


'''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 	lowerCAmelCase__			:




			def __init__(		self  :     Any	,  lowerCamelCase__  :     List[Any]	,  )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       str								=      parent
					_UpperCAmelCase		:       Optional[int]								=      13
					_UpperCAmelCase		:       str								=      7
					_UpperCAmelCase		:       int								=      30
					_UpperCAmelCase		:       List[str]								=      self.seq_length + self.mem_len
					_UpperCAmelCase		:       Union[str, Any]								=      15
					_UpperCAmelCase		:       str								=      True
					_UpperCAmelCase		:       List[str]								=      True
					_UpperCAmelCase		:       Union[str, Any]								=      99
					_UpperCAmelCase		:       int								=      [10, 50, 80]
					_UpperCAmelCase		:       str								=      32
					_UpperCAmelCase		:       Union[str, Any]								=      32
					_UpperCAmelCase		:       List[str]								=      4
					_UpperCAmelCase		:       Optional[int]								=      8
					_UpperCAmelCase		:       int								=      1_28
					_UpperCAmelCase		:       Optional[Any]								=      2
					_UpperCAmelCase		:       Optional[int]								=      2
					_UpperCAmelCase		:       str								=      None
					_UpperCAmelCase		:       List[str]								=      1
					_UpperCAmelCase		:       Optional[Any]								=      0
					_UpperCAmelCase		:       List[Any]								=      3
					_UpperCAmelCase		:       str								=      self.vocab_size - 1
					_UpperCAmelCase		:       List[str]								=      0.0_1




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



					'''simple docstring'''







					_UpperCAmelCase		:       Dict								=      ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )
					_UpperCAmelCase		:       Any								=      ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

					_UpperCAmelCase		:       Any								=      None
					if self.use_labels:
							_UpperCAmelCase		:       Union[str, Any]								=      ids_tensor([self.batch_size, self.seq_length]	,  self.vocab_size )

					_UpperCAmelCase		:       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 							lowerCAmelCase__    (		self  :     List[str] )   ->Optional[int]:



					'''simple docstring'''







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




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     List[str] )   ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      TFTransfoXLModel(lowerCamelCase__ )

					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      model(lowerCamelCase__ ).to_tuple()

					_UpperCAmelCase		:       int								=      {"input_ids": input_ids_a, "mems": mems_a}

					_UpperCAmelCase					,    _UpperCAmelCase		:       str								=      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 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     Any )   ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      TFTransfoXLLMHeadModel(lowerCamelCase__ )

					_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      model(lowerCamelCase__ ).to_tuple()

					_UpperCAmelCase		:       List[str]								=      {"input_ids": input_ids_a, "labels": lm_labels}
					_UpperCAmelCase					,    _UpperCAmelCase		:       List[Any]								=      model(lowerCamelCase__ ).to_tuple()

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

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

					_UpperCAmelCase					,    _UpperCAmelCase		:       List[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 							lowerCAmelCase__    (		self  :     str	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Any	,  lowerCamelCase__  :     List[Any] )   ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      TFTransfoXLForSequenceClassification(lowerCamelCase__ )
					_UpperCAmelCase		:       Tuple								=      model(lowerCamelCase__ )
					self.parent.assertEqual(result.logits.shape	,  (self.batch_size, self.num_labels) )






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



					'''simple docstring'''







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






@require_tf
class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__    ,       unittest.TestCase	):
			lowerCAmelCase :	List[Any]          =     (
			    (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
			)
			lowerCAmelCase :	int          =     () if is_tf_available() else ()
			lowerCAmelCase :	str          =     (
			    {
			        "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
			lowerCAmelCase :	int          =     False
			lowerCAmelCase :	Optional[int]          =     False
			lowerCAmelCase :	Optional[int]          =     False
			lowerCAmelCase :	Dict          =     False




			def 							lowerCAmelCase__    (		self  :     List[str]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     List[Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Optional[Any] )   ->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 							lowerCAmelCase__    (		self  :     str )   ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      TFTransfoXLModelTester(self )
					_UpperCAmelCase		:       List[Any]								=      ConfigTester(self	,  config_class=lowerCamelCase__	,  d_embed=37 )




			def 							lowerCAmelCase__    (		self  :     Optional[Any] )   ->Dict:



