



"""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        __SCREAMING_SNAKE_CASE	:


					'''simple docstring'''





					def __init__(       self							:  str,  lowerCamelCase							:  str,  )->					List[str]:
								lowerCamelCase__		:		Dict			=parent
								lowerCamelCase__		:		List[str]			=13
								lowerCamelCase__		:		List[Any]			=7
								lowerCamelCase__		:		Optional[int]			=30
								lowerCamelCase__		:		Dict			=self.seq_length + self.mem_len
								lowerCamelCase__		:		Any			=15
								lowerCamelCase__		:		Tuple			=True
								lowerCamelCase__		:		Union[str, Any]			=True
								lowerCamelCase__		:		str			=99
								lowerCamelCase__		:		Tuple			=[10, 50, 80]
								lowerCamelCase__		:		Tuple			=32
								lowerCamelCase__		:		List[Any]			=32
								lowerCamelCase__		:		List[Any]			=4
								lowerCamelCase__		:		Optional[int]			=8
								lowerCamelCase__		:		Optional[int]			=128
								lowerCamelCase__		:		int			=2
								lowerCamelCase__		:		Dict			=2
								lowerCamelCase__		:		Optional[int]			=None
								lowerCamelCase__		:		List[Any]			=1
								lowerCamelCase__		:		Dict			=0
								lowerCamelCase__		:		str			=3
								lowerCamelCase__		:		int			=self.vocab_size - 1
								lowerCamelCase__		:		Any			=0.01



					def 		snake_case       (       self							:  Union[str, Any]     )->					Tuple:
								lowerCamelCase__		:		Any			=ids_tensor([self.batch_size, self.seq_length],  self.vocab_size     )
								lowerCamelCase__		:		str			=ids_tensor([self.batch_size, self.seq_length],  self.vocab_size     )

								lowerCamelCase__		:		Optional[Any]			=None
								if self.use_labels:
											lowerCamelCase__		:		List[Any]			=ids_tensor([self.batch_size, self.seq_length],  self.vocab_size     )

								lowerCamelCase__		:		Tuple			=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							:  Dict     )->					Tuple:
								random.seed(self.seed     )
								tf.random.set_seed(self.seed     )



					def 		snake_case       (       self							:  List[str],  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  str,  lowerCamelCase							:  int     )->					Tuple:
								lowerCamelCase__		:		str			=TFTransfoXLModel(lowerCamelCase     )

								lowerCamelCase__	,   lowerCamelCase__		:		str			=model(lowerCamelCase     ).to_tuple()

								lowerCamelCase__		:		Tuple			={'''input_ids''': input_ids_a, '''mems''': mems_a}

								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=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 		snake_case       (       self							:  List[str],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Any     )->					List[str]:
								lowerCamelCase__		:		List[Any]			=TFTransfoXLLMHeadModel(lowerCamelCase     )

								lowerCamelCase__	,   lowerCamelCase__		:		Any			=model(lowerCamelCase     ).to_tuple()

								lowerCamelCase__		:		Optional[Any]			={'''input_ids''': input_ids_a, '''labels''': lm_labels}
								lowerCamelCase__	,   lowerCamelCase__		:		Any			=model(lowerCamelCase     ).to_tuple()

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

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

								lowerCamelCase__	,   lowerCamelCase__		:		Union[str, 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 		snake_case       (       self							:  Union[str, Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Union[str, Any]     )->					Union[str, Any]:
								lowerCamelCase__		:		Dict			=TFTransfoXLForSequenceClassification(lowerCamelCase     )
								lowerCamelCase__		:		str			=model(lowerCamelCase     )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)     )




					def 		snake_case       (       self							:  Tuple     )->					Union[str, Any]:
								lowerCamelCase__		:		List[Any]			=self.prepare_config_and_inputs()
								((lowerCamelCase__)	,   (lowerCamelCase__)	,   (lowerCamelCase__)	,   (lowerCamelCase__))		:		Dict			=config_and_inputs
								lowerCamelCase__		:		Any			={'''input_ids''': input_ids_a}
								return config, inputs_dict





@require_tf
class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							(
					    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
					)
					_a   			=							() if is_tf_available() else ()
					_a   			=							(
					    {
					        '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
					_a   			=							False
					_a   			=							False
					_a   			=							False
					_a   			=							False



					def 		snake_case       (       self							:  Any,  lowerCamelCase							:  List[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  List[Any]     )->					List[Any]:
								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							:  str     )->					Dict:
								lowerCamelCase__		:		List[str]			=TFTransfoXLModelTester(self     )
								lowerCamelCase__		:		List[Any]			=ConfigTester(self,  config_class=lowerCamelCase,  d_embed=37     )



					def 		snake_case       (       self							:  Dict     )->					Optional[int]:
								self.config_tester.run_common_tests()



					def 		snake_case       (       self							:  Any     )->					List[str]:
								self.model_tester.set_seed()
								lowerCamelCase__		:		Optional[int]			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_model(*lowerCamelCase     )



					def 		snake_case       (       self							:  str     )->					Dict:
								self.model_tester.set_seed()
								lowerCamelCase__		:		Optional[int]			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCamelCase     )



					def 		snake_case       (       self							:  Tuple     )->					List[str]:
								lowerCamelCase__		:		List[Any]			=self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCamelCase     )



					def 		snake_case       (       self							:  Any     )->					Any:
								lowerCamelCase__	,   lowerCamelCase__		:		str			=self.model_tester.prepare_config_and_inputs_for_common()
								lowerCamelCase__		:		Tuple			=[TFTransfoXLForSequenceClassification]

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



					def 		snake_case       (       self							:  List[str]     )->					List[str]:
								# TODO JP: Make TransfoXL XLA compliant
								pass



					@slow
					def 		snake_case       (       self							:  List[str]     )->					Any:
								for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCamelCase__		:		str			=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 		snake_case       (       self							:  str     )->					int:
								pass





@require_tf
class        __SCREAMING_SNAKE_CASE	(      unittest.TestCase   ):


					'''simple docstring'''





					@unittest.skip('''Skip test until #12651 is resolved.'''     )
					@slow
					def 		snake_case       (       self							:  str     )->					Optional[Any]:
								lowerCamelCase__		:		Any			=TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''     )
								# fmt: off
								lowerCamelCase__		:		List[str]			=tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0]],  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__		:		Any			=[33,1297,2,1,1009,4,1109,1_1739,4762,358,5,25,245,22,1706,17,2_0098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,6224,831,1_6002,2,8,603,7_8967,2_9546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,2_9546,54,8,3609,5,5_7211,49,4,1,277,18,8,1755,1_5691,3,341,25,416,693,4_2573,71,17,401,94,31,1_7919,2,2_9546,7873,18,1,435,23,1_1011,755,5,5167,3,7983,98,84,2,2_9546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,2_9546,824,1400,1868,2,19,160,2,311,8,5496,2,2_0920,17,25,1_5097,3,24,24,0,33,1,1857,2,1,1009,4,1109,1_1739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,7_1477,2_0098,10_4447,2,2_0961,1,2604,4,1,329,3,0]  # 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__		:		List[Any]			=model.generate(lowerCamelCase,  max_length=200,  do_sample=lowerCamelCase     )
								self.assertListEqual(output_ids[0].numpy().tolist(),  lowerCamelCase     )









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		_lowercase : List[str]      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




