


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 ,       snake_case_ ,       )   ->			List[Any]:







								'''simple docstring'''

								__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 					=   0.0_1






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__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 A      (				self						)   ->			List[Any]:







								'''simple docstring'''

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






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







								'''simple docstring'''

								__lowercase 					=   TFTransfoXLModel(_a						)

								__lowercase 					=   model(_a						).to_tuple()

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

								__lowercase 					=   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 ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   TFTransfoXLLMHeadModel(_a						)

								__lowercase 					=   model(_a						).to_tuple()

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

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

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

								__lowercase 					=   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 ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   TFTransfoXLForSequenceClassification(_a						)
								__lowercase 					=   model(_a						)
								self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.num_labels)						)






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







								'''simple docstring'''

								__lowercase 					=   self.prepare_config_and_inputs()
								(__lowercase) 					=   config_and_inputs
								__lowercase 					=   {"""input_ids""": input_ids_a}
								return config, inputs_dict




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



					'''simple docstring'''







					__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 A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			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'''

								__lowercase 					=   TFTransfoXLModelTester(self						)
								__lowercase 					=   ConfigTester(self ,       config_class=_a ,       d_embed=3_7						)






					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()
								__lowercase 					=   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()
								__lowercase 					=   self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*_a						)






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   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'''

								__lowercase 					=   self.model_tester.prepare_config_and_inputs_for_common()
								__lowercase 					=   [TFTransfoXLForSequenceClassification]

								for model_class in self.all_model_classes:
											__lowercase 					=   model_class(_a						)
											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(_a ,       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 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]:
											__lowercase 					=   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 	lowerCamelCase_       (				unittest.TestCase				):



					'''simple docstring'''







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







								'''simple docstring'''

								__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(_a ,       max_length=2_0_0 ,       do_sample=_a						)
								self.assertListEqual(output_ids[0].numpy().tolist() ,       _a						)






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 		lowercase_  (     *_UpperCamelCase  ):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
						__lowercase 					=   list(_UpperCamelCase  )
			for i in range(len(_UpperCamelCase  )  ):
						__lowercase 					=   None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [
			    '''CUDA out of memory.''',  # CUDA OOM
			    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
			    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
			]
			if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ) and len(exception.args  ) == 1:
						return any(err in exception.args[0] for err in _statements  )
			return False





def 		lowercase_  (     _UpperCamelCase = None       ,			_UpperCamelCase = 1_28  ):


			'''simple docstring'''







			if function is None:
						return functools.partial(_UpperCamelCase       ,			starting_batch_size=_UpperCamelCase  )

			__lowercase 					=   starting_batch_size

			def decorator(*_UpperCamelCase       ,			**_UpperCamelCase  ):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						__lowercase 					=   list(inspect.signature(_UpperCamelCase  ).parameters.keys()  )
						# Guard against user error
						if len(_UpperCamelCase  ) < (len(_UpperCamelCase  ) + 1):
									__lowercase 					=   ''', '''.join([F'{arg}={value}' for arg, value in zip(params[1:]       ,			args[1:]  )]  )
									raise TypeError(
									    F'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'  )
						while True:
									if batch_size == 0:
												raise RuntimeError('''No executable batch size found, reached zero.'''  )
									try:
												return function(_UpperCamelCase       ,			*_UpperCamelCase       ,			**_UpperCamelCase  )
									except Exception as e:
												if should_reduce_batch_size(_UpperCamelCase  ):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator


