


"""simple docstring"""






from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFTransfoXLForSequenceClassification,
     TFTransfoXLLMHeadModel,
     TFTransfoXLModel,
 )
class        _lowercase					:







     def __init__(      self ,						UpperCamelCase_ ,						):
      __magic_name__     =      parent
      __magic_name__     =      13
      __magic_name__     =      7
      __magic_name__     =      30
      __magic_name__     =      self.seq_length + self.mem_len
      __magic_name__     =      15
      __magic_name__     =      True
      __magic_name__     =      True
      __magic_name__     =      99
      __magic_name__     =      [10, 50, 80]
      __magic_name__     =      32
      __magic_name__     =      32
      __magic_name__     =      4
      __magic_name__     =      8
      __magic_name__     =      128
      __magic_name__     =      2
      __magic_name__     =      2
      __magic_name__     =      None
      __magic_name__     =      1
      __magic_name__     =      0
      __magic_name__     =      3
      __magic_name__     =      self.vocab_size - 1
      __magic_name__     =      0.0_1







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size       )
      __magic_name__     =      ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size       )

      __magic_name__     =      None
      if self.use_labels:
       __magic_name__     =      ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size       )

      __magic_name__     =      TransfoXLConfig(
          vocab_size=self.vocab_size ,						mem_len=self.mem_len ,						clamp_len=self.clamp_len ,						cutoffs=self.cutoffs ,						d_model=self.hidden_size ,						d_embed=self.d_embed ,						n_head=self.num_attention_heads ,						d_head=self.d_head ,						d_inner=self.d_inner ,						div_val=self.div_val ,						n_layer=self.num_hidden_layers ,						eos_token_id=self.eos_token_id ,						pad_token_id=self.vocab_size - 1 ,						init_range=self.init_range ,						num_labels=self.num_labels ,						)

      return (config, input_ids_a, input_ids_a, lm_labels)







     def 	lowerCAmelCase__    (      self       ):
      random.seed(self.seed       )
      tf.random.set_seed(self.seed       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      TFTransfoXLModel(UpperCamelCase__       )

      __magic_name__     =      model(UpperCamelCase__       ).to_tuple()

      __magic_name__     =      {'''input_ids''': input_ids_a, '''mems''': mems_a}

      __magic_name__     =      model(UpperCamelCase__       ).to_tuple()

      self.parent.assertEqual(hidden_states_a.shape ,						(self.batch_size, self.seq_length, self.hidden_size)       )
      self.parent.assertEqual(hidden_states_a.shape ,						(self.batch_size, self.seq_length, self.hidden_size)       )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a] ,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,						)
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a] ,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,						)







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      TFTransfoXLLMHeadModel(UpperCamelCase__       )

      __magic_name__     =      model(UpperCamelCase__       ).to_tuple()

      __magic_name__     =      {'''input_ids''': input_ids_a, '''labels''': lm_labels}
      __magic_name__     =      model(UpperCamelCase__       ).to_tuple()

      __magic_name__     =      model([input_ids_a, mems_a]       ).to_tuple()

      __magic_name__     =      {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

      __magic_name__     =      model(UpperCamelCase__       ).to_tuple()

      self.parent.assertEqual(lm_logits_a.shape ,						(self.batch_size, self.seq_length, self.vocab_size)       )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a] ,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,						)

      self.parent.assertEqual(lm_logits_a.shape ,						(self.batch_size, self.seq_length, self.vocab_size)       )
      self.parent.assertListEqual(
          [mem.shape for mem in mems_a] ,						[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers ,						)







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      __magic_name__     =      TFTransfoXLForSequenceClassification(UpperCamelCase__       )
      __magic_name__     =      model(UpperCamelCase__       )
      self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.prepare_config_and_inputs()
      (__magic_name__)     =      config_and_inputs
      __magic_name__     =      {'''input_ids''': input_ids_a}
      return config, inputs_dict


@require_tf
class        _lowercase					(     SCREAMING_SNAKE_CASE_   , SCREAMING_SNAKE_CASE_   , unittest.TestCase     ):
     _lowerCamelCase     =       (
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     _lowerCamelCase     =       () if is_tf_available() else ()
     _lowerCamelCase     =       (
         {
             '''feature-extraction''': TFTransfoXLModel,
             '''text-classification''': TFTransfoXLForSequenceClassification,
             '''text-generation''': TFTransfoXLLMHeadModel,
             '''zero-shot''': TFTransfoXLForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False
     _lowerCamelCase     =       False







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      if pipeline_test_casse_name == "TextGenerationPipelineTests":
       # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
       # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
       # tokenizer.
       return True

      return False







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFTransfoXLModelTester(self       )
      __magic_name__     =      ConfigTester(self ,						config_class=UpperCamelCase__ ,						d_embed=37       )







     def 	lowerCAmelCase__    (      self       ):
      self.config_tester.run_common_tests()







     def 	lowerCAmelCase__    (      self       ):
      self.model_tester.set_seed()
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__       )







     def 	lowerCAmelCase__    (      self       ):
      self.model_tester.set_seed()
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__       )







     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      self.model_tester.prepare_config_and_inputs_for_common()
      __magic_name__     =      [TFTransfoXLForSequenceClassification]

      for model_class in self.all_model_classes:
       __magic_name__     =      model_class(UpperCamelCase__       )
       assert isinstance(model.get_input_embeddings() ,						tf.keras.layers.Layer       )
       if model_class in list_other_models_with_output_ebd:
        __magic_name__     =      model.get_output_embeddings()
        assert isinstance(UpperCamelCase__ ,						tf.keras.layers.Layer       )
        __magic_name__     =      model.get_bias()
        assert name is None
       else:
        __magic_name__     =      model.get_output_embeddings()
        assert x is None
        __magic_name__     =      model.get_bias()
        assert name is None







     def 	lowerCAmelCase__    (      self       ):
      # TODO JP: Make TransfoXL XLA compliant
      pass







     @slow
     def 	lowerCAmelCase__    (      self       ):
      for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __magic_name__     =      TFTransfoXLModel.from_pretrained(UpperCamelCase__       )
       self.assertIsNotNone(UpperCamelCase__       )







     @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''       )
     def 	lowerCAmelCase__    (      self       ):
      pass


@require_tf
class        _lowercase					(     unittest.TestCase     ):







     @unittest.skip('''Skip test until #12651 is resolved.'''       )
     @slow
     def 	lowerCAmelCase__    (      self       ):
      __magic_name__     =      TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''       )
      # fmt: off
      __magic_name__     =      tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]] ,						dtype=tf.intaa       )  # noqa: E231
      # fmt: on
      #  In 1991 , the remains of Russian Tsar Nicholas II and his family
      #  ( except for Alexei and Maria ) are discovered .
      #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
      #  remainder of the story . 1883 Western Siberia ,
      #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
      #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
      #  father initially slaps him for making such an accusation , Rasputin watches as the
      #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
      #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
      #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

      # fmt: off
      __magic_name__     =      [33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # noqa: E231
      # fmt: on
      #  In 1991, the remains of Russian Tsar Nicholas II and his family (
      #  except for Alexei and Maria ) are discovered. The voice of young son,
      #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
      #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
      #  and a group of men to perform magic. Rasputin has a vision and
      #  denounces one of the men as a horse thief. Although his father initially
      #  slaps him for making such an accusation, Rasputin watches as the man
      #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
      #  of the Virgin Mary, prompting him to become a priest.
      #  Rasputin quickly becomes famous, with people, even a bishop, begging for
      #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
      # Nicholas II and his family were discovered. The voice of <unk> young son,
      # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

      __magic_name__     =      model.generate(UpperCamelCase__ ,						max_length=200 ,						do_sample=UpperCamelCase__       )
      self.assertListEqual(output_ids[0].numpy().tolist() ,						UpperCamelCase__       )




