

"""simple docstring"""






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

	_UpperCAmelCase      :							Optional[Any]       =   torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""	)
	logger.info("""device: {}""".format(_lowerCAmelCase	)	)

	# 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 )
	_UpperCAmelCase      :							Dict       =   TransfoXLCorpus.from_pretrained(args.model_name	)

	_UpperCAmelCase      :							Optional[int]       =   corpus.get_iterator("""valid""",					args.batch_size,					args.tgt_len,					device=_lowerCAmelCase,					ext_len=args.ext_len	)
	_UpperCAmelCase      :							List[Any]       =   corpus.get_iterator("""test""",					args.batch_size,					args.tgt_len,					device=_lowerCAmelCase,					ext_len=args.ext_len	)

	# Load a pre-trained model
	_UpperCAmelCase      :							Tuple       =   TransfoXLLMHeadModel.from_pretrained(args.model_name	)
	model.to(_lowerCAmelCase	)

	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:
		_UpperCAmelCase      :							Optional[int]       =   args.clamp_len
	if args.same_length:
		_UpperCAmelCase      :							Tuple       =   True

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

	# Run on test data.
	if args.split == "all":
		_UpperCAmelCase      :							Tuple       =   evaluate(_lowerCAmelCase	)
		_UpperCAmelCase      :							Dict       =   evaluate(_lowerCAmelCase	)
	elif args.split == "valid":
		_UpperCAmelCase      :							Optional[int]       =   evaluate(_lowerCAmelCase	)
		_UpperCAmelCase      :							Any       =   None
	elif args.split == "test":
		_UpperCAmelCase      :							List[Any]       =   evaluate(_lowerCAmelCase	)
		_UpperCAmelCase      :							Any       =   None

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

	_UpperCAmelCase      :							Dict       =   """"""
	if valid_loss is not None:
		log_str += format_log(_lowerCAmelCase,					"""valid"""	)
	if test_loss is not None:
		log_str += format_log(_lowerCAmelCase,					"""test"""	)

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


if __name__ == "__main__":
						main()








"""simple docstring"""






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCamelCase__			:					Dict							  =					logging.get_logger(__name__)

lowerCamelCase__			:					Optional[Any]							  =					{
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class       _UpperCAmelCase     (						__a):
			__a	:    Optional[int]			   =							"""blenderbot"""
			__a	:    Tuple			   =							["""past_key_values"""]
			__a	:    int			   =							{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
			def __init__(						self ,						_A=80_08 ,						_A=1_28 ,						_A=2 ,						_A=1_02_40 ,						_A=32 ,						_A=24 ,						_A=1_02_40 ,						_A=32 ,						_A=0.0 ,						_A=0.0 ,						_A=True ,						_A=True ,						_A="gelu" ,						_A=25_60 ,						_A=0.1 ,						_A=0.0 ,						_A=0.0 ,						_A=0.02 ,						_A=1 ,						_A=False ,						_A=0 ,						_A=1 ,						_A=2 ,						_A=3 ,						_A=2 ,						**_A ,						)      ->      Union[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Any       =   vocab_size
				_UpperCAmelCase      :							Dict       =   max_position_embeddings
				_UpperCAmelCase      :							Optional[int]       =   d_model
				_UpperCAmelCase      :							Optional[Any]       =   encoder_ffn_dim
				_UpperCAmelCase      :							Optional[Any]       =   encoder_layers
				_UpperCAmelCase      :							List[Any]       =   encoder_attention_heads
				_UpperCAmelCase      :							str       =   decoder_ffn_dim
				_UpperCAmelCase      :							int       =   decoder_layers
				_UpperCAmelCase      :							List[str]       =   decoder_attention_heads
				_UpperCAmelCase      :							List[Any]       =   dropout
				_UpperCAmelCase      :							List[str]       =   attention_dropout
				_UpperCAmelCase      :							int       =   activation_dropout
				_UpperCAmelCase      :							Union[str, Any]       =   activation_function
				_UpperCAmelCase      :							List[str]       =   init_std
				_UpperCAmelCase      :							Dict       =   encoder_layerdrop
				_UpperCAmelCase      :							Union[str, Any]       =   decoder_layerdrop
				_UpperCAmelCase      :							str       =   use_cache
				_UpperCAmelCase      :							Optional[int]       =   encoder_layers
				_UpperCAmelCase      :							Union[str, Any]       =   scale_embedding  # scale factor will be sqrt(d_model) if True

				super().__init__(
				    pad_token_id=_A ,						bos_token_id=_A ,						eos_token_id=_A ,						is_encoder_decoder=_A ,						decoder_start_token_id=_A ,						encoder_no_repeat_ngram_size=_A ,						forced_eos_token_id=_A ,						**_A ,						)






class       _UpperCAmelCase     (						__a):
			@property
			def 							__snake_case	(						self       )      ->      Mapping[str, Mapping[int, str]]:






				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					_UpperCAmelCase      :							Tuple       =   OrderedDict(
					    [
					        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
					        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
					    ]       )
					if self.use_past:
						_UpperCAmelCase      :							List[str]       =   {0: """batch"""}
						_UpperCAmelCase      :							List[str]       =   {0: """batch""", 1: """past_decoder_sequence + sequence"""}
					else:
						_UpperCAmelCase      :							Optional[int]       =   {0: """batch""", 1: """decoder_sequence"""}
						_UpperCAmelCase      :							int       =   {0: """batch""", 1: """decoder_sequence"""}
					if self.use_past:
						self.fill_with_past_key_values_(_A ,						direction="""inputs"""       )
				elif self.task == "causal-lm":
					_UpperCAmelCase      :							List[str]       =   OrderedDict(
					    [
					        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
					        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
					    ]       )
					if self.use_past:
						_UpperCAmelCase  ,				_UpperCAmelCase      :							Optional[Any]       =   self.num_layers
						for i in range(_A       ):
							_UpperCAmelCase      :							Any       =   {0: """batch""", 2: """past_sequence + sequence"""}
							_UpperCAmelCase      :							Dict       =   {0: """batch""", 2: """past_sequence + sequence"""}
				else:
					_UpperCAmelCase      :							List[Any]       =   OrderedDict(
					    [
					        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
					        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
					        ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
					        ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
					    ]       )

				return common_inputs
			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def 							__snake_case	(						self       )      ->      Mapping[str, Mapping[int, str]]:






				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					_UpperCAmelCase      :							Optional[Any]       =   super().outputs
				else:
					_UpperCAmelCase      :							Optional[int]       =   super(_A ,						self       ).outputs
					if self.use_past:
						_UpperCAmelCase  ,				_UpperCAmelCase      :							int       =   self.num_layers
						for i in range(_A       ):
							_UpperCAmelCase      :							Tuple       =   {0: """batch""", 2: """past_sequence + sequence"""}
							_UpperCAmelCase      :							Optional[Any]       =   {0: """batch""", 2: """past_sequence + sequence"""}
				return common_outputs
			def 							__snake_case	(						self ,						_A ,						_A = -1 ,						_A = -1 ,						_A = False ,						_A = None ,						)      ->      Mapping[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Dict       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _A ,						_A ,						_A ,						_A ,						_A       )
				# Generate decoder inputs
				_UpperCAmelCase      :							Any       =   seq_length if not self.use_past else 1
				_UpperCAmelCase      :							List[str]       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _A ,						_A ,						_A ,						_A ,						_A       )
				_UpperCAmelCase      :							str       =   {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
				_UpperCAmelCase      :							Dict       =   dict(**_A ,						**_A       )

				if self.use_past:
					if not is_torch_available():
						raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
					else:
						import torch
					_UpperCAmelCase  ,				_UpperCAmelCase      :							Tuple       =   common_inputs["""input_ids"""].shape
					_UpperCAmelCase      :							List[Any]       =   common_inputs["""decoder_input_ids"""].shape[1]
					_UpperCAmelCase  ,				_UpperCAmelCase      :							List[str]       =   self.num_attention_heads
					_UpperCAmelCase      :							List[str]       =   (
					    batch,
					    num_encoder_attention_heads,
					    encoder_seq_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					_UpperCAmelCase      :							List[str]       =   decoder_seq_length
					_UpperCAmelCase      :							Tuple       =   (
					    batch,
					    num_decoder_attention_heads,
					    decoder_past_length,
					    self._config.hidden_size // num_decoder_attention_heads,
					)
					_UpperCAmelCase      :							Tuple       =   torch.cat(
					    [common_inputs["""decoder_attention_mask"""], torch.ones(_A ,						_A       )] ,						dim=1       )
					_UpperCAmelCase      :							Tuple       =   []
					_UpperCAmelCase  ,				_UpperCAmelCase      :							int       =   self.num_layers

					for _ in range(_A       ):
						common_inputs["past_key_values"].append(
						    (
						        torch.zeros(_A       ),
						        torch.zeros(_A       ),
						        torch.zeros(_A       ),
						        torch.zeros(_A       ),
						    )       )
				return common_inputs
			def 							__snake_case	(						self ,						_A ,						_A = -1 ,						_A = -1 ,						_A = False ,						_A = None ,						)      ->      Mapping[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							Union[str, Any]       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _A ,						_A ,						_A ,						_A ,						_A       )

