"""simple docstring"""







import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCamelCase_    :    int											=						{
    """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
    """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
    """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
    """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
}

UpperCamelCase_    :    Optional[int]											=						[
    """Hello, my name is""",
    """Are unicorns real? Unicorns are""",
    """For the first time in several years,""",
    """My name is Julien and I am""",
    """The goal of life is""",
    """Whenever I'm sad, I like to""",
]


def 					a__ (						)->		Optional[int]:
						UpperCAmelCase__   		=						argparse.ArgumentParser(description="""Run and time generations on a big model using Accelerate."""     )
						parser.add_argument("""model_name"""						,     type=UpperCAmelCase__						,     default=UpperCAmelCase__						,     help="""The name of the model to try."""     )
						parser.add_argument(
						    """--tokenizer_name"""						,     type=UpperCAmelCase__						,     default=UpperCAmelCase__						,     help="""The name of the tokenizer (if different from the model."""     )
						parser.add_argument("""--is_causal"""						,     type=UpperCAmelCase__						,     default=UpperCAmelCase__						,     help="""Whether or not the model is causal."""     )
						parser.add_argument(
						    """--model_revision"""						,     type=UpperCAmelCase__						,     default=UpperCAmelCase__						,     help="""The revision to use for the model checkpoint."""     )
						parser.add_argument("""--torch_dtype"""						,     type=UpperCAmelCase__						,     default=UpperCAmelCase__						,     help="""The dtype for the model."""     )
						parser.add_argument("""--disk_offload"""						,     action="""store_true"""     )

						UpperCAmelCase__   		=						parser.parse_args()

						# Sanitize args
						if args.model_name in DEFAULT_MODELS:
												UpperCAmelCase__   		=						DEFAULT_MODELS[args.model_name]
												UpperCAmelCase__   		=						defaults["""model"""]
												if args.tokenizer_name is None:
																		UpperCAmelCase__   		=						defaults.get("""tokenizer"""						,     args.model_name     )
												if args.is_causal is None:
																		UpperCAmelCase__   		=						defaults["""is_causal"""]
												if args.model_revision is None:
																		UpperCAmelCase__   		=						defaults.get("""model_revision"""						,     """main"""     )

						if args.is_causal is None:
												raise ValueError("""Could not infer the default for `--is_causal`, pass either True or False for it."""     )
						if args.tokenizer_name is None:
												UpperCAmelCase__   		=						args.model_name
						if args.model_revision is None:
												UpperCAmelCase__   		=						"""main"""

						return args


def 					a__ (						)->		int:
						transformers.utils.logging.set_verbosity_error()
						UpperCAmelCase__   		=						parse_args()

						if args.torch_dtype is None:
												UpperCAmelCase__   		=						AutoConfig.from_pretrained(args.model_name     )
												UpperCAmelCase__   		=						getattr(UpperCAmelCase__						,     """torch_dtype"""						,     torch.floataa     )
						else:
												UpperCAmelCase__   		=						getattr(UpperCAmelCase__						,     args.torch_dtype     )
						UpperCAmelCase__   		=						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
						UpperCAmelCase__   		=						{
						    """torch_dtype""": torch_dtype,
						    """revision""": args.model_revision,
						}
						if args.disk_offload:
												UpperCAmelCase__   		=						"""tmp_offload"""
												UpperCAmelCase__   		=						True

						UpperCAmelCase__   		=						start_measure()
						UpperCAmelCase__   		=						model_cls.from_pretrained(args.model_name						,     device_map="""auto"""						,     **UpperCAmelCase__     )
						UpperCAmelCase__   		=						end_measure(UpperCAmelCase__     )
						log_measures(UpperCAmelCase__						,     """Model loading"""     )

						UpperCAmelCase__   		=						compute_module_sizes(UpperCAmelCase__     )
						UpperCAmelCase__   		=						{v: 0 for v in model.hf_device_map.values()}
						for module, device in model.hf_device_map.items():
												device_size[device] += module_sizes[module]
						UpperCAmelCase__   		=						"""\n""".join([f'- {device}: {size // 2**2_0}MiB' for device, size in device_size.items()]     )
						print(f'\nTheoretical use:\n{message}'     )

						UpperCAmelCase__   		=						AutoTokenizer.from_pretrained(args.tokenizer_name     )

