

"""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							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    )  ->      List[Any]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	None
           if self.use_labels:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size  )

           SCREAMING_SNAKE_CASE__   :      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  )  ->      List[Any]:


           """simple docstring"""


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

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	TFTransfoXLModel(_a  )

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model(_a  ).to_tuple()

           SCREAMING_SNAKE_CASE__   :      Dict      		=	{"""input_ids""": input_ids_a, """mems""": mems_a}

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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  )  ->      Union[str, Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	TFTransfoXLLMHeadModel(_a  )

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

           SCREAMING_SNAKE_CASE__   :      Dict      		=	{"""input_ids""": input_ids_a, """labels""": lm_labels}
           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Dict      		=	model(_a  ).to_tuple()

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

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	{"""input_ids""": input_ids_a, """mems""": mems_a, """labels""": lm_labels}

           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      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  )  ->      Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	TFTransfoXLForSequenceClassification(_a  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	model(_a  )
           self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)  )





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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self.prepare_config_and_inputs()
           ((SCREAMING_SNAKE_CASE__)		,		(SCREAMING_SNAKE_CASE__)		,		(SCREAMING_SNAKE_CASE__)		,		(SCREAMING_SNAKE_CASE__))   :      Optional[Any]      		=	config_and_inputs
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{"""input_ids""": input_ids_a}
           return config, inputs_dict





@require_tf
class      __a							(UpperCamelCase_   ,	UpperCamelCase_   ,	unittest.TestCase):






     '''simple docstring'''
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = (
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     _SCREAMING_SNAKE_CASE						:Union[str, Any]       = () if is_tf_available() else ()
     _SCREAMING_SNAKE_CASE						:Tuple       = (
         {
             """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
     _SCREAMING_SNAKE_CASE						:List[str]       = False
     _SCREAMING_SNAKE_CASE						:Any       = False
     _SCREAMING_SNAKE_CASE						:Dict       = False
     _SCREAMING_SNAKE_CASE						:int       = False

     def 					_a			(						self	,    _a	,    _a	,    _a	,    _a	,    _a  )  ->      Optional[int]:


           """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  )  ->      int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	TFTransfoXLModelTester(self  )
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	ConfigTester(self	,    config_class=_a	,    d_embed=37  )

     def 					_a			(						self  )  ->      Tuple:


           """simple docstring"""


           self.config_tester.run_common_tests()

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


           """simple docstring"""


           self.model_tester.set_seed()
           SCREAMING_SNAKE_CASE__   :      Tuple      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_model(*_a  )

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


           """simple docstring"""


           self.model_tester.set_seed()
           SCREAMING_SNAKE_CASE__   :      Any      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_lm_head(*_a  )

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Dict      		=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*_a  )

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


           """simple docstring"""


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

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

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           pass

     @slow
     def 					_a			(						self  )  ->      List[str]:


           """simple docstring"""


           for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	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  )  ->      int:


           """simple docstring"""


           pass





@require_tf
class      __a							(unittest.TestCase):






     '''simple docstring'''

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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	TFTransfoXLLMHeadModel.from_pretrained("""transfo-xl-wt103"""  )
           # fmt: off
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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
           SCREAMING_SNAKE_CASE__   :      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>

           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	model.generate(_a	,    max_length=200	,    do_sample=_a  )
           self.assertListEqual(output_ids[0].numpy().tolist()	,    _a  )







"""simple docstring"""


import os

a					:List[str]    = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Any      		=	0

      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      while index < len(__lowerCAmelCase							) - 1:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	SYMBOLS[numerals[index]]
            SCREAMING_SNAKE_CASE__   :      Dict      		=	SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	""""""

      SCREAMING_SNAKE_CASE__   :      int      		=	num // 1000
      numerals += m_count * "M"
      num %= 1000

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	num // 100
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 100

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals




def  _lowercase					(    __lowerCAmelCase = "/p089_roman.txt"							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      int      		=	0

      with open(os.path.dirname(__lowerCAmelCase							) + roman_numerals_filename							) as filea:
            SCREAMING_SNAKE_CASE__   :      str      		=	filea.readlines()

      for line in lines:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	line.strip()
            SCREAMING_SNAKE_CASE__   :      Dict      		=	parse_roman_numerals(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      str      		=	generate_roman_numerals(__lowerCAmelCase							)
            savings += len(__lowerCAmelCase							) - len(__lowerCAmelCase							)

      return savings


if __name__ == "__main__":
  print(f'{solution() = }')




