


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
           TFXLMForMultipleChoice,
           TFXLMForQuestionAnsweringSimple,
           TFXLMForSequenceClassification,
           TFXLMForTokenClassification,
           TFXLMModel,
           TFXLMWithLMHeadModel,
           XLMConfig,
       )


class 	a__						:

       def __init__( self   :  Any   ,				lowerCamelCase_   :  Union[str, Any]   ,				):
            a_     :  str				      =     parent
            a_     :  str				      =     1_3
            a_     :  Optional[int]				      =     7
            a_     :  Tuple				      =     True
            a_     :  List[str]				      =     True
            a_     :  Optional[Any]				      =     True
            a_     :  int				      =     True
            a_     :  str				      =     True
            a_     :  List[str]				      =     False
            a_     :  List[str]				      =     False
            a_     :  Tuple				      =     False
            a_     :  List[str]				      =     2
            a_     :  Dict				      =     9_9
            a_     :  Any				      =     0
            a_     :  Optional[int]				      =     3_2
            a_     :  Union[str, Any]				      =     2
            a_     :  Optional[Any]				      =     4
            a_     :  Optional[int]				      =     0.1
            a_     :  Union[str, Any]				      =     0.1
            a_     :  Optional[Any]				      =     5_1_2
            a_     :  Optional[int]				      =     1_6
            a_     :  str				      =     2
            a_     :  List[Any]				      =     0.0_2
            a_     :  Dict				      =     3
            a_     :  Any				      =     4
            a_     :  Optional[Any]				      =     """last"""
            a_     :  List[str]				      =     True
            a_     :  List[str]				      =     None
            a_     :  Any				      =     0

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  Optional[Any]				      =     ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )
            a_     :  List[Any]				      =     random_attention_mask([self.batch_size, self.seq_length]   ,				dtype=tf.floataa     )

            a_     :  str				      =     None
            if self.use_input_lengths:
                 a_     :  int				      =     (
                     ids_tensor([self.batch_size]   ,				vocab_size=2     ) + self.seq_length - 2
                 )  # small variation of seq_length

            a_     :  Any				      =     None
            if self.use_token_type_ids:
                 a_     :  List[str]				      =     ids_tensor([self.batch_size, self.seq_length]   ,				self.n_langs     )

            a_     :  Tuple				      =     None
            a_     :  Tuple				      =     None
            a_     :  Union[str, Any]				      =     None
            if self.use_labels:
                 a_     :  Any				      =     ids_tensor([self.batch_size]   ,				self.type_sequence_label_size     )
                 a_     :  Dict				      =     ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels     )
                 a_     :  List[str]				      =     ids_tensor([self.batch_size]   ,				2   ,				dtype=tf.floataa     )
                 a_     :  int				      =     ids_tensor([self.batch_size]   ,				self.num_choices     )

            a_     :  Tuple				      =     XLMConfig(
                vocab_size=self.vocab_size   ,				n_special=self.n_special   ,				emb_dim=self.hidden_size   ,				n_layers=self.num_hidden_layers   ,				n_heads=self.num_attention_heads   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				gelu_activation=self.gelu_activation   ,				sinusoidal_embeddings=self.sinusoidal_embeddings   ,				asm=self.asm   ,				causal=self.causal   ,				n_langs=self.n_langs   ,				max_position_embeddings=self.max_position_embeddings   ,				initializer_range=self.initializer_range   ,				summary_type=self.summary_type   ,				use_proj=self.use_proj   ,				bos_token_id=self.bos_token_id   ,				)

            return (
                config,
                input_ids,
                token_type_ids,
                input_lengths,
                sequence_labels,
                token_labels,
                is_impossible_labels,
                choice_labels,
                input_mask,
            )

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Optional[int]   ,				):
            a_     :  List[str]				      =     TFXLMModel(config=A_     )
            a_     :  List[str]				      =     {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
            a_     :  Any				      =     model(A_     )

            a_     :  Any				      =     [input_ids, input_mask]
            a_     :  Optional[int]				      =     model(A_     )
            self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)     )

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Any   ,				):
            a_     :  Union[str, Any]				      =     TFXLMWithLMHeadModel(A_     )

            a_     :  List[Any]				      =     {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
            a_     :  Tuple				      =     model(A_     )

            a_     :  Dict				      =     outputs

            self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)     )

       def     UpperCAmelCase( self   :  List[Any]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  List[Any]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  List[str]   ,				):
            a_     :  Optional[Any]				      =     TFXLMForQuestionAnsweringSimple(A_     )

            a_     :  int				      =     {"""input_ids""": input_ids, """lengths""": input_lengths}

            a_     :  List[Any]				      =     model(A_     )

            self.parent.assertEqual(result.start_logits.shape   ,				(self.batch_size, self.seq_length)     )
            self.parent.assertEqual(result.end_logits.shape   ,				(self.batch_size, self.seq_length)     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Dict   ,				):
            a_     :  Tuple				      =     TFXLMForSequenceClassification(A_     )

            a_     :  Any				      =     {"""input_ids""": input_ids, """lengths""": input_lengths}

            a_     :  int				      =     model(A_     )

            self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  List[Any]   ,				):
            a_     :  List[Any]				      =     self.num_labels
            a_     :  Optional[int]				      =     TFXLMForTokenClassification(config=A_     )
            a_     :  Tuple				      =     {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
            a_     :  Tuple				      =     model(A_     )
            self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)     )

