



import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
snake_case     :    Any						=    logging.getLogger(__name__)
def 						snake_case__							(       )		->		Any:







						"""simple docstring"""

						A__						:				Union[str, Any]         =			argparse.ArgumentParser(description="PyTorch Transformer Language Model"							)
						parser.add_argument("--model_name"				,  type=__SCREAMING_SNAKE_CASE				,  default="transfo-xl-wt103"				,  help="pretrained model name"							)
						parser.add_argument(
						    "--split"				,  type=__SCREAMING_SNAKE_CASE				,  default="test"				,  choices=["all", "valid", "test"]				,  help="which split to evaluate"							)
						parser.add_argument("--batch_size"				,  type=__SCREAMING_SNAKE_CASE				,  default=1_0				,  help="batch size"							)
						parser.add_argument("--tgt_len"				,  type=__SCREAMING_SNAKE_CASE				,  default=1_2_8				,  help="number of tokens to predict"							)
						parser.add_argument("--ext_len"				,  type=__SCREAMING_SNAKE_CASE				,  default=0				,  help="length of the extended context"							)
						parser.add_argument("--mem_len"				,  type=__SCREAMING_SNAKE_CASE				,  default=1_6_0_0				,  help="length of the retained previous heads"							)
						parser.add_argument("--clamp_len"				,  type=__SCREAMING_SNAKE_CASE				,  default=1_0_0_0				,  help="max positional embedding index"							)
						parser.add_argument("--no_cuda"				,  action="store_true"				,  help="Do not use CUDA even though CUA is available"							)
						parser.add_argument("--work_dir"				,  type=__SCREAMING_SNAKE_CASE				,  required=__SCREAMING_SNAKE_CASE				,  help="path to the work_dir"							)
						parser.add_argument("--no_log"				,  action="store_true"				,  help="do not log the eval result"							)
						parser.add_argument("--same_length"				,  action="store_true"				,  help="set same length attention with masking"							)
						parser.add_argument("--server_ip"				,  type=__SCREAMING_SNAKE_CASE				,  default=""				,  help="Can be used for distant debugging."							)
						parser.add_argument("--server_port"				,  type=__SCREAMING_SNAKE_CASE				,  default=""				,  help="Can be used for distant debugging."							)
						A__						:				int         =			parser.parse_args()
						assert args.ext_len >= 0, "extended context length must be non-negative"

						if args.server_ip and args.server_port:
												# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
												import ptvsd

												print("Waiting for debugger attach"							)
												ptvsd.enable_attach(address=(args.server_ip, args.server_port)				,  redirect_output=__SCREAMING_SNAKE_CASE							)
												ptvsd.wait_for_attach()

						A__						:				List[str]         =			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"							)
						logger.info("device: {}".format(__SCREAMING_SNAKE_CASE							)							)

						# Load a pre-processed dataset
						# You can also build the corpus yourself using TransfoXLCorpus methods
						# The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
						# and tokenizing the dataset
						# The pre-processed corpus is a convertion (using the conversion script )
						A__						:				List[str]         =			TransfoXLCorpus.from_pretrained(args.model_name							)

						A__						:				Optional[Any]         =			corpus.get_iterator("valid"				,  args.batch_size				,  args.tgt_len				,  device=__SCREAMING_SNAKE_CASE				,  ext_len=args.ext_len							)
						A__						:				Any         =			corpus.get_iterator("test"				,  args.batch_size				,  args.tgt_len				,  device=__SCREAMING_SNAKE_CASE				,  ext_len=args.ext_len							)

						# Load a pre-trained model
						A__						:				str         =			TransfoXLLMHeadModel.from_pretrained(args.model_name							)
						model.to(__SCREAMING_SNAKE_CASE							)

						logger.info(
						    "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
						        args.batch_size				,  args.tgt_len				,  args.ext_len				,  args.mem_len				,  args.clamp_len							)							)

						model.reset_memory_length(args.mem_len							)
						if args.clamp_len > 0:
												A__						:				int         =			args.clamp_len
						if args.same_length:
												A__						:				List[Any]         =			True

