





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       :






							def __init__( self     :					Dict,			_UpperCAmelCase     :					int,			)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						parent
														SCREAMING_SNAKE_CASE__       :      str     =						1_3
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						7
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						3_0
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self.seq_length + self.mem_len
														SCREAMING_SNAKE_CASE__       :      Tuple     =						1_5
														SCREAMING_SNAKE_CASE__       :      int     =						True
														SCREAMING_SNAKE_CASE__       :      Tuple     =						True
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						9_9
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[1_0, 5_0, 8_0]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						3_2
														SCREAMING_SNAKE_CASE__       :      int     =						3_2
														SCREAMING_SNAKE_CASE__       :      Dict     =						4
														SCREAMING_SNAKE_CASE__       :      Dict     =						8
														SCREAMING_SNAKE_CASE__       :      int     =						1_2_8
														SCREAMING_SNAKE_CASE__       :      int     =						2
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						2
														SCREAMING_SNAKE_CASE__       :      int     =						None
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						1
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						0
														SCREAMING_SNAKE_CASE__       :      Tuple     =						3
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.vocab_size - 1
														SCREAMING_SNAKE_CASE__       :      Tuple     =						0.01






							def  A_     ( self     :					Any						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						ids_tensor([self.batch_size, self.seq_length],			self.vocab_size						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						ids_tensor([self.batch_size, self.seq_length],			self.vocab_size						)

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

														SCREAMING_SNAKE_CASE__       :      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     :					str						)				->			Union[str, Any]:





														"""simple docstring"""
														random.seed(self.seed						)
														tf.random.set_seed(self.seed						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					int						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						TFTransfoXLModel(lowerCAmelCase__						)

														SCREAMING_SNAKE_CASE__       :      Any     =						model(lowerCAmelCase__						).to_tuple()

														SCREAMING_SNAKE_CASE__       :      List[str]     =						{"input_ids": input_ids_a, "mems": mems_a}

														SCREAMING_SNAKE_CASE__       :      Tuple     =						model(lowerCAmelCase__						).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     :					Any,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Any						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						TFTransfoXLLMHeadModel(lowerCAmelCase__						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model(lowerCAmelCase__						).to_tuple()

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						{"input_ids": input_ids_a, "labels": lm_labels}
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model(lowerCAmelCase__						).to_tuple()

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						model([input_ids_a, mems_a]						).to_tuple()

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

														SCREAMING_SNAKE_CASE__       :      Any     =						model(lowerCAmelCase__						).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     :					str,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					Tuple						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						TFTransfoXLForSequenceClassification(lowerCAmelCase__						)
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						model(lowerCAmelCase__						)
														self.parent.assertEqual(result.logits.shape,			(self.batch_size, self.num_labels)						)






							def  A_     ( self     :					Union[str, Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self.prepare_config_and_inputs()
														(SCREAMING_SNAKE_CASE__)       :      List[Any]     =						config_and_inputs
														SCREAMING_SNAKE_CASE__       :      Any     =						{"input_ids": input_ids_a}
														return config, inputs_dict


@require_tf
class 						lowerCamelCase       (__lowerCamelCase							,     __lowerCamelCase							,     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  A_     ( self     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					int						)				->			Optional[Any]:





														"""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						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						TFTransfoXLModelTester(self						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ConfigTester(self,			config_class=lowerCAmelCase__,			d_embed=3_7						)






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





														"""simple docstring"""
														self.config_tester.run_common_tests()






							def  A_     ( self     :					List[Any]						)				->			Dict:





														"""simple docstring"""
														self.model_tester.set_seed()
														SCREAMING_SNAKE_CASE__       :      int     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_model(*lowerCAmelCase__						)






							def  A_     ( self     :					str						)				->			Union[str, Any]:





														"""simple docstring"""
														self.model_tester.set_seed()
														SCREAMING_SNAKE_CASE__       :      Any     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCAmelCase__						)






							def  A_     ( self     :					Dict						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCAmelCase__						)






							def  A_     ( self     :					int						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						self.model_tester.prepare_config_and_inputs_for_common()
														SCREAMING_SNAKE_CASE__       :      str     =						[TFTransfoXLForSequenceClassification]

														for model_class in self.all_model_classes:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						model_class(lowerCAmelCase__						)
																					assert isinstance(model.get_input_embeddings(),			tf.keras.layers.Layer						)
																					if model_class in list_other_models_with_output_ebd:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						model.get_output_embeddings()
																												assert isinstance(lowerCAmelCase__,			tf.keras.layers.Layer						)
																												SCREAMING_SNAKE_CASE__       :      str     =						model.get_bias()
																												assert name is None
																					else:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model.get_output_embeddings()
																												assert x is None
																												SCREAMING_SNAKE_CASE__       :      int     =						model.get_bias()
																												assert name is None






							def  A_     ( self     :					Any						)				->			List[str]:





														"""simple docstring"""
														pass






							@slow
							def  A_     ( self     :					str						)				->			List[Any]:





														"""simple docstring"""




														for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						TFTransfoXLModel.from_pretrained(lowerCAmelCase__						)
																					self.assertIsNotNone(lowerCAmelCase__						)






							@unittest.skip(reason="This model doesn't play well with fit() due to not returning a single loss."						)
							def  A_     ( self     :					List[Any]						)				->			Dict:





														"""simple docstring"""
														pass


@require_tf
class 						lowerCamelCase       (unittest.TestCase      ):






							@unittest.skip("Skip test until #12651 is resolved."						)
							@slow
							def  A_     ( self     :					Dict						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103"						)
														# fmt: off
														SCREAMING_SNAKE_CASE__       :      List[str]     =						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
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[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>

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						model.generate(lowerCAmelCase__,			max_length=2_0_0,			do_sample=lowerCAmelCase__						)
														self.assertListEqual(output_ids[0].numpy().tolist(),			lowerCAmelCase__						)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase :    Union[str, Any]  							= {
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    int  							= [
		    '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BigBirdPegasusForCausalLM''',
		    '''BigBirdPegasusForConditionalGeneration''',
		    '''BigBirdPegasusForQuestionAnswering''',
		    '''BigBirdPegasusForSequenceClassification''',
		    '''BigBirdPegasusModel''',
		    '''BigBirdPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_bigbird_pegasus import (
		    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BigBirdPegasusConfig,
		    BigBirdPegasusOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BigBirdPegasusForCausalLM,
				    BigBirdPegasusForConditionalGeneration,
				    BigBirdPegasusForQuestionAnswering,
				    BigBirdPegasusForSequenceClassification,
				    BigBirdPegasusModel,
				    BigBirdPegasusPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase :    Optional[Any]  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






