






'''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


A:   List[Any]						=					{
    "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"},
}

A:   str						=					[
    "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 						_UpperCAmelCase     (	)      ->      Any:





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

						lowercase_   : Dict   	=      parser.parse_args()

						# Sanitize args
						if args.model_name in DEFAULT_MODELS:
												lowercase_   : Optional[int]   	=      DEFAULT_MODELS[args.model_name]
												lowercase_   : int   	=      defaults['model']
												if args.tokenizer_name is None:
																		lowercase_   : Optional[Any]   	=      defaults.get('tokenizer'    ,      args.model_name				)
												if args.is_causal is None:
																		lowercase_   : Tuple   	=      defaults['is_causal']
												if args.model_revision is None:
																		lowercase_   : int   	=      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:
												lowercase_   : Optional[Any]   	=      args.model_name
						if args.model_revision is None:
												lowercase_   : Any   	=      'main'

						return args






def 						_UpperCAmelCase     (	)      ->      str:





						"""simple docstring"""
						transformers.utils.logging.set_verbosity_error()
						lowercase_   : Dict   	=      parse_args()

						if args.torch_dtype is None:
												lowercase_   : List[Any]   	=      AutoConfig.from_pretrained(args.model_name				)
												lowercase_   : Union[str, Any]   	=      getattr(a    ,      'torch_dtype'    ,      torch.floataa				)
						else:
												lowercase_   : List[Any]   	=      getattr(a    ,      args.torch_dtype				)
						lowercase_   : Union[str, Any]   	=      AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
						lowercase_   : int   	=      {
						    'torch_dtype': torch_dtype,
						    'revision': args.model_revision,
						}
						if args.disk_offload:
												lowercase_   : int   	=      'tmp_offload'
												lowercase_   : List[Any]   	=      True

						lowercase_   : Tuple   	=      start_measure()
						lowercase_   : int   	=      model_cls.from_pretrained(args.model_name    ,      device_map='auto'    ,      **a				)
						lowercase_   : Union[str, Any]   	=      end_measure(a				)
						log_measures(a    ,      'Model loading'				)

						lowercase_   : Optional[int]   	=      compute_module_sizes(a				)
						lowercase_   : Dict   	=      {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]
						lowercase_   : Optional[int]   	=      '\n'.join([f"- {device}: {size // 2**2_0}MiB" for device, size in device_size.items()]				)
						print(f"\nTheoretical use:\n{message}"				)

						lowercase_   : str   	=      AutoTokenizer.from_pretrained(args.tokenizer_name				)

						lowercase_   : str   	=      start_measure()
						lowercase_   : List[Any]   	=      []
						lowercase_   : Union[str, Any]   	=      []
						lowercase_   : Optional[Any]   	=      []
						for prompt in PROMPTS:
												lowercase_   : Dict   	=      tokenizer(a    ,      return_tensors='pt'				).to(0				)
												lowercase_   : Union[str, Any]   	=      inputs['input_ids'][0].tolist()
												lowercase_   : List[Any]   	=      time.time()
												lowercase_   : Any   	=      model.generate(inputs['input_ids']				)
												lowercase_   : List[str]   	=      time.time()
												lowercase_   : str   	=      outputs[0].tolist()
												lowercase_   : Tuple   	=      len(a				) if outputs[: len(a				)] != tokens else len(a				) - len(a				)
												lowercase_   : Optional[Any]   	=      after_generate - before_generate

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

						lowercase_   : List[Any]   	=      end_measure(a				)
						log_measures(a    ,      'Model generation'				)

						lowercase_   : Optional[int]   	=      [gen / tok for gen, tok in zip(a    ,      a				)]
						lowercase_   : Any   	=      sum(a				) / len(a				)
						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"				)
						lowercase_   : List[str]   	=      sum(generation_times_per_token[1:]				) / (len(a				) - 1)
						print(f"Average time of generation per token (excluding the first): {avg_gen:.2f}s"				)


if __name__ == "__main__":
						main()








'''simple docstring'''

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


A:   str						=					get_logger(__name__)

# Datasets
A:   Optional[Any]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/datasets"
A:   Dict						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
A:   List[str]						=					"https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
A:   Any						=					"https://cdn-datasets.huggingface.co/datasets/metric"
A:   List[str]						=					"https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
A:   str						=					os.environ.get("HF_ENDPOINT", "https://huggingface.co")
A:   List[Any]						=					HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
A:   List[Any]						=					"main"

A:   Any						=					version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
						import importlib_metadata
else:
						import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
A:   Optional[Any]						=					{"1", "ON", "YES", "TRUE"}
A:   Optional[int]						=					ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
A:   Any						=					version.parse(importlib_metadata.version("dill"))
A:   str						=					version.parse(importlib_metadata.version("pandas"))
A:   Tuple						=					version.parse(importlib_metadata.version("pyarrow"))