					'''simple docstring'''







					self.config_tester.run_common_tests()




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



					'''simple docstring'''







					self.model_tester.set_seed()
					_UpperCAmelCase		:       Dict								=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_model(*lowerCamelCase__ )




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



					'''simple docstring'''







					self.model_tester.set_seed()
					_UpperCAmelCase		:       Dict								=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_lm_head(*lowerCamelCase__ )




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



					'''simple docstring'''







					_UpperCAmelCase		:       Tuple								=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowerCamelCase__ )




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



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      self.model_tester.prepare_config_and_inputs_for_common()
					_UpperCAmelCase		:       List[Any]								=      [TFTransfoXLForSequenceClassification]

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




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



					'''simple docstring'''







					pass




			@slow
			def 							lowerCAmelCase__    (		self  :     Optional[int] )   ->List[Any]:



					'''simple docstring'''







					for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							_UpperCAmelCase		:       Dict								=      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 							lowerCAmelCase__    (		self  :     str )   ->Any:



					'''simple docstring'''







					pass






@require_tf
class 	lowerCAmelCase__			(			unittest.TestCase	):




			@unittest.skip("Skip test until #12651 is resolved." )
			@slow
			def 							lowerCAmelCase__    (		self  :     Union[str, Any] )   ->Tuple:



					'''simple docstring'''







					_UpperCAmelCase		:       List[Any]								=      TFTransfoXLLMHeadModel.from_pretrained("transfo-xl-wt103" )
					# fmt: off
					_UpperCAmelCase		:       int								=      tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]	,  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
					_UpperCAmelCase		:       str								=      [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # 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>

					_UpperCAmelCase		:       Union[str, Any]								=      model.generate(lowerCamelCase__	,  max_length=2_00	,  do_sample=lowerCamelCase__ )
					self.assertListEqual(output_ids[0].numpy().tolist()	,  lowerCamelCase__ )







'''simple docstring'''


from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
						import timm


if is_torch_available():
						from torch import Tensor






class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__	):
			lowerCAmelCase :	int          =     "pixel_values"
			lowerCAmelCase :	Dict          =     False
			lowerCAmelCase :	Union[str, Any]          =     TimmBackboneConfig




			def __init__(		self  :     List[str]	,  lowerCamelCase__  :     Dict	,  **lowerCamelCase__  :     str )   ->Dict:



					'''simple docstring'''







					requires_backends(self	,  "timm" )
					super().__init__(lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      config

					if config.backbone is None:
							raise ValueError("backbone is not set in the config. Please set it to a timm model name." )

					if config.backbone not in timm.list_models():
							raise ValueError(F"""backbone {config.backbone} is not supported by timm.""" )

					if hasattr(lowerCamelCase__	,  "out_features" ) and config.out_features is not None:
							raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead." )

					_UpperCAmelCase		:       Optional[Any]								=      getattr(lowerCamelCase__	,  "use_pretrained_backbone"	,  lowerCamelCase__ )
					if pretrained is None:
							raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False." )

					# We just take the final layer by default. This matches the default for the transformers models.
					_UpperCAmelCase		:       int								=      config.out_indices if getattr(lowerCamelCase__	,  "out_indices"	,  lowerCamelCase__ ) is not None else (-1,)

