



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     _UpperCAmelCase			:




     def __init__(				self ,						a__ ,						):
         A__		 =					parent
         A__		 =					1_3
         A__		 =					7
         A__		 =					3_0
         A__		 =					self.seq_length + self.mem_len
         A__		 =					1_5
         A__		 =					True
         A__		 =					True
         A__		 =					9_9
         A__		 =					[1_0, 5_0, 8_0]
         A__		 =					3_2
         A__		 =					3_2
         A__		 =					4
         A__		 =					8
         A__		 =					1_2_8
         A__		 =					2
         A__		 =					2
         A__		 =					None
         A__		 =					1
         A__		 =					0
         A__		 =					3
         A__		 =					self.vocab_size - 1
         A__		 =					0.0_1




     def 		snake_case_		(				self):
         A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)
         A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)

         A__		 =					None
         if self.use_labels:
             A__		 =					ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)

         A__		 =					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 		snake_case_		(				self):
         random.seed(self.seed)
         tf.random.set_seed(self.seed)




     def 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         A__		 =					TFTransfoXLModel(UpperCamelCase__)

         A__ ,	A__		 =					model(UpperCamelCase__).to_tuple()

         A__		 =					{'''input_ids''': input_ids_a, '''mems''': mems_a}

         A__ ,	A__		 =					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 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         A__		 =					TFTransfoXLLMHeadModel(UpperCamelCase__)

         A__ ,	A__		 =					model(UpperCamelCase__).to_tuple()

         A__		 =					{'''input_ids''': input_ids_a, '''labels''': lm_labels}
         A__ ,	A__		 =					model(UpperCamelCase__).to_tuple()

         A__ ,	A__		 =					model([input_ids_a, mems_a]).to_tuple()

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

         A__ ,	A__		 =					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 		snake_case_		(				self ,						a__ ,						a__ ,						a__ ,						a__):
         A__		 =					TFTransfoXLForSequenceClassification(UpperCamelCase__)
         A__		 =					model(UpperCamelCase__)
         self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels))




     def 		snake_case_		(				self):
         A__		 =					self.prepare_config_and_inputs()
         ((A__) ,	(A__) ,	(A__) ,	(A__))		 =					config_and_inputs
         A__		 =					{'''input_ids''': input_ids_a}
         return config, inputs_dict







@require_tf
class     _UpperCAmelCase			(       _A			,      _A			,      unittest.TestCase     ):
     UpperCamelCase__		    =				(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     UpperCamelCase__		    =				() if is_tf_available() else ()
     UpperCamelCase__		    =				(
         {
             '''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__		    =				False
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False
     UpperCamelCase__		    =				False




     def 		snake_case_		(				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 		snake_case_		(				self):
         A__		 =					TFTransfoXLModelTester(self)
         A__		 =					ConfigTester(self ,						config_class=UpperCamelCase__ ,						d_embed=3_7)




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




     def 		snake_case_		(				self):
         self.model_tester.set_seed()
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*UpperCamelCase__)




     def 		snake_case_		(				self):
         self.model_tester.set_seed()
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*UpperCamelCase__)




     def 		snake_case_		(				self):
         A__		 =					self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*UpperCamelCase__)




     def 		snake_case_		(				self):
         A__ ,	A__		 =					self.model_tester.prepare_config_and_inputs_for_common()
         A__		 =					[TFTransfoXLForSequenceClassification]

         for model_class in self.all_model_classes:
             A__		 =					model_class(UpperCamelCase__)
             assert isinstance(model.get_input_embeddings() ,						tf.keras.layers.Layer)
             if model_class in list_other_models_with_output_ebd:
                 A__		 =					model.get_output_embeddings()
                 assert isinstance(UpperCamelCase__ ,						tf.keras.layers.Layer)
                 A__		 =					model.get_bias()
                 assert name is None
             else:
                 A__		 =					model.get_output_embeddings()
                 assert x is None
                 A__		 =					model.get_bias()
                 assert name is None




     def 		snake_case_		(				self):
         pass




     @slow
     def 		snake_case_		(				self):






         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A__		 =					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 		snake_case_		(				self):
         pass







@require_tf
class     _UpperCAmelCase			(       unittest.TestCase     ):




     @unittest.skip('''Skip test until #12651 is resolved.''')
     @slow
     def 		snake_case_		(				self):
         A__		 =					TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''')
         # fmt: off
         A__		 =					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__		 =					[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__		 =					model.generate(UpperCamelCase__ ,						max_length=2_0_0 ,						do_sample=UpperCamelCase__)
         self.assertListEqual(output_ids[0].numpy().tolist() ,						UpperCamelCase__)





def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       str	)->  float:

    def get_matched_characters(UpperCamelCase_				:       str ,       UpperCamelCase_				:       str	) -> str:
        A__		 =					[]
        A__		 =					min(len(_stra	) ,       len(_stra	)	) // 2
        for i, l in enumerate(_stra	):
            A__		 =					int(max(0 ,       i - limit	)	)
            A__		 =					int(min(i + limit + 1 ,       len(_stra	)	)	)
            if l in _stra[left:right]:
                matched.append(UpperCamelCase_	)
                A__		 =					f"{_stra[0:_stra.index(UpperCamelCase_	)]} {_stra[_stra.index(UpperCamelCase_	) + 1:]}"

        return "".join(UpperCamelCase_	)

    # matching characters
    A__		 =					get_matched_characters(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					get_matched_characters(UpperCamelCase_ ,       UpperCamelCase_	)
    A__		 =					len(UpperCamelCase_	)

    # transposition
    A__		 =					(
        len([(ca, ca) for ca, ca in zip(UpperCamelCase_ ,       UpperCamelCase_	) if ca != ca]	) // 2
    )

    if not match_count:
        A__		 =					0.0
    else:
        A__		 =					(
            1
            / 3
            * (
                match_count / len(UpperCamelCase_	)
                + match_count / len(UpperCamelCase_	)
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    A__		 =					0
    for ca, ca in zip(stra[:4] ,       stra[:4]	):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(jaro_winkler("hello", "world"))
