpperCAmelCase_=     model(__UpperCAmelCase  )

                   self.assertIsNotNone(__UpperCAmelCase  )
       finally:
             tf.keras.mixed_precision.set_global_policy("""float32"""  )




 @slow
 def 						_SCREAMING_SNAKE_CASE	(			self			:		int  )    ->				Optional[Any]:
       UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()
       # head_mask and decoder_head_mask has different shapes than other input args
       if "head_mask" in inputs_dict:
             del inputs_dict["head_mask"]
       if "decoder_head_mask" in inputs_dict:
             del inputs_dict["decoder_head_mask"]
       if "cross_attn_head_mask" in inputs_dict:
             del inputs_dict["cross_attn_head_mask"]
       UpperCAmelCase_=     {
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__  ),)
           for module_member_name in dir(__UpperCAmelCase  )
           if module_member_name.endswith("""MainLayer"""  )
           for module_member in (getattr(__UpperCAmelCase		,						__UpperCAmelCase  ),)
           if isinstance(__UpperCAmelCase		,						__UpperCAmelCase  )
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(__UpperCAmelCase		,						"""_keras_serializable"""		,						__UpperCAmelCase  )
       }

       for main_layer_class in tf_main_layer_classes:
             # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
             if "T5" in main_layer_class.__name__:
                   # Take the same values than in TFT5ModelTester for this shared layer
                   UpperCAmelCase_=     TFSharedEmbeddings(self.model_tester.vocab_size		,						self.model_tester.hidden_size		,						name="""shared"""  )
                   UpperCAmelCase_=     False
                   UpperCAmelCase_=     main_layer_class(__UpperCAmelCase		,						embed_tokens=__UpperCAmelCase  )
             else:
                   UpperCAmelCase_=     main_layer_class(__UpperCAmelCase  )

             UpperCAmelCase_=     {
                 name: tf.keras.Input(tensor.shape[1:]		,						dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
             }

             if hasattr(self.model_tester		,						"""num_labels"""  ):
                   UpperCAmelCase_=     self.model_tester.num_labels
             else:
                   UpperCAmelCase_=     2

             UpperCAmelCase_=     tf.data.Dataset.from_tensor_slices(
                 (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)  ))  ).batch(1  )

             UpperCAmelCase_=     main_layer(__UpperCAmelCase  )[0]
             UpperCAmelCase_=     tf.keras.layers.Dense(__UpperCAmelCase		,						activation="""softmax"""		,						name="""outputs"""  )(__UpperCAmelCase  )
             UpperCAmelCase_=     tf.keras.models.Model(inputs=__UpperCAmelCase		,						outputs=[outputs]  )

             model.compile(loss="""binary_crossentropy"""		,						optimizer="""adam"""		,						metrics=["""binary_accuracy"""]  )
             model.fit(__UpperCAmelCase		,						epochs=1  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                   UpperCAmelCase_=     os.path.join(__UpperCAmelCase		,						"""keras_model.h5"""  )
                   model.save(__UpperCAmelCase  )
                   if "T5" in main_layer_class.__name__:
                         UpperCAmelCase_=     tf.keras.models.load_model(
                             __UpperCAmelCase		,						custom_objects={
                                 main_layer_class.__name__: main_layer_class,
                                 """TFSharedEmbeddings""": TFSharedEmbeddings,
                             }		,						)
                   else:
                         UpperCAmelCase_=     tf.keras.models.load_model(
                             __UpperCAmelCase		,						custom_objects={main_layer_class.__name__: main_layer_class}  )
                   assert isinstance(__UpperCAmelCase		,						tf.keras.Model  )
                   model(__UpperCAmelCase  )




 @slow
 def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[Any]  )    ->				Optional[Any]:
       UpperCAmelCase_=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes[:2]:
             UpperCAmelCase_=     model_class(__UpperCAmelCase  )

