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						()       ->       Dict:

						'''simple docstring'''



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

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

						# 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						:     str				=    TransfoXLCorpus.from_pretrained(args.model_name						)

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

						# Load a pre-trained model
						_snake_case						:     List[str]				=    TransfoXLLMHeadModel.from_pretrained(args.model_name						)
						model.to(UpperCamelCase_						)

						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						:     Any				=    args.clamp_len
						if args.same_length:
												_snake_case						:     int				=    True

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

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

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

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

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


if __name__ == "__main__":
				main()





import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   SpeechaTextaTokenizer
				lowerCAmelCase__			=   False



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Optional[int]:





								'''simple docstring'''




								super().setUp()

								_lowerCAmelCase			:			Optional[int]        =					"""<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab""".split(""" """					)
								_lowerCAmelCase			:			Optional[int]        =					["""he re</w> 123""", """here a 1456"""]
								_lowerCAmelCase			:			Union[str, Any]        =					dict(zip(_UpperCAmelCase						,		range(len(_UpperCAmelCase					)					)					)					)

								_lowerCAmelCase			:			Any        =					{"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

								_lowerCAmelCase			:			Union[str, Any]        =					tempfile.mkdtemp()
								_lowerCAmelCase			:			Tuple        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase			:			Any        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""merges_file"""]					)

								with open(self.vocab_file						,		"""w"""						,		encoding="""utf-8"""					) as fp:
												fp.write(json.dumps(_UpperCAmelCase					) + """\n"""					)

								with open(self.merges_file						,		"""w"""					) as fp:
												fp.write("""\n""".join(_UpperCAmelCase					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					list(self.get_tokenizer().get_vocab().keys()					)

								self.assertEqual(vocab_keys[0]						,		"""<s>"""					)
								self.assertEqual(vocab_keys[1]						,		"""<pad>"""					)
								self.assertEqual(vocab_keys[-1]						,		"""vocab"""					)
								self.assertEqual(len(_UpperCAmelCase					)						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								self.assertEqual(self.get_tokenizer().vocab_size						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

								# make sure @@ is correctly concatenated
								_lowerCAmelCase			:			int        =					[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
								_lowerCAmelCase			:			List[Any]        =					tokenizer.decode(_UpperCAmelCase					)

								self.assertTrue(output_string == """herecouple words ofthe"""					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												tokenizer.save_pretrained(_UpperCAmelCase					)
												os.remove(os.path.join(_UpperCAmelCase						,		"""merges.txt"""					)					)

												# load tokenizer without merges file should not throw an error
												_lowerCAmelCase			:			Any        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			int        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								self.assertIsNotNone(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								if not self.test_slow_tokenizer:
												return

								_lowerCAmelCase			:			Dict        =					inspect.signature(self.tokenizer_class.__init__					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.get_tokenizer()

								for parameter_name, parameter in signature.parameters.items():
												if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
																self.assertIn(_UpperCAmelCase						,		tokenizer.init_kwargs					)

