
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 					SCREAMING_SNAKE_CASE  (   )			->							str:

				"""simple docstring"""







				A__	:						List[Any]								=				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"]:
								A__	:						List[str]								=				getattr(transformers.models.auto				,    __UpperCamelCase				,    __UpperCamelCase							)
								if module is None:
												continue
								# all mappings in a single auto modeling file
								A__	:						int								=				[
								    x
								    for x in dir(__UpperCamelCase							)
								    if x.endswith('''_MAPPING_NAMES'''							)
								    and (x.startswith('''MODEL_'''							) or x.startswith('''TF_MODEL_'''							) or x.startswith('''FLAX_MODEL_'''							))
								]
								for name in mapping_names:
												A__	:						Any								=				getattr(__UpperCamelCase				,    __UpperCamelCase							)
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(__UpperCamelCase				,    (list, tuple)							):
																								model_names.update(__UpperCamelCase							)
																				elif isinstance(__UpperCamelCase				,    __UpperCamelCase							):
																								model_names.add(__UpperCamelCase							)

				return sorted(__UpperCamelCase							)

def 					SCREAMING_SNAKE_CASE  (   )			->							Optional[Any]:

				"""simple docstring"""







				A__	:						List[str]								=				set(get_all_model_names()							)

				with open('''tests/utils/tiny_model_summary.json'''							) as fp:
								A__	:						Dict								=				json.load(__UpperCamelCase							)
				A__	:						List[Any]								=				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.
				A__	:						Any								=				model_names.difference(__UpperCamelCase							)
				for model_name in copy.copy(__UpperCamelCase							):
								if not model_name.startswith('''TF'''							) and F"TF{model_name}" in not_on_hub:
												tiny_models_names.remove(__UpperCamelCase							)
								elif model_name.startswith('''TF'''							) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(__UpperCamelCase							)

				return sorted(__UpperCamelCase							)

def 					SCREAMING_SNAKE_CASE  (   __UpperCamelCase			:    int							)			->							Optional[int]:

				"""simple docstring"""







				A__	:						Optional[Any]								=				COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				A__	:						int								=				get_all_model_names()
				A__	:						Optional[Any]								=				hf_api.list_models(
				    filter=ModelFilter(
				        author='''hf-internal-testing'''				,    )							)
				A__	:						Any								=				set()
				for x in models:
								A__	:						Optional[Any]								=				x.modelId
								A__							,				A__	:						str								=				model.split('''/'''							)
								if not model.startswith('''tiny-random-'''							):
												continue
								A__	:						int								=				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(__UpperCamelCase							)

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

								A__	:						Optional[Any]								=				sorted(content['''tokenizer_classes''']							)
								A__	:						Union[str, Any]								=				sorted(content['''processor_classes''']							)
								A__	:						str								=				sorted(content['''model_classes''']							)

								A__	:						int								=				content
								with open(os.path.join(__UpperCamelCase				,    '''hub_tiny_model_summary.json'''							)				,    '''w'''							) as fp:
												json.dump(__UpperCamelCase				,    __UpperCamelCase				,    ensure_ascii=__UpperCamelCase				,    indent=4							)


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

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

					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      'tiny_models'
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      True
					_SCREAMING_SNAKE_CASE     :   Dict							 =      None
					_SCREAMING_SNAKE_CASE     :   str							 =      get_tiny_model_names_from_repo()
					_SCREAMING_SNAKE_CASE     :   Dict							 =      True
					_SCREAMING_SNAKE_CASE     :   Tuple							 =      True
					_SCREAMING_SNAKE_CASE     :   int							 =      '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,
					)

import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
					import librosa  # noqa: E402

					_SCREAMING_SNAKE_CASE     :   Tuple							 =      True
					_SCREAMING_SNAKE_CASE     :   List[Any]							 =      ''
except Exception as e:
					_SCREAMING_SNAKE_CASE     :   Optional[Any]							 =      False
					_SCREAMING_SNAKE_CASE     :   Any							 =      (
					    f"""Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it."""
					)


from PIL import Image  # noqa: E402







class 			UpperCamelCase__		(     SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''
	_lowerCAmelCase											=					"mel_config.json"





	@register_to_config
	def __init__(      self					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 256					,							UpperCamelCase__ = 2_2050					,							UpperCamelCase__ = 2048					,							UpperCamelCase__ = 512					,							UpperCamelCase__ = 80					,							UpperCamelCase__ = 32					,							):
					A__	:						Any								=				hop_length
					A__	:						List[Any]								=				sample_rate
					A__	:						Union[str, Any]								=				n_fft
					A__	:						Optional[int]								=				top_db
					A__	:						Optional[Any]								=				n_iter
					self.set_resolution(UpperCamelCase__					,							UpperCamelCase__		)
					A__	:						str								=				None

					if not _librosa_can_be_imported:
									raise ValueError(_import_error		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__		):
					A__	:						int								=				x_res
					A__	:						List[Any]								=				y_res
					A__	:						List[str]								=				self.y_res
					A__	:						int								=				self.x_res * self.hop_length - 1





	def 	__snake_case					(      self					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None		):
					if audio_file is not None:
									A__							,				A__	:						Union[str, Any]								=				librosa.load(UpperCamelCase__					,							mono=UpperCamelCase__					,							sr=self.sr		)
					else:
									A__	:						Tuple								=				raw_audio

					# Pad with silence if necessary.
					if len(self.audio		) < self.x_res * self.hop_length:
									A__	:						List[Any]								=				np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio		),)		)]		)





	def 	__snake_case					(      self		):
					return len(self.audio		) // self.slice_size





	def 	__snake_case					(      self					,							UpperCamelCase__ = 0		):
					return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]





	def 	__snake_case					(      self		):
					return self.sr





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						Optional[Any]								=				librosa.feature.melspectrogram(
					    y=self.get_audio_slice(UpperCamelCase__		)					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_mels=self.n_mels		)
					A__	:						str								=				librosa.power_to_db(UpperCamelCase__					,							ref=np.max					,							top_db=self.top_db		)
					A__	:						str								=				(((log_S + self.top_db) * 255 / self.top_db).clip(0					,							255		) + 0.5).astype(np.uinta		)
					A__	:						Dict								=				Image.fromarray(UpperCamelCase__		)
					return image





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					A__	:						List[Any]								=				np.frombuffer(image.tobytes()					,							dtype='''uint8'''		).reshape((image.height, image.width)		)
					A__	:						Dict								=				bytedata.astype('''float'''		) * self.top_db / 255 - self.top_db
					A__	:						Union[str, Any]								=				librosa.db_to_power(UpperCamelCase__		)
					A__	:						Dict								=				librosa.feature.inverse.mel_to_audio(
					    UpperCamelCase__					,							sr=self.sr					,							n_fft=self.n_fft					,							hop_length=self.hop_length					,							n_iter=self.n_iter		)
					return audio