


'''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_	:     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"""},
}

UpperCAmelCase_	:     Any							     =	[
    """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     snake_case_     (						):







				"""simple docstring"""





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

				_SCREAMING_SNAKE_CASE				: Any  	=   parser.parse_args()

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

				return args







def     snake_case_     (						):







				"""simple docstring"""





				transformers.utils.logging.set_verbosity_error()
				_SCREAMING_SNAKE_CASE				: str  	=   parse_args()

				if args.torch_dtype is None:
								_SCREAMING_SNAKE_CASE				: List[str]  	=   AutoConfig.from_pretrained(args.model_name  )
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   getattr(SCREAMING_SNAKE_CASE__							,   """torch_dtype"""							,   torch.floataa  )
				else:
								_SCREAMING_SNAKE_CASE				: Tuple  	=   getattr(SCREAMING_SNAKE_CASE__							,   args.torch_dtype  )
				_SCREAMING_SNAKE_CASE				: List[str]  	=   AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
				_SCREAMING_SNAKE_CASE				: int  	=   {
				    "torch_dtype": torch_dtype,
				    "revision": args.model_revision,
				}
				if args.disk_offload:
								_SCREAMING_SNAKE_CASE				: int  	=   "tmp_offload"
								_SCREAMING_SNAKE_CASE				: Dict  	=   True

				_SCREAMING_SNAKE_CASE				: Tuple  	=   start_measure()
				_SCREAMING_SNAKE_CASE				: Any  	=   model_cls.from_pretrained(args.model_name							,   device_map="""auto"""							,   **SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   end_measure(SCREAMING_SNAKE_CASE__  )
				log_measures(SCREAMING_SNAKE_CASE__							,   """Model loading"""  )

				_SCREAMING_SNAKE_CASE				: List[Any]  	=   compute_module_sizes(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: Tuple  	=   {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]
				_SCREAMING_SNAKE_CASE				: int  	=   "\n".join([f"""- {device}: {size // 2**20}MiB""" for device, size in device_size.items()]  )
				print(f"""\nTheoretical use:\n{message}"""  )

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

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

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

				_SCREAMING_SNAKE_CASE				: Dict  	=   end_measure(SCREAMING_SNAKE_CASE__  )
				log_measures(SCREAMING_SNAKE_CASE__							,   """Model generation"""  )

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


if __name__ == "__main__":
						main()








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_	:     Union[str, Any]							     =	{'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Dict							     =	['PegasusTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	['PegasusTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	[
				    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'PegasusForCausalLM',
				    'PegasusForConditionalGeneration',
				    'PegasusModel',
				    'PegasusPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[Any]							     =	[
				    'TFPegasusForConditionalGeneration',
				    'TFPegasusModel',
				    'TFPegasusPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'FlaxPegasusForConditionalGeneration',
				    'FlaxPegasusModel',
				    'FlaxPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus import PegasusTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus_fast import PegasusTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_pegasus import (
								    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    PegasusForCausalLM,
								    PegasusForConditionalGeneration,
								    PegasusModel,
								    PegasusPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_pegasus import (
								    FlaxPegasusForConditionalGeneration,
								    FlaxPegasusModel,
								    FlaxPegasusPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     List[str]							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




