


"""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


__magic_name__	:   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'},
}

__magic_name__	:   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 			a_   (  ):
			__lowerCamelCase        =			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"""    )

			__lowerCamelCase        =			parser.parse_args()

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

			return args





def 			a_   (  ):
			transformers.utils.logging.set_verbosity_error()
			__lowerCamelCase        =			parse_args()

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

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

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

			__lowerCamelCase        =			AutoTokenizer.from_pretrained(args.tokenizer_name    )

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

						__lowerCamelCase        =			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'    )

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

			__lowerCamelCase        =			[gen / tok for gen, tok in zip(lowercase__, lowercase__    )]
			__lowerCamelCase        =			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'    )
			__lowerCamelCase        =			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()








"""simple docstring"""




import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types




class  __snake_case      (unittest.TestCase ,		lowerCamelCase     ):




							def      __a      (    self:						Optional[Any]      ):
										__lowerCamelCase        =			load_tool("""translation"""      )
										self.tool.setup()
										__lowerCamelCase        =			load_tool("""translation"""    ,       remote=A_      )




							def      __a      (    self:						int      ):
										__lowerCamelCase        =			self.tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )




							def      __a      (    self:						int      ):
										__lowerCamelCase        =			self.remote_tool("""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )




							def      __a      (    self:						Union[str, Any]      ):
										__lowerCamelCase        =			self.tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )




							def      __a      (    self:						Any      ):
										__lowerCamelCase        =			self.remote_tool(text="""Hey, what's up?"""    ,       src_lang="""English"""    ,       tgt_lang="""French"""      )
										self.assertEqual(A_    ,       """- Hé, comment ça va?"""      )




							def      __a      (    self:						List[Any]      ):
										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
										__lowerCamelCase        =			self.tool(*A_      )

										# There is a single output
										if len(self.tool.outputs      ) == 1:
													__lowerCamelCase        =			[outputs]

										self.assertListEqual(output_types(A_      )    ,       self.tool.outputs      )




							def      __a      (    self:						Union[str, Any]      ):
										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]
										__lowerCamelCase        =			self.tool(*A_      )

										if not isinstance(A_    ,       A_      ):
													__lowerCamelCase        =			[outputs]

										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )

										for output, output_type in zip(A_    ,       self.tool.outputs      ):
													__lowerCamelCase        =			AGENT_TYPE_MAPPING[output_type]
													self.assertTrue(isinstance(A_    ,       A_      )      )




							def      __a      (    self:						Dict      ):
										__lowerCamelCase        =			["""Hey, what's up?""", """English""", """Spanish"""]

										__lowerCamelCase        =			[]

										for _input, input_type in zip(A_    ,       self.tool.inputs      ):
													if isinstance(A_    ,       A_      ):
																_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input      ) for _input_type in input_type]      )
													else:
																_inputs.append(AGENT_TYPE_MAPPING[input_type](_input      )      )

        # Should not raise an error
										__lowerCamelCase        =			self.tool(*A_      )

										if not isinstance(A_    ,       A_      ):
													__lowerCamelCase        =			[outputs]

										self.assertEqual(len(A_      )    ,       len(self.tool.outputs      )      )