				if self.use_past:
					if not is_torch_available():
						raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
					else:
						import torch
					_UpperCAmelCase  ,				_UpperCAmelCase      :							Any       =   common_inputs["""input_ids"""].shape
					_UpperCAmelCase      :							Union[str, Any]       =   seqlen
					_UpperCAmelCase  ,				_UpperCAmelCase      :							Optional[Any]       =   self.num_layers
					_UpperCAmelCase  ,				_UpperCAmelCase      :							Tuple       =   self.num_attention_heads
					_UpperCAmelCase      :							Optional[Any]       =   (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)
					_UpperCAmelCase      :							str       =   common_inputs["""attention_mask"""].dtype
					_UpperCAmelCase      :							Tuple       =   torch.cat(
					    [common_inputs["""attention_mask"""], torch.ones(_A ,						_A ,						dtype=_A       )] ,						dim=1       )
					_UpperCAmelCase      :							str       =   [
					    (torch.zeros(_A       ), torch.zeros(_A       )) for _ in range(_A       )
					]
				return common_inputs
			def 							__snake_case	(						self ,						_A ,						_A = -1 ,						_A = -1 ,						_A = False ,						_A = None ,						)      ->      Mapping[str, Any]:






				'''simple docstring'''







				_UpperCAmelCase      :							str       =   compute_effective_axis_dimension(
				    _A ,						fixed_dimension=OnnxConfig.default_fixed_batch ,						num_token_to_add=0       )

				# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
				_UpperCAmelCase      :							List[str]       =   tokenizer.num_special_tokens_to_add(_A       )
				_UpperCAmelCase      :							Tuple       =   compute_effective_axis_dimension(
				    _A ,						fixed_dimension=OnnxConfig.default_fixed_sequence ,						num_token_to_add=_A       )

				# Generate dummy inputs according to compute batch and sequence
				_UpperCAmelCase      :							str       =   [""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
				_UpperCAmelCase      :							str       =   dict(tokenizer(_A ,						return_tensors=_A       )       )
				return common_inputs
			def 							__snake_case	(						self ,						_A ,						_A = -1 ,						_A = -1 ,						_A = False ,						_A = None ,						)      ->      Mapping[str, Any]:






				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					_UpperCAmelCase      :							Any       =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
					    _A ,						batch_size=_A ,						seq_length=_A ,						is_pair=_A ,						framework=_A       )

				elif self.task == "causal-lm":
					_UpperCAmelCase      :							int       =   self._generate_dummy_inputs_for_causal_lm(
					    _A ,						batch_size=_A ,						seq_length=_A ,						is_pair=_A ,						framework=_A       )
				else:
					_UpperCAmelCase      :							Tuple       =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    _A ,						batch_size=_A ,						seq_length=_A ,						is_pair=_A ,						framework=_A       )

				return common_inputs
			def 							__snake_case	(						self ,						_A ,						_A ,						_A ,						_A       )      ->      Optional[int]:






				'''simple docstring'''







				if self.task in ["default", "seq2seq-lm"]:
					_UpperCAmelCase      :							Union[str, Any]       =   super()._flatten_past_key_values_(_A ,						_A ,						_A ,						_A       )
				else:
					_UpperCAmelCase      :							Tuple       =   super(_A ,						self       )._flatten_past_key_values_(
					    _A ,						_A ,						_A ,						_A       )
			def 							__snake_case	(						self ,						_A ,						_A       )      ->      Optional[Any]:






				'''simple docstring'''







				if direction not in ["inputs", "outputs"]:
					raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''       )

				_UpperCAmelCase      :							Any       =   """past_key_values""" if direction == """inputs""" else """present"""
				_UpperCAmelCase  ,				_UpperCAmelCase      :							Any       =   self.num_layers

				_UpperCAmelCase      :							List[str]       =   """past_encoder_sequence"""
				_UpperCAmelCase      :							int       =   """past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

				for i in range(_A       ):
					_UpperCAmelCase      :							Union[str, Any]       =   {0: """batch""", 2: decoder_sequence}
					_UpperCAmelCase      :							Optional[int]       =   {0: """batch""", 2: decoder_sequence}
					_UpperCAmelCase      :							Tuple       =   {0: """batch""", 2: encoder_sequence}
					_UpperCAmelCase      :							Tuple       =   {0: """batch""", 2: encoder_sequence}





