"""simple docstring"""




import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class 		__A							(				SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Any	      =	GPTaTokenizer
     _UpperCamelCase							:    Optional[int]	      =	GPTaTokenizerFast
     _UpperCamelCase							:    Optional[int]	      =	True
     _UpperCamelCase							:    List[Any]	      =	{"add_prefix_space": True}
     _UpperCamelCase							:    List[Any]	      =	False






     def 					__A						(	self						):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       _lowerCAmelCase		: int						    =  [
           """l""",
           """o""",
           """w""",
           """e""",
           """r""",
           """s""",
           """t""",
           """i""",
           """d""",
           """n""",
           """\u0120""",
           """\u0120l""",
           """\u0120n""",
           """\u0120lo""",
           """\u0120low""",
           """er""",
           """\u0120lowest""",
           """\u0120newer""",
           """\u0120wider""",
           """<unk>""",
           """<|endoftext|>""",
       ]
       _lowerCAmelCase		: str						    =  dict(zip(a__       ,  range(len(a__						)						)						)						)
       _lowerCAmelCase		: List[str]						    =  ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
       _lowerCAmelCase		: Dict						    =  {"""unk_token""": """<unk>"""}

       _lowerCAmelCase		: str						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       _lowerCAmelCase		: Any						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""merges_file"""]						)
       with open(self.vocab_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         fp.write(json.dumps(a__						) + """\n"""						)
       with open(self.merges_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         fp.write("""\n""".join(a__						)						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return GPTaTokenizer.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  **a__						):
       kwargs.update(self.special_tokens_map						)
       return GPTaTokenizerFast.from_pretrained(self.tmpdirname       ,  **a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: Any						    =  """lower newer"""
       _lowerCAmelCase		: Tuple						    =  """lower newer"""
       return input_text, output_text






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  GPTaTokenizer(self.vocab_file       ,  self.merges_file       ,  **self.special_tokens_map						)
       _lowerCAmelCase		: List[str]						    =  """lower newer"""
       _lowerCAmelCase		: Union[str, Any]						    =  ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       self.assertListEqual(a__       ,  a__						)

       _lowerCAmelCase		: List[str]						    =  tokens + [tokenizer.unk_token]
       _lowerCAmelCase		: Tuple						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self						):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase		: List[str]						    =  self.get_tokenizer()
       _lowerCAmelCase		: int						    =  self.get_rust_tokenizer(add_prefix_space=a__						)

       _lowerCAmelCase		: Optional[int]						    =  """lower newer"""

       # Testing tokenization
       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.tokenize(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Dict						    =  rust_tokenizer.tokenize(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids without special tokens
       _lowerCAmelCase		: List[str]						    =  tokenizer.encode(a__       ,  add_special_tokens=a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Optional[Any]						    =  rust_tokenizer.encode(a__       ,  add_special_tokens=a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing conversion to ids with special tokens
       _lowerCAmelCase		: Dict						    =  self.get_rust_tokenizer(add_prefix_space=a__						)
       _lowerCAmelCase		: Optional[int]						    =  tokenizer.encode(a__       ,  add_prefix_space=a__						)
       _lowerCAmelCase		: Any						    =  rust_tokenizer.encode(a__						)
       self.assertListEqual(a__       ,  a__						)

       # Testing the unknown token
       _lowerCAmelCase		: int						    =  tokens + [rust_tokenizer.unk_token]
       _lowerCAmelCase		: Optional[Any]						    =  [14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__						)       ,  a__						)






     def 					__A						(	self       ,  *a__       ,  **a__						):
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass






     def 					__A						(	self       ,  a__=15						):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"						):
           _lowerCAmelCase		: str						    =  self.rust_tokenizer_class.from_pretrained(a__       ,  **a__						)

           # Simple input
           _lowerCAmelCase		: Optional[Any]						    =  """This is a simple input"""
           _lowerCAmelCase		: Any						    =  ["""This is a simple input 1""", """This is a simple input 2"""]
           _lowerCAmelCase		: Any						    =  ("""This is a simple input""", """This is a pair""")
           _lowerCAmelCase		: Tuple						    =  [
               ("""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(a__       ,  tokenizer_r.encode       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Simple input
           self.assertRaises(a__       ,  tokenizer_r.encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Simple input
           self.assertRaises(
               a__       ,  tokenizer_r.batch_encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""       ,  )