             UpperCAmelCase_=     copy.deepcopy(__UpperCAmelCase  )

             if not self.is_encoder_decoder:
                   UpperCAmelCase_=     inputs["input_ids"]
                   del inputs["input_ids"]
             else:
                   UpperCAmelCase_=     inputs["input_ids"]
                   UpperCAmelCase_=     inputs.get("""decoder_input_ids"""		,						__UpperCAmelCase  )
                   del inputs["input_ids"]
                   inputs.pop("""decoder_input_ids"""		,						__UpperCAmelCase  )

             if not self.is_encoder_decoder:
                   UpperCAmelCase_=     model.get_input_embeddings()(__UpperCAmelCase  )
             else:
                   UpperCAmelCase_=     model.get_input_embeddings()(__UpperCAmelCase  )
                   UpperCAmelCase_=     model.get_input_embeddings()(__UpperCAmelCase  )

             UpperCAmelCase_=     self._prepare_for_class(__UpperCAmelCase		,						__UpperCAmelCase  )

             @tf.function
             def run_in_graph_mode():
                   return model(__UpperCAmelCase  )

             UpperCAmelCase_=     run_in_graph_mode()
             self.assertIsNotNone(__UpperCAmelCase  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		int  )    ->				Union[str, Any]:
       UpperCAmelCase_=     []
       if model.config.bos_token_id is not None:
             special_tokens.append(model.config.bos_token_id  )
       if model.config.pad_token_id is not None:
             special_tokens.append(model.config.pad_token_id  )
       if model.config.eos_token_id is not None:
             special_tokens.append(model.config.eos_token_id  )

       # create random bad tokens that are not special tokens
       UpperCAmelCase_=     []
       while len(__UpperCAmelCase  ) < num_bad_tokens:
             UpperCAmelCase_=     tf.squeeze(ids_tensor((1, 1)		,						self.model_tester.vocab_size  )		,						0  ).numpy()[0]
             if token not in special_tokens:
                   bad_tokens.append(__UpperCAmelCase  )
       return bad_tokens




 def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						__UpperCAmelCase			:		Any  )    ->				Dict:







       for token_id in output_ids[0].numpy().tolist():
             self.assertGreaterEqual(__UpperCAmelCase		,						0  )
             self.assertLess(__UpperCAmelCase		,						self.model_tester.vocab_size  )




 def 						_SCREAMING_SNAKE_CASE	(			self			:		str		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Dict  )    ->				str:







       for bad_word_ids in bad_words_ids:
             # for all slices in batch
             for generated_ids_slice in generated_ids:
                   # for all word idx
                   for i in range(len(__UpperCAmelCase  )		,						len(__UpperCAmelCase  )  ):
                         # if tokens match
                         if generated_ids_slice[i - len(__UpperCAmelCase  ) : i] == bad_word_ids:
                               return True
       return False









import copy
from typing import Dict

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


__A       					=		logging.get_logger(__name__)






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Optional[int]	     =							"semantic"




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

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






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"coarse_acoustics"




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

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






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Tuple	     =							"fine_acoustics"




	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Tuple=0.5		,						__UpperCAmelCase			:		Optional[Any]=512		,						__UpperCAmelCase			:		Optional[Any]=1_024		,						__UpperCAmelCase			:		List[str]=8		,						**__UpperCAmelCase			:		str		,						)    ->				Tuple:
							super().__init__(temperature=__UpperCAmelCase  )

							UpperCAmelCase_=     max_fine_history_length
							UpperCAmelCase_=     max_fine_input_length
							UpperCAmelCase_=     n_fine_codebooks






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"bark"
	a__   :      List[str]	     =							True




	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		str=24_000		,						__UpperCAmelCase			:		List[str]=1_024		,						**__UpperCAmelCase			:		Optional[int]		,						)    ->				List[str]:
							if semantic_config is None:
													UpperCAmelCase_=     {}
													logger.info("""semantic_config is None. initializing the semantic model with default values."""  )

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

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

							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )

							UpperCAmelCase_=     sample_rate
							UpperCAmelCase_=     codebook_size




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				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()		,						**__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )

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

							UpperCAmelCase_=     self.__class__.model_type
							return output




