



"""simple docstring"""
import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import (
           MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
           TimesformerForVideoClassification,
           TimesformerModel,
       )
       from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from transformers import VideoMAEImageProcessor


class  SCREAMING_SNAKE_CASE__			:

    def __init__(					self      :					Optional[Any]			,	lowerCAmelCase_      :					Optional[int]			,	lowerCAmelCase_      :					List[str]=1_3			,	lowerCAmelCase_      :					Optional[int]=1_0			,	lowerCAmelCase_      :					Optional[int]=3			,	lowerCAmelCase_      :					str=2			,	lowerCAmelCase_      :					Dict=2			,	lowerCAmelCase_      :					int=True			,	lowerCAmelCase_      :					int=True			,	lowerCAmelCase_      :					Any=3_2			,	lowerCAmelCase_      :					Union[str, Any]=5			,	lowerCAmelCase_      :					Union[str, Any]=4			,	lowerCAmelCase_      :					Any=3_7			,	lowerCAmelCase_      :					int="gelu"			,	lowerCAmelCase_      :					int=0.1			,	lowerCAmelCase_      :					Dict=0.1			,	lowerCAmelCase_      :					List[Any]=1_0			,	lowerCAmelCase_      :					List[Any]=0.02			,	lowerCAmelCase_      :					Dict="divided_space_time"			,	lowerCAmelCase_      :					Optional[Any]=None			,	):







      """simple docstring"""







      lowercase_             =			parent
      lowercase_             =			batch_size
      lowercase_             =			image_size
      lowercase_             =			num_channels
      lowercase_             =			patch_size
      lowercase_             =			num_frames
      lowercase_             =			is_training
      lowercase_             =			use_labels
      lowercase_             =			hidden_size
      lowercase_             =			num_hidden_layers
      lowercase_             =			num_attention_heads
      lowercase_             =			intermediate_size
      lowercase_             =			hidden_act
      lowercase_             =			hidden_dropout_prob
      lowercase_             =			attention_probs_dropout_prob
      lowercase_             =			attention_type
      lowercase_             =			initializer_range
      lowercase_             =			scope
      lowercase_             =			num_labels

      # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
      lowercase_             =			(image_size // patch_size) ** 2
      lowercase_             =			(num_frames) * self.num_patches_per_frame + 1

    def        _UpperCAmelCase						(					self      :					int):







      """simple docstring"""







      lowercase_             =			floats_tensor(
          [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size])

      lowercase_             =			None
      if self.use_labels:
        lowercase_             =			ids_tensor([self.batch_size]			,	self.num_labels)

      lowercase_             =			self.get_config()

      return config, pixel_values, labels

    def        _UpperCAmelCase						(					self      :					Optional[Any]):







      """simple docstring"""







      lowercase_             =			TimesformerConfig(
          image_size=self.image_size			,	patch_size=self.patch_size			,	num_channels=self.num_channels			,	num_frames=self.num_frames			,	hidden_size=self.hidden_size			,	num_hidden_layers=self.num_hidden_layers			,	num_attention_heads=self.num_attention_heads			,	intermediate_size=self.intermediate_size			,	hidden_act=self.hidden_act			,	hidden_dropout_prob=self.hidden_dropout_prob			,	attention_probs_dropout_prob=self.attention_probs_dropout_prob			,	initializer_range=self.initializer_range			,	attention_type=self.attention_type			,	)
      lowercase_             =			self.num_labels
      return config

    def        _UpperCAmelCase						(					self      :					int			,	lowerCAmelCase_      :					List[str]			,	lowerCAmelCase_      :					str			,	lowerCAmelCase_      :					Tuple):







      """simple docstring"""







      lowercase_             =			TimesformerModel(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      lowercase_             =			model(lowerCAmelCase_)
      self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size))

    def        _UpperCAmelCase						(					self      :					int			,	lowerCAmelCase_      :					str			,	lowerCAmelCase_      :					Any			,	lowerCAmelCase_      :					Dict):







      """simple docstring"""







      lowercase_             =			TimesformerForVideoClassification(lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()

      lowercase_             =			model(lowerCAmelCase_)

      # verify the logits shape
      lowercase_             =			torch.Size((self.batch_size, self.num_labels))
      self.parent.assertEqual(result.logits.shape			,	lowerCAmelCase_)




    def        _UpperCAmelCase						(					self      :					List[str]):







