


# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
       import tensorflow as tf


class 	a__						(    TensorFormatter[Mapping, """tf.Tensor""", Mapping]     ):

       def __init__( self   :  Dict   ,				lowerCamelCase_   :  str=None   ,				**lowerCamelCase_   :  Union[str, Any]     ):
            super().__init__(features=A_     )
            a_     :  Optional[int]				      =     tf_tensor_kwargs
            import tensorflow as tf  # noqa: import tf at initialization

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  Optional[Any]     ):
            import tensorflow as tf

            if isinstance(A_   ,				A_     ) and column:
                 if all(
                     isinstance(A_   ,				tf.Tensor     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
                      return tf.stack(A_     )
                 elif all(
                     isinstance(A_   ,				(tf.Tensor, tf.RaggedTensor)     ) and x.ndim == 1 and x.dtype == column[0].dtype
                     for x in column     ):
                      # only rag 1-D tensors, otherwise some dimensions become ragged even though they were consolidated
                      return tf.ragged.stack(A_     )

            return column

       def     UpperCAmelCase( self   :  Dict   ,				lowerCamelCase_   :  int     ):
            import tensorflow as tf

            if value is None:
                 return value

            a_     :  Any				      =     {}

            if isinstance(A_   ,				(np.number, np.ndarray)     ) and np.issubdtype(value.dtype   ,				np.integer     ):
                 a_     :  Dict				      =     {"""dtype""": tf.intaa}
            elif isinstance(A_   ,				(np.number, np.ndarray)     ) and np.issubdtype(value.dtype   ,				np.floating     ):
                 a_     :  List[Any]				      =     {"""dtype""": tf.floataa}
            elif config.PIL_AVAILABLE and "PIL" in sys.modules:
                 import PIL.Image

                 if isinstance(A_   ,				PIL.Image.Image     ):
                      a_     :  List[Any]				      =     np.asarray(A_     )

            return tf.convert_to_tensor(A_   ,				**{**default_dtype, **self.tf_tensor_kwargs}     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Dict     ):
            import tensorflow as tf

            # support for torch, tf, jax etc.
            if config.TORCH_AVAILABLE and "torch" in sys.modules:
                 import torch

                 if isinstance(A_   ,				torch.Tensor     ):
                      return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
            if hasattr(A_   ,				"""__array__"""     ) and not isinstance(A_   ,				tf.Tensor     ):
                 a_     :  Optional[Any]				      =     data_struct.__array__()
            # support for nested types like struct of list of struct
            if isinstance(A_   ,				np.ndarray     ):
                 if data_struct.dtype == object:  # tf tensors cannot be instantied from an array of objects
                      return self._consolidate([self.recursive_tensorize(A_     ) for substruct in data_struct]     )
            elif isinstance(A_   ,				(list, tuple)     ):
                 return self._consolidate([self.recursive_tensorize(A_     ) for substruct in data_struct]     )
            return self._tensorize(A_     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Tuple     ):
            return map_nested(self._recursive_tensorize   ,				A_   ,				map_list=A_     )

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  Dict     ):
            a_     :  Dict				      =     self.numpy_arrow_extractor().extract_row(A_     )
            a_     :  Optional[int]				      =     self.python_features_decoder.decode_row(A_     )
            return self.recursive_tensorize(A_     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  Any     ):
            a_     :  Optional[Any]				      =     self.numpy_arrow_extractor().extract_column(A_     )
            a_     :  List[Any]				      =     self.python_features_decoder.decode_column(A_   ,				pa_table.column_names[0]     )
            a_     :  Union[str, Any]				      =     self.recursive_tensorize(A_     )
            a_     :  List[str]				      =     self._consolidate(A_     )
            return column

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Optional[Any]     ):
            a_     :  Optional[Any]				      =     self.numpy_arrow_extractor().extract_batch(A_     )
            a_     :  Optional[int]				      =     self.python_features_decoder.decode_batch(A_     )
            a_     :  Tuple				      =     self.recursive_tensorize(A_     )
            for column_name in batch:
                 a_     :  Optional[int]				      =     self._consolidate(batch[column_name]     )
            return batch





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   :					List[str]    =			logging.getLogger(__name__)


def  A			(   )  ->    List[Any]:
		'''simple docstring'''

		UpperCamelCase            =	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.'					)
		UpperCamelCase            =	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()

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

		UpperCamelCase            =	corpus.get_iterator('valid'       ,			args.batch_size       ,			args.tgt_len       ,			device=lowercase       ,			ext_len=args.ext_len					)
		UpperCamelCase            =	corpus.get_iterator('test'       ,			args.batch_size       ,			args.tgt_len       ,			device=lowercase       ,			ext_len=args.ext_len					)

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

		###############################################################################
		# Evaluation code
		###############################################################################
		def evaluate(lowercase					):
				# Turn on evaluation mode which disables dropout.
				model.eval()
				UpperCamelCase      ,    UpperCamelCase            =	0, 0.0
				UpperCamelCase            =	time.time()
				with torch.no_grad():
						UpperCamelCase            =	None
						for idx, (data, target, seq_len) in enumerate(lowercase					):
								UpperCamelCase            =	model(lowercase       ,			lm_labels=lowercase       ,			mems=lowercase					)
								UpperCamelCase      ,    UpperCamelCase      ,    UpperCamelCase            =	ret
								UpperCamelCase            =	loss.mean()
								total_loss += seq_len * loss.item()
								total_len += seq_len
						UpperCamelCase            =	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":
				UpperCamelCase            =	evaluate(lowercase					)
				UpperCamelCase            =	evaluate(lowercase					)
		elif args.split == "valid":
				UpperCamelCase            =	evaluate(lowercase					)
				UpperCamelCase            =	None
		elif args.split == "test":
				UpperCamelCase            =	evaluate(lowercase					)
				UpperCamelCase            =	None

		def format_log(lowercase       ,			lowercase					):
				UpperCamelCase            =	'| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(lowercase       ,			lowercase       ,			math.exp(lowercase					)					)
				return log_str

		UpperCamelCase            =	''
		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()
