





'''simple docstring'''

import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowercase_					= get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
class 					a_     (				__UpperCAmelCase      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = XLMProphetNetTokenizer
    UpperCamelCase     = False
    UpperCamelCase     = True


    def 			snake_case_(		self       )		->   Optional[Any]:
        super().setUp()

        # We have a SentencePiece fixture for testing
        _SCREAMING_SNAKE_CASE									=			XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE ,		keep_accents=__SCREAMING_SNAKE_CASE       )
        tokenizer.save_pretrained(self.tmpdirname       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			"""[PAD]"""
        _SCREAMING_SNAKE_CASE									=			0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE       ) ,		__SCREAMING_SNAKE_CASE       )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE       ) ,		__SCREAMING_SNAKE_CASE       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			list(self.get_tokenizer().get_vocab().keys()       )

        self.assertEqual(vocab_keys[0] ,		"""[PAD]"""       )
        self.assertEqual(vocab_keys[1] ,		"""[CLS]"""       )
        self.assertEqual(vocab_keys[-1] ,		"""j"""       )
        self.assertEqual(len(__SCREAMING_SNAKE_CASE       ) ,		1012       )


    def 			snake_case_(		self       )		->   Any:
        self.assertEqual(self.get_tokenizer().vocab_size ,		1012       )


    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			XLMProphetNetTokenizer(__SCREAMING_SNAKE_CASE ,		keep_accents=__SCREAMING_SNAKE_CASE       )

        _SCREAMING_SNAKE_CASE									=			tokenizer.tokenize("""This is a test"""       )
        self.assertListEqual(__SCREAMING_SNAKE_CASE ,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]       )

        self.assertListEqual(
            tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       ) ,		[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,		)

        _SCREAMING_SNAKE_CASE									=			tokenizer.tokenize("""I was born in 92000, and this is falsé."""       )
        self.assertListEqual(
            __SCREAMING_SNAKE_CASE ,		[
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """9""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """é""",
                """.""",
            ] ,		)
        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE       )
        self.assertListEqual(
            __SCREAMING_SNAKE_CASE ,		[
                value + tokenizer.fairseq_offset
                for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
            ] ,		)

        _SCREAMING_SNAKE_CASE									=			tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE       )
        self.assertListEqual(
            __SCREAMING_SNAKE_CASE ,		[
                SPIECE_UNDERLINE + """I""",
                SPIECE_UNDERLINE + """was""",
                SPIECE_UNDERLINE + """b""",
                """or""",
                """n""",
                SPIECE_UNDERLINE + """in""",
                SPIECE_UNDERLINE + """""",
                """[UNK]""",
                """2""",
                """0""",
                """0""",
                """0""",
                """,""",
                SPIECE_UNDERLINE + """and""",
                SPIECE_UNDERLINE + """this""",
                SPIECE_UNDERLINE + """is""",
                SPIECE_UNDERLINE + """f""",
                """al""",
                """s""",
                """[UNK]""",
                """.""",
            ] ,		)


    @cached_property
    def 			snake_case_(		self       )		->   int:
        return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""       )


    @slow
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			"""Hello World!"""
        _SCREAMING_SNAKE_CASE									=			[3_5389, 6672, 49, 2]
        self.assertListEqual(__SCREAMING_SNAKE_CASE ,		self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE       )       )





    @slow
    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			{"""input_ids""": [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=__SCREAMING_SNAKE_CASE ,		model_name="""microsoft/xprophetnet-large-wiki100-cased""" ,		revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" ,		)








import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm
 import torch_xla.debug.metrics as met

class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     List[Any]     ,		*__SCREAMING_SNAKE_CASE							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           super().__init__(*__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)
           __a						       =				eval_examples
           __a						       =				post_process_function



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Optional[Dataset] = None     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=None     ,		__SCREAMING_SNAKE_CASE							:     Optional[List[str]] = None     ,		__SCREAMING_SNAKE_CASE							:     str = "eval"     ,		**__SCREAMING_SNAKE_CASE							:     Any     ,		):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()
           __a						       =				(
               gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length''') is not None else self.args.generation_max_length
           )
           __a						       =				(
               gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams''') is not None else self.args.generation_num_beams
           )
           __a						       =				gen_kwargs

           __a						       =				self.eval_dataset if eval_dataset is None else eval_dataset
           __a						       =				self.get_eval_dataloader(__SCREAMING_SNAKE_CASE)
           __a						       =				self.eval_examples if eval_examples is None else eval_examples

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				time.time()
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Evaluation'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics
           __a						       =				self.args.eval_batch_size * self.args.world_size
           if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
               start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
           output.metrics.update(
               speed_metrics(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		num_samples=output.num_samples     ,		num_steps=math.ceil(output.num_samples / total_batch_size)     ,		))

           if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
               # Only the main node write the results by default
               __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
               __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

               # Prefix all keys with metric_key_prefix + '_'
               for key in list(metrics.keys()):
                   if not key.startswith(F'{metric_key_prefix}_'):
                       __a						       =				metrics.pop(__SCREAMING_SNAKE_CASE)

               metrics.update(output.metrics)
           else:
               __a						       =				output.metrics

           if self.args.should_log:
               # Only the main node log the results by default
               self.log(__SCREAMING_SNAKE_CASE)

           if self.args.tpu_metrics_debug or self.args.debug:
               # tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
               xm.master_print(met.metrics_report())

           __a						       =				self.callback_handler.on_evaluate(self.args     ,		self.state     ,		self.control     ,		__SCREAMING_SNAKE_CASE)
           return metrics





       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Any     ,		__SCREAMING_SNAKE_CASE							:     int     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     str = "test"     ,		**__SCREAMING_SNAKE_CASE							:     Dict):


           '''simple docstring'''

           __a						       =				gen_kwargs.copy()

           __a						       =				self.get_test_dataloader(__SCREAMING_SNAKE_CASE)

           # Temporarily disable metric computation, we will do it in the loop here.
           __a						       =				self.compute_metrics
           __a						       =				None
           __a						       =				time.time()
           __a						       =				self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
           try:
               __a						       =				eval_loop(
                   __SCREAMING_SNAKE_CASE     ,		description='''Prediction'''     ,		prediction_loss_only=True if compute_metrics is None else None     ,		ignore_keys=__SCREAMING_SNAKE_CASE     ,		metric_key_prefix=__SCREAMING_SNAKE_CASE     ,		)
           finally:
               __a						       =				compute_metrics

           __a						       =				self.args.eval_batch_size * self.args.world_size
           if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
               start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
           output.metrics.update(
               speed_metrics(
                   __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		num_samples=output.num_samples     ,		num_steps=math.ceil(output.num_samples / total_batch_size)     ,		))
           if self.post_process_function is None or self.compute_metrics is None:
               return output

           __a						       =				self.post_process_function(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		'''predict''')
           __a						       =				self.compute_metrics(__SCREAMING_SNAKE_CASE)

           # Prefix all keys with metric_key_prefix + '_'
           for key in list(metrics.keys()):
               if not key.startswith(F'{metric_key_prefix}_'):
                   __a						       =				metrics.pop(__SCREAMING_SNAKE_CASE)
           metrics.update(output.metrics)
           return PredictionOutput(predictions=predictions.predictions     ,		label_ids=predictions.label_ids     ,		metrics=__SCREAMING_SNAKE_CASE)





