





'''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 lowerCamelCase						:







  def __init__(		self     ,     lowercase__     ,     )		->						List[str]:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     parent
        _snake_case		:	Union[str, Any]					     =     13
        _snake_case		:	Dict					     =     7
        _snake_case		:	Optional[int]					     =     30
        _snake_case		:	Optional[Any]					     =     self.seq_length + self.mem_len
        _snake_case		:	Optional[Any]					     =     15
        _snake_case		:	Optional[int]					     =     True
        _snake_case		:	Union[str, Any]					     =     True
        _snake_case		:	Dict					     =     99
        _snake_case		:	Optional[Any]					     =     [10, 50, 80]
        _snake_case		:	List[str]					     =     32
        _snake_case		:	str					     =     32
        _snake_case		:	Tuple					     =     4
        _snake_case		:	Tuple					     =     8
        _snake_case		:	Dict					     =     128
        _snake_case		:	str					     =     2
        _snake_case		:	Tuple					     =     2
        _snake_case		:	Tuple					     =     None
        _snake_case		:	Dict					     =     1
        _snake_case		:	Optional[Any]					     =     0
        _snake_case		:	List[str]					     =     3
        _snake_case		:	Dict					     =     self.vocab_size - 1
        _snake_case		:	Tuple					     =     0.01







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size		)
        _snake_case		:	int					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size		)

        _snake_case		:	List[Any]					     =     None
        if self.use_labels:
              _snake_case		:	List[str]					     =     ids_tensor([self.batch_size, self.seq_length]     ,     self.vocab_size		)

        _snake_case		:	List[str]					     =     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 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        random.seed(self.seed		)
        tf.random.set_seed(self.seed		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Optional[int]					     =     TFTransfoXLModel(lowercase__		)

        _snake_case     ,							_snake_case		:	List[Any]					     =     model(lowercase__		).to_tuple()

        _snake_case		:	Union[str, Any]					     =     {'''input_ids''': input_ids_a, '''mems''': mems_a}

        _snake_case     ,							_snake_case		:	Tuple					     =     model(lowercase__		).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 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     TFTransfoXLLMHeadModel(lowercase__		)

        _snake_case     ,							_snake_case		:	Dict					     =     model(lowercase__		).to_tuple()

        _snake_case		:	int					     =     {'''input_ids''': input_ids_a, '''labels''': lm_labels}
        _snake_case     ,							_snake_case		:	Optional[int]					     =     model(lowercase__		).to_tuple()

        _snake_case     ,							_snake_case		:	List[str]					     =     model([input_ids_a, mems_a]		).to_tuple()

        _snake_case		:	Optional[Any]					     =     {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

        _snake_case     ,							_snake_case		:	List[str]					     =     model(lowercase__		).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 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     TFTransfoXLForSequenceClassification(lowercase__		)
        _snake_case		:	Dict					     =     model(lowercase__		)
        self.parent.assertEqual(result.logits.shape     ,     (self.batch_size, self.num_labels)		)







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	Optional[Any]					     =     self.prepare_config_and_inputs()
        ((_snake_case)     ,							(_snake_case)     ,							(_snake_case)     ,							(_snake_case))		:	Tuple					     =     config_and_inputs
        _snake_case		:	int					     =     {'''input_ids''': input_ids_a}
        return config, inputs_dict





@require_tf
class lowerCamelCase						(a__						,      a__						,      unittest.TestCase					):
  _lowercase  :	List[str]       					=				(
      (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
  )
  _lowercase  :	Tuple       					=				() if is_tf_available() else ()
  _lowercase  :	Union[str, Any]       					=				(
      {
          """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
  _lowercase  :	Optional[Any]       					=				False
  _lowercase  :	Tuple       					=				False
  _lowercase  :	Optional[int]       					=				False
  _lowercase  :	List[str]       					=				False







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        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 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     TFTransfoXLModelTester(self		)
        _snake_case		:	Any					     =     ConfigTester(self     ,     config_class=lowercase__     ,     d_embed=37		)







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        self.config_tester.run_common_tests()







  def 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        self.model_tester.set_seed()
        _snake_case		:	Optional[Any]					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Union[str, Any]:






        """simple docstring"""





        self.model_tester.set_seed()
        _snake_case		:	Dict					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        _snake_case		:	Any					     =     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	int					     =     self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case		:	Optional[Any]					     =     [TFTransfoXLForSequenceClassification]

        for model_class in self.all_model_classes:
              _snake_case		:	Optional[Any]					     =     model_class(lowercase__		)
              assert isinstance(model.get_input_embeddings()     ,     tf.keras.layers.Layer		)
              if model_class in list_other_models_with_output_ebd:
                    _snake_case		:	List[Any]					     =     model.get_output_embeddings()
                    assert isinstance(lowercase__     ,     tf.keras.layers.Layer		)
                    _snake_case		:	str					     =     model.get_bias()
                    assert name is None
              else:
                    _snake_case		:	Optional[Any]					     =     model.get_output_embeddings()
                    assert x is None
                    _snake_case		:	List[str]					     =     model.get_bias()
                    assert name is None







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






        """simple docstring"""





        pass







  @slow
  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _snake_case		:	List[str]					     =     TFTransfoXLModel.from_pretrained(lowercase__		)
              self.assertIsNotNone(lowercase__		)







  @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''		)
  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        pass





@require_tf
class lowerCamelCase						(unittest.TestCase					):







  @unittest.skip('''Skip test until #12651 is resolved.'''		)
  @slow
  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	Any					     =     TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''		)
        # fmt: off
        _snake_case		:	Union[str, Any]					     =     tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]     ,     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
        _snake_case		:	Optional[int]					     =     [33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # 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>

        _snake_case		:	str					     =     model.generate(lowercase__     ,     max_length=200     ,     do_sample=lowercase__		)
        self.assertListEqual(output_ids[0].numpy().tolist()     ,     lowercase__		)









'''simple docstring'''

import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
   import optax
   from flax.training.common_utils import onehot

   from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
   from transformers.models.ta.modeling_flax_ta import shift_tokens_right





@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class lowerCamelCase						(unittest.TestCase					):







  @slow
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	Tuple					     =     FlaxMTaForConditionalGeneration.from_pretrained('''google/mt5-small'''		)
        _snake_case		:	Any					     =     AutoTokenizer.from_pretrained('''google/mt5-small'''		)

        _snake_case		:	List[str]					     =     tokenizer('''Hello there'''     ,     return_tensors='''np'''		).input_ids
        _snake_case		:	Dict					     =     tokenizer('''Hi I am'''     ,     return_tensors='''np'''		).input_ids

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     model.config.pad_token_id     ,     model.config.decoder_start_token_id		)

        _snake_case		:	Any					     =     model(lowercase__     ,     decoder_input_ids=lowercase__		).logits
        _snake_case		:	Tuple					     =     optax.softmax_cross_entropy(lowercase__     ,     onehot(lowercase__     ,     logits.shape[-1]		)		).mean()

        _snake_case		:	Tuple					     =     -(labels.shape[-1] * loss.item())

        _snake_case		:	Union[str, Any]					     =     -84.9_127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1E-4		)


