


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
)
A :	List[Any]												=     logging.getLogger(__name__)






def 				UpperCamelCase      (			)     ->						Optional[int]:
		"""simple docstring"""




		lowercase__      =	argparse.ArgumentParser(description="""PyTorch Transformer Language Model"""  )
		parser.add_argument("""--model_name"""					,					type=__magic_name__					,					default="""transfo-xl-wt103"""					,					help="""pretrained model name"""  )
		parser.add_argument(
		    """--split"""					,					type=__magic_name__					,					default="""test"""					,					choices=["""all""", """valid""", """test"""]					,					help="""which split to evaluate"""  )
		parser.add_argument("""--batch_size"""					,					type=__magic_name__					,					default=10					,					help="""batch size"""  )
		parser.add_argument("""--tgt_len"""					,					type=__magic_name__					,					default=128					,					help="""number of tokens to predict"""  )
		parser.add_argument("""--ext_len"""					,					type=__magic_name__					,					default=0					,					help="""length of the extended context"""  )
		parser.add_argument("""--mem_len"""					,					type=__magic_name__					,					default=1600					,					help="""length of the retained previous heads"""  )
		parser.add_argument("""--clamp_len"""					,					type=__magic_name__					,					default=1000					,					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=__magic_name__					,					required=__magic_name__					,					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=__magic_name__					,					default=""""""					,					help="""Can be used for distant debugging."""  )
		parser.add_argument("""--server_port"""					,					type=__magic_name__					,					default=""""""					,					help="""Can be used for distant debugging."""  )
		lowercase__      =	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=__magic_name__  )
				ptvsd.wait_for_attach()

		lowercase__      =	torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""  )
		logger.info("""device: {}""".format(__magic_name__  )  )

		# 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 )
		lowercase__      =	TransfoXLCorpus.from_pretrained(args.model_name  )

		lowercase__      =	corpus.get_iterator("""valid"""					,					args.batch_size					,					args.tgt_len					,					device=__magic_name__					,					ext_len=args.ext_len  )
		lowercase__      =	corpus.get_iterator("""test"""					,					args.batch_size					,					args.tgt_len					,					device=__magic_name__					,					ext_len=args.ext_len  )

		# Load a pre-trained model
		lowercase__      =	TransfoXLLMHeadModel.from_pretrained(args.model_name  )
		model.to(__magic_name__  )

		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:
				lowercase__      =	args.clamp_len
		if args.same_length:
				lowercase__      =	True

		###############################################################################
		# Evaluation code
		###############################################################################
		def evaluate(__magic_name__  :							Optional[Any]  ):
				# Turn on evaluation mode which disables dropout.
				model.eval()
				lowercase__    ,			lowercase__      =	0, 0.0
				lowercase__      =	time.time()
				with torch.no_grad():
						lowercase__      =	None
						for idx, (data, target, seq_len) in enumerate(__magic_name__  ):
								lowercase__      =	model(__magic_name__					,					lm_labels=__magic_name__					,					mems=__magic_name__  )
								lowercase__    ,			lowercase__    ,			lowercase__      =	ret
								lowercase__      =	loss.mean()
								total_loss += seq_len * loss.item()
								total_len += seq_len
						lowercase__      =	time.time() - start_time
				logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(__magic_name__					,					1000 * total_time / (idx + 1)  )  )
				return total_loss / total_len

		# Run on test data.
		if args.split == "all":
				lowercase__      =	evaluate(__magic_name__  )
				lowercase__      =	evaluate(__magic_name__  )
		elif args.split == "valid":
				lowercase__      =	evaluate(__magic_name__  )
				lowercase__      =	None
		elif args.split == "test":
				lowercase__      =	evaluate(__magic_name__  )
				lowercase__      =	None

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

		lowercase__      =	""""""
		if valid_loss is not None:
				log_str += format_log(__magic_name__					,					"""valid"""  )
		if test_loss is not None:
				log_str += format_log(__magic_name__					,					"""test"""  )

		logger.info("""=""" * 100  )
		logger.info(__magic_name__  )
		logger.info("""=""" * 100  )


if __name__ == "__main__":
	main()





import copy

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


A :	Optional[Any]												=     logging.get_logger(__name__)

