code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import copy
  from typing import Dict
  
  from ...generation.configuration_utils import GenerationConfig
  from ...utils import logging
  
  
  _lowerCamelCase							:		Union[str, Any]										=						logging.get_logger(__name__)
  
  
  
  
  
  class   lowerCamelCase_							( lowerCamelCase__							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				'semantic'
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase=10_000,						__UpperCamelCase=True,						__UpperCamelCase=768,						__UpperCamelCase=False,						__UpperCamelCase=False,						__UpperCamelCase=False,						__UpperCamelCase=False,						__UpperCamelCase=0.7,						__UpperCamelCase=True,						__UpperCamelCase=10_048,						__UpperCamelCase=129_595,						__UpperCamelCase=129_599,						__UpperCamelCase=10_000,						__UpperCamelCase=256,						__UpperCamelCase=49.9,						**__UpperCamelCase,						):
  							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_,						)
  
  							_snake_case			 = text_encoding_offset
  							_snake_case			 = text_pad_token
  							_snake_case			 = eos_token_id
  							_snake_case			 = semantic_infer_token
  							_snake_case			 = semantic_vocab_size
  							_snake_case			 = max_input_semantic_length
  							_snake_case			 = semantic_rate_hz
  
  
  
  
  
  class   lowerCamelCase_							( lowerCamelCase__							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				'coarse_acoustics'
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase=True,						__UpperCamelCase=False,						__UpperCamelCase=False,						__UpperCamelCase=False,						__UpperCamelCase=False,						__UpperCamelCase=0.7,						__UpperCamelCase=True,						__UpperCamelCase=12_048,						__UpperCamelCase=75,						__UpperCamelCase=2,						__UpperCamelCase=12_050,						__UpperCamelCase=256,						__UpperCamelCase = 630,						__UpperCamelCase = 60,						**__UpperCamelCase,						):
  							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_,						)
  
  							_snake_case			 = coarse_semantic_pad_token
  							_snake_case			 = coarse_rate_hz
  							_snake_case			 = n_coarse_codebooks
  							_snake_case			 = coarse_infer_token
  							_snake_case			 = max_coarse_input_length
  							_snake_case			 = max_coarse_history
  							_snake_case			 = sliding_window_len
  
  
  
  
  
  class   lowerCamelCase_							( lowerCamelCase__							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				'fine_acoustics'
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase=0.5,						__UpperCamelCase=512,						__UpperCamelCase=1_024,						__UpperCamelCase=8,						**__UpperCamelCase,						):
  							super().__init__(temperature=lowercase_				)
  
  							_snake_case			 = max_fine_history_length
  							_snake_case			 = max_fine_input_length
  							_snake_case			 = n_fine_codebooks
  
  
  
  
  
  class   lowerCamelCase_							( lowerCamelCase__							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				'bark'
  					lowercase__					=				True
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase = None,						__UpperCamelCase = None,						__UpperCamelCase = None,						__UpperCamelCase=24_000,						__UpperCamelCase=1_024,						**__UpperCamelCase,						):
  
  
  
  
  
  
  							if semantic_config is None:
  									_snake_case			 = {}
  									logger.info("semantic_config is None. initializing the semantic model with default values."				)
  
  							if coarse_acoustics_config is None:
  									_snake_case			 = {}
  									logger.info("coarse_acoustics_config is None. initializing the coarse model with default values."				)
  
  							if fine_acoustics_config is None:
  									_snake_case			 = {}
  									logger.info("fine_acoustics_config is None. initializing the fine model with default values."				)
  
  							_snake_case			 = BarkSemanticGenerationConfig(**lowercase_				)
  							_snake_case			 = BarkCoarseGenerationConfig(**lowercase_				)
  							_snake_case			 = BarkFineGenerationConfig(**lowercase_				)
  
  							_snake_case			 = sample_rate
  							_snake_case			 = codebook_size
  
  
  
  
  
  
  					@classmethod
  					def        __SCREAMING_SNAKE_CASE			(					cls,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						**__UpperCamelCase,						):
  							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        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = copy.deepcopy(self.__dict__				)
  
  							_snake_case			 = self.semantic_config.to_dict()
  							_snake_case			 = self.coarse_acoustics_config.to_dict()
  							_snake_case			 = self.fine_acoustics_config.to_dict()
  
  							_snake_case			 = self.__class__.model_type
  							return output
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import argparse
  import collections
  import importlib.util
  import os
  import re
  
  
  # All paths are set with the intent you should run this script from the root of the repo with the command
  # python utils/check_table.py
  _lowerCamelCase							:		Any										=						'src/diffusers'
  _lowerCamelCase							:		Any										=						'docs/source/en'
  _lowerCamelCase							:		int										=						'.'
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		with open(lowercase_		,							"r"		,							encoding="utf-8"		,							newline="\n"						) as f:
  				_snake_case			 = f.readlines()
  		# Find the start prompt.
  		_snake_case			 = 0
  		while not lines[start_index].startswith(lowercase_						):
  				start_index += 1
  		start_index += 1
  
  		_snake_case			 = start_index
  		while not lines[end_index].startswith(lowercase_						):
  				end_index += 1
  		end_index -= 1
  
  		while len(lines[start_index]						) <= 1:
  				start_index += 1
  		while len(lines[end_index]						) <= 1:
  				end_index -= 1
  		end_index += 1
  		return "".join(lines[start_index:end_index]						), start_index, end_index, lines
  