      """simple docstring"""







      lowercase_             =			self.prepare_config_and_inputs()
      lowercase_             =			config_and_inputs
      lowercase_             =			{'''pixel_values''': pixel_values}
      return config, inputs_dict







@require_torch
class  SCREAMING_SNAKE_CASE__			(				__snake_case   ,	__snake_case   ,	unittest.TestCase ):
    lowercase__  			= (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
    lowercase__  			= (
        {"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
        if is_torch_available()
        else {}
    )

    lowercase__  			= False
    lowercase__  			= False
    lowercase__  			= False
    lowercase__  			= False

    def        _UpperCAmelCase						(					self      :					Optional[Any]):







      """simple docstring"""







      lowercase_             =			TimesformerModelTester(self)
      lowercase_             =			ConfigTester(
          self			,	config_class=lowerCAmelCase_			,	has_text_modality=lowerCAmelCase_			,	hidden_size=3_7)

    def        _UpperCAmelCase						(					self      :					Dict			,	lowerCAmelCase_      :					int			,	lowerCAmelCase_      :					Any			,	lowerCAmelCase_      :					Dict=False):







      """simple docstring"""







      lowercase_             =			copy.deepcopy(lowerCAmelCase_)

      if return_labels:
        if model_class in get_values(lowerCAmelCase_):
          lowercase_             =			torch.zeros(
              self.model_tester.batch_size			,	dtype=torch.long			,	device=lowerCAmelCase_)

      return inputs_dict

    def        _UpperCAmelCase						(					self      :					Optional[int]):







      """simple docstring"""







      self.config_tester.run_common_tests()

    @unittest.skip(reason="""TimeSformer does not use inputs_embeds""")
    def        _UpperCAmelCase						(					self      :					List[Any]):







      """simple docstring"""







      pass

    def        _UpperCAmelCase						(					self      :					str):







      """simple docstring"""







      lowercase_             =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase_             =			model_class(lowerCAmelCase_)
        self.assertIsInstance(model.get_input_embeddings()			,	(nn.Module))
        lowercase_             =			model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCAmelCase_			,	nn.Linear))

    def        _UpperCAmelCase						(					self      :					List[str]):







      """simple docstring"""







      lowercase_             =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase_             =			model_class(lowerCAmelCase_)
        lowercase_             =			inspect.signature(model.forward)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowercase_             =			[*signature.parameters.keys()]

        lowercase_             =			['''pixel_values''']
        self.assertListEqual(arg_names[:1]			,	lowerCAmelCase_)

    def        _UpperCAmelCase						(					self      :					List[Any]):







      """simple docstring"""







      lowercase_             =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase_)

    def        _UpperCAmelCase						(					self      :					int):







      """simple docstring"""







      lowercase_             =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_video_classification(*lowerCAmelCase_)

    @slow
    def        _UpperCAmelCase						(					self      :					int):







      """simple docstring"""







      for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowercase_             =			TimesformerModel.from_pretrained(lowerCAmelCase_)
        self.assertIsNotNone(lowerCAmelCase_)

    def        _UpperCAmelCase						(					self      :					int):







      """simple docstring"""







      if not self.has_attentions:
        pass

      else:
        lowercase_             =			self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_             =			True

        for model_class in self.all_model_classes:
          lowercase_             =			self.model_tester.seq_length
          lowercase_             =			self.model_tester.num_frames

          lowercase_             =			True
          lowercase_             =			False
          lowercase_             =			True
          lowercase_             =			model_class(lowerCAmelCase_)
          model.to(lowerCAmelCase_)
          model.eval()
          with torch.no_grad():
            lowercase_             =			model(**self._prepare_for_class(lowerCAmelCase_			,	lowerCAmelCase_))
          lowercase_             =			outputs.attentions
          self.assertEqual(len(lowerCAmelCase_)			,	self.model_tester.num_hidden_layers)

          # check that output_attentions also work using config
          del inputs_dict["output_attentions"]
          lowercase_             =			True
          lowercase_             =			model_class(lowerCAmelCase_)
          model.to(lowerCAmelCase_)
          model.eval()
          with torch.no_grad():
            lowercase_             =			model(**self._prepare_for_class(lowerCAmelCase_			,	lowerCAmelCase_))
          lowercase_             =			outputs.attentions
          self.assertEqual(len(lowerCAmelCase_)			,	self.model_tester.num_hidden_layers)

          # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
          self.assertListEqual(
              list(attentions[0].shape[-3:])			,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,	)
          lowercase_             =			len(lowerCAmelCase_)

          # Check attention is always last and order is fine
          lowercase_             =			True
          lowercase_             =			True
          lowercase_             =			model_class(lowerCAmelCase_)
          model.to(lowerCAmelCase_)
          model.eval()
          with torch.no_grad():
            lowercase_             =			model(**self._prepare_for_class(lowerCAmelCase_			,	lowerCAmelCase_))

          self.assertEqual(out_len + 1			,	len(lowerCAmelCase_))

          lowercase_             =			outputs.attentions

          self.assertEqual(len(lowerCAmelCase_)			,	self.model_tester.num_hidden_layers)

          # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
          self.assertListEqual(
              list(self_attentions[0].shape[-3:])			,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]			,	)




    def        _UpperCAmelCase						(					self      :					Optional[int]):







      """simple docstring"""







      def check_hidden_states_output(lowerCAmelCase_      :					Dict			,	lowerCAmelCase_      :					int			,	lowerCAmelCase_      :					List[Any]):
        lowercase_             =			model_class(lowerCAmelCase_)
        model.to(lowerCAmelCase_)
        model.eval()

        with torch.no_grad():
          lowercase_             =			model(**self._prepare_for_class(lowerCAmelCase_			,	lowerCAmelCase_))

        lowercase_             =			outputs.hidden_states
        lowercase_             =			self.model_tester.num_hidden_layers + 1
        self.assertEqual(len(lowerCAmelCase_)			,	lowerCAmelCase_)

        lowercase_             =			self.model_tester.seq_length

        self.assertListEqual(
            list(hidden_states[0].shape[-2:])			,	[seq_length, self.model_tester.hidden_size]			,	)

      lowercase_             =			self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        lowercase_             =			True
        check_hidden_states_output(lowerCAmelCase_			,	lowerCAmelCase_			,	lowerCAmelCase_)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        lowercase_             =			True

        check_hidden_states_output(lowerCAmelCase_			,	lowerCAmelCase_			,	lowerCAmelCase_)







def       _SCREAMING_SNAKE_CASE    ()			->    Tuple:







  '''simple docstring'''


  lowercase_             =			hf_hub_download(
      repo_id="""hf-internal-testing/spaghetti-video"""			,	filename="""eating_spaghetti.npy"""			,	repo_type="""dataset"""				)
  lowercase_             =			np.load(snake_case__				)
  return list(snake_case__				)


@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__			(				unittest.TestCase ):

    @cached_property
    def        _UpperCAmelCase						(					self      :					List[Any]):







      """simple docstring"""







      return (
          VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]			,	image_std=[0.5, 0.5, 0.5])
          if is_vision_available()
          else None
      )




    @slow
    def        _UpperCAmelCase						(					self      :					Tuple):







      """simple docstring"""







      lowercase_             =			TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400""").to(
          lowerCAmelCase_)

      lowercase_             =			self.default_image_processor
      lowercase_             =			prepare_video()
      lowercase_             =			image_processor(video[:8]			,	return_tensors="""pt""").to(lowerCAmelCase_)

      # forward pass
      with torch.no_grad():
        lowercase_             =			model(**lowerCAmelCase_)

      # verify the logits
      lowercase_             =			torch.Size((1, 4_0_0))
      self.assertEqual(outputs.logits.shape			,	lowerCAmelCase_)

      lowercase_             =			torch.tensor([-0.3_016, -0.7_713, -0.4_205]).to(lowerCAmelCase_)

      self.assertTrue(torch.allclose(outputs.logits[0, :3]			,	lowerCAmelCase_			,	atol=1E-4))



'''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 					A							:





     def __init__(				self		,     SCREAMING_SNAKE_CASE		,     )					->							int:






         """simple docstring"""