       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Dict   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Tuple   ,				lowerCamelCase_   :  Any   ,				):
            a_     :  Optional[int]				      =     self.num_choices
            a_     :  Optional[Any]				      =     TFXLMForMultipleChoice(config=A_     )
            a_     :  Tuple				      =     tf.tile(tf.expand_dims(A_   ,				1     )   ,				(1, self.num_choices, 1)     )
            a_     :  Tuple				      =     tf.tile(tf.expand_dims(A_   ,				1     )   ,				(1, self.num_choices, 1)     )
            a_     :  List[Any]				      =     tf.tile(tf.expand_dims(A_   ,				1     )   ,				(1, self.num_choices, 1)     )
            a_     :  Any				      =     {
                """input_ids""": multiple_choice_inputs_ids,
                """attention_mask""": multiple_choice_input_mask,
                """token_type_ids""": multiple_choice_token_type_ids,
            }
            a_     :  Optional[Any]				      =     model(A_     )
            self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)     )

       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Tuple				      =     self.prepare_config_and_inputs()
            (
                (
                a_    
            )      ,      (
                a_    
            )      ,      (
                a_    
            )      ,      (
                a_    
            )      ,      (
                a_    
            )      ,      (
                a_    
            )      ,      (
                a_    
            )      ,      (
                a_    
            )      ,      (
                a_    
            )      ,          
            )     :  Union[str, Any]				      =     config_and_inputs
            a_     :  str				      =     {
                """input_ids""": input_ids,
                """token_type_ids""": token_type_ids,
                """langs""": token_type_ids,
                """lengths""": input_lengths,
            }
            return config, inputs_dict


@require_tf
class 	a__						(    _SCREAMING_SNAKE_CASE    ,							_SCREAMING_SNAKE_CASE    ,							unittest.TestCase     ):
       lowerCamelCase__:  Any				     =  (
           (
               TFXLMModel,
               TFXLMWithLMHeadModel,
               TFXLMForSequenceClassification,
               TFXLMForQuestionAnsweringSimple,
               TFXLMForTokenClassification,
               TFXLMForMultipleChoice,
           )
           if is_tf_available()
           else ()
       )
       lowerCamelCase__:  Optional[Any]				     =  (
           (TFXLMWithLMHeadModel,) if is_tf_available() else ()
       )  # TODO (PVP): Check other models whether language generation is also applicable
       lowerCamelCase__:  int				     =  (
           {
               "feature-extraction": TFXLMModel,
               "fill-mask": TFXLMWithLMHeadModel,
               "question-answering": TFXLMForQuestionAnsweringSimple,
               "text-classification": TFXLMForSequenceClassification,
               "text-generation": TFXLMWithLMHeadModel,
               "token-classification": TFXLMForTokenClassification,
               "zero-shot": TFXLMForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       lowerCamelCase__:  List[str]				     =  False
       lowerCamelCase__:  Optional[int]				     =  False

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  Union[str, Any]   ,				lowerCamelCase_   :  Optional[int]     ):
            if (
                pipeline_test_casse_name == "QAPipelineTests"
                and tokenizer_name is not None
                and not tokenizer_name.endswith("""Fast"""     )
            ):
                 # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                 # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                 # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                 return True

            return False

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            a_     :  Dict				      =     TFXLMModelTester(self     )
            a_     :  Any				      =     ConfigTester(self   ,				config_class=A_   ,				emb_dim=3_7     )

       def     UpperCAmelCase( self   :  List[Any]     ):
            self.config_tester.run_common_tests()

       def     UpperCAmelCase( self   :  List[Any]     ):
            a_     :  List[Any]				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_model(*A_     )

       def     UpperCAmelCase( self   :  Optional[int]     ):
            a_     :  Optional[int]				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_lm_head(*A_     )

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Optional[Any]				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_qa(*A_     )

       def     UpperCAmelCase( self   :  Any     ):
            a_     :  List[Any]				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_sequence_classif(*A_     )

       def     UpperCAmelCase( self   :  Tuple     ):
            a_     :  Dict				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_token_classification(*A_     )

       def     UpperCAmelCase( self   :  int     ):
            a_     :  int				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_xlm_for_multiple_choice(*A_     )

       @slow
       def     UpperCAmelCase( self   :  Dict     ):
            for model_name in TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a_     :  str				      =     TFXLMModel.from_pretrained(A_     )
                 self.assertIsNotNone(A_     )


@require_tf
class 	a__						(    unittest.TestCase     ):

       @slow
       def     UpperCAmelCase( self   :  int     ):
            a_     :  Any				      =     TFXLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""     )
            a_     :  List[str]				      =     tf.convert_to_tensor([[1_4, 4_4_7]]   ,				dtype=tf.intaa     )  # the president
            a_     :  Any				      =     [
                1_4,
                4_4_7,
                1_4,
                4_4_7,
                1_4,
                4_4_7,
                1_4,
                4_4_7,
                1_4,
                4_4_7,
                1_4,
                4_4_7,
                1_4,
                4_4_7,
                1_4,
                4_4_7,
                1_4,
                4_4_7,
                1_4,
                4_4_7,
            ]  # the president the president the president the president the president the president the president the president the president the president
            # TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
            a_     :  Dict				      =     model.generate(A_   ,				do_sample=A_     )
            self.assertListEqual(output_ids[0].numpy().tolist()   ,				A_     )





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()
