

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

					A     =   parser.parse_args()

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

					return args
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					transformers.utils.logging.set_verbosity_error()
					A     =   parse_args()

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

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

					A     =   compute_module_sizes(lowercase__ )
					A     =   {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]
					A     =   "\n".join([F"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()] )
					print(F"""\nTheoretical use:\n{message}""" )

					A     =   AutoTokenizer.from_pretrained(args.tokenizer_name )

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

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

					A     =   end_measure(lowercase__ )
					log_measures(lowercase__     ,				"Model generation" )

					A     =   [gen / tok for gen, tok in zip(lowercase__     ,				lowercase__ )]
					A     =   sum(lowercase__ ) / len(lowercase__ )
					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""" )
					A     =   sum(generation_times_per_token[1:] ) / (len(lowercase__ ) - 1)
					print(F"""Average time of generation per token (excluding the first): {avg_gen:.2f}s""" )


if __name__ == "__main__":
	main()







import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self	,				__snake_case	,				__snake_case=False							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     (
								    filename_skip_take["filename"],
								    filename_skip_take["skip"] if "skip" in filename_skip_take else None,
								    filename_skip_take["take"] if "take" in filename_skip_take else None,
								)
								open(os.path.join(__snake_case							)	,				"wb"							).close()
								UpperCAmelCase:					Any 						=     pa.Table.from_pydict({"filename": [Path(__snake_case							).name] * 1_0_0}							)
								if take == -1:
									UpperCAmelCase:					List[Any] 						=     len(__snake_case							) - skip
								if skip is not None and take is not None:
									UpperCAmelCase:					Optional[int] 						=     pa_table.slice(__snake_case	,				__snake_case							)
								return pa_table





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     "my_name"
								UpperCAmelCase:					str 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     [train_info, test_info]
								UpperCAmelCase:					Tuple 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[int] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					int 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					int 						=     "test[:33%]"
									UpperCAmelCase:					Dict 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-test'							)
									self.assertEqual(dset.num_rows	,				3_3							)
									self.assertEqual(dset.num_columns	,				1							)

									UpperCAmelCase:					int 						=     ["train", "test[:33%]"]
									UpperCAmelCase:					str 						=     [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
									for instructions in [instructionsa, instructionsa]:
										UpperCAmelCase:					List[str] 						=     [reader.read(__snake_case	,				__snake_case	,				__snake_case							) for instr in instructions]
										UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     (Dataset(**__snake_case							) for dataset_kwargs in datasets_kwargs)
										self.assertEqual(train_dset["filename"][0]	,				F'{name}-train'							)
										self.assertEqual(train_dset.num_rows	,				1_0_0							)
										self.assertEqual(train_dset.num_columns	,				1							)
										self.assertIsInstance(train_dset.split	,				__snake_case							)
										self.assertEqual(str(train_dset.split							)	,				"train"							)
										self.assertEqual(test_dset["filename"][0]	,				F'{name}-test'							)
										self.assertEqual(test_dset.num_rows	,				3_3							)
										self.assertEqual(test_dset.num_columns	,				1							)
										self.assertIsInstance(test_dset.split	,				__snake_case							)
										self.assertEqual(str(test_dset.split							)	,				"test[:33%]"							)
										del train_dset, test_dset



							def     A__ (     self							)						->     Optional[Any]:






								"""simple docstring"""



								UpperCAmelCase:					str 						=     "my_name"
								UpperCAmelCase:					List[Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0_0	,				shard_lengths=[1_0_0] * 1_0							)
								UpperCAmelCase:					List[Any] 						=     [train_info]
								UpperCAmelCase:					Dict 						=     SplitDict()
								split_dict.add(__snake_case							)
								UpperCAmelCase:					int 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					Optional[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					Optional[Any] 						=     "train[:33%]"
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-train-00000-of-00010'							)
									self.assertEqual(dset["filename"][-1]	,				F'{name}-train-00003-of-00010'							)
									self.assertEqual(dset.num_rows	,				3_3_0							)
									self.assertEqual(dset.num_columns	,				1							)




							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					int 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					List[Any] 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					List[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					List[str] 						=     [
									    {"filename": os.path.join(__snake_case	,				"train"							)},
									    {"filename": os.path.join(__snake_case	,				"test"							), "skip": 1_0, "take": 1_0},
									]
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read_files(__snake_case	,				original_instructions="train+test[10:20]"							)							)
									self.assertEqual(dset.num_rows	,				1_1_0							)
									self.assertEqual(dset.num_columns	,				1							)
									del dset



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		str				,					snake_case_							:		Any				):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					int 						=     ArrowReader.read_table(snake_case_				,					in_memory=snake_case_				)
	assert table.shape == dataset.data.shape
	assert set(table.column_names				) == set(dataset.data.column_names				)
	assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		List[str]				,					snake_case_							:		Optional[int]				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     arrow_file
	UpperCAmelCase:					Tuple 						=     ArrowReader(""				,					snake_case_				)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					Any 						=     reader.read_files([{"filename": filename}]				,					in_memory=snake_case_				)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	UpperCAmelCase:					Any 						=     dataset_kwargs["arrow_table"]
	assert table.shape == dataset.data.shape
	assert set(table.column_names				) == set(dataset.data.column_names				)
	assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	assert ReadInstruction("train"				,					to=1_0				,					unit="abs"				).to_spec() == "train[:10]"
	assert ReadInstruction("train"				,					from_=-8_0				,					to=1_0				,					unit="%"				).to_spec() == "train[-80%:10%]"

	UpperCAmelCase:					str 						=     "train+test"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test

	UpperCAmelCase:					List[Any] 						=     "train[2:10]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_abs

	UpperCAmelCase:					Tuple 						=     "train[15%:-20%]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct

	UpperCAmelCase:					Dict 						=     "train[:10%](closest)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == "train[:10%]"

	UpperCAmelCase:					List[str] 						=     "train[:10%](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct_rounding

	UpperCAmelCase:					Any 						=     "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test_pct_rounding



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     "dummy"
	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					List[str] 						=     "train[:33%]"
	UpperCAmelCase:					Optional[int] 						=     "arrow"
	UpperCAmelCase:					List[Any] 						=     "prefix"

	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train.arrow'				), "skip": 0, "take": 3_3}
	]

	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				,					shard_lengths=[1_0] * 1_0				)]
	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00000-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00001-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00002-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00003-of-00010.arrow'				), "skip": 0, "take": 3},
	]



@pytest.mark.parametrize("name, expected_exception"				,					[(None, TypeError), ("", ValueError)]				)
def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Dict				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					Union[str, Any] 						=     "train"
	UpperCAmelCase:					List[str] 						=     "arrow"
	UpperCAmelCase:					Any 						=     "prefix_path"
	with pytest.raises(snake_case_				):
		UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