A:   str						=					os.environ.get("USE_TF", "AUTO").upper()
A:   List[Any]						=					os.environ.get("USE_TORCH", "AUTO").upper()
A:   Union[str, Any]						=					os.environ.get("USE_JAX", "AUTO").upper()

A:   Dict						=					"N/A"
A:   Optional[int]						=					False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
						A:   List[str]						=					importlib.util.find_spec("torch") is not None
						if TORCH_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("torch"))
																		logger.info(f"""PyTorch version {TORCH_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling PyTorch because USE_TF is set")

A:   Union[str, Any]						=					"N/A"
A:   Dict						=					False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
						A:   Optional[Any]						=					importlib.util.find_spec("tensorflow") is not None
						if TF_AVAILABLE:
												# For the metadata, we have to look for both tensorflow and tensorflow-cpu
												for package in [
												    "tensorflow",
												    "tensorflow-cpu",
												    "tensorflow-gpu",
												    "tf-nightly",
												    "tf-nightly-cpu",
												    "tf-nightly-gpu",
												    "intel-tensorflow",
												    "tensorflow-rocm",
												    "tensorflow-macos",
												]:
																		try:
																								A:   Any						=					version.parse(importlib_metadata.version(package))
																		except importlib_metadata.PackageNotFoundError:
																								continue
																		else:
																								break
												else:
																		A:   Any						=					False
						if TF_AVAILABLE:
												if TF_VERSION.major < 2:
																		logger.info(f"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
																		A:   Tuple						=					False
												else:
																		logger.info(f"""TensorFlow version {TF_VERSION} available.""")
else:
						logger.info("Disabling Tensorflow because USE_TORCH is set")


A:   Any						=					"N/A"
A:   int						=					False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
						A:   Dict						=					importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
						if JAX_AVAILABLE:
												try:
																		A:   List[str]						=					version.parse(importlib_metadata.version("jax"))
																		logger.info(f"""JAX version {JAX_VERSION} available.""")
												except importlib_metadata.PackageNotFoundError:
																		pass
else:
						logger.info("Disabling JAX because USE_JAX is set to False")


A:   Optional[Any]						=					os.environ.get("USE_BEAM", "AUTO").upper()
A:   Any						=					"N/A"
A:   List[Any]						=					False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
						try:
												A:   Tuple						=					version.parse(importlib_metadata.version("apache_beam"))
												A:   Dict						=					True
												logger.info(f"""Apache Beam version {BEAM_VERSION} available.""")
						except importlib_metadata.PackageNotFoundError:
												pass
else:
						logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
A:   Union[str, Any]						=					importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
A:   Optional[int]						=					importlib.util.find_spec("PIL") is not None
A:   Dict						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
A:   Any						=					importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
A:   Optional[Any]						=					importlib.util.find_spec("rarfile") is not None
A:   Any						=					importlib.util.find_spec("zstandard") is not None
A:   Union[str, Any]						=					importlib.util.find_spec("lz4") is not None
A:   str						=					importlib.util.find_spec("py7zr") is not None


# Cache location
A:   Any						=					"~/.cache"
A:   Tuple						=					os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
A:   Dict						=					os.path.join(XDG_CACHE_HOME, "huggingface")
A:   List[str]						=					os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

A:   int						=					os.path.join(HF_CACHE_HOME, "datasets")
A:   int						=					Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

A:   int						=					os.path.join(HF_CACHE_HOME, "metrics")
A:   int						=					Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

A:   Any						=					os.path.join(HF_CACHE_HOME, "modules")
A:   Tuple						=					Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

A:   List[str]						=					"downloads"
A:   Tuple						=					os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
A:   Union[str, Any]						=					Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

A:   Tuple						=					"extracted"
A:   Optional[int]						=					os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
A:   Optional[int]						=					Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
A:   Union[str, Any]						=					(
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
A:   List[str]						=					1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
A:   Tuple						=					1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
A:   List[Any]						=					4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
A:   List[str]						=					"500MB"

# Parquet configuration
A:   Union[str, Any]						=					1_0_0
A:   Optional[int]						=					1_0_0
A:   Any						=					1_0_0

# Offline mode
A:   List[str]						=					os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
A:   Any						=					0  # Disabled
A:   List[str]						=					float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
A:   List[str]						=					"dataset.arrow"
A:   Optional[int]						=					"indices.arrow"
A:   List[str]						=					"state.json"
A:   Tuple						=					"dataset_info.json"
A:   Tuple						=					"dataset_infos.json"
A:   Dict						=					"LICENSE"
A:   Optional[Any]						=					"metric_info.json"
A:   Any						=					"dataset_dict.json"

A:   Dict						=					"datasets_modules"

A:   List[str]						=					2_5_5

# Streaming
A:   Any						=					2_0
A:   Dict						=					5

# Datasets without script
A:   Union[str, Any]						=					2_0_0
A:   Union[str, Any]						=					1_0
A:   List[Any]						=					2_0_0

# Progress bars
A:   Tuple						=					0.05  # 20 progress updates per sec
