



'''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
)
UpperCAmelCase			=    logging.getLogger(__name__)






def 		_snake_case		(					)			->						str:







	"""simple docstring"""
	lowerCAmelCase    =	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=10							,		help="""batch size"""							)
	parser.add_argument("""--tgt_len"""							,		type=_SCREAMING_SNAKE_CASE							,		default=128							,		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_600							,		help="""length of the retained previous heads"""							)
	parser.add_argument("""--clamp_len"""							,		type=_SCREAMING_SNAKE_CASE							,		default=1_000							,		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."""							)
	lowerCAmelCase    =	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()

	lowerCAmelCase    =	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 )
	lowerCAmelCase    =	TransfoXLCorpus.from_pretrained(args.model_name							)

	lowerCAmelCase    =	corpus.get_iterator("""valid"""							,		args.batch_size							,		args.tgt_len							,		device=_SCREAMING_SNAKE_CASE							,		ext_len=args.ext_len							)
	lowerCAmelCase    =	corpus.get_iterator("""test"""							,		args.batch_size							,		args.tgt_len							,		device=_SCREAMING_SNAKE_CASE							,		ext_len=args.ext_len							)

	# Load a pre-trained model
	lowerCAmelCase    =	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:
		lowerCAmelCase    =	args.clamp_len
	if args.same_length:
		lowerCAmelCase    =	True

	###############################################################################
	# Evaluation code
	###############################################################################
	def evaluate(_SCREAMING_SNAKE_CASE     :						int							):
		# Turn on evaluation mode which disables dropout.
		model.eval()
		lowerCAmelCase,   lowerCAmelCase    =	0, 0.0
		lowerCAmelCase    =	time.time()
		with torch.no_grad():
			lowerCAmelCase    =	None
			for idx, (data, target, seq_len) in enumerate(_SCREAMING_SNAKE_CASE							):
				lowerCAmelCase    =	model(_SCREAMING_SNAKE_CASE							,		lm_labels=_SCREAMING_SNAKE_CASE							,		mems=_SCREAMING_SNAKE_CASE							)
				lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase    =	ret
				lowerCAmelCase    =	loss.mean()
				total_loss += seq_len * loss.item()
				total_len += seq_len
			lowerCAmelCase    =	time.time() - start_time
		logger.info("""Time : {:.2f}s, {:.2f}ms/segment""".format(_SCREAMING_SNAKE_CASE							,		1_000 * total_time / (idx + 1)							)							)
		return total_loss / total_len

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

	def format_log(_SCREAMING_SNAKE_CASE     :						Optional[int]							,		_SCREAMING_SNAKE_CASE     :						List[Any]							):
		lowerCAmelCase    =	"""| {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

	lowerCAmelCase    =	""""""
	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("""=""" * 100							)
	logger.info(_SCREAMING_SNAKE_CASE							)
	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 ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase			=    logging.get_logger(__name__)

UpperCAmelCase			=    {
    'google/bigbird-pegasus-large-arxiv': (
        'https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-pubmed': (
        'https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'
    ),
    'google/bigbird-pegasus-large-bigpatent': (
        'https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						UpperCAmelCase							:    Union[str, Any]           =		"bigbird_pegasus"
						UpperCAmelCase							:    Optional[Any]           =		["past_key_values"]
						UpperCAmelCase							:    Optional[Any]           =		{
						    "num_attention_heads": "encoder_attention_heads",
						    "hidden_size": "d_model",
						    "attention_probs_dropout_prob": "attention_dropout",
						}


						def __init__(					self	,       A_=9_6103	,       A_=4096	,       A_=16	,       A_=4096	,       A_=16	,       A_=16	,       A_=4096	,       A_=16	,       A_=0.0	,       A_=0.0	,       A_=True	,       A_=True	,       A_="gelu_new"	,       A_=1024	,       A_=0.1	,       A_=0.0	,       A_=0.0	,       A_=0.0_2	,       A_=2	,       A_=0.0	,       A_=True	,       A_=0	,       A_=2	,       A_=1	,       A_="block_sparse"	,       A_=64	,       A_=3	,       A_=False	,       **A_	,       )	->	List[str]:
							lowerCAmelCase    =	vocab_size
							lowerCAmelCase    =	max_position_embeddings
							lowerCAmelCase    =	d_model
							lowerCAmelCase    =	encoder_ffn_dim
							lowerCAmelCase    =	encoder_layers
							lowerCAmelCase    =	encoder_attention_heads
							lowerCAmelCase    =	decoder_ffn_dim
							lowerCAmelCase    =	decoder_layers
							lowerCAmelCase    =	decoder_attention_heads
							lowerCAmelCase    =	dropout
							lowerCAmelCase    =	attention_dropout
							lowerCAmelCase    =	activation_dropout
							lowerCAmelCase    =	activation_function
							lowerCAmelCase    =	init_std
							lowerCAmelCase    =	encoder_layerdrop
							lowerCAmelCase    =	decoder_layerdrop
							lowerCAmelCase    =	classifier_dropout
							lowerCAmelCase    =	use_cache
							lowerCAmelCase    =	encoder_layers
							lowerCAmelCase    =	scale_embedding  # scale factor will be sqrt(d_model) if True

							# extra config
							lowerCAmelCase    =	attention_type
							lowerCAmelCase    =	block_size
							lowerCAmelCase    =	num_random_blocks
							lowerCAmelCase    =	use_bias

							super().__init__(
							    pad_token_id=A_	,       bos_token_id=A_	,       eos_token_id=A_	,       is_encoder_decoder=A_	,       decoder_start_token_id=A_	,       **A_	,       )







class __snake_case(      _lowerCAmelCase					):


