

"""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 					_lowerCAmelCase  :







 """simple docstring"""







 def __init__(			self							:			Any,					UpperCAmelCase__							:			List[Any],					):
    __lowercase								=						parent
    __lowercase								=						1_3
    __lowercase								=						7
    __lowercase								=						3_0
    __lowercase								=						self.seq_length + self.mem_len
    __lowercase								=						1_5
    __lowercase								=						True
    __lowercase								=						True
    __lowercase								=						9_9
    __lowercase								=						[1_0, 5_0, 8_0]
    __lowercase								=						3_2
    __lowercase								=						3_2
    __lowercase								=						4
    __lowercase								=						8
    __lowercase								=						1_2_8
    __lowercase								=						2
    __lowercase								=						2
    __lowercase								=						None
    __lowercase								=						1
    __lowercase								=						0
    __lowercase								=						3
    __lowercase								=						self.vocab_size - 1
    __lowercase								=						0.01


 def 					_lowercase (			self							:			List[str]	):
    __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 					_lowercase (			self							:			List[Any]	):
    random.seed(self.seed	)
    tf.random.set_seed(self.seed	)


 def 					_lowercase (			self							:			int,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			Optional[int],					UpperCAmelCase__							:			int,					UpperCAmelCase__							:			Tuple	):
    __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 					_lowercase (			self							:			List[str],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Dict,					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			List[str]	):
    __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 					_lowercase (			self							:			Optional[Any],					UpperCAmelCase__							:			Union[str, Any],					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			str,					UpperCAmelCase__							:			Optional[int]	):
    __lowercase								=						TFTransfoXLForSequenceClassification(UpperCAmelCase__	)
    __lowercase								=						model(UpperCAmelCase__	)
    self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)	)



 def 					_lowercase (			self							:			Any	):
    __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  (	lowercase     ,lowercase     ,unittest.TestCase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Optional[Any]   		=	(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 __UpperCAmelCase	:					Union[str, Any]   		=	() if is_tf_available() else ()
 __UpperCAmelCase	:					List[str]   		=	(
     {
         "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	:					Tuple   		=	False
 __UpperCAmelCase	:					List[str]   		=	False
 __UpperCAmelCase	:					Any   		=	False
 __UpperCAmelCase	:					List[Any]   		=	False


 def 					_lowercase (			self							:			str,					UpperCAmelCase__							:			List[str],					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			Any,					UpperCAmelCase__							:			Tuple,					UpperCAmelCase__							:			int	):
    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 					_lowercase (			self							:			List[str]	):
    __lowercase								=						TFTransfoXLModelTester(self	)
    __lowercase								=						ConfigTester(self,					config_class=UpperCAmelCase__,					d_embed=3_7	)


 def 					_lowercase (			self							:			int	):
    self.config_tester.run_common_tests()


 def 					_lowercase (			self							:			Union[str, Any]	):
    self.model_tester.set_seed()
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_model(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			Tuple	):
    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 					_lowercase (			self							:			Optional[int]	):
    __lowercase								=						self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCAmelCase__	)


 def 					_lowercase (			self							:			int	):
    __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 					_lowercase (			self							:			List[str]	):
    # TODO JP: Make TransfoXL XLA compliant
    pass


 @slow
 def 					_lowercase (			self							:			Tuple	):
    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 					_lowercase (			self							:			Any	):
    pass







@require_tf
class 					_lowerCAmelCase  (	unittest.TestCase   ):







 """simple docstring"""







 @unittest.skip("Skip test until #12651 is resolved."	)
 @slow
 def 					_lowercase (			self							:			List[str]	):
    __lowercase								=						TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"	)
    # fmt: off
    __lowercase								=						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
    __lowercase								=						[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>

    __lowercase								=						model.generate(UpperCAmelCase__,					max_length=2_0_0,					do_sample=UpperCAmelCase__	)
    self.assertListEqual(output_ids[0].numpy().tolist(),					UpperCAmelCase__	)







"""simple docstring"""





def   _A  (  UpperCamelCase_     :       Any)	->						List[str]:
   '''simple docstring'''


   __lowercase		,__lowercase								=						[], []
   while len(UpperCamelCase_) > 1:
      __lowercase		,__lowercase								=						min(UpperCamelCase_), max(UpperCamelCase_)
      start.append(UpperCamelCase_)
      end.append(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
   end.reverse()
   return start + collection + end


if __name__ == "__main__":
   _a       =							input('Enter numbers separated by a comma:\n').strip()
   _a       =							[int(item) for item in user_input.split(',')]
   print(*merge_sort(unsorted), sep=',')