A :	Union[str, Any]												=     {
    '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					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Dict   ,      _UpperCAmelCase					:		List[str]=256   ,      _UpperCAmelCase					:		Tuple=1024   ,      _UpperCAmelCase					:		List[str]=16   ,      _UpperCAmelCase					:		Optional[Any]=16   ,      _UpperCAmelCase					:		Union[str, Any]=4   ,      _UpperCAmelCase					:		str="gelu"   ,      _UpperCAmelCase					:		List[str]=1E-6   ,      **_UpperCAmelCase					:		List[Any]   ,      )		->     Tuple:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	hidden_size
							lowercase__      =	image_size
							lowercase__      =	patch_size
							lowercase__      =	image_size // patch_size
							lowercase__      =	mask_input_channels
							lowercase__      =	num_point_embeddings
							lowercase__      =	hidden_act
							lowercase__      =	layer_norm_eps






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		Optional[int]=256   ,      _UpperCAmelCase					:		int="relu"   ,      _UpperCAmelCase					:		Union[str, Any]=2048   ,      _UpperCAmelCase					:		int=2   ,      _UpperCAmelCase					:		Optional[Any]=8   ,      _UpperCAmelCase					:		Any=2   ,      _UpperCAmelCase					:		Tuple=3   ,      _UpperCAmelCase					:		List[str]=3   ,      _UpperCAmelCase					:		List[str]=256   ,      _UpperCAmelCase					:		Tuple=1E-6   ,      **_UpperCAmelCase					:		int   ,      )		->     Tuple:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	hidden_size
							lowercase__      =	hidden_act
							lowercase__      =	mlp_dim
							lowercase__      =	num_hidden_layers
							lowercase__      =	num_attention_heads
							lowercase__      =	attention_downsample_rate
							lowercase__      =	num_multimask_outputs
							lowercase__      =	iou_head_depth
							lowercase__      =	iou_head_hidden_dim
							lowercase__      =	layer_norm_eps






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					def __init__(self					:		Optional[int]   ,      _UpperCAmelCase					:		List[Any]=768   ,      _UpperCAmelCase					:		Union[str, Any]=256   ,      _UpperCAmelCase					:		Union[str, Any]=12   ,      _UpperCAmelCase					:		Optional[Any]=12   ,      _UpperCAmelCase					:		Tuple=3   ,      _UpperCAmelCase					:		Optional[int]=1024   ,      _UpperCAmelCase					:		str=16   ,      _UpperCAmelCase					:		Tuple="gelu"   ,      _UpperCAmelCase					:		Any=1E-0_6   ,      _UpperCAmelCase					:		Any=0.0   ,      _UpperCAmelCase					:		str=1E-1_0   ,      _UpperCAmelCase					:		Tuple=True   ,      _UpperCAmelCase					:		Tuple=4.0   ,      _UpperCAmelCase					:		Optional[Any]=True   ,      _UpperCAmelCase					:		Optional[int]=True   ,      _UpperCAmelCase					:		Optional[Any]=14   ,      _UpperCAmelCase					:		Optional[int]=[2, 5, 8, 11]   ,      _UpperCAmelCase					:		List[Any]=128   ,      _UpperCAmelCase					:		int=None   ,      **_UpperCAmelCase					:		int   ,      )		->     Dict:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)

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






class 							A					( UpperCAmelCase__							):
					'''simple docstring'''






					A__    =				'''sam'''
					A__    =				True

					def __init__(self					:		List[str]   ,      _UpperCAmelCase					:		Any=None   ,      _UpperCAmelCase					:		List[Any]=None   ,      _UpperCAmelCase					:		List[Any]=None   ,      _UpperCAmelCase					:		List[Any]=0.02   ,      **_UpperCAmelCase					:		Optional[Any]   ,      )		->     Dict:




							"""simple docstring"""


							super().__init__(**_UpperCAmelCase	)
							lowercase__      =	vision_config if vision_config is not None else {}
							lowercase__      =	prompt_encoder_config if prompt_encoder_config is not None else {}
							lowercase__      =	mask_decoder_config if mask_decoder_config is not None else {}

							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	vision_config.to_dict()
							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	prompt_encoder_config.to_dict()
							if isinstance(_UpperCAmelCase   ,      _UpperCAmelCase	):
									lowercase__      =	mask_decoder_config.to_dict()

							lowercase__      =	SamVisionConfig(**_UpperCAmelCase	)
							lowercase__      =	SamPromptEncoderConfig(**_UpperCAmelCase	)
							lowercase__      =	SamMaskDecoderConfig(**_UpperCAmelCase	)
							lowercase__      =	initializer_range


					def 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Tuple:




							"""simple docstring"""


							lowercase__      =	copy.deepcopy(self.__dict__	)
							lowercase__      =	self.vision_config.to_dict()
							lowercase__      =	self.prompt_encoder_config.to_dict()
							lowercase__      =	self.mask_decoder_config.to_dict()
							lowercase__      =	self.__class__.model_type
							return output

