code: 
  
  
  
  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
  
  
  
  
  
style_context: 
  
  
  
  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
  
  
  
  
  