

"""simple docstring"""




import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= set()
			# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
			for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
						SCREAMING_SNAKE_CASE__									= getattr(transformers.models.auto						,			snake_case__						,			snake_case__      )
						if module is None:
									continue
						# all mappings in a single auto modeling file
						SCREAMING_SNAKE_CASE__									= [
						    x
						    for x in dir(snake_case__      )
						    if x.endswith("""_MAPPING_NAMES"""      )
						    and (x.startswith("""MODEL_"""      ) or x.startswith("""TF_MODEL_"""      ) or x.startswith("""FLAX_MODEL_"""      ))
						]
						for name in mapping_names:
									SCREAMING_SNAKE_CASE__									= getattr(snake_case__						,			snake_case__      )
									if mapping is not None:
												for v in mapping.values():
															if isinstance(snake_case__						,			(list, tuple)      ):
																		model_names.update(snake_case__      )
															elif isinstance(snake_case__						,			snake_case__      ):
																		model_names.add(snake_case__      )

			return sorted(snake_case__      )

def 		A    (       ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= set(get_all_model_names()      )

			with open("""tests/utils/tiny_model_summary.json"""      ) as fp:
						SCREAMING_SNAKE_CASE__									= json.load(snake_case__      )
			SCREAMING_SNAKE_CASE__									= set()
			for model_base_name in tiny_model_info:
						tiny_models_names.update(tiny_model_info[model_base_name]["""model_classes"""]      )

			# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
			SCREAMING_SNAKE_CASE__									= model_names.difference(snake_case__      )
			for model_name in copy.copy(snake_case__      ):
						if not model_name.startswith("""TF"""      ) and f"""TF{model_name}""" in not_on_hub:
									tiny_models_names.remove(snake_case__      )
						elif model_name.startswith("""TF"""      ) and model_name[2:] in not_on_hub:
									tiny_models_names.remove(snake_case__      )

			return sorted(snake_case__      )

def 		A    (       snake_case__      ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__									= COMPOSITE_MODELS.values()

			# All tiny model base names on Hub
			SCREAMING_SNAKE_CASE__									= get_all_model_names()
			SCREAMING_SNAKE_CASE__									= hf_api.list_models(
			    filter=ModelFilter(
			        author="""hf-internal-testing"""						,			)      )
			SCREAMING_SNAKE_CASE__									= set()
			for x in models:
						SCREAMING_SNAKE_CASE__									= x.modelId
						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__									= model.split("""/"""      )
						if not model.startswith("""tiny-random-"""      ):
									continue
						SCREAMING_SNAKE_CASE__									= model.replace("""tiny-random-"""						,			""""""      )
						if not model[0].isupper():
									continue
						if model not in model_names and model not in special_models:
									continue
						_models.add(snake_case__      )

			SCREAMING_SNAKE_CASE__									= sorted(_models      )
			# All tiny model names on Hub
			SCREAMING_SNAKE_CASE__									= {}
			for model in models:
						SCREAMING_SNAKE_CASE__									= f"""hf-internal-testing/tiny-random-{model}"""
						SCREAMING_SNAKE_CASE__									= model.split("""-"""      )[0]
						try:
									SCREAMING_SNAKE_CASE__									= hf_api.repo_info(snake_case__      )
									SCREAMING_SNAKE_CASE__									= {
									    """tokenizer_classes""": set(),
									    """processor_classes""": set(),
									    """model_classes""": set(),
									    """sha""": repo_info.sha,
									}
						except Exception:
									continue
						try:
									time.sleep(1      )
									SCREAMING_SNAKE_CASE__									= AutoTokenizer.from_pretrained(snake_case__      )
									content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__      )
						except Exception:
									pass
						try:
									time.sleep(1      )
									SCREAMING_SNAKE_CASE__									= AutoTokenizer.from_pretrained(snake_case__						,			use_fast=snake_case__      )
									content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__      )
						except Exception:
									pass
						try:
									time.sleep(1      )
									SCREAMING_SNAKE_CASE__									= AutoImageProcessor.from_pretrained(snake_case__      )
									content["processor_classes"].add(img_p.__class__.__name__      )
						except Exception:
									pass
						try:
									time.sleep(1      )
									SCREAMING_SNAKE_CASE__									= AutoFeatureExtractor.from_pretrained(snake_case__      )
									if not isinstance(snake_case__						,			snake_case__      ):
												content["processor_classes"].add(feat_p.__class__.__name__      )
						except Exception:
									pass
						try:
									time.sleep(1      )
									SCREAMING_SNAKE_CASE__									= getattr(snake_case__						,			snake_case__      )
									SCREAMING_SNAKE_CASE__									= model_class.from_pretrained(snake_case__      )
									content["model_classes"].add(m.__class__.__name__      )
						except Exception:
									pass
						try:
									time.sleep(1      )
									SCREAMING_SNAKE_CASE__									= getattr(snake_case__						,			f"""TF{model}"""      )
									SCREAMING_SNAKE_CASE__									= model_class.from_pretrained(snake_case__      )
									content["model_classes"].add(m.__class__.__name__      )
						except Exception:
									pass

