


"""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


lowercase  		=			{
    """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"""},
}

lowercase  		=			[
    """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]:

	'''simple docstring'''


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

	snake_case__  :						int		=      parser.parse_args()

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

	return args



def 			A__		(					)							->	List[str]:

	'''simple docstring'''


	transformers.utils.logging.set_verbosity_error()
	snake_case__  :						Tuple		=      parse_args()

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

	snake_case__  :						Optional[int]		=      start_measure()
	snake_case__  :						Optional[int]		=      model_cls.from_pretrained(args.model_name				,    device_map="auto"				,    **__lowerCamelCase	)
	snake_case__  :						Dict		=      end_measure(__lowerCamelCase	)
	log_measures(__lowerCamelCase				,    "Model loading"	)

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

	snake_case__  :						Any		=      AutoTokenizer.from_pretrained(args.tokenizer_name	)

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

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

	snake_case__  :						Optional[Any]		=      end_measure(__lowerCamelCase	)
	log_measures(__lowerCamelCase				,    "Model generation"	)

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


if __name__ == "__main__":
					main()













"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase           =							logging.get_logger(__name__)

lowercase           =							{
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}

class     A_				(   snake_case_	):







				def __init__(				self				:    List[Any]    ,     __lowerCamelCase				:    str=2_5_6    ,     __lowerCamelCase				:    str=1_0_2_4    ,     __lowerCamelCase				:    Optional[Any]=1_6    ,     __lowerCamelCase				:    Union[str, Any]=1_6    ,     __lowerCamelCase				:    str=4    ,     __lowerCamelCase				:    Union[str, Any]="gelu"    ,     __lowerCamelCase				:    Optional[int]=1e-6    ,     **__lowerCamelCase				:    str    ,     )					->       List[Any]:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  hidden_size
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  image_size // patch_size
										__magic_name__      =  mask_input_channels
										__magic_name__      =  num_point_embeddings
										__magic_name__      =  hidden_act
										__magic_name__      =  layer_norm_eps





class     A_				(   snake_case_	):







				def __init__(				self				:    List[str]    ,     __lowerCamelCase				:    Optional[Any]=2_5_6    ,     __lowerCamelCase				:    int="relu"    ,     __lowerCamelCase				:    List[str]=2_0_4_8    ,     __lowerCamelCase				:    Optional[Any]=2    ,     __lowerCamelCase				:    Optional[Any]=8    ,     __lowerCamelCase				:    Optional[Any]=2    ,     __lowerCamelCase				:    Dict=3    ,     __lowerCamelCase				:    Optional[int]=3    ,     __lowerCamelCase				:    Tuple=2_5_6    ,     __lowerCamelCase				:    List[str]=1e-6    ,     **__lowerCamelCase				:    Any    ,     )					->       str:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  hidden_size
										__magic_name__      =  hidden_act
										__magic_name__      =  mlp_dim
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  attention_downsample_rate
										__magic_name__      =  num_multimask_outputs
										__magic_name__      =  iou_head_depth
										__magic_name__      =  iou_head_hidden_dim
										__magic_name__      =  layer_norm_eps





class     A_				(   snake_case_	):







				def __init__(				self				:    Union[str, Any]    ,     __lowerCamelCase				:    str=7_6_8    ,     __lowerCamelCase				:    Optional[int]=2_5_6    ,     __lowerCamelCase				:    Tuple=1_2    ,     __lowerCamelCase				:    int=1_2    ,     __lowerCamelCase				:    int=3    ,     __lowerCamelCase				:    int=1_0_2_4    ,     __lowerCamelCase				:    Any=1_6    ,     __lowerCamelCase				:    Tuple="gelu"    ,     __lowerCamelCase				:    Dict=1e-06    ,     __lowerCamelCase				:    List[Any]=0.0    ,     __lowerCamelCase				:    Optional[Any]=1e-10    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    List[str]=4.0    ,     __lowerCamelCase				:    Union[str, Any]=True    ,     __lowerCamelCase				:    str=True    ,     __lowerCamelCase				:    Optional[Any]=1_4    ,     __lowerCamelCase				:    str=[2, 5, 8, 1_1]    ,     __lowerCamelCase				:    List[Any]=1_2_8    ,     __lowerCamelCase				:    Dict=None    ,     **__lowerCamelCase				:    List[str]    ,     )					->       Optional[int]:
										super().__init__(**__lowerCamelCase						)

										__magic_name__      =  hidden_size
										__magic_name__      =  output_channels
										__magic_name__      =  num_hidden_layers
										__magic_name__      =  num_attention_heads
										__magic_name__      =  num_channels
										__magic_name__      =  image_size
										__magic_name__      =  patch_size
										__magic_name__      =  hidden_act
										__magic_name__      =  layer_norm_eps
										__magic_name__      =  attention_dropout
										__magic_name__      =  initializer_range
										__magic_name__      =  qkv_bias
										__magic_name__      =  mlp_ratio
										__magic_name__      =  use_abs_pos
										__magic_name__      =  use_rel_pos
										__magic_name__      =  window_size
										__magic_name__      =  global_attn_indexes
										__magic_name__      =  num_pos_feats
										__magic_name__      =  int(hidden_size * mlp_ratio						) if mlp_dim is None else mlp_dim





class     A_				(   snake_case_	):
				UpperCAmelCase__										=					'''sam'''
				UpperCAmelCase__										=					True







				def __init__(				self				:    Optional[Any]    ,     __lowerCamelCase				:    Dict=None    ,     __lowerCamelCase				:    Dict=None    ,     __lowerCamelCase				:    Optional[Any]=None    ,     __lowerCamelCase				:    int=0.02    ,     **__lowerCamelCase				:    List[Any]    ,     )					->       List[str]:
										super().__init__(**__lowerCamelCase						)
										__magic_name__      =  vision_config if vision_config is not None else {}
										__magic_name__      =  prompt_encoder_config if prompt_encoder_config is not None else {}
										__magic_name__      =  mask_decoder_config if mask_decoder_config is not None else {}

										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  vision_config.to_dict()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  prompt_encoder_config.to_dict()
										if isinstance(__lowerCamelCase    ,     __lowerCamelCase						):
																__magic_name__      =  mask_decoder_config.to_dict()

										__magic_name__      =  SamVisionConfig(**__lowerCamelCase						)
										__magic_name__      =  SamPromptEncoderConfig(**__lowerCamelCase						)
										__magic_name__      =  SamMaskDecoderConfig(**__lowerCamelCase						)
										__magic_name__      =  initializer_range







				def      _snake_case       (				self				:    Optional[int]						)					->       str:
										__magic_name__      =  copy.deepcopy(self.__dict__						)
										__magic_name__      =  self.vision_config.to_dict()
										__magic_name__      =  self.prompt_encoder_config.to_dict()
										__magic_name__      =  self.mask_decoder_config.to_dict()
										__magic_name__      =  self.__class__.model_type
										return output



