



"""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:     List[Any]				,		__A:     int				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  13
												a__          =  7
												a__          =  30
												a__          =  self.seq_length + self.mem_len
												a__          =  15
												a__          =  True
												a__          =  True
												a__          =  99
												a__          =  [10, 50, 80]
												a__          =  32
												a__          =  32
												a__          =  4
												a__          =  8
												a__          =  128
												a__          =  2
												a__          =  2
												a__          =  None
												a__          =  1
												a__          =  0
												a__          =  3
												a__          =  self.vocab_size - 1
												a__          =  0.0_1




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												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  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

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




							def  lowercase		(   self:     List[str]				,		__A:     Dict				,		__A:     Dict				,		__A:     str				,		__A:     List[str]    ):

												'''simple docstring'''

												a__          =  TFTransfoXLModel(__A    )

												a__       ,a__          =  model(__A    ).to_tuple()

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

												a__       ,a__          =  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  lowercase		(   self:     int				,		__A:     Union[str, Any]				,		__A:     Optional[Any]				,		__A:     Any				,		__A:     List[str]    ):

												'''simple docstring'''

												a__          =  TFTransfoXLLMHeadModel(__A    )

												a__       ,a__          =  model(__A    ).to_tuple()

												a__          =  {'''input_ids''': input_ids_a, '''labels''': lm_labels}
												a__       ,a__          =  model(__A    ).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(__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  lowercase		(   self:     str				,		__A:     Tuple				,		__A:     List[str]				,		__A:     Union[str, Any]				,		__A:     List[Any]    ):

												'''simple docstring'''

												a__          =  TFTransfoXLForSequenceClassification(__A    )
												a__          =  model(__A    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)    )





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												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        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =(
							    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
							)
							_SCREAMING_SNAKE_CASE    =() if is_tf_available() else ()
							_SCREAMING_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
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     Union[str, Any]				,		__A:     Tuple				,		__A:     Optional[int]				,		__A:     Tuple				,		__A:     Optional[int]				,		__A:     Union[str, 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  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  TFTransfoXLModelTester(self    )
												a__          =  ConfigTester(self				,		config_class=__A				,		d_embed=37    )




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							def  lowercase		(   self:     Optional[Any]    ):

												'''simple docstring'''

												self.model_tester.set_seed()
												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_model(*__A    )




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												self.model_tester.set_seed()
												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_lm_head(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()
												a__          =  [TFTransfoXLForSequenceClassification]

												for model_class in self.all_model_classes:
																	a__          =  model_class(__A    )
																	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(__A				,		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  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												pass




							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	a__          =  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  lowercase		(   self:     int    ):

												'''simple docstring'''

												pass







@require_tf
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@unittest.skip('''Skip test until #12651 is resolved.'''    )
							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''    )
												# fmt: off
												a__          =  tf.convert_to_tensor([[33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0]]				,		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
												a__          =  [33,1297,2,1,1009,4,1109,11739,4762,358,5,25,245,22,1706,17,20098,5,3215,21,37,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,6224,831,16002,2,8,603,78967,29546,23,803,20,25,416,5,8,232,4,277,6,1855,4601,3,29546,54,8,3609,5,57211,49,4,1,277,18,8,1755,15691,3,341,25,416,693,42573,71,17,401,94,31,17919,2,29546,7873,18,1,435,23,11011,755,5,5167,3,7983,98,84,2,29546,3267,8,3609,4,1,4865,1075,2,6087,71,6,346,8,5854,3,29546,824,1400,1868,2,19,160,2,311,8,5496,2,20920,17,25,15097,3,24,24,0,33,1,1857,2,1,1009,4,1109,11739,4762,358,5,25,245,28,1110,3,13,1041,4,24,603,490,2,71477,20098,104447,2,20961,1,2604,4,1,329,3,0]  # 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>

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










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__a       :	str             = {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	int             = [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	__a       :	Dict             = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