						SCREAMING_SNAKE_CASE__									= sorted(content["""tokenizer_classes"""]      )
						SCREAMING_SNAKE_CASE__									= sorted(content["""processor_classes"""]      )
						SCREAMING_SNAKE_CASE__									= sorted(content["""model_classes"""]      )

						SCREAMING_SNAKE_CASE__									= content
						with open(os.path.join(snake_case__						,			"""hub_tiny_model_summary.json"""      )						,			"""w"""      ) as fp:
									json.dump(snake_case__						,			snake_case__						,			ensure_ascii=snake_case__						,			indent=4      )


if __name__ == "__main__":
						A_							:     List[str]     =	argparse.ArgumentParser()
						parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
						A_							:     int     =	parser.parse_args()

						# This has to be `spawn` to avoid hanging forever!
						multiprocessing.set_start_method("spawn")

						A_							:     List[Any]     =	"tiny_models"
						A_							:     Tuple     =	True
						A_							:     Optional[Any]     =	None
						A_							:     int     =	get_tiny_model_names_from_repo()
						A_							:     int     =	True
						A_							:     List[str]     =	True
						A_							:     Tuple     =	"hf-internal-testing"

						create_tiny_models(
						    output_path,
						    all,
						    model_types,
						    models_to_skip,
						    no_check,
						    upload,
						    organization,
						    token=os.environ.get("TOKEN", None),
						    num_workers=args.num_workers,
						)





"""simple docstring"""




import copy
from typing import Dict

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


A_							:     str     =	logging.get_logger(__name__)





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Any   						=							'semantic'

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  List[str]=True      ,			__UpperCAmelCase			:  Tuple=7_6_8      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Optional[int]=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Dict=False      ,			__UpperCAmelCase			:  Any=0.7      ,			__UpperCAmelCase			:  Optional[int]=True      ,			__UpperCAmelCase			:  str=1_0_0_4_8      ,			__UpperCAmelCase			:  Tuple=1_2_9_5_9_5      ,			__UpperCAmelCase			:  List[Any]=1_2_9_5_9_9      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_0_0_0      ,			__UpperCAmelCase			:  Dict=2_5_6      ,			__UpperCAmelCase			:  int=49.9      ,			**__UpperCAmelCase			:  Any      ,			)				->      List[Any]:
						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      ,			)

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





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'coarse_acoustics'

			def __init__(   self			:  Optional[int]      ,			__UpperCAmelCase			:  Tuple=True      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  List[Any]=False      ,			__UpperCAmelCase			:  Tuple=False      ,			__UpperCAmelCase			:  Optional[Any]=False      ,			__UpperCAmelCase			:  Dict=0.7      ,			__UpperCAmelCase			:  Dict=True      ,			__UpperCAmelCase			:  List[str]=1_2_0_4_8      ,			__UpperCAmelCase			:  Optional[Any]=7_5      ,			__UpperCAmelCase			:  List[str]=2      ,			__UpperCAmelCase			:  List[str]=1_2_0_5_0      ,			__UpperCAmelCase			:  Optional[Any]=2_5_6      ,			__UpperCAmelCase			:  int = 6_3_0      ,			__UpperCAmelCase			:  int = 6_0      ,			**__UpperCAmelCase			:  Any      ,			)				->      Dict:
						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      ,			)

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





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Union[str, Any]   						=							'fine_acoustics'

			def __init__(   self			:  List[Any]      ,			__UpperCAmelCase			:  Union[str, Any]=0.5      ,			__UpperCAmelCase			:  List[Any]=5_1_2      ,			__UpperCAmelCase			:  Union[str, Any]=1_0_2_4      ,			__UpperCAmelCase			:  Optional[Any]=8      ,			**__UpperCAmelCase			:  Tuple      ,			)				->      Optional[Any]:
						super().__init__(temperature=__UpperCAmelCase    )

						SCREAMING_SNAKE_CASE__									= max_fine_history_length
						SCREAMING_SNAKE_CASE__									= max_fine_input_length
						SCREAMING_SNAKE_CASE__									= n_fine_codebooks





class 				lowerCamelCase  (A__     ):
			lowerCamelCase__				:						Tuple   						=							'bark'
			lowerCamelCase__				:						Dict   						=							True

			def __init__(   self			:  int      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  Dict = None      ,			__UpperCAmelCase			:  List[Any]=2_4_0_0_0      ,			__UpperCAmelCase			:  Any=1_0_2_4      ,			**__UpperCAmelCase			:  int      ,			)				->      Any:
						if semantic_config is None:
									SCREAMING_SNAKE_CASE__									= {}
									logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

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

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

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

						SCREAMING_SNAKE_CASE__									= sample_rate
						SCREAMING_SNAKE_CASE__									= codebook_size

			@classmethod
			def    SCREAMING_SNAKE_CASE			(   cls			:  int      ,			__UpperCAmelCase			:  BarkSemanticGenerationConfig      ,			__UpperCAmelCase			:  BarkCoarseGenerationConfig      ,			__UpperCAmelCase			:  BarkFineGenerationConfig      ,			**__UpperCAmelCase			:  Optional[int]      ,			)				->      Tuple:
						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			:  Any    )				->      Any:
						SCREAMING_SNAKE_CASE__									= copy.deepcopy(self.__dict__    )

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

						SCREAMING_SNAKE_CASE__									= self.__class__.model_type
						return output