  
  # Add here suffixes that are used to identify models, separated by |
  _lowerCamelCase							:		List[Any]										=						'Model|Encoder|Decoder|ForConditionalGeneration'
  # Regexes that match TF/Flax/PT model names.
  _lowerCamelCase							:		List[Any]										=						re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  _lowerCamelCase							:		Any										=						re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  # Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
  _lowerCamelCase							:		Optional[int]										=						re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
  
  
  # This is to make sure the diffusers module imported is the one in the repo.
  _lowerCamelCase							:		Union[str, Any]										=						importlib.util.spec_from_file_location(
      'diffusers',
      os.path.join(TRANSFORMERS_PATH, '__init__.py'),
      submodule_search_locations=[TRANSFORMERS_PATH],
  )
  _lowerCamelCase							:		Optional[int]										=						spec.loader.load_module()
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,							lowercase_						)
  		return [m.group(0						) for m in matches]
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = 2 if text == "✅" or text == "❌" else len(lowercase_						)
  		_snake_case			 = (width - text_length) // 2
  		_snake_case			 = width - text_length - left_indent
  		return " " * left_indent + text + " " * right_indent
  
  
  
  
  
  
  def 	_lowercase	(			):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  		_snake_case			 = {
  		    name: config_mapping_names[code]
  		    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
  		    if code in config_mapping_names
  		}
  		_snake_case			 = {name: config.replace("ConfigMixin"		,							""						) for name, config in model_name_to_config.items()}
  
  		# Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  		_snake_case			 = collections.defaultdict(lowercase_						)
  		_snake_case			 = collections.defaultdict(lowercase_						)
  		_snake_case			 = collections.defaultdict(lowercase_						)
  		_snake_case			 = collections.defaultdict(lowercase_						)
  		_snake_case			 = collections.defaultdict(lowercase_						)
  
  		# Let's lookup through all diffusers object (once).
  		for attr_name in dir(lowercase_						):
  				_snake_case			 = None
  				if attr_name.endswith("Tokenizer"						):
  						_snake_case			 = slow_tokenizers
  						_snake_case			 = attr_name[:-9]
  				elif attr_name.endswith("TokenizerFast"						):
  						_snake_case			 = fast_tokenizers
  						_snake_case			 = attr_name[:-13]
  				elif _re_tf_models.match(lowercase_						) is not None:
  						_snake_case			 = tf_models
  						_snake_case			 = _re_tf_models.match(lowercase_						).groups()[0]
  				elif _re_flax_models.match(lowercase_						) is not None:
  						_snake_case			 = flax_models
  						_snake_case			 = _re_flax_models.match(lowercase_						).groups()[0]
  				elif _re_pt_models.match(lowercase_						) is not None:
  						_snake_case			 = pt_models
  						_snake_case			 = _re_pt_models.match(lowercase_						).groups()[0]
  
  				if lookup_dict is not None:
  						while len(lowercase_						) > 0:
  								if attr_name in model_name_to_prefix.values():
  										_snake_case			 = True
  										break
  								# Try again after removing the last word in the name
  								_snake_case			 = "".join(camel_case_split(lowercase_						)[:-1]						)
  
      # Let's build that table!
  		_snake_case			 = list(model_name_to_config.keys()						)
  		model_names.sort(key=str.lower						)
  		_snake_case			 = ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
  		# We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  		_snake_case			 = [len(lowercase_						) + 2 for c in columns]
  		_snake_case			 = max([len(lowercase_						) for name in model_names]						) + 2
  
  		# Build the table per se
  		_snake_case			 = "|" + "|".join([_center_text(lowercase_		,							lowercase_						) for c, w in zip(lowercase_		,							lowercase_						)]						) + "|\n"
  		# Use ":-----:" format to center-aligned table cell texts
  		table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]						) + "|\n"
  
  		_snake_case			 = {True: "✅", False: "❌"}
  		for name in model_names:
  				_snake_case			 = model_name_to_prefix[name]
  				_snake_case			 = [
  				    name,
  				    check[slow_tokenizers[prefix]],
  				    check[fast_tokenizers[prefix]],
  				    check[pt_models[prefix]],
  				    check[tf_models[prefix]],
  				    check[flax_models[prefix]],
  				]
  				table += "|" + "|".join([_center_text(lowercase_		,							lowercase_						) for l, w in zip(lowercase_		,							lowercase_						)]						) + "|\n"
  		return table
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_=False						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case ,					_snake_case ,					_snake_case ,					_snake_case			 = _find_text_in_file(
  		    filename=os.path.join(lowercase_		,							"index.mdx"						)		,							start_prompt="<!--This table is updated automatically from the auto modules"		,							end_prompt="<!-- End table-->"		,							)
  		_snake_case			 = get_model_table_from_auto_modules()
  
  		if current_table != new_table:
  				if overwrite:
  						with open(os.path.join(lowercase_		,							"index.mdx"						)		,							"w"		,							encoding="utf-8"		,							newline="\n"						) as f:
  								f.writelines(lines[:start_index] + [new_table] + lines[end_index:]						)
  				else:
  						raise ValueError(
  						    "The model table in the `index.mdx` has not been updated. Run `make fix-copies` to fix this."						)
  
  
  if __name__ == "__main__":
  						_lowerCamelCase							:		Dict										=						argparse.ArgumentParser()
  						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  						_lowerCamelCase							:		Dict										=						parser.parse_args()
  
  						check_model_table(args.fix_and_overwrite)
  
  
  
  
  
  