


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
snake_case       :					List[Any]        =  '''src/diffusers'''
snake_case       :					Tuple        =  '''docs/source/en'''
snake_case       :					List[str]        =  '''.'''
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	List[Any]		):




						"""simple docstring"""





						with open(a_				,						'''r'''				,						encoding='''utf-8'''				,						newline='''\n'''		) as f:
												a		:int											= f.readlines()
						# Find the start prompt.
						a		:List[Any]											= 0
						while not lines[start_index].startswith(a_		):
												start_index += 1
						start_index += 1

						a		:Any											= start_index
						while not lines[end_index].startswith(a_		):
												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 |
snake_case       :					Optional[Any]        =  '''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
snake_case       :					Any        =  re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
snake_case       :					Union[str, 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.
snake_case       :					List[Any]        =  re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the diffusers module imported is the one in the repo.
snake_case       :					Optional[int]        =  importlib.util.spec_from_file_location(
    '''diffusers''',
    os.path.join(TRANSFORMERS_PATH, '''__init__.py'''),
    submodule_search_locations=[TRANSFORMERS_PATH],
)
snake_case       :					Dict        =  spec.loader.load_module()
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[Any]		):




						"""simple docstring"""





						a		:List[Any]											= re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''				,						a_		)
						return [m.group(0		) for m in matches]
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	List[Any]		):




						"""simple docstring"""





						a		:Dict											= 2 if text == '''✅''' or text == '''❌''' else len(a_		)
						a		:Optional[Any]											= (width - text_length) // 2
						a		:List[str]											= width - text_length - left_indent
						return " " * left_indent + text + " " * right_indent
def        __lowerCamelCase		(				):




						"""simple docstring"""





						a		:str											= diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						a		:List[Any]											= {
						    name: config_mapping_names[code]
						    for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
						    if code in config_mapping_names
						}
						a		:Union[str, Any]											= {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.
						a		:List[str]											= collections.defaultdict(a_		)
						a		:Union[str, Any]											= collections.defaultdict(a_		)
						a		:Tuple											= collections.defaultdict(a_		)
						a		:Dict											= collections.defaultdict(a_		)
						a		:Optional[int]											= collections.defaultdict(a_		)

						# Let's lookup through all diffusers object (once).
						for attr_name in dir(a_		):
												a		:int											= None
												if attr_name.endswith('''Tokenizer'''		):
																		a		:Union[str, Any]											= slow_tokenizers
																		a		:Optional[Any]											= attr_name[:-9]
												elif attr_name.endswith('''TokenizerFast'''		):
																		a		:str											= fast_tokenizers
																		a		:Optional[Any]											= attr_name[:-13]
												elif _re_tf_models.match(a_		) is not None:
																		a		:List[Any]											= tf_models
																		a		:List[Any]											= _re_tf_models.match(a_		).groups()[0]
												elif _re_flax_models.match(a_		) is not None:
																		a		:List[str]											= flax_models
																		a		:List[str]											= _re_flax_models.match(a_		).groups()[0]
												elif _re_pt_models.match(a_		) is not None:
																		a		:Optional[Any]											= pt_models
																		a		:Dict											= _re_pt_models.match(a_		).groups()[0]

												if lookup_dict is not None:
																		while len(a_		) > 0:
																								if attr_name in model_name_to_prefix.values():
																														a		:Optional[int]											= True
																														break
																								# Try again after removing the last word in the name
																								a		:List[Any]											= ''''''.join(camel_case_split(a_		)[:-1]		)

    # Let's build that table!
						a		:Optional[Any]											= list(model_name_to_config.keys()		)
						model_names.sort(key=str.lower		)
						a		:Union[str, Any]											= ['''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).
						a		:Any											= [len(a_		) + 2 for c in columns]
						a		:int											= max([len(a_		) for name in model_names]		) + 2

						# Build the table per se
						a		:Optional[int]											= '''|''' + '''|'''.join([_center_text(a_				,						a_		) for c, w in zip(a_				,						a_		)]		) + '''|\n'''
						# Use ":-----:" format to center-aligned table cell texts
						table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]		) + "|\n"

						a		:List[str]											= {True: '''✅''', False: '''❌'''}
						for name in model_names:
												a		:Tuple											= model_name_to_prefix[name]
												a		:List[Any]											= [
												    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(a_				,						a_		) for l, w in zip(a_				,						a_		)]		) + "|\n"
						return table
def        __lowerCamelCase		(				UpperCAmelCase_		:	Union[str, Any]=False		):




						"""simple docstring"""





						a							,       a							,       a							,       a		:Tuple											= _find_text_in_file(
						    filename=os.path.join(a_				,						'''index.mdx'''		)				,						start_prompt='''<!--This table is updated automatically from the auto modules'''				,						end_prompt='''<!-- End table-->'''				,						)
						a		:int											= get_model_table_from_auto_modules()

						if current_table != new_table:
												if overwrite:
																		with open(os.path.join(a_				,						'''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__":
					snake_case       :					Optional[Any]        =  argparse.ArgumentParser()
					parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
					snake_case       :					List[Any]        =  parser.parse_args()

					check_model_table(args.fix_and_overwrite)









import copy
from typing import Dict

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


snake_case       :					Dict        =  logging.get_logger(__name__)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = '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      ,						):
       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      ,						)

       a		:Dict											= text_encoding_offset
       a		:Any											= text_pad_token
       a		:List[Any]											= eos_token_id
       a		:str											= semantic_infer_token
       a		:Any											= semantic_vocab_size
       a		:Any											= max_input_semantic_length
       a		:Dict											= semantic_rate_hz







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = '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      ,						):
       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      ,						)

       a		:Optional[Any]											= coarse_semantic_pad_token
       a		:Dict											= coarse_rate_hz
       a		:List[str]											= n_coarse_codebooks
       a		:List[Any]											= coarse_infer_token
       a		:Optional[Any]											= max_coarse_input_length
       a		:Any											= max_coarse_history
       a		:Optional[int]											= sliding_window_len







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'fine_acoustics'




 def __init__(	self      ,						_lowerCamelCase=0.5      ,						_lowerCamelCase=512      ,						_lowerCamelCase=1024      ,						_lowerCamelCase=8      ,						**_lowerCamelCase      ,						):
       super().__init__(temperature=_lowerCamelCase					)

       a		:Union[str, Any]											= max_fine_history_length
       a		:int											= max_fine_input_length
       a		:str											= n_fine_codebooks







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = 'bark'
 SCREAMING_SNAKE_CASE__   = True




 def __init__(	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase=2_4000      ,						_lowerCamelCase=1024      ,						**_lowerCamelCase      ,						):
       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		:List[str]											= {}
             logger.info('''coarse_acoustics_config is None. initializing the coarse model with default values.'''					)

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

       a		:Any											= BarkSemanticGenerationConfig(**_lowerCamelCase					)
       a		:int											= BarkCoarseGenerationConfig(**_lowerCamelCase					)
       a		:Optional[int]											= BarkFineGenerationConfig(**_lowerCamelCase					)

       a		:Tuple											= sample_rate
       a		:Dict											= codebook_size




 @classmethod
 def  SCREAMING_SNAKE_CASE__       (	cls      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						**_lowerCamelCase      ,						):
       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  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= copy.deepcopy(self.__dict__					)

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

       a		:Tuple											= self.__class__.model_type
       return output





