




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


				def        a				(	self					:					Union[str, Any]       )				->	Union[str, Any]:
									lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )
									lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

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

									lowerCAmelCase__          =			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					:					Tuple       )				->	List[str]:
									random.seed(self.seed       )
									tf.random.set_seed(self.seed       )


				def        a				(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Any:
									lowerCAmelCase__          =			TFTransfoXLModel(SCREAMING_SNAKE_CASE__       )

									lowerCAmelCase__     ,		lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ).to_tuple()

									lowerCAmelCase__          =			{"input_ids": input_ids_a, "mems": mems_a}

									lowerCAmelCase__     ,		lowerCAmelCase__          =			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        a				(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	str:
									lowerCAmelCase__          =			TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE__       )

									lowerCAmelCase__     ,		lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ).to_tuple()

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

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

									lowerCAmelCase__          =			{"input_ids": input_ids_a, "mems": mems_a, "labels": lm_labels}

									lowerCAmelCase__     ,		lowerCAmelCase__          =			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        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					List[str]       )				->	Tuple:
									lowerCAmelCase__          =			TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE__       )
									lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       )
									self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)       )


				def        a				(	self					:					Union[str, Any]       )				->	List[Any]:
									lowerCAmelCase__          =			self.prepare_config_and_inputs()
									((lowerCAmelCase__)     ,		(lowerCAmelCase__)     ,		(lowerCAmelCase__)     ,		(lowerCAmelCase__))          =			config_and_inputs
									lowerCAmelCase__          =			{"input_ids": input_ids_a}
									return config, inputs_dict






@require_tf
class 			__lowerCamelCase	(   UpperCamelCase__     ,    UpperCamelCase__     ,    unittest.TestCase   ):




				"""simple docstring"""

				snake_case__ =				(
				    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
				)
				snake_case__ =				() if is_tf_available() else ()
				snake_case__ =				(
				    {
				        "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__ =				False
				snake_case__ =				False
				snake_case__ =				False
				snake_case__ =				False


				def        a				(	self					:					List[str]       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Tuple:
									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					:					Dict       )				->	List[str]:
									lowerCAmelCase__          =			TFTransfoXLModelTester(self       )
									lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       ,     d_embed=37       )


				def        a				(	self					:					Union[str, Any]       )				->	List[Any]:
									self.config_tester.run_common_tests()


				def        a				(	self					:					Any       )				->	Dict:
									self.model_tester.set_seed()
									lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE__       )


				def        a				(	self					:					Optional[Any]       )				->	int:
									self.model_tester.set_seed()
									lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE__       )


				def        a				(	self					:					str       )				->	Dict:
									lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE__       )


				def        a				(	self					:					Any       )				->	Tuple:
									lowerCAmelCase__     ,		lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()
									lowerCAmelCase__          =			[TFTransfoXLForSequenceClassification]

									for model_class in self.all_model_classes:
														lowerCAmelCase__          =			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:
																			lowerCAmelCase__          =			model.get_output_embeddings()
																			assert isinstance(SCREAMING_SNAKE_CASE__       ,     tf.keras.layers.Layer       )
																			lowerCAmelCase__          =			model.get_bias()
																			assert name is None
														else:
																			lowerCAmelCase__          =			model.get_output_embeddings()
																			assert x is None
																			lowerCAmelCase__          =			model.get_bias()
																			assert name is None


				def        a				(	self					:					Any       )				->	Tuple:
									# TODO JP: Make TransfoXL XLA compliant
									pass


				@slow
				def        a				(	self					:					Optional[Any]       )				->	Optional[int]:
									for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase__          =			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        a				(	self					:					Union[str, Any]       )				->	int:
									pass






@require_tf
class 			__lowerCamelCase	(   unittest.TestCase   ):




				"""simple docstring"""

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

									lowerCAmelCase__          =			model.generate(SCREAMING_SNAKE_CASE__       ,     max_length=200       ,     do_sample=SCREAMING_SNAKE_CASE__       )
									self.assertListEqual(output_ids[0].numpy().tolist()       ,     SCREAMING_SNAKE_CASE__       )






import argparse
from collections import defaultdict



def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'{file}_{class_name}_{test_name}'
     done_test[_id] += 1

     with open(lowerCAmelCase_       ,    "r"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     lowerCAmelCase__          =			F'class {class_name}('
     lowerCAmelCase__          =			F'{4 * " "}def {test_name}('
     lowerCAmelCase__          =			F'{8 * " "}{correct_line.split()[0]}'
     lowerCAmelCase__          =			F'{16 * " "}{correct_line.split()[0]}'
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			0
     lowerCAmelCase__          =			0

     lowerCAmelCase__          =			[]
     for line in lines:
          if line.startswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			True
          elif in_class and line.startswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			True
          elif in_class and in_func and (line.startswith(lowerCAmelCase_      ) or line.startswith(lowerCAmelCase_      )):
               lowerCAmelCase__          =			len(line.split(correct_line.split()[0]      )[0]      )
               count += 1

               if count == done_test[_id]:
                    lowerCAmelCase__          =			True

          if in_class and in_func and in_line:
               if ")" not in line:
                    continue
               else:
                    lowerCAmelCase__          =			True

          if in_class and in_func and in_line and insert_line:
               new_lines.append(F'{spaces * " "}{correct_line}'      )
               lowerCAmelCase__          =			lowerCAmelCase__          =			lowerCAmelCase__          =			lowerCAmelCase__          =			False
          else:
               new_lines.append(lowerCAmelCase_      )

     with open(lowerCAmelCase_       ,    "w"      ) as f:
          for line in new_lines:
               f.write(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

     if fail is not None:
          with open(lowerCAmelCase_       ,    "r"      ) as f:
               lowerCAmelCase__          =			{l.strip() for l in f.readlines()}
     else:
          lowerCAmelCase__          =			None

     with open(lowerCAmelCase_       ,    "r"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     lowerCAmelCase__          =			defaultdict(lowerCAmelCase_      )
     for line in correct_lines:
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			line.split(";"      )
          if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
               overwrite_file(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     parser.add_argument('--correct_filename', help='filename of tests with expected result')
     parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
     UpperCamelCase								=					parser.parse_args()

     main(args.correct_filename, args.fail_filename)