						###############################################################################
						# Evaluation code
						###############################################################################
						def evaluate(__lowercase							):
												# Turn on evaluation mode which disables dropout.
												model.eval()
												A__						:				Union[str, Any]         =			0, 0.0
												A__						:				str         =			time.time()
												with torch.no_grad():
																		A__						:				Dict         =			None
																		for idx, (data, target, seq_len) in enumerate(__SCREAMING_SNAKE_CASE							):
																								A__						:				List[str]         =			model(__SCREAMING_SNAKE_CASE				,  lm_labels=__SCREAMING_SNAKE_CASE				,  mems=__SCREAMING_SNAKE_CASE							)
																								A__						:				Optional[Any]         =			ret
																								A__						:				List[Any]         =			loss.mean()
																								total_loss += seq_len * loss.item()
																								total_len += seq_len
																		A__						:				str         =			time.time() - start_time
												logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(__SCREAMING_SNAKE_CASE				,  1_0_0_0 * total_time / (idx + 1)							)							)
												return total_loss / total_len

						# Run on test data.
						if args.split == "all":
												A__						:				Tuple         =			evaluate(__SCREAMING_SNAKE_CASE							)
												A__						:				Union[str, Any]         =			evaluate(__SCREAMING_SNAKE_CASE							)
						elif args.split == "valid":
												A__						:				Dict         =			evaluate(__SCREAMING_SNAKE_CASE							)
												A__						:				Dict         =			None
						elif args.split == "test":
												A__						:				Union[str, Any]         =			evaluate(__SCREAMING_SNAKE_CASE							)
												A__						:				Tuple         =			None

						def format_log(__lowercase				,  __lowercase							):
												A__						:				Optional[int]         =			"""| {0} loss {1:5.2f} | {0} ppl {2:9.3f} """.format(__SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  math.exp(__SCREAMING_SNAKE_CASE							)							)
												return log_str

						A__						:				str         =			""""""
						if valid_loss is not None:
												log_str += format_log(__SCREAMING_SNAKE_CASE				,  "valid"							)
						if test_loss is not None:
												log_str += format_log(__SCREAMING_SNAKE_CASE				,  "test"							)

						logger.info("=" * 1_0_0							)
						logger.info(__SCREAMING_SNAKE_CASE							)
						logger.info("=" * 1_0_0							)


if __name__ == "__main__":
		main()

'''simple docstring'''
import copy

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


A__ :		str					=logging.get_logger(__name__)

A__ :		Optional[int]					={
    '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					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Tuple					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Union[str, Any]=10_24					, lowerCamelCase      :     List[str]=16					, lowerCamelCase      :     str=16					, lowerCamelCase      :     Union[str, Any]=4					, lowerCamelCase      :     Optional[Any]="gelu"					, lowerCamelCase      :     Union[str, Any]=1e-6					, **lowerCamelCase      :     Union[str, Any]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[str]       				=		hidden_size
   __A		:  Any       				=		image_size
   __A		:  Dict       				=		patch_size
   __A		:  Union[str, Any]       				=		image_size // patch_size
   __A		:  List[Any]       				=		mask_input_channels
   __A		:  str       				=		num_point_embeddings
   __A		:  Tuple       				=		hidden_act
   __A		:  List[str]       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     int=2_56					, lowerCamelCase      :     Tuple="relu"					, lowerCamelCase      :     Optional[Any]=20_48					, lowerCamelCase      :     int=2					, lowerCamelCase      :     Tuple=8					, lowerCamelCase      :     int=2					, lowerCamelCase      :     List[str]=3					, lowerCamelCase      :     Dict=3					, lowerCamelCase      :     Any=2_56					, lowerCamelCase      :     int=1e-6					, **lowerCamelCase      :     Tuple					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  List[Any]       				=		hidden_size
   __A		:  Union[str, Any]       				=		hidden_act
   __A		:  Tuple       				=		mlp_dim
   __A		:  Any       				=		num_hidden_layers
   __A		:  Dict       				=		num_attention_heads
   __A		:  Optional[Any]       				=		attention_downsample_rate
   __A		:  List[Any]       				=		num_multimask_outputs
   __A		:  Optional[Any]       				=		iou_head_depth
   __A		:  Optional[int]       				=		iou_head_hidden_dim
   __A		:  Any       				=		layer_norm_eps







