"""simple docstring"""




import json
import sys





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int    ,_lowerCamelCase		: List[str]			) ->   Union[str, Any]:



  with open(lowerCamelCase_    ,encoding="""utf-8"""			) as f:
    _lowerCAmelCase		: Union[str, Any]						    =  json.load(lowerCamelCase_			)

  _lowerCAmelCase		: Optional[int]						    =  ["""<details>""", """<summary>Show updated benchmarks!</summary>""", """ """]

  for benchmark_name in sorted(lowerCamelCase_			):
    _lowerCAmelCase		: str						    =  results[benchmark_name]

    _lowerCAmelCase		: Union[str, Any]						    =  benchmark_name.split("""/"""			)[-1]
    output_md.append(f"### Benchmark: {benchmark_file_name}"			)

    _lowerCAmelCase		: Optional[Any]						    =  """| metric |"""
    _lowerCAmelCase		: Dict						    =  """|--------|"""
    _lowerCAmelCase		: int						    =  """| new / old (diff) |"""
    for metric_name in sorted(lowerCamelCase_			):
      _lowerCAmelCase		: Optional[Any]						    =  benchmark_res[metric_name]
      _lowerCAmelCase		: Tuple						    =  metric_vals["""new"""]
      _lowerCAmelCase		: Optional[int]						    =  metric_vals.get("""old"""    ,lowerCamelCase_			)
      _lowerCAmelCase		: Tuple						    =  metric_vals.get("""diff"""    ,lowerCamelCase_			)

      _lowerCAmelCase		: Optional[Any]						    =  f" {new_val:f}" if isinstance(lowerCamelCase_    ,(int, float)			) else """None"""

      if old_val is not None:
        val_str += f" / {old_val:f}" if isinstance(lowerCamelCase_    ,(int, float)			) else "None"
      if dif_val is not None:
        val_str += f" ({dif_val:f})" if isinstance(lowerCamelCase_    ,(int, float)			) else "None"

      title += " " + metric_name + " |"
      lines += "---|"
      value += val_str + " |"

    output_md += [title, lines, value, " "]

  output_md.append("""</details>"""			)

  with open(lowerCamelCase_    ,"""w"""    ,encoding="""utf-8"""			) as f:
    f.writelines("""\n""".join(lowerCamelCase_			)			)


if __name__ == "__main__":
     _a   :	Tuple 					=     sys.argv[1]
     _a   :	Dict 					=     sys.argv[2]

     format_json_to_md(input_json_file, output_md_file)

"""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__						)