						UpperCAmelCase__   		=						start_measure()
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						UpperCAmelCase__   		=						[]
						for prompt in PROMPTS:
												UpperCAmelCase__   		=						tokenizer(UpperCAmelCase__						,     return_tensors="""pt"""     ).to(0     )
												UpperCAmelCase__   		=						inputs["""input_ids"""][0].tolist()
												UpperCAmelCase__   		=						time.time()
												UpperCAmelCase__   		=						model.generate(inputs["""input_ids"""]     )
												UpperCAmelCase__   		=						time.time()
												UpperCAmelCase__   		=						outputs[0].tolist()
												UpperCAmelCase__   		=						len(UpperCAmelCase__     ) if outputs[: len(UpperCAmelCase__     )] != tokens else len(UpperCAmelCase__     ) - len(UpperCAmelCase__     )
												UpperCAmelCase__   		=						after_generate - before_generate

												UpperCAmelCase__   		=						tokenizer.decode(UpperCAmelCase__						,     skip_special_tokens=UpperCAmelCase__     )
												texts_outs.append(UpperCAmelCase__     )
												generation_times.append(UpperCAmelCase__     )
												gen_tokens.append(UpperCAmelCase__     )
												print(f'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'     )

						UpperCAmelCase__   		=						end_measure(UpperCAmelCase__     )
						log_measures(UpperCAmelCase__						,     """Model generation"""     )

						UpperCAmelCase__   		=						[gen / tok for gen, tok in zip(UpperCAmelCase__						,     UpperCAmelCase__     )]
						UpperCAmelCase__   		=						sum(UpperCAmelCase__     ) / len(UpperCAmelCase__     )
						print(f'Average time of generation per token: {avg_gen:.2f}s'     )
						print(f'First generation (avg time per token): {generation_times_per_token[0]:.2f}s'     )
						UpperCAmelCase__   		=						sum(generation_times_per_token[1:]     ) / (len(UpperCAmelCase__     ) - 1)
						print(f'Average time of generation per token (excluding the first): {avg_gen:.2f}s'     )


if __name__ == "__main__":
						main()


"""simple docstring"""







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

						UpperCamelCase_    :    Union[str, Any]											=						True
						UpperCamelCase_    :    int											=						""""""
except Exception as e:
						UpperCamelCase_    :    Any											=						False
						UpperCamelCase_    :    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      _lowercase						(				__lowerCamelCase , __lowerCamelCase					):
				lowerCamelCase__									=    'mel_config.json'

				@register_to_config
				def __init__(       self    :      int    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_2_0_5_0    ,      _lowercase    :      int = 2_0_4_8    ,      _lowercase    :      int = 5_1_2    ,      _lowercase    :      int = 8_0    ,      _lowercase    :      int = 3_2    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						hop_length
										UpperCAmelCase__   		=						sample_rate
										UpperCAmelCase__   		=						n_fft
										UpperCAmelCase__   		=						top_db
										UpperCAmelCase__   		=						n_iter
										self.set_resolution(_lowercase    ,      _lowercase       )
										UpperCAmelCase__   		=						None

										if not _librosa_can_be_imported:
																raise ValueError(_import_error       )

				def     A	(       self    :      Any    ,      _lowercase    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						x_res
										UpperCAmelCase__   		=						y_res
										UpperCAmelCase__   		=						self.y_res
										UpperCAmelCase__   		=						self.x_res * self.hop_length - 1

				def     A	(       self    :      Dict    ,      _lowercase    :      str = None    ,      _lowercase    :      np.ndarray = None       ):


										'''simple docstring'''
										if audio_file is not None:
																UpperCAmelCase__		, UpperCAmelCase__   		=						librosa.load(_lowercase    ,      mono=_lowercase    ,      sr=self.sr       )
										else:
																UpperCAmelCase__   		=						raw_audio

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

				def     A	(       self    :      int       ):


										'''simple docstring'''
										return len(self.audio       ) // self.slice_size

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      int = 0       ):


										'''simple docstring'''
										return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										return self.sr

				def     A	(       self    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						librosa.feature.melspectrogram(
										    y=self.get_audio_slice(_lowercase       )    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_mels=self.n_mels       )
										UpperCAmelCase__   		=						librosa.power_to_db(_lowercase    ,      ref=np.max    ,      top_db=self.top_db       )
										UpperCAmelCase__   		=						(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0    ,      2_5_5       ) + 0.5).astype(np.uinta       )
										UpperCAmelCase__   		=						Image.fromarray(_lowercase       )
										return image

				def     A	(       self    :      Dict    ,      _lowercase    :      Image.Image       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						np.frombuffer(image.tobytes()    ,      dtype="""uint8"""       ).reshape((image.height, image.width)       )
										UpperCAmelCase__   		=						bytedata.astype("""float"""       ) * self.top_db / 2_5_5 - self.top_db
										UpperCAmelCase__   		=						librosa.db_to_power(_lowercase       )
										UpperCAmelCase__   		=						librosa.feature.inverse.mel_to_audio(
										    _lowercase    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_iter=self.n_iter       )
										return audio

