




'''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		:			Tuple     ,       lowercase		:			Optional[Any]     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	13
           UpperCAmelCase     =	7
           UpperCAmelCase     =	30
           UpperCAmelCase     =	self.seq_length + self.mem_len
           UpperCAmelCase     =	15
           UpperCAmelCase     =	True
           UpperCAmelCase     =	True
           UpperCAmelCase     =	99
           UpperCAmelCase     =	[10, 50, 80]
           UpperCAmelCase     =	32
           UpperCAmelCase     =	32
           UpperCAmelCase     =	4
           UpperCAmelCase     =	8
           UpperCAmelCase     =	128
           UpperCAmelCase     =	2
           UpperCAmelCase     =	2
           UpperCAmelCase     =	None
           UpperCAmelCase     =	1
           UpperCAmelCase     =	0
           UpperCAmelCase     =	3
           UpperCAmelCase     =	self.vocab_size - 1
           UpperCAmelCase     =	0.01
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )
           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

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

           UpperCAmelCase     =	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		:			Optional[Any]       ):



           '''simple docstring'''

           random.seed(self.seed       )
           tf.random.set_seed(self.seed       )
       def 					A   (			self		:			Tuple     ,       lowercase		:			Any     ,       lowercase		:			List[Any]     ,       lowercase		:			Dict     ,       lowercase		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFTransfoXLModel(lowercase       )

           UpperCAmelCase ,  UpperCAmelCase     =	model(lowercase       ).to_tuple()

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

           UpperCAmelCase ,  UpperCAmelCase     =	model(lowercase       ).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		:			int     ,       lowercase		:			Dict     ,       lowercase		:			List[Any]     ,       lowercase		:			List[Any]     ,       lowercase		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFTransfoXLLMHeadModel(lowercase       )

           UpperCAmelCase ,  UpperCAmelCase     =	model(lowercase       ).to_tuple()

           UpperCAmelCase     =	{'''input_ids''': input_ids_a, '''labels''': lm_labels}
           UpperCAmelCase ,  UpperCAmelCase     =	model(lowercase       ).to_tuple()

           UpperCAmelCase ,  UpperCAmelCase     =	model([input_ids_a, mems_a]       ).to_tuple()

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

           UpperCAmelCase ,  UpperCAmelCase     =	model(lowercase       ).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		:			int     ,       lowercase		:			Optional[int]     ,       lowercase		:			List[str]     ,       lowercase		:			Tuple     ,       lowercase		:			Any       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFTransfoXLForSequenceClassification(lowercase       )
           UpperCAmelCase     =	model(lowercase       )
           self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.num_labels)       )







       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           ((UpperCAmelCase) ,  (UpperCAmelCase) ,  (UpperCAmelCase) ,  (UpperCAmelCase))     =	config_and_inputs
           UpperCAmelCase     =	{'''input_ids''': input_ids_a}
           return config, inputs_dict






@require_tf
class     _a						(			__a							,							__a							,							unittest.TestCase				):
       __a       :  List[Any]            =      (
           (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
       )
       __a       :  List[Any]            =      () if is_tf_available() else ()
       __a       :  Any            =      (
           {
               """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       :  List[Any]            =      False
       __a       :  Optional[int]            =      False
       __a       :  Optional[Any]            =      False
       __a       :  Dict            =      False
       def 					A   (			self		:			str     ,       lowercase		:			Tuple     ,       lowercase		:			Any     ,       lowercase		:			Tuple     ,       lowercase		:			Union[str, Any]     ,       lowercase		:			Tuple       ):



           '''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       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFTransfoXLModelTester(self       )
           UpperCAmelCase     =	ConfigTester(self     ,       config_class=lowercase     ,       d_embed=37       )
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def 					A   (			self		:			str       ):



           '''simple docstring'''

           self.model_tester.set_seed()
           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_model(*lowercase       )
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           self.model_tester.set_seed()
           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_lm_head(*lowercase       )
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowercase       )
       def 					A   (			self		:			Dict       ):



           '''simple docstring'''

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

           for model_class in self.all_model_classes:
               UpperCAmelCase     =	model_class(lowercase       )
               assert isinstance(model.get_input_embeddings()     ,       tf.keras.layers.Layer       )
               if model_class in list_other_models_with_output_ebd:
                   UpperCAmelCase     =	model.get_output_embeddings()
                   assert isinstance(lowercase     ,       tf.keras.layers.Layer       )
                   UpperCAmelCase     =	model.get_bias()
                   assert name is None
               else:
                   UpperCAmelCase     =	model.get_output_embeddings()
                   assert x is None
                   UpperCAmelCase     =	model.get_bias()
                   assert name is None
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

           pass
       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     =	TFTransfoXLModel.from_pretrained(lowercase       )
               self.assertIsNotNone(lowercase       )







       @unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.'''       )
       def 					A   (			self		:			Optional[int]       ):



           '''simple docstring'''

           pass






@require_tf
class     _a						(			unittest.TestCase				):
       @unittest.skip('''Skip test until #12651 is resolved.'''       )
       @slow
       def 					A   (			self		:			int       ):



           '''simple docstring'''

           UpperCAmelCase     =	TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103'''       )
           # fmt: off
           UpperCAmelCase     =	tf.convert_to_tensor([[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0]]     ,       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     =	[33,1_297,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,22,1_706,17,20_098,5,3_215,21,37,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,6_224,831,16_002,2,8,603,78_967,29_546,23,803,20,25,416,5,8,232,4,277,6,1_855,4_601,3,29_546,54,8,3_609,5,57_211,49,4,1,277,18,8,1_755,15_691,3,341,25,416,693,42_573,71,17,401,94,31,17_919,2,29_546,7_873,18,1,435,23,11_011,755,5,5_167,3,7_983,98,84,2,29_546,3_267,8,3_609,4,1,4_865,1_075,2,6_087,71,6,346,8,5_854,3,29_546,824,1_400,1_868,2,19,160,2,311,8,5_496,2,20_920,17,25,15_097,3,24,24,0,33,1,1_857,2,1,1_009,4,1_109,11_739,4_762,358,5,25,245,28,1_110,3,13,1_041,4,24,603,490,2,71_477,20_098,104_447,2,20_961,1,2_604,4,1,329,3,0]  # 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>

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







'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A									={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A									=[
       'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TableTransformerForObjectDetection',
       'TableTransformerModel',
       'TableTransformerPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_table_transformer import (
       TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TableTransformerConfig,
       TableTransformerOnnxConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_table_transformer import (
          TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TableTransformerForObjectDetection,
          TableTransformerModel,
          TableTransformerPreTrainedModel,
      )

else:
   import sys

   A									=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