					_UpperCAmelCase		:       List[Any]								=      timm.create_model(
					    config.backbone	,  pretrained=lowerCamelCase__	,  features_only=config.features_only	,  in_chans=config.num_channels	,  out_indices=lowerCamelCase__	,  **lowerCamelCase__	,  )
					# These are used to control the output of the model when called. If output_hidden_states is True, then
					# return_layers is modified to include all layers.
					_UpperCAmelCase		:       List[str]								=      self._backbone.return_layers
					_UpperCAmelCase		:       Optional[int]								=      {layer["module"]: str(lowerCamelCase__ ) for i, layer in enumerate(self._backbone.feature_info.info )}
					super()._init_backbone(lowerCamelCase__ )




			@classmethod
			def 							lowerCAmelCase__    (		cls  :     List[str]	,  lowerCamelCase__  :     str	,  *lowerCamelCase__  :     Tuple	,  **lowerCamelCase__  :     Optional[Any] )   ->Optional[Any]:



					'''simple docstring'''







					requires_backends(cls	,  ["vision", "timm"] )
					from ...models.timm_backbone import TimmBackboneConfig

					_UpperCAmelCase		:       Any								=      kwargs.pop("config"	,  TimmBackboneConfig() )

					_UpperCAmelCase		:       Dict								=      kwargs.pop("use_timm_backbone"	,  lowerCamelCase__ )
					if not use_timm:
							raise ValueError("use_timm_backbone must be True for timm backbones" )

					_UpperCAmelCase		:       str								=      kwargs.pop("num_channels"	,  config.num_channels )
					_UpperCAmelCase		:       Dict								=      kwargs.pop("features_only"	,  config.features_only )
					_UpperCAmelCase		:       str								=      kwargs.pop("use_pretrained_backbone"	,  config.use_pretrained_backbone )
					_UpperCAmelCase		:       Optional[Any]								=      kwargs.pop("out_indices"	,  config.out_indices )
					_UpperCAmelCase		:       Dict								=      TimmBackboneConfig(
					    backbone=lowerCamelCase__	,  num_channels=lowerCamelCase__	,  features_only=lowerCamelCase__	,  use_pretrained_backbone=lowerCamelCase__	,  out_indices=lowerCamelCase__	,  )
					return super()._from_config(lowerCamelCase__	,  **lowerCamelCase__ )




			def 							lowerCAmelCase__    (		self  :     Tuple	,  lowerCamelCase__  :     str )   ->Optional[int]:



					'''simple docstring'''







					pass






			def 							lowerCAmelCase__    (		self  :     Union[str, Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Union[str, Any]=None	,  lowerCamelCase__  :     List[Any]=None	,  lowerCamelCase__  :     Union[str, Any]=None	,  **lowerCamelCase__  :     Dict )   ->Union[BackboneOutput, Tuple[Tensor, ...]]:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      return_dict if return_dict is not None else self.config.use_return_dict
					_UpperCAmelCase		:       Optional[int]								=      (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					_UpperCAmelCase		:       Dict								=      output_attentions if output_attentions is not None else self.config.output_attentions

					if output_attentions:
							raise ValueError("Cannot output attentions for timm backbones at the moment" )

					if output_hidden_states:
							# We modify the return layers to include all the stages of the backbone
							_UpperCAmelCase		:       Optional[int]								=      self._all_layers
							_UpperCAmelCase		:       List[str]								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       List[Any]								=      self._return_layers
							_UpperCAmelCase		:       Tuple								=      tuple(hidden_states[i] for i in self.out_indices )
					else:
							_UpperCAmelCase		:       Any								=      self._backbone(lowerCamelCase__	,  **lowerCamelCase__ )
							_UpperCAmelCase		:       Tuple								=      None

					_UpperCAmelCase		:       Dict								=      tuple(lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      tuple(lowerCamelCase__ ) if hidden_states is not None else None

					if not return_dict:
							_UpperCAmelCase		:       Dict								=      (feature_maps,)
							if output_hidden_states:
									_UpperCAmelCase		:       List[str]								=      output + (hidden_states,)
							return output

					return BackboneOutput(feature_maps=lowerCamelCase__	,  hidden_states=lowerCamelCase__	,  attentions=lowerCamelCase__ )