         A			:  str   =		parent
         A			:  Optional[int]   =		13
         A			:  Union[str, Any]   =		7
         A			:  Optional[Any]   =		30
         A			:  Optional[int]   =		self.seq_length + self.mem_len
         A			:  int   =		15
         A			:  Any   =		True
         A			:  Union[str, Any]   =		True
         A			:  Optional[int]   =		99
         A			:  Any   =		[10, 50, 80]
         A			:  Any   =		32
         A			:  Any   =		32
         A			:  Any   =		4
         A			:  Any   =		8
         A			:  Optional[int]   =		128
         A			:  Union[str, Any]   =		2
         A			:  List[str]   =		2
         A			:  Optional[Any]   =		None
         A			:  int   =		1
         A			:  str   =		0
         A			:  Optional[int]   =		3
         A			:  Union[str, Any]   =		self.vocab_size - 1
         A			:  str   =		0.01





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  Optional[int]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )
         A			:  str   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

         A			:  List[str]   =		None
         if self.use_labels:
             A			:  Optional[int]   =		ids_tensor([self.batch_size, self.seq_length]		,     self.vocab_size      )

         A			:  Optional[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      )					->							Optional[int]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  int   =		TFTransfoXLModel(SCREAMING_SNAKE_CASE      )

         A,    A			:  int   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A			:  Union[str, Any]   =		{'''input_ids''': input_ids_a, '''mems''': mems_a}

         A,    A			:  Any   =		model(SCREAMING_SNAKE_CASE      ).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		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Tuple   =		TFTransfoXLLMHeadModel(SCREAMING_SNAKE_CASE      )

         A,    A			:  Optional[int]   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A			:  Tuple   =		{'''input_ids''': input_ids_a, '''labels''': lm_labels}
         A,    A			:  Dict   =		model(SCREAMING_SNAKE_CASE      ).to_tuple()

         A,    A			:  Optional[Any]   =		model([input_ids_a, mems_a]      ).to_tuple()

         A			:  Union[str, Any]   =		{'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}

         A,    A			:  List[Any]   =		model(SCREAMING_SNAKE_CASE      ).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		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							int:






         """simple docstring"""






         A			:  List[Any]   =		TFTransfoXLForSequenceClassification(SCREAMING_SNAKE_CASE      )
         A			:  Optional[Any]   =		model(SCREAMING_SNAKE_CASE      )
         self.parent.assertEqual(result.logits.shape		,     (self.batch_size, self.num_labels)      )





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






         """simple docstring"""






         A			:  List[Any]   =		self.prepare_config_and_inputs()
         ((A),    (A),    (A),    (A))			:  Dict   =		config_and_inputs
         A			:  List[str]   =		{'''input_ids''': input_ids_a}
         return config, inputs_dict





@require_tf
class 					A							(				__snake_case       ,     __snake_case       ,     unittest.TestCase		):
     __magic_name__          =						(
         (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
     )
     __magic_name__          =						() if is_tf_available() else ()
     __magic_name__          =						(
         {
             '''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
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False
     __magic_name__          =						False





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )					->							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 							__lowerCAmelCase				(				self      )					->							Optional[Any]:






         """simple docstring"""






         A			:  str   =		TFTransfoXLModelTester(self      )
         A			:  Optional[int]   =		ConfigTester(self		,     config_class=SCREAMING_SNAKE_CASE		,     d_embed=37      )





     def 							__lowerCAmelCase				(				self      )					->							Optional[int]:






         """simple docstring"""






         self.config_tester.run_common_tests()





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         self.model_tester.set_seed()
         A			:  Optional[Any]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_model(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         self.model_tester.set_seed()
         A			:  Tuple   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_lm_head(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         A			:  List[str]   =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							Any:






         """simple docstring"""






         A,    A			:  int   =		self.model_tester.prepare_config_and_inputs_for_common()
         A			:  int   =		[TFTransfoXLForSequenceClassification]

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





     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         pass





     @slow
     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A			:  Union[str, Any]   =		TFTransfoXLModel.from_pretrained(SCREAMING_SNAKE_CASE      )
             self.assertIsNotNone(SCREAMING_SNAKE_CASE      )





     @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''      )
     def 							__lowerCAmelCase				(				self      )					->							Dict:






         """simple docstring"""






         pass





@require_tf
class 					A							(				unittest.TestCase		):





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






         """simple docstring"""






         A			:  List[str]   =		TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''      )
         # fmt: off
         A			:  List[str]   =		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			:  str   =		[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			:  Optional[Any]   =		model.generate(SCREAMING_SNAKE_CASE		,     max_length=200		,     do_sample=SCREAMING_SNAKE_CASE      )
         self.assertListEqual(output_ids[0].numpy().tolist()		,     SCREAMING_SNAKE_CASE      )