"""simple docstring"""






import os
from tempfile import TemporaryDirectory
from unittest import TestCase

import pytest
from absl.testing import parameterized

from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path


__lowerCamelCase										=		[
    {"dataset": "wikipedia", "config_name": "20220301.de"},
    {"dataset": "wikipedia", "config_name": "20220301.en"},
    {"dataset": "wikipedia", "config_name": "20220301.fr"},
    {"dataset": "wikipedia", "config_name": "20220301.frr"},
    {"dataset": "wikipedia", "config_name": "20220301.it"},
    {"dataset": "wikipedia", "config_name": "20220301.simple"},
    {"dataset": "snli", "config_name": "plain_text"},
    {"dataset": "eli5", "config_name": "LFQA_reddit"},
    {"dataset": "wiki40b", "config_name": "en"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.compressed"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.nq.no_index"},
    {"dataset": "wiki_dpr", "config_name": "psgs_w100.multiset.no_index"},
    {"dataset": "natural_questions", "config_name": "default"},
]





def 	lowercase      (   __UpperCamelCase=True						) ->      List[Any]:
 if with_config:
  return [
      {
          "testcase_name": d["dataset"] + "/" + d["config_name"],
          "dataset": d["dataset"],
          "config_name": d["config_name"],
      }
      for d in DATASETS_ON_HF_GCP
  ]
 else:
  return [
      {"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
  ]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=__UpperCAmelCase     )     )
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       None
     _lowerCamelCase     =       None







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_       ):
      with TemporaryDirectory() as tmp_dir:
       __magic_name__     =      dataset_module_factory(UpperCamelCase_ ,						cache_dir=UpperCamelCase_       )

       __magic_name__     =      import_main_class(dataset_module.module_path ,						dataset=UpperCamelCase_       )

       __magic_name__     =      builder_cls(
           cache_dir=UpperCamelCase_ ,						config_name=UpperCamelCase_ ,						hash=dataset_module.hash ,						)

       __magic_name__     =      '''/'''.join(
           [
               HF_GCP_BASE_URL,
               builder_instance._relative_data_dir(with_hash=UpperCamelCase_       ).replace(os.sep ,						'''/'''       ),
               config.DATASET_INFO_FILENAME,
           ]       )
       __magic_name__     =      cached_path(UpperCamelCase_ ,						cache_dir=UpperCamelCase_       )
       self.assertTrue(os.path.exists(UpperCamelCase_       )       )





@pytest.mark.integration
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      tmp_path_factory.mktemp('''test_hf_gcp'''						) / '''test_wikipedia_simple'''
 __magic_name__     =      dataset_module_factory('''wikipedia'''      ,   cache_dir=__UpperCamelCase						)
 __magic_name__     =      import_main_class(dataset_module.module_path						)
 __magic_name__     =      builder_cls(
     cache_dir=__UpperCamelCase      ,   config_name='''20220301.frr'''      ,   hash=dataset_module.hash      ,   )
 # use the HF cloud storage, not the original download_and_prepare that uses apache-beam
 __magic_name__     =      None
 builder_instance.download_and_prepare()
 __magic_name__     =      builder_instance.as_dataset()
 assert ds





@pytest.mark.integration
def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      dataset_module_factory('''wikipedia'''      ,   cache_dir=__UpperCamelCase						)
 __magic_name__     =      import_main_class(dataset_module.module_path      ,   dataset=__UpperCamelCase						)
 __magic_name__     =      builder_cls(
     cache_dir=__UpperCamelCase      ,   config_name='''20220301.frr'''      ,   hash=dataset_module.hash      ,   )
 __magic_name__     =      builder_instance.as_streaming_dataset()
 assert ds
 assert isinstance(__UpperCamelCase      ,   __UpperCamelCase						)
 assert "train" in ds
 assert isinstance(ds['''train''']      ,   __UpperCamelCase						)
 assert next(iter(ds['''train''']						)						)