           # Pair input
           self.assertRaises(a__       ,  tokenizer_r.encode       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Pair input
           self.assertRaises(a__       ,  tokenizer_r.encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""						)

           # Pair input
           self.assertRaises(
               a__       ,  tokenizer_r.batch_encode_plus       ,  a__       ,  max_length=a__       ,  padding="""max_length"""       ,  )






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  GPTaTokenizer.from_pretrained(self.tmpdirname       ,  pad_token="""<pad>"""						)

       # Simple input
       _lowerCAmelCase		: Any						    =  """This is a simple input"""
       _lowerCAmelCase		: List[Any]						    =  ["""This is a simple input looooooooong""", """This is a simple input"""]
       _lowerCAmelCase		: List[str]						    =  ("""This is a simple input""", """This is a pair""")
       _lowerCAmelCase		: Union[str, Any]						    =  [
           ("""This is a simple input loooooong""", """This is a simple input"""),
           ("""This is a simple pair loooooong""", """This is a simple pair"""),
       ]

       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.pad_token_id

       _lowerCAmelCase		: Tuple						    =  tokenizer(a__       ,  padding="""max_length"""       ,  max_length=30       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: Optional[Any]						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: List[str]						    =  tokenizer(*a__       ,  padding="""max_length"""       ,  max_length=60       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: Tuple						    =  tokenizer(a__       ,  padding=a__       ,  truncate=a__       ,  return_tensors="""np"""						)

       # s
       # test single string max_length padding
       self.assertEqual(out_s["""input_ids"""].shape[-1]       ,  30						)
       self.assertTrue(pad_token_id in out_s["""input_ids"""]						)
       self.assertTrue(0 in out_s["""attention_mask"""]						)

       # s2
       # test automatic padding
       self.assertEqual(out_sa["""input_ids"""].shape[-1]       ,  33						)
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa["""input_ids"""][0]						)
       self.assertFalse(0 in out_sa["""attention_mask"""][0]						)
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa["""input_ids"""][1]						)
       self.assertTrue(0 in out_sa["""attention_mask"""][1]						)

       # p
       # test single pair max_length padding
       self.assertEqual(out_p["""input_ids"""].shape[-1]       ,  60						)
       self.assertTrue(pad_token_id in out_p["""input_ids"""]						)
       self.assertTrue(0 in out_p["""attention_mask"""]						)

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa["""input_ids"""].shape[-1]       ,  52						)
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa["""input_ids"""][0]						)
       self.assertFalse(0 in out_pa["""attention_mask"""][0]						)
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa["""input_ids"""][1]						)
       self.assertTrue(0 in out_pa["""attention_mask"""][1]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  """$$$"""
       _lowerCAmelCase		: Dict						    =  GPTaTokenizer.from_pretrained(self.tmpdirname       ,  bos_token=a__       ,  add_bos_token=a__						)

       _lowerCAmelCase		: List[str]						    =  """This is a simple input"""
       _lowerCAmelCase		: Optional[int]						    =  ["""This is a simple input 1""", """This is a simple input 2"""]

       _lowerCAmelCase		: Optional[int]						    =  tokenizer.bos_token_id

       _lowerCAmelCase		: Optional[int]						    =  tokenizer(a__						)
       _lowerCAmelCase		: Optional[Any]						    =  tokenizer(a__						)

       self.assertEqual(out_s.input_ids[0]       ,  a__						)
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids						)						)

