

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							,     lowerCAmelCase							,     )						->				Optional[int]:
        '''simple docstring'''






        _lowercase			 =parent
        _lowercase			 =13
        _lowercase			 =7
        _lowercase			 =30
        _lowercase			 =self.seq_length + self.mem_len
        _lowercase			 =15
        _lowercase			 =True
        _lowercase			 =True
        _lowercase			 =99
        _lowercase			 =[10, 50, 80]
        _lowercase			 =32
        _lowercase			 =32
        _lowercase			 =4
        _lowercase			 =8
        _lowercase			 =128
        _lowercase			 =2
        _lowercase			 =2
        _lowercase			 =None
        _lowercase			 =1
        _lowercase			 =0
        _lowercase			 =3
        _lowercase			 =self.vocab_size - 1
        _lowercase			 =0.01




  def 						A__  (       self      )						->				Any:
        '''simple docstring'''






        _lowercase			 =ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size      )
        _lowercase			 =ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size      )

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

        _lowercase			 =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 						A__  (       self      )						->				Dict:
        '''simple docstring'''






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




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        _lowercase			 =TFTransfoXLModel(UpperCamelCase__      )

        _lowercase , _lowercase			 =model(UpperCamelCase__      ).to_tuple()

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

        _lowercase , _lowercase			 =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 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






        _lowercase			 =TFTransfoXLLMHeadModel(UpperCamelCase__      )

        _lowercase , _lowercase			 =model(UpperCamelCase__      ).to_tuple()

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

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

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

        _lowercase , _lowercase			 =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 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Any:
        '''simple docstring'''






        _lowercase			 =TFTransfoXLForSequenceClassification(UpperCamelCase__      )
        _lowercase			 =model(UpperCamelCase__      )
        self.parent.assertEqual(result.logits.shape							,     (self.batch_size, self.num_labels)      )




  def 						A__  (       self      )						->				Optional[Any]:
        '''simple docstring'''






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





@require_tf
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						(
      (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
  )
  _a		    =						() if is_tf_available() else ()
  _a		    =						(
      {
          "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
  _a		    =						False
  _a		    =						False
  _a		    =						False
  _a		    =						False




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Tuple:
        '''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 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFTransfoXLModelTester(self      )
        _lowercase			 =ConfigTester(self							,     config_class=UpperCamelCase__							,     d_embed=37      )




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






        self.config_tester.run_common_tests()




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        self.model_tester.set_seed()
        _lowercase			 =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__      )




  def 						A__  (       self      )						->				List[str]:
        '''simple docstring'''






        self.model_tester.set_seed()
        _lowercase			 =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__      )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        _lowercase			 =self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__      )




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        _lowercase , _lowercase			 =self.model_tester.prepare_config_and_inputs_for_common()
        _lowercase			 =[TFTransfoXLForSequenceClassification]

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




  def 						A__  (       self      )						->				Optional[int]:
        '''simple docstring'''






        pass




  @slow
  def 						A__  (       self      )						->				Union[str, Any]:
        '''simple docstring'''






        for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _lowercase			 =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 						A__  (       self      )						->				List[Any]:
        '''simple docstring'''






        pass





@require_tf
class 				__lowerCAmelCase  (      unittest.TestCase							):




  @unittest.skip('Skip test until #12651 is resolved.'      )
  @slow
  def 						A__  (       self      )						->				str:
        '''simple docstring'''






        _lowercase			 =TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'      )
        # fmt: off
        _lowercase			 =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
        _lowercase			 =[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>

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





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE    ,    unittest.TestCase							):
  _a		    =						OpenAIGPTTokenizer
  _a		    =						OpenAIGPTTokenizerFast
  _a		    =						True
  _a		    =						False




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        _lowercase			 =[
            'l',
            'o',
            'w',
            'e',
            'r',
            's',
            't',
            'i',
            'd',
            'n',
            'w</w>',
            'r</w>',
            't</w>',
            'lo',
            'low',
            'er</w>',
            'low</w>',
            'lowest</w>',
            'newer</w>',
            'wider</w>',
            '<unk>',
        ]
        _lowercase			 =dict(zip(lowerCAmelCase							,     range(len(lowerCAmelCase      )      )      )      )
        _lowercase			 =['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

        _lowercase			 =os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES['vocab_file']      )
        _lowercase			 =os.path.join(self.tmpdirname							,     VOCAB_FILES_NAMES['merges_file']      )
        with open(self.vocab_file							,     'w'      ) as fp:
              fp.write(json.dumps(lowerCAmelCase      )      )
        with open(self.merges_file							,     'w'      ) as fp:
              fp.write('\n'.join(lowerCAmelCase      )      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






        return "lower newer", "lower newer"




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






        _lowercase			 =OpenAIGPTTokenizer(self.vocab_file							,     self.merges_file      )

        _lowercase			 ='lower'
        _lowercase			 =['low', 'er</w>']
        _lowercase			 =tokenizer.tokenize(lowerCAmelCase      )
        self.assertListEqual(lowerCAmelCase							,     lowerCAmelCase      )

        _lowercase			 =tokens + ['<unk>']
        _lowercase			 =[14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase      )							,     lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase=15      )						->				Union[str, Any]:
        '''simple docstring'''






        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                    _lowercase			 =self.rust_tokenizer_class.from_pretrained(lowerCAmelCase							,     **lowerCAmelCase      )

                    # Simple input
                    _lowercase			 ='This is a simple input'
                    _lowercase			 =['This is a simple input 1', 'This is a simple input 2']
                    _lowercase			 =('This is a simple input', 'This is a pair')
                    _lowercase			 =[
                        ('This is a simple input 1', 'This is a simple input 2'),
                        ('This is a simple pair 1', 'This is a simple pair 2'),
                    ]

                    # Simple input tests
                    self.assertRaises(lowerCAmelCase							,     tokenizer_r.encode							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'      )

                    # Simple input
                    self.assertRaises(lowerCAmelCase							,     tokenizer_r.encode_plus							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'      )

                    # Simple input
                    self.assertRaises(
                        lowerCAmelCase							,     tokenizer_r.batch_encode_plus							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'							,     )

                    # Pair input
                    self.assertRaises(lowerCAmelCase							,     tokenizer_r.encode							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'      )

                    # Pair input
                    self.assertRaises(lowerCAmelCase							,     tokenizer_r.encode_plus							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'      )

                    # Pair input
                    self.assertRaises(
                        lowerCAmelCase							,     tokenizer_r.batch_encode_plus							,     lowerCAmelCase							,     max_length=lowerCAmelCase							,     padding='max_length'							,     )




  def 						A__  (       self      )						->				Dict:
        '''simple docstring'''






        pass





@require_ftfy
@require_spacy
@require_tokenizers
class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  pass


