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
)
__SCREAMING_SNAKE_CASE						:    Any     = logging.getLogger(__name__)
def 					snake_case						()       ->       List[Any]:

						'''simple docstring'''



						_snake_case						:     Dict				=    argparse.ArgumentParser(description="PyTorch Transformer Language Model"						)
						parser.add_argument("--model_name"					,			type=__lowercase					,			default="transfo-xl-wt103"					,			help="pretrained model name"						)
						parser.add_argument(
						    "--split"					,			type=__lowercase					,			default="test"					,			choices=["all", "valid", "test"]					,			help="which split to evaluate"						)
						parser.add_argument("--batch_size"					,			type=__lowercase					,			default=10					,			help="batch size"						)
						parser.add_argument("--tgt_len"					,			type=__lowercase					,			default=128					,			help="number of tokens to predict"						)
						parser.add_argument("--ext_len"					,			type=__lowercase					,			default=0					,			help="length of the extended context"						)
						parser.add_argument("--mem_len"					,			type=__lowercase					,			default=1_600					,			help="length of the retained previous heads"						)
						parser.add_argument("--clamp_len"					,			type=__lowercase					,			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=__lowercase					,			required=__lowercase					,			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=__lowercase					,			default=""					,			help="Can be used for distant debugging."						)
						parser.add_argument("--server_port"					,			type=__lowercase					,			default=""					,			help="Can be used for distant debugging."						)
						_snake_case						:     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=__lowercase						)
												ptvsd.wait_for_attach()

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

						# 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 )
						_snake_case						:     Union[str, Any]				=    TransfoXLCorpus.from_pretrained(args.model_name						)

						_snake_case						:     Union[str, Any]				=    corpus.get_iterator("valid"					,			args.batch_size					,			args.tgt_len					,			device=__lowercase					,			ext_len=args.ext_len						)
						_snake_case						:     int				=    corpus.get_iterator("test"					,			args.batch_size					,			args.tgt_len					,			device=__lowercase					,			ext_len=args.ext_len						)

						# Load a pre-trained model
						_snake_case						:     Optional[Any]				=    TransfoXLLMHeadModel.from_pretrained(args.model_name						)
						model.to(__lowercase						)

						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:
												_snake_case						:     List[str]				=    args.clamp_len
						if args.same_length:
												_snake_case						:     str				=    True

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

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

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

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

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


if __name__ == "__main__":
				main()
import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
				import tensorflow as tf

				from transformers.activations_tf import get_tf_activation



@require_tf
class    lowercase_    (     unittest.TestCase		):



							def        UpperCamelCase       (						self				):
													_snake_case						:     int				=    tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]				)
													_snake_case						:     List[Any]				=    get_tf_activation("gelu"				)
													_snake_case						:     Any				=    get_tf_activation("gelu_10"				)

													_snake_case						:     Dict				=    gelu(lowercase_				)
													_snake_case						:     Optional[int]				=    geluaa(lowercase_				)

													_snake_case						:     Tuple				=    tf.where(y_gelu_aa < 10.0      ,				1.0      ,				0.0				)

													self.assertEqual(tf.math.reduce_max(lowercase_				).numpy().item()      ,				10.0				)
													self.assertTrue(np.allclose(y_gelu * clipped_mask      ,				y_gelu_aa * clipped_mask				)				)



							def        UpperCamelCase       (						self				):
													get_tf_activation("gelu"				)
													get_tf_activation("gelu_10"				)
													get_tf_activation("gelu_fast"				)
													get_tf_activation("gelu_new"				)
													get_tf_activation("glu"				)
													get_tf_activation("mish"				)
													get_tf_activation("quick_gelu"				)
													get_tf_activation("relu"				)
													get_tf_activation("sigmoid"				)
													get_tf_activation("silu"				)
													get_tf_activation("swish"				)
													get_tf_activation("tanh"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation("bogus"				)
													with self.assertRaises(lowercase_				):
																			get_tf_activation(lowercase_				)