


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  SCREAMING_SNAKE_CASE__  :
      def __init__(       self				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							)		->			List[str]:
             a_					:		Optional[Any]							=  parent
             a_					:		Optional[int]							=  1_3
             a_					:		Tuple							=  7
             a_					:		List[Any]							=  3_0
             a_					:		Tuple							=  self.seq_length + self.mem_len
             a_					:		Any							=  1_5
             a_					:		List[Any]							=  True
             a_					:		Optional[int]							=  True
             a_					:		Any							=  9_9
             a_					:		Optional[int]							=  [1_0, 5_0, 8_0]
             a_					:		List[Any]							=  3_2
             a_					:		int							=  3_2
             a_					:		Dict							=  4
             a_					:		Any							=  8
             a_					:		Optional[int]							=  1_2_8
             a_					:		Tuple							=  2
             a_					:		Any							=  2
             a_					:		int							=  None
             a_					:		Union[str, Any]							=  1
             a_					:		List[Any]							=  0
             a_					:		Optional[Any]							=  3
             a_					:		int							=  self.vocab_size - 1
             a_					:		List[Any]							=  0.01
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			List[Any]:
             a_					:		Tuple							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size     )
             a_					:		Union[str, Any]							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size     )

             a_					:		Union[str, Any]							=  None
             if self.use_labels:
                    a_					:		Tuple							=  ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size     )

             a_					:		int							=  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        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			Tuple:
             random.seed(self.seed     )
             tf.random.set_seed(self.seed     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     ,							SCREAMING_SNAKE_CASE__				:       Union[str, Any]     ,							SCREAMING_SNAKE_CASE__				:       Tuple     ,							SCREAMING_SNAKE_CASE__				:       List[Any]     )		->			List[Any]:
             a_					:		int							=  TFTransfoXLModel(SCREAMING_SNAKE_CASE__     )

             a_							,		a_					:		Tuple							=  model(SCREAMING_SNAKE_CASE__     ).to_tuple()

             a_					:		Any							=  {'input_ids': input_ids_a, 'mems': mems_a}

             a_							,		a_					:		int							=  model(SCREAMING_SNAKE_CASE__     ).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        SCREAMING_SNAKE_CASE							(       self				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     )		->			str:
             a_					:		Any							=  TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE__     )

             a_							,		a_					:		List[Any]							=  model(SCREAMING_SNAKE_CASE__     ).to_tuple()

             a_					:		Dict							=  {'input_ids': input_ids_a, 'labels': lm_labels}
             a_							,		a_					:		Any							=  model(SCREAMING_SNAKE_CASE__     ).to_tuple()

             a_							,		a_					:		int							=  model([input_ids_a, mems_a]     ).to_tuple()

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

             a_							,		a_					:		Dict							=  model(SCREAMING_SNAKE_CASE__     ).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        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       Dict     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			Dict:
             a_					:		Union[str, Any]							=  TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE__     )
             a_					:		int							=  model(SCREAMING_SNAKE_CASE__     )
             self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)     )

      def        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Union[str, Any]:
             a_					:		str							=  self.prepare_config_and_inputs()
             ((a_)							,		(a_)							,		(a_)							,		(a_))					:		int							=  config_and_inputs
             a_					:		Union[str, Any]							=  {'input_ids': input_ids_a}
             return config, inputs_dict
@require_tf
class  SCREAMING_SNAKE_CASE__  (				lowercase__						,    lowercase__						,    unittest.TestCase			):
      snake_case__				:     Optional[Any]        =						(
          (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
      )
      snake_case__				:     Tuple        =						() if is_tf_available() else ()
      snake_case__				:     Optional[int]        =						(
          {
              '''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
      snake_case__				:     Any        =						False
      snake_case__				:     List[Any]        =						False
      snake_case__				:     Tuple        =						False
      snake_case__				:     Optional[Any]        =						False
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     ,							SCREAMING_SNAKE_CASE__				:       Any     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       int     ,							SCREAMING_SNAKE_CASE__				:       str     ,							SCREAMING_SNAKE_CASE__				:       str     )		->			str:
             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        SCREAMING_SNAKE_CASE							(       self				:       Union[str, Any]     )		->			Optional[Any]:
             a_					:		int							=  TFTransfoXLModelTester(self     )
             a_					:		str							=  ConfigTester(self     ,							config_class=SCREAMING_SNAKE_CASE__     ,							d_embed=3_7     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Tuple     )		->			Optional[int]:
             self.config_tester.run_common_tests()
      def        SCREAMING_SNAKE_CASE							(       self				:       Dict     )		->			Optional[int]:
             self.model_tester.set_seed()
             a_					:		int							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			int:
             self.model_tester.set_seed()
             a_					:		Union[str, Any]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             a_					:		List[str]							=  self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE__     )
      def        SCREAMING_SNAKE_CASE							(       self				:       str     )		->			str:
             a_							,		a_					:		Dict							=  self.model_tester.prepare_config_and_inputs_for_common()
             a_					:		Optional[Any]							=  [TFTransfoXLForSequenceClassification]