       _lowerCAmelCase		: Optional[Any]						    =  tokenizer.decode(out_s.input_ids						)
       _lowerCAmelCase		: List[Any]						    =  tokenizer.batch_decode(out_sa.input_ids						)

       self.assertEqual(decode_s.split()[0]       ,  a__						)
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa						)						)






     def 					__A						(	self						):
       pass






     def 					__A						(	self						):
       # TODO: change to self.get_tokenizers() when the fast version is implemented
       _lowerCAmelCase		: List[Any]						    =  [self.get_tokenizer(do_lower_case=a__       ,  add_bos_token=a__						)]
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"						):
           _lowerCAmelCase		: int						    =  """Encode this."""
           _lowerCAmelCase		: Dict						    =  """This one too please."""
           _lowerCAmelCase		: Tuple						    =  tokenizer.encode(a__       ,  add_special_tokens=a__						)
           encoded_sequence += tokenizer.encode(a__       ,  add_special_tokens=a__						)
           _lowerCAmelCase		: List[Any]						    =  tokenizer.encode_plus(
               a__       ,  a__       ,  add_special_tokens=a__       ,  return_special_tokens_mask=a__       ,  )
           _lowerCAmelCase		: Tuple						    =  encoded_sequence_dict["""input_ids"""]
           _lowerCAmelCase		: List[str]						    =  encoded_sequence_dict["""special_tokens_mask"""]
           self.assertEqual(len(a__						)       ,  len(a__						)						)

           _lowerCAmelCase		: Dict						    =  [
               (x if not special_tokens_mask[i] else None) for i, x in enumerate(a__						)
           ]
           _lowerCAmelCase		: List[str]						    =  [x for x in filtered_sequence if x is not None]
           self.assertEqual(a__       ,  a__						)







@require_tokenizers
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       # More context:
       # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
       # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
       # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

       _lowerCAmelCase		: Optional[int]						    =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  from_slow=a__						)
       _lowerCAmelCase		: Tuple						    =  """A photo of a cat"""

       _lowerCAmelCase		: List[str]						    =  tokenizer.encode(
           a__       ,  )
       self.assertEqual(a__       ,  [2, 250, 1345, 9, 10, 4758]						)
       tokenizer.save_pretrained("""test_opt"""						)

       _lowerCAmelCase		: Optional[int]						    =  AutoTokenizer.from_pretrained("""./test_opt"""						)
       _lowerCAmelCase		: List[Any]						    =  tokenizer.encode(
           a__       ,  )
       self.assertEqual(a__       ,  [2, 250, 1345, 9, 10, 4758]						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  use_slow=a__						)
       _lowerCAmelCase		: Any						    =  """A photo of a cat"""

       _lowerCAmelCase		: Dict						    =  tokenizer.encode(
           a__       ,  )
       # Same as above
       self.assertEqual(a__       ,  [2, 250, 1345, 9, 10, 4758]						)






     @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""						)
     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  AutoTokenizer.from_pretrained("""facebook/opt-350m"""       ,  from_slow=a__						)

       _lowerCAmelCase		: Dict						    =  """bos"""
       _lowerCAmelCase		: str						    =  tokenizer.get_vocab()["""bos"""]

       _lowerCAmelCase		: Any						    =  """A photo of a cat"""
       _lowerCAmelCase		: List[Any]						    =  tokenizer.encode(
           a__       ,  )
       # We changed the bos token
       self.assertEqual(a__       ,  [31957, 250, 1345, 9, 10, 4758]						)
       tokenizer.save_pretrained("""./tok"""						)
       _lowerCAmelCase		: int						    =  AutoTokenizer.from_pretrained("""./tok"""						)
       self.assertTrue(tokenizer.is_fast						)
       _lowerCAmelCase		: Union[str, Any]						    =  tokenizer.encode(
           a__       ,  )
       self.assertEqual(a__       ,  [31957, 250, 1345, 9, 10, 4758]						)





"""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 		__A							:






     def __init__(	self       ,  a__       ,  ):
       _lowerCAmelCase		: Optional[Any]						    =  parent
       _lowerCAmelCase		: Tuple						    =  13
       _lowerCAmelCase		: Tuple						    =  7
       _lowerCAmelCase		: Any						    =  30
       _lowerCAmelCase		: Optional[int]						    =  self.seq_length + self.mem_len
       _lowerCAmelCase		: Dict						    =  15
       _lowerCAmelCase		: List[Any]						    =  True
       _lowerCAmelCase		: Any						    =  True
       _lowerCAmelCase		: List[str]						    =  99
       _lowerCAmelCase		: List[Any]						    =  [10, 50, 80]
       _lowerCAmelCase		: Tuple						    =  32
       _lowerCAmelCase		: int						    =  32
       _lowerCAmelCase		: Dict						    =  4
       _lowerCAmelCase		: List[str]						    =  8
       _lowerCAmelCase		: Tuple						    =  128
       _lowerCAmelCase		: Any						    =  2
       _lowerCAmelCase		: List[Any]						    =  2
       _lowerCAmelCase		: List[Any]						    =  None
       _lowerCAmelCase		: Optional[Any]						    =  1
       _lowerCAmelCase		: Tuple						    =  0
       _lowerCAmelCase		: List[Any]						    =  3
       _lowerCAmelCase		: Optional[int]						    =  self.vocab_size - 1
       _lowerCAmelCase		: Dict						    =  0.0_1






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)
       _lowerCAmelCase		: Optional[Any]						    =  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size						)

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

       _lowerCAmelCase		: Union[str, Any]						    =  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						):
       random.seed(self.seed						)
       tf.random.set_seed(self.seed						)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Optional[int]						    =  TFTransfoXLModel(a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  model(a__						).to_tuple()

       _lowerCAmelCase		: Optional[Any]						    =  {"""input_ids""": input_ids_a, """mems""": mems_a}

       _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  model(a__						).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       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: int						    =  TFTransfoXLLMHeadModel(a__						)

       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  model(a__						).to_tuple()

       _lowerCAmelCase		: Dict						    =  {"""input_ids""": input_ids_a, """labels""": lm_labels}
       _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  model(a__						).to_tuple()

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[Any]						    =  model([input_ids_a, mems_a]						).to_tuple()

       _lowerCAmelCase		: Any						    =  {"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

       _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  model(a__						).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       ,  a__       ,  a__       ,  a__       ,  a__						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLForSequenceClassification(a__						)
       _lowerCAmelCase		: int						    =  model(a__						)
       self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)						)






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  self.prepare_config_and_inputs()
       ((_lowerCAmelCase)    ,			(_lowerCAmelCase)    ,			(_lowerCAmelCase)    ,			(_lowerCAmelCase))		: Dict						    =  config_and_inputs
       _lowerCAmelCase		: List[Any]						    =  {"""input_ids""": input_ids_a}
       return config, inputs_dict







@require_tf
class 		__A							(				SCREAMING_SNAKE_CASE_					,			SCREAMING_SNAKE_CASE_					,			unittest.TestCase	):
     _UpperCamelCase							:    Dict	      =	(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     _UpperCamelCase							:    Tuple	      =	() if is_tf_available() else ()
     _UpperCamelCase							:    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
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    str	      =	False
     _UpperCamelCase							:    Tuple	      =	False
     _UpperCamelCase							:    Any	      =	False






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__						):
       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						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLModelTester(self						)
       _lowerCAmelCase		: List[Any]						    =  ConfigTester(self       ,  config_class=a__       ,  d_embed=37						)






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






     def 					__A						(	self						):
       self.model_tester.set_seed()
       _lowerCAmelCase		: Union[str, Any]						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_model(*a__						)






     def 					__A						(	self						):
       self.model_tester.set_seed()
       _lowerCAmelCase		: int						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_lm_head(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a__						)






     def 					__A						(	self						):
       _lowerCAmelCase    ,			_lowerCAmelCase		: Any						    =  self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase		: List[Any]						    =  [TFTransfoXLForSequenceClassification]

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






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






     @slow
     def 					__A						(	self						):
       for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCAmelCase		: Union[str, Any]						    =  TFTransfoXLModel.from_pretrained(a__						)
         self.assertIsNotNone(a__						)






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







@require_tf
class 		__A							(				unittest.TestCase	):






     @unittest.skip("""Skip test until #12651 is resolved."""						)
     @slow
     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""						)
       # fmt: off
       _lowerCAmelCase		: List[str]						    =  tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]       ,  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
       _lowerCAmelCase		: List[Any]						    =  [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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>

       _lowerCAmelCase		: Tuple						    =  model.generate(a__       ,  max_length=200       ,  do_sample=a__						)
       self.assertListEqual(output_ids[0].numpy().tolist()       ,  a__						)