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     List[str]=7_68					, lowerCamelCase      :     Tuple=2_56					, lowerCamelCase      :     Optional[Any]=12					, lowerCamelCase      :     Tuple=12					, lowerCamelCase      :     int=3					, lowerCamelCase      :     Dict=10_24					, lowerCamelCase      :     int=16					, lowerCamelCase      :     Any="gelu"					, lowerCamelCase      :     Tuple=1e-0_6					, lowerCamelCase      :     Optional[int]=0.0					, lowerCamelCase      :     List[str]=1e-1_0					, lowerCamelCase      :     Optional[int]=True					, lowerCamelCase      :     Union[str, Any]=4.0					, lowerCamelCase      :     List[Any]=True					, lowerCamelCase      :     Optional[Any]=True					, lowerCamelCase      :     str=14					, lowerCamelCase      :     str=[2, 5, 8, 11]					, lowerCamelCase      :     Dict=1_28					, lowerCamelCase      :     Any=None					, **lowerCamelCase      :     Optional[int]					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)

   __A		:  Any       				=		hidden_size
   __A		:  Union[str, Any]       				=		output_channels
   __A		:  int       				=		num_hidden_layers
   __A		:  Any       				=		num_attention_heads
   __A		:  Tuple       				=		num_channels
   __A		:  Tuple       				=		image_size
   __A		:  Optional[Any]       				=		patch_size
   __A		:  List[str]       				=		hidden_act
   __A		:  Dict       				=		layer_norm_eps
   __A		:  Optional[int]       				=		attention_dropout
   __A		:  Dict       				=		initializer_range
   __A		:  Union[str, Any]       				=		qkv_bias
   __A		:  Optional[Any]       				=		mlp_ratio
   __A		:  Optional[int]       				=		use_abs_pos
   __A		:  int       				=		use_rel_pos
   __A		:  str       				=		window_size
   __A		:  Union[str, Any]       				=		global_attn_indexes
   __A		:  Dict       				=		num_pos_feats
   __A		:  int       				=		int(hidden_size * mlp_ratio							) if mlp_dim is None else mlp_dim







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 lowerCamelCase												='''sam'''
 lowerCamelCase												=True
 def __init__( self      :     Optional[int]					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Dict=None					, lowerCamelCase      :     Optional[Any]=None					, lowerCamelCase      :     List[Any]=0.02					, **lowerCamelCase      :     Any					, ):



   """simple docstring"""





   super().__init__(**lowerCamelCase							)
   __A		:  str       				=		vision_config if vision_config is not None else {}
   __A		:  int       				=		prompt_encoder_config if prompt_encoder_config is not None else {}
   __A		:  Dict       				=		mask_decoder_config if mask_decoder_config is not None else {}

   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  List[str]       				=		vision_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		prompt_encoder_config.to_dict()
   if isinstance(lowerCamelCase					, lowerCamelCase							):
     __A		:  Any       				=		mask_decoder_config.to_dict()

   __A		:  Any       				=		SamVisionConfig(**lowerCamelCase							)
   __A		:  List[Any]       				=		SamPromptEncoderConfig(**lowerCamelCase							)
   __A		:  Union[str, Any]       				=		SamMaskDecoderConfig(**lowerCamelCase							)
   __A		:  Any       				=		initializer_range






 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  str       				=		copy.deepcopy(self.__dict__							)
   __A		:  Dict       				=		self.vision_config.to_dict()
   __A		:  Any       				=		self.prompt_encoder_config.to_dict()
   __A		:  str       				=		self.mask_decoder_config.to_dict()
   __A		:  Optional[int]       				=		self.__class__.model_type
   return output