             for model_class in self.all_model_classes:
                    a_					:		Optional[Any]							=  model_class(SCREAMING_SNAKE_CASE__     )
                    assert isinstance(model.get_input_embeddings()     ,							tf.keras.layers.Layer     )
                    if model_class in list_other_models_with_output_ebd:
                           a_					:		Union[str, Any]							=  model.get_output_embeddings()
                           assert isinstance(SCREAMING_SNAKE_CASE__     ,							tf.keras.layers.Layer     )
                           a_					:		Any							=  model.get_bias()
                           assert name is None
                    else:
                           a_					:		Union[str, Any]							=  model.get_output_embeddings()
                           assert x is None
                           a_					:		Optional[int]							=  model.get_bias()
                           assert name is None
      def        SCREAMING_SNAKE_CASE							(       self				:       Optional[int]     )		->			Tuple:
             # TODO JP: Make TransfoXL XLA compliant
             pass
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Optional[Any]:
             for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    a_					:		List[str]							=  TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE__     )
                    self.assertIsNotNone(SCREAMING_SNAKE_CASE__     )

      @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'     )
      def        SCREAMING_SNAKE_CASE							(       self				:       List[str]     )		->			Optional[Any]:
             pass
@require_tf
class  SCREAMING_SNAKE_CASE__  (				unittest.TestCase			):
      @unittest.skip('Skip test until #12651 is resolved.'     )
      @slow
      def        SCREAMING_SNAKE_CASE							(       self				:       List[Any]     )		->			Any:
             a_					:		Union[str, Any]							=  TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'     )
             # fmt: off
             a_					:		str							=  tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]]     ,							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
             a_					:		List[str]							=  [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0]  # 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>

             a_					:		Dict							=  model.generate(SCREAMING_SNAKE_CASE__     ,							max_length=2_0_0     ,							do_sample=SCREAMING_SNAKE_CASE__     )
             self.assertListEqual(output_ids[0].numpy().tolist()     ,							SCREAMING_SNAKE_CASE__     )






from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments







def        SCREAMING_SNAKE_CASE_						(			)	->		Any:

       """simple docstring"""

       a_					:		Optional[Any]							=  HfArgumentParser(__A				)
       a_					:		Optional[int]							=  parser.parse_args_into_dataclasses()[0]
       a_					:		List[Any]							=  TensorFlowBenchmark(args=__A				)
       try:
              a_					:		List[str]							=  parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              a_					:		Dict							=  'Arg --no_{0} is no longer used, please use --no-{0} instead.'
              a_					:		Dict							=  ' '.join(str(__A				).split(' '				)[:-1]				)
              a_					:		int							=  ''
              a_					:		int							=  eval(str(__A				).split(' '				)[-1]				)
              a_					:		Any							=  []
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]				)
                     else:
                            wrong_args.append(__A				)
              if len(__A				) > 0:
                     a_					:		str							=  full_error_msg + begin_error_msg + str(__A				)
              raise ValueError(__A				)
       benchmark.run()


if __name__ == "__main__":
   main()