						'''simple docstring'''



						@property
						def 				__snake_case    (					self      )	->	Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
								lowerCAmelCase    =	OrderedDict(
								    [
								        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
								        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
								    ]      )

								if self.use_past:
									lowerCAmelCase    =	{0: """batch"""}
									lowerCAmelCase    =	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
								else:
									lowerCAmelCase    =	{0: """batch""", 1: """decoder_sequence"""}
									lowerCAmelCase    =	{0: """batch""", 1: """decoder_sequence"""}

								if self.use_past:
									self.fill_with_past_key_values_(A_	,       direction="""inputs"""      )
							elif self.task == "causal-lm":
								# TODO: figure this case out.
								lowerCAmelCase    =	OrderedDict(
								    [
								        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
								        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
								    ]      )
								if self.use_past:
									lowerCAmelCase,   lowerCAmelCase    =	self.num_layers
									for i in range(A_      ):
										lowerCAmelCase    =	{0: """batch""", 2: """past_sequence + sequence"""}
										lowerCAmelCase    =	{0: """batch""", 2: """past_sequence + sequence"""}
							else:
								lowerCAmelCase    =	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
						def 				__snake_case    (					self      )	->	Mapping[str, Mapping[int, str]]:
							if self.task in ["default", "seq2seq-lm"]:
								lowerCAmelCase    =	super().outputs
							else:
								lowerCAmelCase    =	super(A_	,       self      ).outputs
								if self.use_past:
									lowerCAmelCase,   lowerCAmelCase    =	self.num_layers
									for i in range(A_      ):
										lowerCAmelCase    =	{0: """batch""", 2: """past_sequence + sequence"""}
										lowerCAmelCase    =	{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]:
							lowerCAmelCase    =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    A_	,       A_	,       A_	,       A_	,       A_      )

							# Generate decoder inputs
							lowerCAmelCase    =	seq_length if not self.use_past else 1
							lowerCAmelCase    =	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    A_	,       A_	,       A_	,       A_	,       A_      )
							lowerCAmelCase    =	{f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
							lowerCAmelCase    =	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
								lowerCAmelCase,   lowerCAmelCase    =	common_inputs["""input_ids"""].shape
								lowerCAmelCase    =	common_inputs["""decoder_input_ids"""].shape[1]
								lowerCAmelCase,   lowerCAmelCase    =	self.num_attention_heads
								lowerCAmelCase    =	(
								    batch,
								    num_encoder_attention_heads,
								    encoder_seq_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								lowerCAmelCase    =	decoder_seq_length + 3
								lowerCAmelCase    =	(
								    batch,
								    num_decoder_attention_heads,
								    decoder_past_length,
								    self._config.hidden_size // num_decoder_attention_heads,
								)

								lowerCAmelCase    =	torch.cat(
								    [common_inputs["""decoder_attention_mask"""], torch.ones(A_	,       A_      )]	,       dim=1      )

								lowerCAmelCase    =	[]
								# If the number of encoder and decoder layers are present in the model configuration, both are considered
								lowerCAmelCase,   lowerCAmelCase    =	self.num_layers
								lowerCAmelCase    =	min(A_	,       A_      )
								lowerCAmelCase    =	max(A_	,       A_      ) - min_num_layers
								lowerCAmelCase    =	"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

								for _ in range(A_      ):
									common_inputs["past_key_values"].append(
									    (
									        torch.zeros(A_      ),
									        torch.zeros(A_      ),
									        torch.zeros(A_      ),
									        torch.zeros(A_      ),
									    )      )
								# TODO: test this.
								lowerCAmelCase    =	encoder_shape if remaining_side_name == """encoder""" else decoder_shape
								for _ in range(A_	,       A_      ):
									common_inputs["past_key_values"].append((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]:
							lowerCAmelCase    =	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
								lowerCAmelCase,   lowerCAmelCase    =	common_inputs["""input_ids"""].shape
								# Not using the same length for past_key_values
								lowerCAmelCase    =	seqlen + 2
								lowerCAmelCase,   lowerCAmelCase    =	self.num_layers
								lowerCAmelCase,   lowerCAmelCase    =	self.num_attention_heads
								lowerCAmelCase    =	(
								    batch,
								    num_encoder_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)

								lowerCAmelCase    =	common_inputs["""attention_mask"""].dtype
								lowerCAmelCase    =	torch.cat(
								    [common_inputs["""attention_mask"""], torch.ones(A_	,       A_	,       dtype=A_      )]	,       dim=1      )
								lowerCAmelCase    =	[
								    (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]:
							# Copied from OnnxConfig.generate_dummy_inputs
							# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							lowerCAmelCase    =	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
							lowerCAmelCase    =	tokenizer.num_special_tokens_to_add(A_      )
							lowerCAmelCase    =	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
							lowerCAmelCase    =	[""" """.join([tokenizer.unk_token]      ) * seq_length] * batch_size
							lowerCAmelCase    =	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]:
							if self.task in ["default", "seq2seq-lm"]:
								lowerCAmelCase    =	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":
								lowerCAmelCase    =	self._generate_dummy_inputs_for_causal_lm(
								    A_	,       batch_size=A_	,       seq_length=A_	,       is_pair=A_	,       framework=A_      )
							else:
								lowerCAmelCase    =	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_      )	->	Tuple:
							if self.task in ["default", "seq2seq-lm"]:
								lowerCAmelCase    =	super()._flatten_past_key_values_(A_	,       A_	,       A_	,       A_      )
							else:
								lowerCAmelCase    =	super(A_	,       self      )._flatten_past_key_values_(
								    A_	,       A_	,       A_	,       A_      )